oversight

Food Stamp Automation: Some Benefits Achieved; Federal Incentive Funding No Longer Needed

Published by the Government Accountability Office on 1990-01-24.

Below is a raw (and likely hideous) rendition of the original report. (PDF)

                 United   States   Gene@   Accounting   Office
                 Report to i=ongressional Requesters             ’,




January   1990
                 F’OODSTAMP
                 AUTOMATION
                 SomeBenefits
                 Achieved; Federal
                 Incentive Funding No
                 Longer Needed




GAO/RCED-90-9
 Resources, Community,   and
 Economic Development    Division

 B-217883

 January 24,199O

 The Honorable Rudy Boschwitz
 The Honorable Jesse A. Helms
 The Honorable Richard G. Lugar
 United States Senate

 The Honorable William Emerson
 House of Representatives

 As requested in your March 1, and May 25, 1988, requests, this report discusses the benefits
 and the costs of automating the Food Stamp Program in selected states and the need for
 continued enhanced federal funding to encourage program automation nationwide.

 This report includes a recommendation to the Congress to eliminate the 75-percent federal
 incentive funding for automating the Food Stamp Program. The report also recommends that
 the Secretary of Agriculture improve the accountability for Food Stamp Program funding,
 expenditures, and equipment.

 As arranged with your office, unless you publicly announce its contents earlier, we plan no
 further distribution of this report until 14 days after the date of this letter. At that time, we
 will send copies of this report to the appropriate House and Senate committees and
 subcommittees; interested Members of Congress; the Secretary of Agriculture; the Director,
 Office of Management and Budget; and other interested parties.

 This report was prepared under the direction of John W. Harman, Director, Food and
 Agriculture Issues, (202) 275-5138. Other major contributors to this report are listed in
 appendix X.




/!Y.wq
 Assistant Comptroller General
Executive Summ~


                   In fiscal year 1987 about $10.5 billion in food stamps was distributed,
Purpose            including about $1 billion in erroneously issued food stamps. As the
                   Food Stamp Program has grown, so has the cost of administering the
                   program - from about $119 million in fiscal year 1974 when the fed-
                   eral government began paying 50 percent of the administrative costs to
                   over $2 billion in fiscal year 1987. To improve the program’s administra-
                   tion and combat increasing costs, legislation was passed in 1980 and
                   1985 to encourage Food Stamp Program automation. Since 1980, state
                   agencies have spent about $524 million in federal and state funds to
                   automate their Food Stamp Programs.

                   In response to congressional requests, GAO discusses the benefits and the
                   costs of automating the Food Stamp Program in selected states. Specifi-
                   cally, GAO was asked to determine (1) whether automated programs
                   were helping state and local agencies improve program administration
                   and control program errors, (2) the costs of these automated systems,
                   and (3) the continued need for federal incentives to encourage program
                   automation. Additionally, in determining costs, GAO reviewed the con-
                   trols over expenditures of government funds and the safeguards for
                   property purchased with government funds.


                   The Food Stamp Program is administered by state welfare agencies
Background         under the supervision of the U.S. Department of Agriculture’s Food and
                   Nutrition Service. Generally, federal funding for state administrative
                   expenses, including automatic data processing (ADP) system develop-
                   ment and operation costs, is provided at the 50-percent level. The Food
                   Stamp Act Amendments of 1980 encouraged state agencies without
                   existing automated systems to plan, design, develop, or install such sys-
                   tems by authorizing an increase in the federal funding rate to 75 percent
                   of the cost. (See ch. 1.)


                   The four statewide automated Food Stamp Programs in Vermont, Xorth
Results in Brief   Dakota, Kentucky, and Texas, and the three local office automated Food
                   Stamp Programs in Texas and California that GAO reviewed, improved
                   certain administrative procedures and caseload management, and ena-
                   bled workers to avoid or detect certain program errors usually made
                   when determining program eligibility. However, GAO did not find that
                   automation has achieved all of the expected benefits in improving pro-
                   gram administration, such as reducing program staff. Some of these
                   goals were beyond the capability of the automated systems.



                   Page 2                                    GAO/RCED-90-9 Food Stamp Automation
--
                          JhxutiveSummary




                          Although Service regional officials approved from about $1.1 million in
                          North Dakota to over $22 million in Texas to develop the automated
                          systems that GAO reviewed, the five state agencies did not always main-
                          tain adequate records to account for the costs incurred to develop and
                          operate each automated system. As a result, GAO could not always deter-
                          mine costs. Additionally, the Service did not always monitor state claims
                          for cost reimbursement. Because of these weaknesses, payments to at
                          least one state, North Dakota, exceeded the amount approved for its
                          system’s development. Furthermore, not one of the five state agencies
                          reviewed could account for all of its federally funded automated sys-
                          tems’ equipment, increasing the risk of fraud, waste, and abuse.

                          Responses from all state agencies to a GAO questionnaire disclosed that
                          Food Stamp Programs in each state are automated to some extent at
                          either the state office level, local office level, or both. Therefore, GAO
                          believes that the 75-percent funding level established by the Congress to
                          encourage states without existing automated systems to automate their
                          programs is no longer needed.



Principal Findings

Automation’s Effects on   The Congress and program administrators at all levels have long
                          thought automation to be a major factor in helping state and local agen-
Program Operations        cies control program errors, manage large caseloads, improve services to
                          participants, and implement complex requirements. The majority of
                          state agencies, when requesting federal funding to develop automated
                          programs, highlighted the systems’ planned capability to reduce pro-
                          gram errors and to streamline administrative procedures. At the loca-
                          tions that GAO reviewed, automation improved certain administrative
                          procedures and caseload management and enabled eligibility workers to
                          avoid or detect certain program errors. For example, the seven auto-
                          mated systems were designed to compare social security numbers of all
                          participants to prevent an individual from participating in two separate
                          households in the same state. Some achieved benefits varied from loca-
                          tion to location. For example, GAO'S analysis showed that the impact of
                          automation decreased error rates in North Dakota but had no effect in
                          Vermont. Benefits varied because of differences in program administra-
                          tion and automated system capability. GAO'S analyses of the impact of
                          automation was limited in some cases by the quantity and quality of
                          data available.


                          Page 3                                    GAOiRCEDM-9   Food Stamp Automation
-
                            Executive Summary




                            The locations reviewed, however, did not achieve all of the expected
                            benefits from automation. For example, North Dakota expected program
                            workers to spend less time in processing food stamp cases after its pro-
                            gram was automated. But, GAO'Sanalysis showed that the automated
                            system had no effect on the amount of time spent on processing food
                            stamp cases.

                            In addition, automation has limitations that prevent it from achieving
                            certain benefits. For example, automation cannot always prevent certain
                            types of errors, such as unreported income, because the program must
                            rely primarily on the applicant to identify the source of that income.
                            (See ch. 2.)


Inadequate Records and      GAOidentified the costs, which ranged from $1.2 million to $19.8 million,
                            claimed by the states to develop the automated systems in Vermont,
Control of Automated        North Dakota, and Kentucky. However, because of inadequate state
Systems’ Costs and          agency and Service accounting records, the costs of the four automated
Equipment                   systems reviewed in Texas and California could not be identified. Fed-
                            eral, state, and local office records did not routinely account for actual
                            costs incurred to develop and operate each of the seven systems. For
                            example, at the request of Service regional officials in 1985, Texas state
                            officials had to reconstruct costs incurred for the development of the
                            state’s automated systems in order to reconcile expenditures with
                            approved funding requests. Also, although required by federal regula-
                            tions, none of the state agencies included in the review could accurately
                            account for all systems-related equipment for which federal funds had
                            been provided. Because of inadequate internal accounting and adminis-
                            trative controls, the states have no assurance that the equipment is safe-
                            guarded against loss, unauthorized use, and misappropriation. (See ch.
                            3.)


Increased Federal Funding   The Congress intended the 75-percent funding level to encourage states
                            without existing automated systems to automate. According to state
and Program Automation      agency officials’ responses to GAO'Squestionnaire, this objective has
                            been met. All 53 state agencies have automated their programs to some
                            extent. For the 37 state agencies receiving 75-percent funding, 4 state
                            agencies initiated automated systems development, 13 upgraded or mod-
                            ified an existing system, 16 replaced existing systems entirely, and 4
                            partially automated their systems. The remaining 16 state agencies
                            received 50-percent funding for similar purposes. (See ch. 4.)



                            Page 4                                    GAO/RCFZD-9&9 Food Stamp Automation
                  Executive Summary




                  Since all of the state agencies have automated their Food Stamp Pro-
Recommendations   grams to some extent, GAOrecommends that the Congress amend the
                  Food Stamp Act to end the use of 75percent federal funding for Food
                  Stamp automation. (See ch. 4.) GAOalso recommends that the Secretary
                  of Agriculture improve accountability for program funding, expendi-
                  tures, and equipment. (See ch. 3.)


                  The Service disagrees with GAO'Sinterpretation that the originating con-
Agency Comments   gressional committee intended that after the first year of the program
                  the 75-percent funding provision was to be used only to encourage
                  states not computerizing their programs to automate. GAObelieves that
                  its interpretation of the intent is correct and that based on the report’s
                  findings, all states have automated to some degree, thus fulfilling the
                  intent of the originating committee. The Service states that the method-
                  ology used in the report to measure the effects of automation on the
                  program has limitations that are recognized by GAObut that the signifi-
                  cance of these limitations is downplayed in the report. GAOacknowl-
                  edges the limitations of the data and the statistical results pertaining to
                  program changes caused by automation and, accordingly, has high-
                  lighted these limitations. In addition, the Service stated that it is prohib-
                  ited by an Office of Management and Budget circular from requiring
                  greater accountability for state expenditures for specific Anp-related
                  costs as recommended by GAO.The report recommendation has been
                  revised to clarify the level of cost data needed, which GAObelieves is not
                  prohibited by the circular. GAOalso obtained comments from the states
                  covered in this review. These comments, related largely to the clarity
                  and technical accuracy of the report, have been incorporated where
                  appropriate. (The Service’s and states’ comments and GAO'Sresponses
                  are included at the end of chapters 2,3, and 4, and in appendixes V
                  through IX.)




                  Page 6                                     GAO/RCED-90-9 Food Stamp Automation
Contents


Executive Summary                                                                                     2

Chapter 1                                                                                         10
Introduction             Background                                                               10
                         Objectives, Scope, and Methodology                                       12

Chapter 2                                                                                         18
Benefits Achieved        Many Administrative Improvements Were Experienced as                     18
                             a Result of Automation
From Automation Not      Improvements in Program Results Not Always Achieved                     24
Always Reflected in          From Automation
Program Results          Conclusions                                                             35
                         Agency and State Comments and Our Evaluation                            36

Chapter 3                                                                                        38
States and the Service   Financial Integrity and Internal Control Requirements
                         State Agencies’ Accounting and Service’s Monitoring of
                                                                                                 38
                                                                                                 39
Did Not Maintain              ADP Costs Are Not Adequate
Adequate Records of      State Agencies’ ADP Equipment Inventory Records Were                    44
Automated System              Not Accurate
                         Conclusions                                                             46
Costs and Equipment      Recommendations to the Secretary of Agriculture                         47
Inventories              Agency Comments and Our Evaluation                                      48

Chapter 4                                                                                        50
Enhanced Funding for     All State Agencies Have Automated to Some Extent                        50
                         Funding at 75 Percent Encouraged Development of                         63
Automation Has                Automation
Achieved Its Objective   Conclusions                                                             72
                         Recommendation to the Congress                                          72
                         Agency and State Comments and Our Evaluation                            73

Appendixes               Appendix I: Estimating the Effects of Automation on the                 76
                             Operations of State/Local Food Stamp Programs
                         Appendix II: Description of the Automated Food Stamp                    99
                             Programs GAO Reviewed
                         Appendix III: U.S. General Accounting Office Survey of                 107
                             State Food Stamp Programs




                         Page 6                                 GAO/RCED-So-9 Food Stamp Automation
         Contents




         Appendix IV: Model Plan Requirements for Certification;                 118
             Issuance, Reconciliation, and Reporting; And General
             Standards
         Appendix V: Comments From the U.S. Department of                        122
             Agriculture’s Food and Nutrition Service
         Appendix VI: Comments From the State of Kentucky                        131
         Appendix VII: Comments From the State of North Dakota                   140
         Appendix VIII: Comments From the State of Texas                         144
         Appendix IX: Comments From the State of Vermont                         158
         Appendix X: Major Contributors to This Report                           163

Tables   Table 2.1: Major Manual Tasks Assumed by the Seven
             Automated Systems GAO Reviewed to Improve
             Application Processing and Make Policy Changes
         Table 2.2: Locations Where Appropriate Data Were                         25
             Available to Determine the Effect of the Automated
             Systems on Specific Program Activity
         Table 2.3: Claims and Collections for Food Stamp                         30
             Overissuances for Fiscal Years 1982-87
         Table 3.1: Costs Claimed by State Agencies to Develop                    41
             and Operate Approved Automated Systems GAO
             Reviewed for Fiscal Years 1981-87
         Table 3.2: Inventory of Kentucky’s Automated Food                        44
             Stamp Program Equipment
         Table 3.3: Inventory of Vermont’s Automated Food Stamp                   45
             Program Equipment
         Table 4.1: Status of Automation With Regard to the Model                 52
             Plan’s Requirements for Certification
         Table 4.2: Status of Automation With Regard to the Model                 56
             Plan’s Requirements for Issuance, Reconciliation, and
             Reporting
         Table 4.3: Status of Automation With Regard to the Model                 60
             Plan’s Requirements for General Standards
         Table 4.4: Importance Placed on Incentives to Automate                   66
             the Food Stamp Program Statewide
         Table 4.5: Purposes of Three Most Recent State Agencies’                 68
              Requests for 75-Percent Funding
         Table 4.6: Impact of 75-Percent Funding on State Agency                  69
              Systems’ Characteristics
         Table 4.7: States’ Integration of Automated Food Stamp                   70
              and AFDC Programs With 75-Percent Funding




         Page 7                                  GAO/RCED-90-9 Faod Stamp Automation
          Contents




          Table 4.8: Extent of State Agencies’ Automation, by                      71
              Function, With 75-Percent Funding
          Table 1.1: Expectations of Automation and Other Key                      80
              Factors Affecting Efficiency Measures
          Table I.2 List of Variables Used in Empirical Analysis                   82
          Table 1.3: Vermont Estimation Results- Error Rates                       86
          Table 1.4: Vermont Estimation Results- Claims and                        89
              Collections
          Table 1.5: Vermont Estimation Results- Staffing                          90
          Table 1.6: North Dakota Estimation Results-Error     Rates               92
          Table 1.7: North Dakota Estimation Results-Minutes     of                93
              Staff Time Spent Per Food Stamp Case
          Table 1.8: San Antonio Estimation Results-Timeliness    in               94
              Processing Food Stamp Cases
          Table 1.9: San Antonio Estimation Results-Staffing                       96
          Table I. 10: Dallas Estimation Results- Timeliness in                    97
              Processing Food Stamp Cases
          Table I. 11: Dallas Estimation Results- Staffing                         98

Figures   Figure I. 1: Structure of Model of Food Stamp Program                    79
               Operations




          Page 8                                   GAO/WED-t&%9 Foad Stamp Automation
Contents




Abbreviations

ADP     automatic data processing
        Aid to Families With Dependent Children
AFNC    Aid to Families With Needy Children
ATP     Authorization to Participate
BENDEX Beneficiary Data Exchange
FNS     Food and Nutrition Service
FrExxIs Food Stamp Automated On-Line Issuance System
GAO     General Accounting Office
HHS     Health and Human Services
HIR     Household Issuance Record
IEVS     Income Eligibility Verification System
KAMES-Fs Kentucky Automated Management and Eligibility System--
             Food Stamps
KACIS   Kentucky Automated Certification and Issuance System
OMB     Office of Management and Budget
SAVERR System for Application, Verification, Eligibility, Referral, and
             Reporting
SDX     State Data Exchange
TECS     Technical Eligibility Computer System
USDA     United States Department of Agriculture
WCDS     Welfare Case Data System
WELNET Welfare Network


Page 9                                   GAO/RCED-90-9 Food Stamp Automation
Chapter 1

Introduction


               Since 1980 the Congress and federal, state, and local Food Stamp Pro-
               gram administrators have placed special emphasis on program automa-
               tion. In addition to the normal 50-percent funding rate, beginning in
               fiscal year 1981, the federal government began providing 75-percent
               funding to further encourage states to automate their programs. In
               requests for federal funding to automate, state program administrators
               stated that automation would enable them to control program errors,
               manage increasing caseloads, implement complex program require-
               ments, and improve services to clients. During fiscal years 1981-87,
               state agencies report having spent about $524 million in federal and
               state funds to develop and operate automated Food Stamp Programs.


               The Congress established the basic authority for the current Food Stamp
Background     Program in 1964 to improve the nutrition of low-income households, and
               required all states to participate in the program beginning in 1971. The
               program is federally designed and generally requires applicants to apply
               in person at their local food stamp office and meet numerous program
               requirements pertaining to their household composition, residency,
               financial resources, and income to be eligible for the monthly food stamp
               benefits, which are federally funded. State welfare agencies administer
               the program under the supervision of the U.S. Department of Agricul-
               ture’s Food and Nutrition Service. Generally, since October 1974 the fed-
               eral government has paid 50-percent of the state agencies’ costs to
               administer the program. According to the Service’s records for fiscal
               year 1987, about $10.5 billion worth of food stamps was distributed to
               participants, and about one-tenth of this amount, or about $1 billion,
               involving overpayments and under-payments, was issued erroneously.
               According to Service financial reports, federal and state costs to admin-
               ister the program amounted to about $2 billion that year.

               The high cost of food stamp issuances, erroneous issuances, and admin-
               istration prompted efforts to improve program administration and to
               reduce waste, fraud, and abuse. The Congress decided that providing an
               incentive to automate the program would improve administration and
               reduce errors. Thus, to encourage states to computerize their Food
               Stamp Programs, the Food Stamp Act Amendments of 1980 (P.L. 96-
               249) amended the Food Stamp Act of 1977 and authorized the Secretary
               of Agriculture to pay, beginning October 1, 1980,75 percent of the costs
               incurred by state agencies who met the 75-percent requirements to plan,
               design, develop, or install automatic data processing (ADP) and informa-
               tion retrieval systems for administering the Food Stamp Program. State



               Page 10                                  GAO/RCED-90-9 Faod Stamp Automation
Chapter 1
Introduction




agencies not meeting the requirements for 75percent funding continued
to receive the 50-percent federal funding rate for ADP development.

Continuing this emphasis on automating the Food Stamp Program, the
Food Security Act of 1985 (P.L. 99-198) required the Secretary of Agri-
culture to develop a model plan for the comprehensive automation of
program information systems by February 1, 1987. Additionally, by
October 1, 1987, each administering state agency was to develop and
submit to the Food and Nutrition Service for approval a plan, based on
the Service’s model, to implement an automated system. The Service
developed the required model plan and issued regulations implementing
the Food Security Act’s Model Plan requirements on September 18,
 1987. Service headquarters records show that by May 1989, the Service
had approved program automation model plans for all the states.

To obtain federal funding to develop the automated systems, the Food
Stamp Program requires that state agencies planning an acquisition of
$200,000 or more in federal and state funds over a 12-month period, or
$300,000 or more in funds for the total acquisition, must submit
requests to the Service for approval prior to purchasing such systems.1
Service guidelines require that acquisition requests be submitted in the
form of an advance planning document, which is a written plan of action
containing, among other things, a proposed budget for development and
operations cost.* Service regional officials review and approve state
agency requests. For requests in which the Service’s share of the cost
will be over $1 million, the regional staff prepare and submit for concur-
rence an executive summary of the request with their recommendations
to the Advance Planning Document Oversight Committee at the Ser-
vice’s national office in Washington, D.C.

Once the state agencies have an approved advance planning document
with a stated dollar limit for the automated systems’ development, state
agency expenditures are claimed for reimbursement by the Service up to
the approved dollar limit. Because ADP systems development usually
evolves over several years, state agencies submit to their cognizant Ser-
vice regional office annual program budgets or estimates of the state’s
total cost of administering the Food Stamp Program, including the share

‘In February 1987 a policy memorandum raised the limits for prior approval cost thresholds from
$100,000 for a 1%month period and $200,000 for total acquisition costs. The higher thresholds also
are reflected in a draft lvle published August 8, 1988, and a final rule now in clearance.

‘ADP Advance Planning Document Handbook for State Agencies, Food and Nut&on ,&vice Hand-
book 151.



Page 11                                                 GAO/RCEDW-9 Food Stamp Automation
--
                         Chapter 1
                         introduction




                         of the ADP development and operating costs to be funded by the Service.
                         The Service then issues a letter of credit to the state agency for the
                         approved program budget amount, against which the agency funds its
                         administrative expenditures. During the fiscal year, the state agencies
                         submit quarterly expenditure reports and claims for reimbursement to
                         the Service.


                         Senator Richard Lugar, Ranking Minority Member of the Senate Com-
Objectives, Scope, and   mittee on Agriculture, Nutrition, and Forestry, and Senator Jesse Helms
Methodology              of the same Committee asked that we review state efforts to automate
                         the Food Stamp Program to determine (1) whether the automated pro-
                         grams were helping state and local agencies improve administration, (2)
                         the costs of these automated systems, and (3) the continued need for
                         federal incentives to encourage program automation. Later, Senator
                         Rudy Boschwitz of the Senate Committee on Agriculture, Nutrition, and
                         Forestry and Congressman William Emerson of the House Committee on
                         Agriculture requested that we include in our review the state of Ken-
                         tucky’s automated Food Stamp Program system to determine whether
                         the newly developed system enabled the state to reduce its program
                         error rates.

                         Because there is no typical type of automated Food Stamp Program, we
                         selected the locations discussed below to obtain a broad view of differ-
                         ent automated systems with different automated capabilities in differ-
                         ent parts of the country. (Detailed descriptions of each of the automated
                         systems are provided in app. II.) We chose the statewide systems oper-
                         ated by Vermont and North Dakota for review because each (1) is an on-
                         line automated system used to determine eligibility for program partici-
                         pation and to maintain food stamp case information; (2) serves other
                         public assistance programs, such as the Aid to Families with Dependent
                         Children (AFDC) and Medicaid Programs; and (3) was cited by Service
                         headquarters and regional officials and state Food Stamp Program
                         administrators as an automated program that has been used as a model
                         for other state agency programs. Also, these state agencies had informa-
                         tion available on program operations for several years before the auto-
                         mated system was developed, during system development, and after its
                         implementation.

                         We selected for review the automated Food Stamp Program operations
                         in Texas and California to achieve geographic balance in our review and
                         to include states that had multiple automated systems. Unlike Vermont
                         and North Dakota, where we reviewed statewide automated systems, we


                         Page 12                                  GAO/RCED-9@9 Food Stamp Automation
Chapter 1
Introduction




could review only local office automated systems in Texas and Califor-
nia. Although Texas has a statewide Food Stamp Program system in
operation, we could not compare program operations before and after
automation to determine benefits of automation on the statewide pro-
gram because pre-automation program operation data were not availa-
ble. However, at the time of our review, in addition to the statewide
system, Texas had two different types of automated systems in opera-
tion at various local offices.3 Therefore, we selected for review the local
office systems in San Antonio and Dallas because, together, they repre-
sented both types of local office automated systems in use in the state
and because each had available for review program information for sev-
eral years before and after the systems were automated.

California does not have a statewide automated Food Stamp Program.
Therefore, as we did in Texas, we selected for review local office auto-
mated programs. However, we found that before-and-after program
operations data were generally not available at the local office level in
California. As a result, we compared program operations at one of the
state’s nonautomated local office operations-in      Red Bluff, California-
to the operations of an automated local office of comparable caseload
size in Vallejo, California. In addition, we selected for review the auto-
mated system at the San Francisco local office because, unlike the other
systems we reviewed, it was the only system we found during our sur-
vey that was designed specifically for the food stamp benefit issuance
part of the Food Stamp Program.

Furthermore, as requested, we reviewed the statewide system in Ken-
tucky, but we were able to review program operations only for a period
prior to the beginning of the system’s statewide operations in 1988, fis-
cal years 1984-88. Because so little time had passed after automation,
data were not available to perform a before-and-after comparison.

We had discussions about the benefits and costs of Food Stamp Program
automated systems with Service officials at the Northeast, Southeast,
Southwest, Mountain Plains, and Western regional offices, and Service
headquarters in Alexandria, Virginia. We also interviewed state and
local Food Stamp Program officials in the states we visited. At each
location we reviewed pertinent records, such as state program policy
and procedures and applicable ADPplanning documents and operating

“Local office systems separately maintain food stamp cases with data entry overnight into the state-
wide system for eligibility validation and benefit issuance. As of May 1989, the Texas state agency
was developing a third local office system, which will be an on-line system used to determine program
eligibility and maintain case information.



Page 13                                                  GAO/RCJD-9@9 Food Stamp Automation
                          Chapter 1
                          Introduction




                          manuals pertaining to the state Food Stamp Programs and ADP systems.
                          Also, at each location we discussed major deficiencies that we found
                          with appropriate officials and incorporated their comments where
                          appropriate.


Determining Benefits of   To determine the benefits resulting from program automation, in each
Automation                state we focused on the benefits of automation cited (1) most often by
                          federal, state, and local Food Stamp Program administrators and (2) in
                          the state agencies’ requests for Service funding to develop automated
                          systems since fiscal year 1981. These benefits centered on program
                          administration of the application process and case management as
                          reflected by more accurate eligibility determinations, program staff
                          reductions, less time to process food stamp cases, more cases processed
                          within required time frames, and reduced paperwork.

                          However, the task of determining whether these benefits were achieved
                          as a result of automation is complicated by the fact that changes in pro-
                          gram operations can be caused by a host of factors not related to the
                          automated system. For example, a decline in error rates after an auto-
                          mated system begins operations is not a sufficient basis for concluding
                          that the automated system caused the decline. The error rate may have
                          declined because the number of staff increased or the caseload
                          decreased. An increase in staff and/or a decrease in caseload could pro-
                          vide workers more time to process food stamp cases and thus could
                          reduce the chance for error.

                          Therefore, we used regression analyses to isolate the effects of automa-
                          tion on various components of program administration apart from the
                          effects of changes in other measurable program activity, such as
                          changes in staffing or caseload, for program operations data in Vermont,
                          North Dakota, and Texas locations where sufficient data were available.
                          These regression models, which are described in detail in appendix I,
                          enabled us to determine the statistical significance4 of possible relation-
                          ships between automation and each of the different measures of pro-
                          gram benefits, while controlling for the effects of other program-related
                          factors. Our analysis does not include all of the factors that could affect
                          program operations because of the lack of adequate data. These factors



                          4We refer to a relationship as statistically significant if we can be 80 percent confident (90 percent for
                          a one-tailed test), based on the results of our analysis, that the relationship exists. Appendix I pro-
                          vides a detailed description of our regression models and corresponding results



                          Page 14                                                     GAO/RCElMO-9 Food Stamp Automation
Chapter 1
Introduction




include such things as the quality of staff-education    and training, spe-
cial programs designed to affect program activity, and socioeconomic
factors within the community served by the program. The factors we
did include were (1) the number of food stamp cases, (2) the number of
AFDCProgram and Medicaid Program cases,6(3) the number of public
assistance workers-clerks,    eligibility workers, and supervisors-who
also may process other assistance program cases in addition to food
stamp cases, (4) the frequency with which eligibility determinations
were made within program time requirements, (6) the amount of time
spent to process food stamp cases, (6) the number of claims established
for overissued benefits, (7) the amount of overissuance claims collected,
(8) certain changes in program policy, and (9) the percentage of pro-
gram errors. Administrators of the programs covered by our review
 agreed that these program-related factors are those needed to determine
 changes in program operations that could have resulted from automa-
tion. In addition, they also agreed that other factors such as quality of
 staff and socioeconomic factors within the community served by the
 program, but not included in our analysis, are factors that could affect
 program operations.

The results from any regression analyses, though, are only as good as
the theory of the proposed relationships assumed and the quality and
quantity of data used for the analyses. Using Food Stamp Program and
Anp-related information, we obtained a general consensus for the theo-
ries of the proposed relationships we assumed through discussions with
Service and state program officials. The quantity and quality of data
used in our regression analyses, however, had limitations. Our tests to
determine the effect that automation had on the various program meas-
ures were based on a short period of time, fiscal years 1981-87, for an
analysis of this kind. Also, for some program measures, particularly
staffing, it was necessary to transform annual data to quarterly figures,
which could result in some measurement error in the data and in our
 analysis. Except as noted above, the results of our empirical analyses
 and regression models also seemed plausible to cognizant state and local
 program office officials with whom we spoke.




 “Participants of the Food Stamp Program often participate in other public assistance programs such
 as the AFDC and Medicaid Programs, which are administered by the U.S. Department of Health and
 Human Services. As a result, in many states generic (public assistance) workers process the applica-
 tions and maintain the case information for all of the programs. Thus, the food stamp case could be
 affected by changes in the number of AFDC and Medicaid cases.



 Page 16                                                  GAO/RCED-909 Food Stamp Autmuation
                         Chapter 1
                         kroduction




                         However, we did not examine each automated system to determine if
                         design flaws and/or operational problems may have prevented the auto-
                         mated system from achieving its specific goals or objectives. For exam-
                         ple, should Vermont’s system not reduce program error rates as
                         planned, it may be because the system does not conform to its design or
                         operational plans. Instead, our objective was to determine only if the
                         presence of the automated system had made a difference in the results
                         of the program’s operations.

                         With the exception of Kentucky and the three offices in California, we
                         obtained program-related data, as described earlier, since fiscal year
                         1981, when program administrators began placing special emphasis on
                         automation pertaining to each program we reviewed. Because state and
                         local offices in Kentucky and California did not always maintain file
                         data beyond 5 years, we generally obtained Food Stamp Program data
                         for only fiscal years 1983-87 in those states.


Determining Automation   To determine the costs of Food Stamp Program automation, we reviewed
                         the appropriate local office, state agency, and Service regional office
costs
                         accounting records in the five states covered by our review. We also
                         accounted for expenditures pursuant to specific approval of federal
                         funds to develop and operate these systems and tested the records
                         against supporting documentation. For fiscal years 1981-87 when
                         records were available, we determined the Food Stamp Program share of
                         the cost to develop each system and the program’s share of the cost to
                         operate the system since it began operations. In Texas, we expanded our
                         records testing for the local office automated systems because, from a
                         sample of claims, we found that not all claims for federal funding to
                         develop automated systems were supported by vouchers. As a result, we
                         reviewed all of the claims for the Food Stamp Program’s share of costs
                         to develop Texas’ automated systems.


Determining the          To determine whether federal incentives are still needed to encourage
                         automation, we sent a questionnaire (see app. III) to all 53 state agencies
Continuing Need for      that administer the Food Stamp Program” and conducted a follow up
Incentives               telephone survey to clarify agency responses. Our questionnaire and tel-
                         ephone survey asked about each state’s need for and use of federal
                         incentives to automate the Food Stamp Program, as well as the effect of

                         “The 53 state agencies include the 50 states and the three administrating agencies in the District of
                         Columbia, the Virgin Islands, and Guam



                         Page 16                                                   GAO/RCED-90-9 Food Stamp Automation
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Introduction




such incentives on its Food Stamp Program automation efforts. All 53
state agencies responded to our questionnaire and telephone survey.

Our work was done between January 1988 and April 1989. We con-
ducted our review in accordance with generally accepted government
auditing standards. We made only limited tests to assess the reliability
of the Services’ computer-generated information. For example, we did
not test the validity of state agency reported program information, such
as the reported program quality control error rates, caseloads, and staff-
ing. However, through our review of Service regional and state agency
records and discussions with Service and state agency officials, we
determined that the data had been compiled and reported consistently in
fiscal years 1981-88. In addition, in a previous report, Food Stamp Pro-
gram: Statistical Validity of Agriculture’s Payment Error-Rate Estimates
(GAO/RCEDW-4,Oct. 1986), we noted that the Service’s quality control
system provides the most statistically valid estimate available of a
state’s Food Stamp Program error rate.




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Benefits Achieved From Automation Not
Always Reflected in Program Results

                       Since fiscal year 198 1, state agencies requesting federal funding to
                       develop automated systems indicated that states were seeking to
                       improve Food Stamp Program administration through automation. The
                       Food Stamp Program agencies we reviewed believed automating the pro-
                       grams should enable them to do such things as more accurately deter-
                       mine applicant eligibility and benefits, use fewer staff to manage larger
                       caseloads, process applications faster and reduce paperwork. These
                       changes would allow program workers more time to serve applicants
                       and to verify reported information, which in turn would reduce the
                       number of errors made in determining program eligibility. Each of the
                       automated systems we reviewed, to some extent, (1) improved program
                       administrative procedures and caseload management and (2) enabled
                       eligibility workers to avoid and detect certain types of errors sometimes
                       made when determining program eligibility.

                       However, the improvements brought by the automated systems in the
                       states we reviewed were not always measurable in the results of pro-
                       gram operations. While error reduction was a major goal of automation,
                       its introduction was only one of many error reduction strategies, For
                       example, Kentucky achieved one of the objectives of its automated sys-
                       tem-to reduce errors-before      the program was automated. In all of the
                       locations we reviewed, the types of errors occurring in the Food Stamp
                       Programs were often beyond the systems’ capabilities because these
                       errors reflected the accuracy or inaccuracy of household-provided infor-
                       mation. Additionally, the automated systems’ effect on changing pro-
                       gram staffing, over-issuances, case processing time, processing time
                       limits, and paperwork was not always as expected. Our comparison
                       between an automated local office and a nonautomated local office in
                       California also showed that the presence of an automated system did
                       not necessarily reduce the number of staff or the cost to process food
                       stamp cases.


                       According to Food Stamp Program officials in Vermont, North Dakota,
Many Administrative    Kentucky, Texas, and the Vallejo, California, local office, their auto-
Improvements Were      mated systems improved program administration. They told us that
Experienced as a       their automated systems enabled eligibility workers to better process
                       applications and maintain caseloads, more easily notify applicants of
Result of Automation   case action, and routinely avoid and detect errors to more accurately
                       determine program eligibility. Based on discussions with state and local
                       office program personnel at each of the offices we visited and demon-
                       strations of each automated system’s capabilities, we believe that in
                       many respects the automated systems achieved these benefits.


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                            Benefits Achieved From Automation Not
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Improvements in             Each automated system we reviewed assumed many of the manual tasks
                            previously performed by eligibility worker& and improved the workers’
Processing Applicat ;ions   ability to process Food Stamp Program applications, maintain current
and Policy Changes          case file information, and implement program policy changes. Table 2.1
                            lists the manual tasks-previously    done by program clerks, eligibility
                            workers, and supervisors-performed      by the systems we reviewed. As a
                            result, eligibility workers can more easily ensure complete and accurate
                            food stamp applications.




                            ‘Eligibility workers are program staff who process and maintain food stamp applications and cases.



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                                            Benefits Achieved From Automation Not
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Table 2.1: Major Manual Tasks Assumed by the Seven Automated Systems GAO Reviewed to Improve Application Processing
and Make Policv Changes
                                                                                          Local office systems
                                               Statewide systems                          Texas               California
Processing capability of                           North
automated systems                 Vermont        Dakota     Kentucky     Texas        Dallas     &Antonio          Vallejo
Guide rntervrew through
applrcation
  Required                              X                                   X
  Optional                                                 X                                             X             X
Prevent rnvalrd entnes                  X                  X                X                 X
Valrdate entrres                        X                  X                X                 X          X             X                X
Compute calculatrons                    X                  X                X                            X             X                X
Consrstent pol~cv applrcatron           X                  X                X                 X          X             X
Compare rnformatron for                 X                  X                X                 X          X             X
consrstency
Deny/termrnate cases for:
  Mrssrng monthly reports               X                  X                X                 X                                         X
  End of certrfrcatron period           X                  X                X                 X                                         X
Alert caseworkers to
  Supervisory notes                     X                  X                X                            X
   Errors ~-                            X                  X                X                 X          X             X                X
   Household chanaes                    X                  X                X                 X          X             X                X
Determrne whether elrgrbrlrty
cnterra are met
   Resource lrmrt                       X                  X                X                            X             X
   Gross Income                         X                  X                X                            X             X
   Net Income                           X                  X                X                            X             X
Venfrcatlon with other
automated svstems’ data,
   nlrect nn-lrne                                          X                X                 X
  Batched overnight                     X                  X                X                 X                                         X
  Batched rrreaularlv                                      X                X                 X
                                            Legend “X rndrcates that the capabiltty existed

Automated Systems Help Ensure               Although the extent is not quantifiable, the automated systems
Complete Coverage of                        improved the entire application processing activity. Eligibility workers
Application Process                         process and maintain food stamp applications and cases using the com-
                                            puters to more easily compare or screen reported applicant information
                                            at the time of the request for assistance. The automated systems search
                                            the statewide food stamp case file information to identify previous or
                                            ongoing pubiic assistance received by the applicant and other household
                                            members. For example, the Korth Dakota system compares the names of



                                            Page 20                                               GAO/RCED-90-9 Food Stamp Automation
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Benefits Achieved From Automation Not
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each member of an applicant’s household to every person receiving food
stamps in the state. This helps to prevent an applicant from receiving
double benefits because he or she has applied for assistance as a mem-
ber of two separate households. Furthermore, it aids the worker in
processing the current application if the applicant has previously
applied for food stamps because much of the information needed may
have already been recorded and in the automated system.

Following the initial applicant screening, each of the automated sys-
tems, to varying degrees, can guide the eligibility worker through the
applicant’s interview to help ensure complete and accurate coverage.
For example, the automated systems in Vermont, Kentucky, and the two
Texas local offices have screens that appear on the workers’ terminal in
food stamp application sequence. Further, these systems will not permit
the worker to bypass any of the information requested on the applica-
tion Generally, the systems recognize the type of entries permitted for
each data query and prevent or alert the worker of invalid or unaccept-
able entries. Also, the systems validate certain entries, such as double
checking social security numbers for 9 digits, and compute certain calcu-
lations for eligibility, such as household budgets and benefits allowed.
Furthermore, the automated systems apply program policy as appropri-
ate to each application. For example, for household members reported
as students or elderly, Kentucky’s system compares their reported ages
to ensure that the program-required age limits are met.

Following the eligibility worker’s completion of the application process-
ing activity, supervisors can make direct inquiries into the case file at
remote terminals and review the completed application. Also, workers
can more easily make changes in the case files as the participants’
household circumstances change. For example, the eligibility worker can
immediately make changes in the automated case file in response to a
change in household income. The system then automatically recomputes
the effect on allowable household benefits.

Eligibility workers frequently cited their respective systems’ capability
to automatically prepare for mailing notices of action to food stamp
applicants or recipients as a major improvement in food stamp case
processing. Generally, each of the systems initiate, print, and prepare
for mailing notices of case action, such as appointments for interviews,
application approvals and denials, and recipient termination. For exam-
ple, without direct involvement by the eligibility worker, the systems we
reviewed routinely mail reporting forms to the participants who are



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                             Benefits Achieved From Automation Not
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                             required to report monthly about changes in household income. More-
                             over, all seven automated systems we reviewed immediately identify
                             program participants who are delinquent in submitting monthly reports.
                             In all cases, if the required monthly reports are not received by the
                             extended filing date, the automated system will terminate benefits.
                             Some systems, such as the Texas statewide system, automatically mail a
                             package of information for participants to reapply for food stamps as
                             the end of their period of eligibility approaches.

Certain Program Changes      Program administrators and eligibility workers told us that another
Quickly Implemented          major benefit of their automated systems was the ease in implementing
                             certain across-the-board or “mass changes” to the Food Stamp Program.
                             These include such program changes as seasonal or annual adjustments
                             to social security, supplemental security income, income eligibility stan-
                             dards, or dependent care deductions. The automated systems can change
                             food stamp allotments overnight to reflect changes brought about by
                             certain program changes. According to program administrators, before
                             automation, eligibility workers spent weekends working overtime to
                             manually change all of the case files to reflect program changes.


Automated Systems Axe        Each of the seven automated systems we reviewed improved the eligibil-
                             ity workers’ ability to accurately determine applicant eligibility to par-
Designed to Help             ticipate in the Food Stamp Program. The automated systems were
Eligibility Workers to       designed to help the workers to prevent, discover, and take corrective
Prevent, Detect, and         action on errors. The following four sections describe the general
Correct Certain Errors       improvements in error prevention, detection, and correction in several
                             important areas in the applicant eligibility determination process-the
                             process of appropriately determining the applicant’s household income,
                             household-related deductions, other household resources, and whether
                             nonfinancial requirements are met -brought about by the automated
                             systems we reviewed.

Automated Systems Help       The automated systems increased the likelihood of the eligibility
Determine Household Income   worker’s accurate use of reported household income in determining eligi-
                             bility, including verifying reported income and detecting unreported
                             income. Household income consists of any earned or unearned gain or
                             benefit, (wages, salaries, and monies from additional sources, such as
                             other public assistance programs) by any household member. For exam-
                             ple, the automated systems were designed to automatically calculate
                             household income and credits according to program policy guidelines,
                             thus ensuring accurate application of policy. To illustrate, Vermont’s



                             Page 22                                   GAO/RCED-90-9 Food Stamp Automation
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                                 Renef¶b Achieved From Automation Not
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                                 system is designed to compute the household’s total income and the ben-
                                 efit amount based on that income. The systems in Kentucky, North
                                 Dakota, and Texas convert income reported on a weekly basis into a
                                 monthly figure as required by the program. Each system can also test
                                 certain households for eligibility based on the households’ net income,
                                 gross income, or both.

                                 Also, each of the automated systems help eligibility workers verify
                                 reported information by matching the data with other sources. This type
                                 of verification became a program requirement in 1987, when the Service
                                 began requiring workers to compare applicant reported income to infor-
                                 mation obtained in the Income Eligibility Verification Systems (IEVS),
                                 which is maintained by the state social services agency. IEVSis a data
                                 system that is separate and apart from the automated Food Stamp Pro-
                                 grams and contains earned and unearned income information main-
                                 tained by federal and state agencies, such as unemployment
                                 compensation and Internal Revenue Service information. However, each
                                 state agency periodically compares automated statewide Food Stamp
                                 Program case income information to information contained in IEVS.Not
                                 only does this data matching process help verify reported income, it also
                                 helps identify income that the applicant failed to report.

                                 The computer match flags discrepancies. Then, so that eligibility work-
                                 ers can compare the data, state agency program staff route notices of
                                 the discrepancies through the automated system to individual computer
                                 terminals, such as those in North Dakota’s system, or through written
                                 printouts for local office systems such as those in Dallas and San
                                 Antonio. Workers can then determine if an error has indeed occurred
                                 and, if so, correct it.

Automated Systems Help           The automated systems we reviewed assisted in the consistent applica-
Determine Household Deductions   tion of Food Stamp Program regulation pertaining to allowable deduc-
                                 tions from household income. In establishing an adjusted household
                                 income amount, program regulation allows applicants to deduct either a
                                 standard amount or the actual amount for certain expenses such as the
                                 costs of shelter and utilities, some medical expenses, dependent care
                                 expenses, and a standard 20-percent earned income deduction. For
                                 example, for deductions such as the utility allowance, the automated
                                 systems total the applicant’s reported electric, gas, and phone bills, com-
                                 pare the total to the program’s standard allowance, and allow the eligi-
                                 bility worker to apply the appropriate amounts in determining adjusted
                                 household income.



                                 Page 23                                   GAO/RCED-90-9 Food Stamp Automation
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                                Chapter 2
                                ReneKts Achieved From Automation Not
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Aubmated Systems Help           The automated systems generally help to ensure mathematical accuracy
Determine Household Resources   of the applicable household resource calculations and help in making
                                accurate determinations of whether the applicant meets the program’s
                                resource eligibility requirement. Resources include liquid and nonliquid
                                funds, such as cash, bank accounts, and the cash surrender value of life
                                insurance policies. Program regulations provide that generally the maxi-
                                mum allowable resources of all members of the household should not
                                exceed $2,000. Because this program policy is built into the software,
                                the systems automatically consider each type of resource listed on the
                                food stamp application and determine whether the applicant’s total
                                resources meet the eligibility limit.

Automated Systems Help          According to program administrators and our observations of demon-
Determine Compliance With       strations, each system has improved the workers’ ability to obtain and
Nonfinancial Requirements       verify certain applicant-reported information used to determine the
                                household’s compliance with nonfinancial program requirements. Pro-
                                gram eligibility depends initially on the applicant meeting certain nonfi-
                                nancial standards, such as age, citizenship, residency, work registration,
                                and proper household composition. For example, the automated systems
                                we reviewed, such as Kentucky’s, automatically determine whether a
                                household member listed as a “student” meets the program definition of
                                a student. If the student definition is met, the systems automatically
                                compute appropriate income, school expenses, and deductions for
                                students.


                                The seven automated systems we reviewed achieved many of the
Improvements in                 expected administrative improvements or benefits. These improve-
Program Results Not             ments, however, have not always changed the results of program opera-
Always Achieved                 tions as expected. Some expected benefits preceded automation. For
                                example, as a result of a nonautomated, concerted effort, Kentucky
From Automation                 experienced large drops in its program error rates2 prior to its auto-
                                mated systems operation. In Vermont, North Dakota, and Dallas and San
                                Antonio, Texas, we found that the automated systems did not always
                                change the results of program operations as expected. For example,

                                “Program error rates consist mainly of two reported measures of program errors: “case error rate”
                                and “issuance error rate.” Prom annual statistical samples of their food stamp caseload, states deter-
                                mine and report the percentage of cases containing errors. The result is the case error rate. Using this
                                case error sample, the states estimate and report the percentage of benefits resulting from the errors,
                                This is the issuance error rate and is often referred to as the “payment error” rate. Because our
                                analysis was based on fiscal years 198l-87 data, the issuance error rate includes erroneous overis-
                                suances, not under-issuances.The Hunger Prevention Act of 19EB(P.L. 100-435) amended the Food
                                Stamp Act to require the issuance error rate to include erroneous underissuances as well as erroneous
                                overissuances.



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                                         preventing major types of errors, such as those involving household
                                         income, was often beyond each automated system’s capability because
                                         the system did not always have access to the necessary information.
                                         Also, improvements such as reducing the number of program forms
                                         needed to process applications were countered by new automated sys-
                                         tem-required forms to process applications.

                                         Table 2.2 lists the specific Food Stamp Program activity for which we
                                         attempted to determine the effect of each automated system in each
                                         location we visited and the extent to which data was available to assess
                                         the system’s impact. As shown in the table, the information available
                                         enabled us to address only part of the program activity which should
                                         have improved or benefited from each automated system. Appendix 1
                                         describes the regression models we used, where sufficient information
                                         was available, to determine the effect that the automated system had on
                                         each of the expected benefits, that is, the change in the results of pro-
                                         gram operations, such as reducing errors or program staff due to
                                         automation.3

Table 2.2: Locations Where Appropriate
Data Were Available to Determine the     Program activity for which                          Availability of appropriate data
Effect of the Automated Systems on       the effect of the automated                           North           Dallas,       San Antonio,
Specific Program Activity                system was determined                Vermont          Dakota          Texas         Texas
                                         Program error rates:
                                            Issuance errors                   Yes              Yes               No                No
                                            Case errors                       Yes              Yes               No                No
                                         Program staffing                     Yes              No                Yes               Yes
                                         Claims for overissuances             Yes              No                No                No
                                         Amount of collections for            Yes              No                No                No
                                         overissuances
                                         Amount of time spent on food         No               Yes               No                No
                                         stamp cases
                                         Timeliness of case action            No               No                Yes               Yes


                                         The Kentucky program and the automated local office in California are
                                         not included in the list because the information was not available for us


                                         3Program outcomes-such as error rates, costs, staffing levels, and timeliness of application process-
                                         ing-are affected by the interaction of many characteristics of this environment. The results pre-
                                         sented here control for some, but not all, of the factors that affect program operations. For example,
                                         we account for changes in total caseload and certain types of staff and policy changes. We do not,
                                         however, account for the characteristics of the cases served; corrective actions and management
                                         practices other than automation; differences in organizations, staff qualifications, and job responsibil-
                                         ities; or measures of the enthusiasm or commitment of managers and staff.



                                         Page 26                                                     GAO/RCJZD-So-9 Food Stamp Automation
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                          to evaluate the results of their automated systems operations. As dis-
                          cussed in chapter 1, our analysis was based on data in fiscal years 1983-
                          87; the Kentucky system did not begin operating statewide until March
                          1988. In California, information about the local office’s automated sys-
                          tem in Vallejo, California, which had been operating since 1972, was not
                          available and thus we were prevented from comparing operations before
                          and after automation. We were, however, able to compare this office to a
                          similar nonautomated local office in Red Bluff, California, to determine
                          whether automation, alone, may have made a difference in the results in
                          the two offices’ program operation.


Kentucky’s Error Rates    Although we could not measure or determine the impact of Kentucky’s
Began Decreasing Before   automated system on program operations, we found that the state of
                          Kentucky had success in decreasing both its case and issuance error
Aiitomation               rates in recent years. This success, though, cannot be attributed to the
                          state’s automated system because major management initiatives caused
                          the rates to decline before the system became operational in the spring
                          of 1988.

                          The Kentucky Food Stamp Program case error rate decreased from 26.5
                          percent in fiscal year 1984 to 18.3 percent in fiscal year 1987. For the
                          same period, the issuance error rate decreased from 8.9 percent to 4.1
                          percent. State program officials attribute much of the rate of decrease to
                          measures they took to reduce program errors. Specifically, they changed
                          some program requirements, increased the number of staff, provided
                          additional staff program training, and increased the amount of supervi-
                          sory monitoring and review. For example, the state shortened the time
                          period between caseworker reviews of recipient household circum-
                          stances from the once-per-year requirement to at least once every 3
                          months for specific types of cases based on earnings and earnings his-
                          tory. Also, the state increased the number of staff administering the
                          program from 1,942 to 2,139 between fiscal years 1984-87, while at the
                          same time the number of food stamp cases had decreased.

                          According to state program officials, although they do not expect the
                          system to automatically decrease error rates, they believe that as the
                          automated system becomes more of a routine part of the program opera-
                          tion, it should enable workers to avoid making certain errors. In turn,
                          error rates should decrease even further.




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Automated Systems’      In general, the state agencies covered by our review expected that auto-
                        mating their programs would help reduce the number of Food Stamp
Effect on Error Rates   Program errors. Reducing the number of errors should, in turn, reduce
Varied in Vermont and   the state’s overall program error rates. For example, Vermont expected
North Dakota            to reduce its issuance error rate from about 11 percent to about 4 per-
                        cent, while North Dakota, which traditionally has had low issuance
                        error rates, expected its system not to increase the error rates during its
                        development and to assist in maintaining its low error rates once it
                        became operational. Our regression models for the program results data
                        from Vermont and North Dakota, which were the only locations with
                        the necessary information for this analysis,4 suggest that Vermont’s
                        automated system was not instrumental in reducing its error rates, but
                        North Dakota’s did cause its error rates to decrease. However, in both
                        states the major types of program errors involving household income,
                        resources, and nonfinancial eligibility requirements continue to occur
                        because the necessary information to prevent these types of errors
                        remains beyond the automated systems’ reach.

                        In Vermont, the models indicated that the state’s automated system was
                        not a statistically significant factor in decreasing the state’s issuance
                        and case error rates. However, as described in appendix I, the model
                        suggests that other factors, such as the number of food stamp cases, had
                        a statistically significant effect on the overall decrease in the error rates
                        during fiscal years 1981-87. Specifically, the issuance error rate declined
                        from 9.6 percent to 6.3 percent, while the case error rate decreased from
                         17.1 to 14.7 percent. Throughout the period before this automated sys-
                        tem-essentially     fiscal years 1981 through 1983-and after its develop-
                        ment-fiscal years 1984 through 1987-the error rate fluctuated up
                        and down. For example, the issuance error rates began at 9.6 percent in
                        fiscal year 1981, increased to about 14.0 percent in fiscal year 1982, and
                        decreased to about 7.1 percent in fiscal year 1983. During the system’s
                        first year of operation in fiscal year 1984, the case error rate increased
                        to 9.0 percent, decreased to 7.3 percent in fiscal year 1985, then to 5.9
                        percent in fiscal year 1986, and ended at 6.3 percent in fiscal year 1987.
                        It has not yet met the 4 percent goal. The state’s case error rate followed
                        a similar up and down pattern.


                        4Reported state agency quality control error rates are statistically valid estimates only for the total
                        statewide food stamp caseloads. No statistically valid estimates exist for local food stamp office oper-
                        ations such as those we reviewed in California and Texas. Also, since the use of our regression models
                        requires information for a period of time before and after automation, Texas’ statewide and Ken-
                        tucky’s systems could not be included because, as discussed in chapter 1, information was not availa-
                        ble before the Texas system or after the Kentucky system became operational.



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                                On the other hand, in North Dakota, our models indicated that its auto-
                                mated system was a statistically significant factor in decreasing its issu-
                                ance and case error rates. This is better than North Dakota state
                                officials expected for their automated system. The raw data, too,
                                showed that the issuance and case error rates declined since the auto-
                                mated system began operating in 1985. However, this decline in error
                                rates began even before the automated system began operations. Specifi-
                                cally, the issuance error rate was about 6.6 percent in fiscal year 1981,
                                and decreased to about 5.0 percent by fiscal year 1983. After the pro-
                                gram was automated in 1984, the error rate began at 6.2 percent, then
                                dropped to 5.4 percent in fiscal year 1985, to 1.9 percent in fiscal year
                                1986, and ended at 4.2 percent in fiscal year 1987. The case error rate
                                followed the same pattern, beginning at 22.1 percent in fiscal year 1981
                                and ending at 12.9 percent in fiscal year 1987.

Error Prevention Often Beyond   We found that the types of errors occurring in the Food Stamp Programs
Systems’ Capabilities           before automation are continuing to occur after automation. According
                                to state program and quality control system administrators, the consis-
                                tency in the types of errors being made is not surprising even though the
                                automated systems have enhanced the eligibility workers’ ability to
                                avoid, detect, and correct many errors in these same categories. They
                                said that many of the same avenues for errors continue to exist. For
                                example, the automated systems do not enable eligibility workers to dis-
                                cover all types of unreported income or other resources, such as motor
                                vehicles, or to always accurately determine household composition,
                                which includes establishing the living and eating arrangements of all
                                household members. To illustrate, the North Dakota automated system
                                has on-line capability that enables the eligibility worker to access the
                                state department of motor vehicles to determine whether the applicant
                                has unreported motor vehicles. However, the automated system would
                                not help the worker discover unreported vehicles registered out of state
                                or in someone else’s name.

                                Furthermore, even though the automated systems’ data matching capa-
                                bilities have enhanced the eligibility workers’ ability to detect unre-
                                ported income and other resources, many times the data bases used in
                                the matching process are not available in a timely manner to prevent
                                errors from being made. Computer matches with the IEVSand other data
                                bases may be monthly or semi-monthly. Additionally, the data may be
                                several months old. For example, according to eligibility workers in
                                Texas, the IEVSdata base information, such as employer reports to the
                                state employment commission, is usually from 3 to 6 months old at the
                                time the applicant applies for food stamps, The Texas Department of


                                Page 28                                   GAO/RCED90-9 Food Stamp Automation
                          chapter 2
                          Reneflts Achieved From Automation Not
                          Always Reflected in Program Results




                          Human Services has indicated that it is testing on-line access with the
                          Texas Employment Commission for applicants’ wage and unemployment
                          compensation history. However, while the unearned income data may be
                          more current and could enhance the detection of failing to report the
                          receipt of income from this source, the information pertaining to earn-
                          ings and wages reported by employers will be at least 3 months old, as
                          stated above.

                          In addition, Internal Revenue Service information is usually at least a
                          year old by the time the eligibility worker receives it. By the time the
                          match is made and unreported income or other resources are discovered,
                          an error, such as in determining an applicant eligible when in fact the
                          unreported income or other resources renders the applicant ineligible,
                          has been made.

                          Moreover, if eligibility workers did not act on the results of the com-
                          puter matching process, errors may not be discovered and corrected.
                          Because the information contained in the data bases used in the com-
                          puter matching process is usually dated, the workers cannot rely only on
                          the fact that the match indicates a problem, such as unreported income.
                          The applicant cannot be denied benefits until additional facts are
                          obtained. The worker must call or write to confirm with the cognizant
                          source that the problem indeed exists. The discrepancy must be
                          resolved, and resolution of the discrepancy depends on the willingness
                          and capability of the eligibility worker, as well as on the availability of
                          documentation or third-party contacts for confirmation.


Vermont’s Automated       According to Food Stamp Program administrators in each of the states
                          we reviewed, automated systems enhanced their capability to establish
System Had No Effect on   claims and increase collections for overissuances. For example, North
Claims or Collections     Dakota administrators explained that once eligibility workers discover
                          that participants have been overissued benefits, claims are established
                          and collection attempts are made. With their automated system, the
                          workers and state agency administrators can inquire into the case files,
                          physically located anywhere in their state, to determine whether claims
                          were filed promptly and to monitor the amount and timing of collections
                          by local offices. As shown in table 2.3, except for one local office in Cali-
                          fornia, the amounts of overissuance claims and collections generally
                          have increased in recent years in the locations we visited.




                          Page 29                                    GAO/RCED-9&9 Food Stamp Automation
                                                                                                                                               -
                                        Chapter 2
                                        Benefits Achieved From Automation Not
                                        Always Reflected in Program Results




Table 2.3: Claims and Collections for
Food Stamp Overissuances for Fiscal     Dollars in thousands
Years 1982-87                                                                     State agency or local office
                                                          North                                                                 Vallejo,
                                        Fiscal year       Dakota          Vermont          Texas               Kentucky         California
                                        1982
                                        Claims            N/A”            $ 63             $ 8,047             N/A”             N/A”
                                        Collections       N/A”              12               1,184             N/As             N/A”
                                        1983
                                        Clarms            N/A”              101               8,010            N/A”             $ 97
                                        Collections       N/A”               28               1,578            N/As               38
                                        1984
                                        Claims            $293              233               9,614            $1,144            100
                                        Collections         91               69               3,912               586             57
                                        1985
                                        Clarms             174              286               8,761             1,802            137
                                        Collections         96               82               3,801               872             55
                                        1986
                                        Clarms             212              205              11,482             2,324            288
                                        Collections        124               78               5,254             1,119             67
                                        1987
                                        Claims             435              224              12,480             2,419            172
                                        Collections        159               80               5,744             1,496             63
                                        Note. N/A = not avaIlable
                                        %ate agency and cognizant reglonal Food and Nutrition Serwce offuals     did not have the records of
                                        claims and collections


                                        In Vermont, the only location we visited that had sufficient information
                                        for use in our regression analysis, our model showed that the automated
                                        system was not statistically significant in increasing claims or collec-
                                        tions. Our discussions with eligibility workers, however, revealed that
                                        they expected the automated system to increase claims because estab-
                                        lishing a claim merely involves recording the over-issuance and estab-
                                        lishing an accounts receivable. Collections, they told us, involved some
                                        activities beyond the automated system’s capabilities. Collections
                                        depend more on the state’s power to enforce and its effort to make the
                                        collections.




                                        Page 30                                                 GAO/RCEIMO-9 Food Stamp Automation
                               Chapter 2
                               Benefits Achieved From Automation Not
                               Always Reflected in Frogram Results




Automation’s Effect on         Officials in Vermont and Texas expected that their automated systems
Program Staffing Varied   in   would reduce the number of people needed to administer food stamp
                               caseloads, Our models showed that the automated system had a statisti-
Vermont and Dallas and         cally significant effect on the number of program staff needed to admin-
San Antonio, Texas             ister the Food Stamp Program, increasing the number in Vermont and
                               generally decreasing the number in Dallas and San Antonio. Also, the
                               effect varied according to the type of program worker-clerical   work-
                               ers, eligibility workers, and supervisors-which  often differed at each
                               location.

                               In Vermont, program officials expected the automated system to reduce
                               total program staff by about 11 people. However, the actual number of
                               program staff remained relatively constant, increasing by 1 person,
                               from 136 in fiscal year 1981 to 137 in fiscal year 1987. Furthermore, our
                               models indicated that the automated system, which was implemented in
                               fiscal year 1983, was a statistically significant factor in increasing staff
                               levels. Specifically, the models suggest that the automated system had
                               the greatest impact on increasing the number of eligibility worker
                               review specialists needed to administer the program. The system had no
                               statistically significant effect on the number of eligibility intake work-
                               ers. (Eligibility workers in Vermont are classified as intake workers who
                               process initial applications or review specialists who maintain ongoing
                               participants.)

                               Texas program officials expected that the local office automated sys-
                               tems implemented in Dallas and San Antonio would greatly reduce the
                               number of program staff needed to administer the program. In Dallas,
                               while the number of staff increased from 53 in fiscal year 1981 to 65 in
                               fiscal year 1987, our model indicated that the increase in staff probably
                               would have been even higher had it not been for the automated system.
                               For example, the models indicated that the automated system was sta-
                               tistically significant in decreasing the number of eligibility workers. Yet,
                               the raw data showed that the actual number of eligibility workers
                               increased by 10 in fiscal years 1981-87. Thus, had the automated system
                               not decreased the number of workers needed, the actual number of
                               workers would have been greater than 10. On the other hand, the auto-
                               mated system had no statistically significant effect on the number of
                               supervisors needed in the office. The actual number increased by four,
                               from two to six during fiscal years 1981-87.

                               In the San Antonio local office, total staff increased from 57 in fiscal
                               year 1981 to 83 in fiscal year 1987. Our model suggests that although



                               Page 31                                    GAO,‘RCED-90-S Food Stamp Automation
                             Chapter 2
                             Benefits Achieved From Automation Not
                             Always Reflected in Program Results




Automation’s Effect on       Officials in Vermont and Texas expected that their automated systems
Program Staffing Varied in   would reduce the number of people needed to administer food stamp
                             caseloads. Our models showed that the automated system had a statisti-
Vermont and Dallas and       tally significant effect on the number of program staff needed to admin-
San Antonio, Texas           ister the Food Stamp Program, increasing the number in Vermont and
                             generally decreasing the number in Dallas and San Antonio. Also, the
                             effect varied according to the type of program worker-clerical   work-
                             ers, eligibility workers, and supervisors- which often differed at each
                             location.

                             In Vermont, program officials expected the automated system to reduce
                             total program staff by about 11 people. However, the actual number of
                             program staff remained relatively constant, increasing by 1 person,
                             from 136 in fiscal year 1981 to 137 in fiscal year 1987. Furthermore, our
                             models indicated that the automated system, which was implemented in
                             fiscal year 1983, was a statistically significant factor in increasing staff
                             levels. Specifically, the models suggest that the automated system had
                             the greatest impact on increasing the number of eligibility worker
                             review specialists needed to administer the program. The system had no
                             statistically significant effect on the number of eligibility intake work-
                             ers. (Eligibility workers in Vermont are classified as intake workers who
                             process initial applications or review specialists who maintain ongoing
                             participants.)

                             Texas program officials expected that the local office automated sys-
                             tems implemented in Dallas and San Antonio would greatly reduce the
                             number of program staff needed to administer the program. In Dallas,
                             while the number of staff increased from 53 in fiscal year 1981 to 65 in
                             fiscal year 1987, our model indicated that the increase in staff probably
                             would have been even higher had it not been for the automated system.
                             For example, the models indicated that the automated system was sta-
                             tistically significant in decreasing the number of eligibility workers. Yet,
                             the raw data showed that the actual number of eligibility workers
                             increased by 10 in fiscal years 1981-87. Thus, had the automated system
                             not decreased the number of workers needed, the actual number of
                             workers would have been greater than 10. On the other hand, the auto-
                             mated system had no statistically significant effect on the number of
                             supervisors needed in the office. The actual number increased by four,
                             from two to six during fiscal years 1981-87.

                             In the San Antonio local office, total staff increased from 57 in fiscal
                             year 1981 to 83 in fiscal year 1987. Our model suggests that although



                             Page 31                                    GAO/RCELMO-9 Food Stamp Automation
                            Chapter 2
                            Benefits Achieved From Automation Not
                            Always Reflected in Program Results




                            total staff increased during this period, the automated system was sta-
                            tistically significant in reducing the number of clerical staff needed to
                            administer the program. The number of clerical workers, though, actu-
                            ally increased from 18 to 35 in fiscal years 1981-87. Thus, as was the
                            case in the Dallas office, had it not been for the automated system, the
                            San Antonio office may have needed more than 17 additional clerks to
                            administer the program. On the other hand, our model suggests that the
                            automated system had a statistically insignificant effect on both the
                            number of supervisors and eligibility workers needed.

                            According to Texas state agency and San Antonio local office program
                            officials, the effect of the automated system on the number and type of
                            staff seems reasonable. They told us that the automated systems permit-
                            ted the eligibility workers to do more, negating the need for additional
                            staff to handle the increase in the eligibility workers’ tasks. For exam-
                            ple, along with the introduction of the automated system came a pro-
                            gram change that required most of the participating households to
                            report monthly about household circumstances. Although additional
                            staff may have been needed to handle this additional paperwork, the
                            automated system enabled the same workers to track receipt of the
                            reports, process them, and make necessary changes automatically.


Automated System Had No     Korth Dakota expected workers to spend less time on food stamp cases
                            after its program was automated. Our model for North Dakota, which
Effect on Time Spent on     was the only location that had sufficient information available to test
Food Stamp Cases in North   the effect of automation on the amount of time spent by workers on
Dakota                      cases, indicated that the automated system was not a significant factor
                            affecting the amount of time spent on food stamp case processing. In
                            any event, the program results data show that the average time spent
                            by program workers on food stamp only cases increased from 35.4 min-
                            utes per case in fiscal year 1983, before the system was automated, to
                            47 minutes per case in fiscal year 1987, after the system was automated.

                            As we mentioned earlier, determining why the automated system had a
                            certain effect-in this case increasing the amount of time spent on food
                            stamp cases-was beyond the scope of our review. However, according
                            to program workers these results seem reasonable. For example, in
                            North Dakota, eligibility workers told us that they usually complete the
                            food stamp applicant interview without using the automated system.
                            Following the interview, they spend additional time entering the infor-
                            mation obtained into the automated system. Thus, the two-step process



                            Page 32                                   GAO/RCED-So-9 Food Stamp Automation
                               Chapter 2
                               Benefits Achieved From Automation Not
                               Always Reflected in Program Results




                               could account for the increase in the total time spent by workers
                               processing food stamp cases.


Automated Systems Had          According to planning documents and state agency officials, the Texas
                               local offices’ (the only locations with the information we needed to ana-
Little Effect on Eligibility   lyze the impact of automation on the timeliness of food stamp case
Determination Timeliness       actions) automated systems were expected to standardize the benefit
in Texas                       determination process at the eligibility worker level. This standardized
                               procedure would result in more timely food stamp eligibility determina-
                               tions.” However, our models indicated that the automated systems had
                               no statistically significant effect on the timeliness of case processing in
                               either the Dallas or the San Antonio office.

                               In both offices, as described in appendix I, the automated system was
                               not statistically significant in increasing case processing timeliness.
                               However, the percentage of cases processed within the required
                               timeframes did improve for both offices. For Dallas, the percentage of
                               cases processed in a timely manner averaged about 63 percent for the 3-
                               year period before the automated system went into effect and increased
                               to about 87 percent in fiscal year 1986-the first full year of the auto-
                               mated system’s operations. For San Antonio, the percentage of cases
                               processed in a timely manner averaged about 70 percent for the 3-year
                               period before automation, 77 percent in fiscal year 1984-the first full
                               year of the automated systems operations-and        increased to 82 percent
                               in fiscal year 1986.

                               According to state agency program officials, the automated systems
                               should have improved the timeliness of the workers’ case actions in each
                               of the offices. They told us that in the Dallas office, the standardization
                               and consistent policy application that came with the automated system
                               should have improved the timeliness of the eligibility workers’ actions.
                               In San Antonio, however, they agree that the effect of automation may
                               not be readily apparent. That office had several major reorganiza-
                               tions-which     are considered in our models in appendix l-that      could
                               have actually caused a decrease in the timeliness had it not been for the
                               automated system.


                               ‘Timely food stamp cases are those in which program eligibility is determined (1) within 30 days
                               from the date of initial application for regular program participants or (2) within 5 days from the
                               date of initial application for participants needing expedited services--whereby immediate benefits
                               are provided to households that have accessto less than $150. Texas’ program, however, requires
                               expedited service to be determined within 1 day of initial application.



                               Page 33                                                  GAO/RCED9@9 Food Stamp Automation
                            Chapter 2
                            Benefits Achieved From Automation Not
                            Always Reflected in Program ReEdt5




Automated Systems Have      In comparing automated and nonautomated operations, we found that in
Not Always Reduced          general, the number of forms used to process food stamp applications
                            and to maintain food stamp cases before automation remained about the
Paperwork in the States     same or increased after automation. Even though the on-line systems
We Reviewed                 developed by Vermont, North Dakota, and Kentucky permit paperless,
                            direct entry into the automated systems, eligibility workers still must
                            maintain paper files for each case and some changes in paperwork
                            accompanied the automated operations. Paperwork increased for the
                            batch-process systems in the Texas and California local offices mostly
                            because of the need to duplicate the paper file information for entry into
                            the automated systems.

                            For example, the number of forms needed to process food stamp cases in
                            Kentucky remained about the same. While the automated system
                            reduced the need for 11 forms used under the manual system, the sys-
                            tem required 9 new forms to the process the cases. In North Dakota, the
                            standard federal Food Stamp Program application form was changed to
                            meet the needs of the automated system in North Dakota. Instead of the
                            previous 5-page application, food stamp applicants must complete a 40-
                            page application. According to state and local office administrators, this
                            enabled the workers to obtain more accurate and complete information
                            on all household members, not just the head of the household. Also, the
                            information can be used to apply for assistance in other programs, such
                            as AFDCand medical assistance. In September 1988, the application form
                            was revised down to 34 pages. In the Vallejo, California office, a
                            batched-process system, we found that the number of forms used in the
                            automated food stamp office to process food stamp cases used 34 forms,
                            while the nonautomated local office used 25 forms.


Comparison Shows That       Our comparison of two local office operations in California showed that
                            the automated office processed fewer food stamp cases per eligibility
an Automated Office         worker at a greater average cost per food stamp case than did the
Processed Fewer Cases Per   nonautomated office. We reviewed the results of each office’s program
Worker at a Greater Cost    operations to determine only whether the presence of the automated
Than a Nonautomated         system appeared to make a difference in the number of staff or adminis-
Office                      trative cost to process food stamp cases. We did not include other fac-
                            tors that could have influenced the efficiency of either the automated or
                            nonautomated office. These factors could include such activities as the
                            offices’ organization and operating procedures, the characteristics of the
                            cases processed, as well as the efficiency of the automated system itself.




                            Page 34                                   GAO/RCEDSQ9 Food Stamp Automation
              Chapter 2
              Benefits Achieved From Automation Not
              Always Reflected in Program Results




              From the results of each office’s program operations data, we found that
              in fiscal year 1983, the automated office had an average monthly
              caseload of 2,915 food stamp cases and about 42 eligibility workers, a
              ratio of about 69 cases to 1 person. In 1987 the caseload decreased to an
              average monthly caseload of 2,362 and the number of staff increased to
              49, causing the ratio to decrease to 48 to 1. On the other hand, the
              nonautomated office had an average monthly caseload of 1,473 and 20
              eligibility workers or a ratio of about 74 to 1 in fiscal year 1983. By
              fiscal year 1987 the ratio had increased to about 78 to l-an average
              caseload of 1,793 and 23 eligibility workers.

              Correspondingly, our comparison between the two offices’ administra-
              tive costs to process food stamp only cases also showed that the
              nonautomated office spent less per case than did the automated office.
              Specifically, the automated office’s administrative cost to process a food
              stamp only case averaged about $107 in June 1984. In June 1987, the
              average cost per case increased to about $129.63 per case. While in the
              nonautomated office, the average cost per food stamp only case was
              about $92.99 in June 1984 and about $94.71 in June 1987.


              Many of the expected benefits have been achieved by the automated
Conclusions   systems we reviewed in Vermont, North Dakota, Kentucky, Texas, and
              California. We found that automation enabled workers to (1) automati-
              cally avoid certain program errors and (2) better identify certain pro-
              gram errors for correction. Automation also improved many aspects of
              the food stamp case processing activity, such as guiding the client inter-
              view, managing participant cases, and notifying applicants of case
              action.

              However, in the states with the information needed to perform our anal-
              yses, we found that these improvements have not always reduced state
              agency program error rates or improved program administration. Cer-
              tain types of program errors prevented by automation, such as arithme-
              tic errors, were never a major problem. Thus, automation has had a
              limited effect in reducing error rates. On the other hand, preventing or
              detecting certain major types of program errors, such as earned income
              errors, has been beyond the automated systems’ capabilities. As a result,
              the major categories of program errors continue to be the same after
              automation. Furthermore, our analysis suggests that automation has not
              always resulted in administrative improvements such as less time




              Page 35                                   GAO/RCEDSO-9 Food Stamp Automation
                   Chapter 2
                   Jk.nel’its Achieved From Automation Not
                   Always Reflected ln Fl-ogram Resulta




                   processing food stamp cases, fewer staff needed to administer the pro-
                   gram, or more timely eligibility determinations. Automation, for exam-
                   ple, has resulted in more forms needed to process food stamp cases in
                   some of the programs we reviewed.

                   We also found that measures of program performance, such as error
                   rates, may be affected by changes in any of a number of program
                   related factors other than automation, such as staffing levels or
                   caseloads. Kentucky experienced a decline in issuance and case error
                   rates following such changes but prior to automation of its program. By
                   considering these other changes along with the impact of the automated
                   systems, our analysis suggested, for example, that North Dakota’s auto-
                   mated system played a significant role in reducing its program error
                   rate, whereas in Vermont, the system did not. In doing our regression
                   models, as with all regression analyses, we could consider only a limited
                   number of changes affecting program activity for a short period of time.
                   In addition, more time may be needed to determine whether the auto-
                   mated systems will eventually cause more of the expected improve-
                   ments in the results of program operations.


                   The Food and Nutrition Service recognizes that the report “addressed
Agency and State   the complex subject of the costs and benefits of automation in the Food
Comments and Our   Stamp Program... .” However, the Service indicates that the methodology
Evaluation         used by us to measure the effects of automation on the Food Stamp Pro-
                   gram has serious limitations that it stated are not adequately empha-
                   sized in the report. The Service notes that while our regression models
                   include a number of relevant variables, a number of equally important
                   factors are left out which can be expected to influence the outcome of
                   automation. Service examples of these factors include the economic
                   health of state and local governments, changes in state funding priori-
                   ties, and differences in the type of households served. The Service
                   acknowledges our awareness of these limitations, but states that we
                   downplay their significance.

                   We believe that throughout the report we discuss the limitations of the
                   data and the statistical results pertaining to program changes caused by
                   Food Stamp Program automation. Because we had neither adequate data
                   on the factors cited by the Service nor controls in our models for them,
                   we have qualified our report accordingly.




                   Page 36                                   GAO/RCEIMO-9 Food Stamp Automation
Clmpter 2
Benefits Achieved From Automation Not
Always Reflected 111Progrluu Resulta




We also obtained comments from the states of Kentucky, North Dakota,
Texas, and Vermont covered in this review. Generally, the states indi-
cate that it is difficult if not impossible to accurately measure the
impact of automation on their programs due to the large number of vari-
ables involved and the lack of reliable data. We acknowledge these diffi-
culties and have stated in the report that we did not include all the
variables affecting automation such as quality of program staff and
socioeconomic factors within the community served by a program
because of lack of adequate data. However, the variables that are
included in our analysis enabled us to determine the statistical signifi-
cance of possible relationships between automation and each of the dif-
ferent measures of program benefits, while controlling for the effects of
other program-related factors, such as changes in staffing or caseload.
Other comments, related largely to the clarity and technical accuracy of
specific statements in the draft report, have been incorporated where
appropriate. (See apps. V through IX for the Food and Nutrition Ser-
vice’s and the states’ comments of this report and our response.)




Page 37                                  GAO/RCED!W4l Food Stamp Automation
States and the Service Did Not Maintain
Adequate Records of Automated System Costs
and Equipment Inventories
                       Although no specific federal or state agency requirements exist for state
                       agencies to account for the development or operations costs of specific
                       automated systems, we were able to identify the costs of the automated
                       Food Stamp Programs in Vermont, North Dakota, and Kentucky. How-
                       ever, state agency and Service accounting records were not sufficient for
                       us to identify, in Texas and California, the cost to develop or operate
                       each of the automated systems. In these two states, agency records in
                       general did not identify expenditures related to each specific Service
                       approved funding request. Records at each of the five state agencies did
                       not always account for the operating costs of the system that the Ser-
                       vice approved for development. Moreover, despite federal requirements,
                       none of the state agencies could account for all of the automated sys-
                       tems-related equipment in their inventories purchased pursuant to the
                       approved ADP funding requests. Similarly, Service regional office
                       records did not account for approved funding provided to the states.
                       State agency and Service accounting and records problems (1) prevented
                       us from identifying the actual costs of ADP systems developed with Ser-
                       vice funds in some states, (2) resulted in state agencies inappropriately
                       allocating expenditures between approved projects and, in at least one
                       state, exceeding approved federal funding levels for ADP development,
                       and (3) increased the potential for fraud, waste, and abuse of system
                       equipment.


                       The Federal Managers’ Financial Integrity Act of 1982 requires, in part,
Financial Integrity    government agencies to evaluate their internal controls and report
and Internal Control   whether they comply with prescribed internal control standards and
Requirements           provide reasonable assurance that revenues and expenditures are prop-
                       erly recorded and accounted for so that reliable financial reports may be
                       prepared and accountability of assets may be maintained. To ensure
                       such accountability, the act requires that the internal controls be consis-
                       tent with the Comptroller General “Standards.” In addition, the Office
                       of Management and Budget’s Internal Control Guidelines of 1982 iden-
                       tify several specific objectives of grant activities that agencies should
                       seek to achieve, some of which the agencies can require of grantees; e.g.,
                       state agencies administering the Food Stamp Program need to undertake
                       certain internal control actions.

                       Thus, while the act does not address the extent to which it applies to
                       grant programs, it is within the contemplation of the act and implement-
                       ing guidelines that agencies will identify specific internal control objec-
                       tives for their grant programs and monitor their grant agreements in a



                       Page 38                                    GAO/RCED909   Food Stamp Automation
                          Chapter 3
                          States and the Service Did Not Maintain
                          Adequate Records of Automated System
                          Costs and Equipment Inventories




                          manner that seeks to achieve the specific internal control objectives
                          identified by the agencies.


                          Unlike Vermont, North Dakota, and Kentucky, Texas and California
State Agencies’           state agency accounting records did not, in general, account for the costs
Accounting and            of the specific Food Stamp Program automated systems approved by the
Service’s Monitoring      Service. While the state agencies’ requests for Service funding to
                          develop the automated systems provided estimates of the total costs to
of ADP Costs Are Not      develop the ADPsystem and its annual operating costs, Service regional
Adequate                  supervisory personnel told us that state agencies are not required to
                          determine or report the systems’ actual development and operating
                          costs to the Service. Also, the Service regions, which approve the state
                          agencies’ requests for ADPfunding, are not required to monitor or deter-
                          mine the actual expenditures for the ADPsystems’ development or oper-
                          ations As a result, we could not determine the actual costs to develop
                          and operate federally funded ADPsystems, and in at least one state, the
                          Service-approved cost ceiling was exceeded.


Accounting Practices by   Inadequate accounting practices have resulted in state agencies inappro-
                          priately allocating costs or exceeding approved federal funding limits.
States Need Improvement   For example, Texas inappropriately allocated costs to develop its sys-
                          tems, and California developed the San Francisco automated issuance
                          system without accounting for its specific expenditures. Because of
                          these accounting problems we could not determine the development and
                          operating costs for the Texas and California automated systems. More-
                          over, inadequate accounting and oversight resulted in North Dakota
                          exceeding the original approved federal funding limit for developing its
                          system.

                          The five state agencies generally grouped together all ADP-related
                          expenditures charged to the Food Stamp Program and submitted quar-
                          terly claims to the Service regions for federal funding during the annual
                          Food Stamp Program budgeting process, as described in chapter 1. For
                          example, at the time of our review, the Texas state agency had 13 sepa-
                          rate Service-approved ADP funding requests. All of the expenditures
                          claimed by the state against these funding requests, including those for
                          the three different local office automated systems developed in fiscal
                          years 1981-87, were combined on the required state agency’s quarterly
                          claims to the Service Southwest Region and identified as “ADP develop-
                          ment expenditures” or “ADP operating costs.” According to state pro-
                          gram officials, Texas was not required to separate related ADP


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development costs or to separate related ADP operating costs among the
different automated systems’ funding requests.

Because it realized that these accounting problems existed, in fiscal year
1985 the Service’s Southwest Region asked the Texas state agency to
account for expenditures relating to each Service-approved ADP funding
request. According to the two state budget officers involved in the task
of reconstructing the expenditure records, they assigned each ADP-
related expenditure voucher dating back 5 years to approved ALIPfund-
ing requests on the basis of their knowledge of each automated system.
Table 3.1 shows that on the basis of the reconstructed records, the Food
Stamp Program’s share of the cost of developing the first local office
automated system was about $1.1 million. The program’s share of the
second local office system cost was about $11 million and the program
share of the cost of the third local office system, which is currently
under development, is about $1.9 million to date.




Page 40                                   GAO/RCTDWB   Feud Stamp Automation
                                    Chapter 3
                                    States and the Service Did Not Maintain
                                    Adequate Records of Automated System
                                    Costs and Equipment Inventories




Table 3.1: Costs Claimed by State
Agencies to Develop and Operate     Dollars in millions
Approved Automated Systems GAO                                                                           Food Stamp Program share of
Reviewed for Fiscal Years 1981-87                                                                                   costs
                                    Automated system/date the system began                                Development      Operations
                                    operations                                                                   costs         costs’
                                    Vermont (Sept 1983 /86)b                                                        $1.25                    $1.96
                                    North Dakota (Ott 1984)                                                         $1.40                    $1.13
                                    Kentucky (Mar. 1988)                                                           $19.75          Not Applicable
                                    Texas:
                                      Statewrde system (Oct. 1979) estimated operating
                                      costs srnce FY 1981                                                                   c             $29.83
                                      First local office system - 17 offrces (May 1983)                             $1.06             UnknownC
                                      Second local office svstem - 37 offices (Mav 1985)                           $11.35             UnknownC
                                      Third local office system (not operational)                                   $1.87       In development
                                    Californra:
                                      On-Line issuance
                                      -16 Local Offices (Sept. 1983)                                            UnknownC                UnknownC
                                      WCDSd
                                      ;‘996p)cal Offrces (First Office began operating In
                                                                                                                            c           UnknownC
                                    aWrth the exceptrons of Texas and the WCDS systems, cumulatrve costs srnce date systems operatrons
                                    began.
                                    bVermont’s system rnrtrally developed for the Food Stamp Program began operations in 1983 Added
                                    features to the system to serve other assrstance programs began operations In 1986

                                    ‘With the exceptions of Texas and the WCDS systems, development and operation costs were
                                    “unknown” because the state or local office records did not Identify the costs or officrals could not
                                    estimate the applicable costs Texas and WDCS was developed prior to the period. fiscal years 198167,
                                    covered by our review
                                    dWCDS=Welfare     Case Data System, of which the Valleto, Calrfornra, local offrce IS a part


                                    We found, however, that the reconstructed records may not reflect an
                                    appropriate allocation of costs among the various automated systems.
                                    Although each voucher we reviewed documented ADP-related expendi-
                                    tures, because of the judgmental method used to allocate the expendi-
                                    tures to the different automated systems, the voucher totals did not
                                    correspond with the Texas state agencies’ claims to the Service for reim-
                                    bursement. For example, the reconstructed records showed expendi-
                                    tures of only $10,444 in fiscal year 1987 for the first local office
                                    automated system, but the state agency claimed expenditures of
                                    $211,888. On the other hand, for the second local office system, the
                                    reconstructed records showed expenditures of $6,255,553 in fiscal year
                                     1985, but only $4,682,970 in expenditures was claimed. Thus, on the
                                    basis of the reconstructed records, it appears that the state agency
                                    claimed federal reimbursement in excess of expenditures to develop the



                                    Page 41                                                       GAO/RCEDWB         Food Stamp Automation
                           Chapter 3
                           States snd the Service Did Not Maintain
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                           first system, and claimed less than actual costs to develop the second
                           system.

                           Because of the way the Texas state agency accounted for expenditures,
                           we could not verify the accuracy of the reconstructed records or the
                           state’s allocation of costs. The Service’s Southwest Regional Administra-
                           tor told us in February 1989 that the region was in the process of deter-
                           mining the appropriateness of the state agency’s ,allocation of ADP
                           expenditures. Also, according to Texas state agency budget and account-
                           ing personnel, the state agency began in fiscal year 1988 to account for
                           expenditures related to each specific approved ADP funding request.

                           Because some state agencies did not always account separately for the
                           automated systems developed with funds received from each Service
                           approved ADP funding request, we could not always identify the actual
                           cost to develop and operate each of the automated systems, as shown in
                           table 3.1. For example, the state agency accounting records for Texas
                           did not identify the operating costs of either statewide system or local
                           office system. Similarly, for California, we could not identify the devel-
                           opment or operating costs for the San Francisco issuance system or the
                           operating costs for the California local office system we reviewed.
                           Although Texas state agency officials could estimate the cost to operate
                           the Texas statewide automated system, California state agency and local
                           office officials did not have the information to estimate the San Fran-
                           cisco issuance system’s operating costs.

                           On the other hand, based on cited limitations table 3.1 shows that we
                           identified the actual costs claimed by the state agencies to develop and
                           operate the automated systems in Vermont, North Dakota, and Ken-
                           tucky. Although these state agencies also pooled ADP development and
                           operations costs as a state agency total, they had only one automated
                           system each and essentially only one overall Service-approved ADP fund-
                           ing request to develop the Food Stamp Program’s share of the system.
                           Thus, the cost of the automated system was the state agency’s allocated
                           share of its total ADP expenditures to the Food Stamp Program.


Limited ADP Funding        Service regions have not, in general, ascertained the costs of developing
                           or operating the state agencies’ automated systems, Service regional
Oversight by the Service   officials told us that there is no requirement that expenditures for ADP
Regions                    development or operations costs be compared to the Service-approved
                           systems development plan. Service regulations and ADPAdvance Plan-
                           ning Document Handbook 103 provide that the Service regions perform


                           Page 42                                   GAO/RCED-90-O Food Stamp Automation
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on-site reviews. There are generally three types of on-site reviews: pre-
installation, utilization, and post-installation. The post-installation
review is to be performed after the automated system becomes opera-
tional to determine whether the system adequately reflects the system
in the state agency’s request. The timing and content of this review is
left to the regions’ discretion. Consequently, we found that the timing
and content of the Service post-installation reviews varied from region
to region. Also, because state claims for expenditure reimbursement are
not evaluated and post-installation reviews do not always include a
review of systems’ costs, adequate controls do not exist to ensure that
approved federal funding amounts are not exceeded.

The Service’s Northeast Region did not perform the post-installation
review of Vermont’s system, which initially began operations in 1983,
until May 1988. The region reviewed the system’s functional capability
but did not address the cost of development or operations. According to
Service’s Southwest Region program management personnel, because of
lack of resources they have not performed a review of the two Texas
local office automated systems that have been operational since 1983
and 1985, respectively. They told us that, instead, they have monitored
the state’s performance through correspondence, on-site visits, and
numerous meetings throughout the systems’ development. The Service’s
Western Region has not performed a post-installation review of the San
Francisco on-line issuance system, which began operations in September
 1983.

The Service’s Mountain Plains Region performed a post-installation
review in 1984, shortly after the North Dakota system began operations.
It included a financial review of the system’s development costs. From
this review, the Service discovered that the state had claimed federal
funding in excess of the approved amount. The Service originally
approved North Dakota’s ADP request for about $1.10 million in January
1984; the Service share of the cost was about $844,000. However, the
state claimed expenditures of about $1.3’7 million. According to Service
regional ADP staff, however, the Service retroactively approved the
expenditures for the system following their post-installation review,
including the approximately $270,000 that was in excess of the origi-
nally approved amount.

Although North Dakota was the only instance in which we found evi-
dence that a state agency exceeded its approved amount to develop its
automated system, regions need to monitor ADP expenditures and claims
for reimbursement during the Food Stamp Program budgeting process to


Page 43                                   GAO/RCED-90-9 Food Stamp Automation
                                     Chapter 3
                                     States and the Service Did Not Maintain
                                     Adequate Records of Automated System
                                     Costs and Equipment Inventories




                                     ensure approved amounts are not exceeded. We found that this was gen-
                                     erally not done. In fact, until October 1988, only the Service Southeast
                                     Region required that state agency claims for federal reimbursement be
                                     reconciled to approved ADP funding requests. In October 1988 the Ser-
                                     vice Southwest Region began reconciling state agency ADP budgets and
                                     quarterly claims to approved request amounts.


                                     None of the state agencies we reviewed could account for all ADP equip-
State Agencies’ ADP                  ment purchased pursuant to their approved ADP funding requests. The
Equipment Inventory                  Kentucky, North Dakota, California, Vermont, and Texas state agencies
Records Were Not                     did not maintain current or accurate inventories of the automated sys-
                                     terns equipment purchased in conformance with Service-approved fund-
Accurate                             ing requests. These states did not have accurate records of the amounts
                                     of equipment purchased or of the locations where the equipment was
                                     used. Such inadequate record keeping is contrary to Title 7, part 277, of
                                     USDAregulations and Office of Management and Budget Circular A-102,
                                     which require that each state agency account for all equipment pur-
                                     chased with federal funds.

                                     Specifically, the regulations and circular require that state agencies’
                                     property management records include the equipment’s description, iden-
                                     tification number, acquisition date and cost, source, percent of Service
                                     funds used, location, use, and disposition information. The guidance also
                                     provides that where discrepancies between the inventory records and
                                     on-hand quantities exist, an investigation be made to determine the
                                     cause of the discrepancy.

                                     We found that the Kentucky state agency maintained an automated rec-
                                     ord of its ADP equipment purchases and individual property record cards
                                     identifying the location of the equipment. However, as shown in table
                                     3.2, the lists of equipment requested, the lists of equipment purchased,
                                     and the property records did not agree.

Table 3.2: Inventory of Kentucky’s
Automated Food Stamp Program                                 Total                     Program                     State ADP
Equipment                            Equipment        planned as             Total     property    No record of       section
                                     description      per request      purchased        records        location      records
                                     Controllers               166               166         163              3           166
                                     Terminals               1,834             1,834       1,805             29         1,834
                                     Printers                  633               633         629              4           630
                                     Modems                    270               275   No record              2           273




                                     Page 44                                             GAO/RCED-90-9 Food Stamp Automation
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                                    According to the state agency program manager, the ADP section main-
                                    tains the records of the Food Stamp Program’s automated system equip-
                                    ment. The ADP section manager supplied us with an equipment inventory
                                    record showing 273 modems and their locations. She told us that their
                                    record was the most complete and accurate. On the other hand, accord-
                                    ing to her, the Food Stamp Program property records are not kept cur-
                                    rent. She could not explain why the modems were not identified in the
                                    property records. Also, she could not explain the difference between the
                                    275 modems purchased, and the 273 shown on the inventory record.

                                    The North Dakota state agency could not provide us with a list of the
                                    systems-related equipment purchased with Service-approved funding.
                                    According to the state agency ADP systems project director, a detailed
                                    inventory would have to be developed by contacting each of the state’s
                                    53 local food stamp offices to determine what equipment it had. Simi-
                                    larly, the San Francisco office also could not provide us a list of equip-
                                    ment for its on-line issuance system.

                                    In Vermont, state agency officials gave us two inventory listings of
                                    equipment purchased for the state’s automated Food Stamp Program
                                    system, one as of April 1988 and one as of August 1988. However, as
                                    shown in table 3.3, the two lists did not agree. The officials could not tell
                                    us which list was more accurate nor could they account for the differ-
                                    ences between the two lists. Furthermore, they could not explain the dif-
                                    ference between the numbers of terminals and keyboards at some local
                                    offices. In Newport, Vermont, for example, the April 1988 inventory
                                    listed 15 terminals but only 5 keyboards. In Springfield, Vermont, the
                                    list showed 19 keyboards and 18 terminals. Yet, the automated system is
                                    designed to require one keyboard for each computer terminal.

Table 3.3: Inventory of Vermont’s
Automated Food Stamp Program                                                                Number
Equipment                           Listed equipment                          April 1988   August 1988    Difference
                                    Keyboards                                       341            397            56
                                    Terminals                                       361            387            26
                                    Printers                                         38             35                 3
                                    Controllers                                       14            14                 0
                                    Modems                                             1             3                 2
                                    Other                                              1             1                 0


                                    Although auditing the ALIPinventory in Vermont was beyond the scope
                                    of our review, we checked the equipment on hand at an office we visited



                                    Page 45                                     GAO/RCED-90-9 Food Stamp Automation
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              Chapter 3
              States and the Service Did Not Maintain
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              Costs and Equipment Inventories




              in October 1988 against the state’s record of the equipment located
              there. We were provided information to document the existence of 30
              terminals and 30 keyboards in the office; however, the state’s inventory
              list showed 28 terminals and 29 keyboards at that location. Neither local
              office nor state agency officials could explain the difference.

              In Texas, although state agency officials provided us with computer
              printouts that identified mp-related equipment and the location of each
              item, we could not determine which equipment belonged to which auto-
              mated system because the inventory did not identify the name of the
              system or the approved federal funding account. An audit performed by
              the USDA'SSouthwest Regional Office of the Inspector General in fiscal
              year 1986 identified incomplete property records and missing ADP equip-
              ment. According to the state agency’s response to the audit, they located
              the missing equipment. However, according to the Assistant Deputy
              Commissioner of Information Systems in Texas, as of January 1989, the
              ADP equipment listed in the Service-approved ADPfunding requests and
              purchased by the state still cannot be traced to the specific automated
              system developed.’


              In furtherance of the purposes of the Federal Managers’ Financial Integ-
Conclusions   rity Act of 1982, the Food and Nutrition Service needs to improve its
              internal controls over the Food Stamp Program’s automated systems
              development and operations costs, and equipment inventories. In addi-
              tion, the Service should require that state agencies establish controls
              that allow the Service to properly monitor these Food Stamp Program
              activities.

              For the states we reviewed, specific expenditures to develop and operate
              automated Food Stamp Program systems were generally not identifiable
              in the accounting records of those states with multiple ADP systems.
              Neither Service regions nor state agencies we examined required that
              accounting records be maintained for specific ADP systems’ expendi-
              tures. Furthermore, even though state agencies are required to maintain
              an accurate accounting for ADP-related equipment, they did not maintain

              ‘In an August 18, 1989. letter providing comments to the report, the Texas Department of Human
              Services Commissioner explained that the department can identify the number of workstations, file
              services, and other equipment purchased to support a particular project. In the department’s view,
              whether or not the equipment identity can be directly related to a specific project seems an unneces-
              sary requirement that could prevent them from using equipment from different systems interchange-
              ably when unexpected delays occur for some systems. According to the Commissioner, once the
              delays in equipment acquisition are overcome, each system receives the approved amount of
              equipment.



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                             States and the Service Did Not Maintain
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                             Costs and Equipment Inventories




                             such records in any of the states we reviewed. Consequently, the Service
                             has no assurance that the state agencies (1) spend the funds as agreed
                             upon in their approved ADPfunding requests, (2) do not exceed the ADP
                             development costs approved by the Service, or (3) account for equip-
                             ment and other assets obtained with federal funds.

                             To overcome these shortcomings in state agency accounting for and Ser-
                             vice regional oversight of ADPexpenditures, the state agencies need to
                             account for expenditures for services and equipment against each spe-
                             cific Service-approved ADP funding request, a practice recently imple-
                             mented by the Texas state agency. In addition, the Service needs to
                             compare state agencies’ claims for reimbursement, to the amount
                             approved by the Service. In addition, the Service regions need to include
                             in their post-installation reviews, which are required by Service Hand-
                             book 103, a timely financial review of the states’ expenditures closely
                             following the systems’ development and an inventory of all ADP-related
                             equipment purchased pursuant to the approved funding request.


                             To help ensure good internal controls over the Food Stamp Program’s
Recommendations to           automated systems development, operations costs, and equipment
the Secretary of             inventories, we recommend that the Secretary of Agriculture direct the
Agriculture                  Administrator of the Food and Nutrition Service to:

                     l       Amend Service Handbook 103 to require post-installation reviews to be
                             performed as soon as the state agency’s automated system becomes
                             operational and require that the reviews include (1) reconciliation of
                             state agency expenditures with each approved ADPrequest for funding
                             and (2) reconciliation of state agency equipment purchased, pursuant to
                             the approved ADPrequest for funding, with state agency property
                             records.
                     l       Amend the Service Food Stamp Program budgeting process to require
                             state agencies to (1) account for total expenditures for each Service-
                             approved request for ADPdevelopment funding and (2) account annually
                             for all ADP-related equipment purchased pursuant to each Service-
                             approved request for ADPdevelopment funding.
                         l   Amend Service regional operating procedures to require Service officials
                             to monitor agency quarterly claims for federal reimbursement to ensure
                             that state agencies do not exceed the approved ADPfunding level.




                             Page 47                                   GAO/RCED-90-9 Food Stamp Automation
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                      The Food and Nutrition Service states that although we did not question
Agency Comments and   any specific costs charged to the Service by any of the states, we never-
Our Evaluation        theless assert that greater controls are needed over mp-related charges
                      to the Food Stamp Program. Furthermore, the Service states that the
                      controls recommended by us, which center on the Service collecting,
                      recording and reconciling state expenditures for specific mp-related
                      costs, are prohibited by Office of Management and Budget (OMB) Circu-
                      lar A-102.

                      We agree that we did not find any questionable costs charged to the Ser-
                      vice. However, although there is no Service requirement to reconcile
                      costs incurred to develop a system, the Service’s Mountain Plains Region
                      discovered that a $270,000 expenditure was incurred by a state which
                      was beyond the approved ADP amount. We believe that such a require-
                      ment represents basic minimal internal controls. Furthermore, we are
                      not recommending that the Service collect, record or reconcile expendi-
                      tures for specific Anp-related costs as the systems are developed. We are
                      recommending that once the system is operational the Service include in
                      its required post-installation review a reconciliation of the cost incurred
                      to develop the system.

                      Finally, in our view, the revised OMBcircular A-102 does not prohibit
                      federal agencies from requiring state agencies to report the level of
                      detail envisioned by our recommendation. We are not recommending
                      that the Service account for or require state agencies to account for spe-
                      cific “object costs” expenditures, as stated in the OMBcircular, for ADP
                      development or operation costs. We are recommending that the Service
                      and the state agencies account for the total actual costs to develop each
                      system. To more clearly convey our recommended action, we revised the
                      recommendation to the state agencies to refer to accounting for total
                      costs for each approved request only. In fact, in fiscal year 1985 the
                      Service’s Southwest Regional Office requested our recommended
                      accounting detail from Texas. The state began reporting the requested
                      level of detail in fiscal year 1988. Specifically, all we are recommending
                      is that state agencies, which must request specific approval for ADP
                      development funding, account for associated costs and report the total
                      actual expenditures associated with each specific request approved for
                      funding. Because all expenditures submitted for federal reimbursement
                      are subject to federal audit, the states must be able to account for all
                      claimed expenditures whether for ADP development, operations, or other
                      program related expenditures.




                      Page 48                                   GAO/RCRWtO-9 Food Stamp Automation
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The Service disagrees that it should reconcile state agency equipment
acquisition with funds used and state agency property records. We are
not recommending that the Service reconcile state agency equipment
acquisition with funds used and state agency property records. We agree
that this is a state agency responsibility. In fact, we are recommending
that the Service specifically require the state agencies to do this
accounting for each approved ADP funding request. For the Service, we
recommend only that it include in its post-installation review, once the
system becomes operational, a requirement to reconcile the equipment
purchased for the ADP system approved for development.




Page 49                                   GAO/RCED90-9 Food Stamp Automation
Enhanced Funding for Automation Has
Achieved Its Objective

                     In a 1980 House Agriculture Committee report,’ the Committee
                     expressed the need for increasing the rate of federal funding for ADP
                     development from 50 percent to 75 percent to encourage state agencies
                     with manually operated Food Stamp Programs to initiate automation
                     efforts. Subsequently, the Food Stamp Act Amendments of 1980 pro-
                     vided for 75-percent funding for states to plan, design, develop, or
                     install ADP systems for administering the Program. According to
                     responses to our questionnaire (see app. III), all of the state agencies
                     that received the 75-percent funding stated that the increased funding
                     was very important to either begin automation efforts or to modify,
                     upgrade, and replace existing automated systems. Now, 50 of the 53
                     state agencies administering the Food Stamp Program have automated
                     systems that support their Food Stamp Programs statewide. The
                     remaining three state agencies have partially automated systems. Thus,
                     it appears that the increased rate of funding at the 75-percent level has
                     achieved its objective.


                     All of the state agencies administering the Food Stamp Program have
All State Agencies   automated at least portions of their Food Stamp Program using 75-per-
Have Automated to    cent and/or 50-percent federal funding. According to responses to our
Some Extent          questionnaire, 50 of the 53 state agencies have developed automated
                     systems that support their program statewide.* The other three agencies
                     have automated systems at the local level and plan future automated
                     capabilities at the state level. The majority of functions identified in the
                     Service’s regulations for the model plan as discussed below, required by
                     the Food Security Act of 1985, have been completely or partially auto-
                     mated by most of the state agencies. According to our questionnaire
                     results, states that developed systems using only the normal 50-percent
                     funding perform similar program functions to those developed using 75-
                     percent funding.

                     Tables 4.1, 4.2, and 4.3 present the status of automation in the states
                     with regard to the Service program function requirements of the model

                     ‘House of Representatives Report No. i’S&96 Gong., 2nd Sess.

                     “The sophistication level of an automated system can vary widely from state to state and within the
                     state. For example, a simple system could be a client index where the computer is essentially a stor-
                     age mechanism for information. The eligibility worker calculates information and then enters the
                     information into the computer. In a sophisticated system, each eligibility worker has a computer ter-
                     minal that is used to enter raw data during the interview and the computer then determines eligibility
                     at the time of the interview. In addition, this same system can control benefits and determine the
                     amount of assistance the client will receive. We did not address the sophistication level of automation
                     in this report Instead, we asked the states to report the extent of automation based on their interpre
                     tation of what program automation consists of in their Food Stamp Program



                     Page 50                                                   GAO/RCJXIHO-9 Food Stamp Automation
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EnhancedFundhg for AutomationHas
Achieved Its Objective




plan. The model plan calls for systems to be designed to have the capa-
bility to perform up to 34 different functions: 12 functions for a certifi-
cation system including determining applicant eligibility, 13 functions
for an issuance, reconciliation, and reporting system including generat-
ing authorization for benefits, and 9 general functions, including timeli-
ness and data quality requirements, to be performed by all systems.
According to our questionnaire results, states that developed systems
using only the normal 50-percent funding perform similar program func-
tions to those developed using 75-percent funding at least once to
develop part or all of systems development. All 53 state agencies have
developed systems with some of the automated functions identified for
a certification system, an issuance, reconciliation, and reporting system,
and a general system standard.

Table 4.1 shows that most of the different certification functions are
automated to some extent in the majority of the states. Specifically, 28
of the 37 agencies approved for 75-percent funding have partially or
completely automated each of the functions essentially used to deter-
mine an applicant’s eligibility for program participation. Eight of the 16
agencies receiving 50-percent funding for ADP development are partially
or completely automated with regard to the certification functions.
Table 4.2 shows that the majority of issuance, reconciliation, and report-
ing functions are partially or completely automated. Twenty-six of the
37 state agencies developing systems with these types of functional
capabilities obtained 75-percent funding, while 9 of the 16 state agencies
developing these functions used only the SO-percent level of funding.
Finally, table 4.3 shows that most of the state agencies developed sys-
tems that are completely or partially capable of performing the majority
of the general functions. Thirty-two of the 37 states receiving 75-percent
funding automated the general functions, while 11 of the 16 states
receiving only 50-percent funding automated general functions.

The state agencies reporting the fewest automated capabilities and hav-
ing partially automated systems include Montana, the Virgin Islands,
and Ohio. Specifically, of the 34 possible functional requirements listed
in the USDAmodel plan regulations, Montana reported that 13 were auto-
mated statewide, the Virgin Islands reported that 11 were automated,
and Ohio reported that 5 program functions were automated statewide.
However, for Ohio many of these functions were automated at the local
office level. State agency officials reported that in Ohio, 27 of the 34
program functional standards were automated at the local office level.
All three state agencies reported that they were currently planning addi-
tional ADP development, which should be implemented in 1989 or 1990.


Page 51                                   GAO/RCEDsO-9 Food Stamp Automation
                                            chapter 4
                                            Enhanced Fundhg for Automation Has
                                            Achieved Ita Objective




Table 4.1: Status of Automation With Regard to the Model Plan’s Requirements for Certification
__

                                                                                  Identify elements   Provide        Notify
                                                                   Determine      that affect         automatic      certification
State                                                              eligibility    eligibility         cutoff         unit
Alaba-a                                                            C              C                   C              P
Alaska         ~-                                                  C              C                   C              C
Arizona.                                                           C              C                   C              C
Arkansas                                                           C              C                   C              P
Callforn,a’:                                                       N              N                   N              N
Ciloradc                                                           C              P                   C              C
Connectlcuta                                                       P              P                   C              P
Delanare /                                                         C              P                   C              C
Dlstrlct of Columbiaa                                              P              P                   C              P
Florlda,T                                                          P              P                   C              P
Georgia‘;                                                          C              P                   C              C
Guam<                                                              C              P                   C              C
Hawail                                                             C              C                   C              C
Idaho”                                                             C              C                   C              C
lllinols”                                                          P              P                   C              C
IndIanad                                                           P              P                   c              P
Iowa”                                                              P              P                   C              C
Kansas                                                             C              P                   C              C
Kentuckyd                                                          C              C                   C              C
Louisiana0                                                         C              C                   C              P
Malne”                                                             C              C                   C              P
Maryland”                                                          P              P                   C              C
Massachusettsa                                                     P              P                   C              P
Michigan”                                                          P              P                   C              P
Mlnnesotad c                                                       N              N                   N              N
MISSISSIDDI~                                                       C              P                   C              C
Mtssourl”                                                          C              C                   C              P
Montanal                                                           N              N                   N              N
Nebraska”                                                          C              C                   C              C
Nevadaa                                                            P              C                   C              C
New Hampshire                                                      C              C                   C              C
New Jerseya                                                        C              C                   C              C
New MexIcoa                                                        C              P                   C              C
New York”                                                          C              C                   P              P
North CarolInaa                                                    C              C                   C              P
North Dakota                                                       C              C                   C              C




                                            Page 52                                          GAO/RcED-W9   Food Stamp Automation
                                               Chapter 4
                                               Enhanced Funding for Automation Has
                                               Achieved 1t.aObjective




                                Provide             Identify                         Store               Provide for     Monthly
 Check for       Meet the       mass                cases        Calculate or        household           social          repotting and
 duplicate       IEVS system    change              pending      validate            characteristics     security        retrospective
 cases           requirements   capabilities        action       benefits            information         enumeration     budgeting
 C
__~             -C              C                   C            C                   C                   C               P
 C               C              C                   C            C                   C                   C               C
 C               C              C                   C            C                   C                   C               C
 C               C              C                   N            N                   C                   C               C
 C               C              N                   N            N                   N                   C               N
 C               C              C                   C            C                   C                   P               C
 N               C              C                   N            N                   P                   N               C
 C               C              C                   P            C                   C                   C               C
 C
__~              C              C                   N            N                   C                   P               P
 P               C              C                   N            N                   P                   C               C
 C
__~~~            C              C                   C            C                   C                   C               C
 C
___       ~~~ ~- C              C                   P            C                   C                   N               C
 C               P              C                   C            P                   C                   C               C


  C                 C           P                   N            P                   C                   P               P
  C
___~_    -~         C           C                   P            N                   C                   C               C
  C                 C           C                   C            C                   C                   C               C
  C                 C           C                   C            C                   C                   C               C
  C                 C           C                   C            C                   C                   C               C
  C                 C           C                   C            C                   C                   P               C
  C
___~     .--        P           C                   P            N                   P                   C               P
  C
___                 C           C                   P            C                   C                   C               N
  C          ~   .-~C           C                   C            C                   C                   C
  N                 C           N                   N            N                   P                   P               N
  C                 C           P                   C            C                   C                   C               C
  C                 C           C                   C            P                   C                   C               P
  C                 C           N                   N            N                   C                   C               P
  C                 C           C                   C            C                   C                   C               C
  P
__--                P           P                   N            C                   P                   P               P
  C                 C           C                   C            P                   C                   C               C
  c      -          c           C                   C            C                   C                   C               C
  C                 C           C                   C            C                   C                   C               C
  C                 C           C                   C            P                   C                   C               C
  C                 C           C                   C            C                   C                   C               C
                    C           C                   C            C                   C                   C               C
-~-C
                                                                                                                             (continued)




                                               Page 63                                             GAO/RCED99-9 Food Stamp Automation
                    Chapter 4
                    Enhanced Funding for Automation Haa
                    Achieved 1t.aObjective




                                                      Identify elements     Provide        Notify
                                      Determine       that affect           automatic      certification
State                                 eligibility     eligibility           cutoff         unit
Ohlo” b                               N               N                     N              N
Oklahoma”                             C               C                     C              C
OregorY                               C               C                     C              C
PennsylvanIaa                         P               C                     C              C
Rhode Islanda                         P               P                     C              C
South CarolInaa                       C               C                     C              C
South Dakotaa                         C               C                     C              C
Tennesseea                            P               P                     C              P
Texas?                                C               C                     C              C
Utah                                  C               P                     C              C
Vermont                               C               C                     C              C
VirgInIaa                             C               P                     C              C
Virgin Islandsa b                                     C                                    N
Washingtona                           P               P                     C              C
West VirgIniaa                        P               P                     C              C
WisconsM                              C               C                     C              C
Wyoming                               C               C                     C              P




                    Page 64                                        GAO/RCED-9@9 Food Stamp Automation
                                       Chapter 4
                                       Enhanced Funding for Automation Has
                                       Achieved Ita Objective




                           Provide               Identify                          Store                   Provide for        Monthly
Check for   Meet the       mass                  cases           Calculate or      household               social             reporting and
duplicate   IEVS system    than e                f$gg            validate          characteristics         security           retrospective
cases       requirements   capa %ilities                         benefits          information             enumeration        budgeting
N           P              N                      N              N                 N                       P                  N
P           C              C                      C              P                 C                       P                  C
C           P              C                      C              C                 C                       C                  C
C           C              P                      P              P                 C                       C                  C
P           P              C                      P              P                 C                       C                  P
C           C              C                      C              C                 C                       C                  C
C           C              C                      C              C                 C                       C                  C
C           C              P                      P              C                 P                       C                  C




C                                                 C                                C                       C
P           P              C                      N              P                 C                       P                  N
C           P              C                      P              N                 C                       C                  C
N           C              C                      P              P                 C                       C                  C
P           C              C                      C              C                 C                       C                  C
C           C              C                      C              C                 C                       C                  C
                                       Note. The program functronal standards presented here are in abbreviated   form. For complete version,
                                       see appendrx IV

                                       %tate IS In the process of planmng or developrng a new system or addrtronal automated system capa-
                                       brlrtres.

                                       bState does not have an automated system that supports the Food Stamp Program statewide


                                       F     = Function IS completely automated
                                           P = Function IS partially automated.
                                           N = Functron is not automated at all.




                                           Page 66                                                   GAO/lZCED-90-9 Food Stamp Automation
                                          chapter 4
                                          Enhanced Fuuding for Automation Haa
                                          Achieved Ita Objective




Table 4.2: Status of Automation With Regard to the Model Plan’s Requirements for Issuance, Reconciliation, and Reporting*
                                                                                              Reconciliation     Redemption of
                                     Genemte                                                  of transacted      more than one
                                     authorizations    Prevent           Allow for under-     authorization      authorization
State                                for benefits      duplicate HlRs    or over-issuance     documents          document
Alabama’                             C                 C                  C                   C                  N
Alaska                               C                 C                  C                   C                  C
Arizona” -                           C                 C                  C                   N/A                N/A
Arkansa9                             C                 C                  P                   WA                 N/A
CalIforniaa D                        C                 N                  C                   C                  C
Colorado                             P                 C                  C                   WA                 WA
ConnectIcuta                         P                 N                  C                   P                  C
Delawarea                            C                                    C                   C                  C
Dlstnct of Columbiaa                 P                 C                  P                   C                  C
Floridaa                             C                 P                  C                   WA                 N/A
Georalaa                             C                 C                  P                   C                  C
Guama
--.
                                     C                 C                  C                   C                  C
Hawall                               C                 C                  P                   C                  C
Idahoa
__..--                               C                 P                  C                   C                  C
Illinoisa
__---                                C                 C                  C                   C                  N
IndIanaa                             C                 C                  P                   C                  C
Iowaa                                 P                 P                 C                   N/A                N/A
Kansasa                               C                 C                 C                   C                  C
Kentuckya                             C                 C                 C                   C                  C
Louisianaa                            C                 C                 C                   C                  C
Maine”                                C                 P                 C                   WA                 WA
Marylanda                             C                 C                 C                   C                  C
MassachusetW                          C                 C                 C                   C                  C
Michiaana                             C                 C                 C                   C                  C
MInnesotaa’                         P                  P                 N                    N                 N
M~ssiss~pp~~                        C                  C                 C                    C                 C
Mlssour?                             P                 C                 N                    P                 N
Montanaa                             N                 C                 N                    N                 N
Nebraskaa                            C                 C                 C                    N/A               N/A
___-
Nevadaa                              C                 C                 C                    C                 C
New Hampshlrea                       C                 C                 C                    N/A               N/A
New Jerseya                          C                 C                 C                    C                 C
New Mexicoa                          C                 P                 C                    N/A               N/A
New Yorka                            C                 C                 C                    C                 C
North Carolinaa                      P                 C                 C                    C                 C




                                           Page 56                                        GAO/RcXWKL9 Food Stamp Automation
                                       Chapter 4
                                       Enhanced Funding for Automation Ras
                                       Achieved Its Objective




 Generate data   Generate data                   Program-wide                    Participation                  Tracking
 to meet fed.    to meet other   Sample          reduction and     Expedited     history                        collection of
 reporting       reporting       selection for   restoration of    issuance of   covering 3       cutoff of     recipient
 requirements    requirements    QC reviews      benefits          benefits      years            benefits      claims
 C               C               C               C                 C             C                C             C
 C
__-              C               C               C                 C             C                C             C
 C               C               C               C                 C             C                C             C
 C               P               C               N                 C             C                C             C
 C               N               C               N                 C             C                C             N
 C               P               C               C                 C             C                C             C
 C               P               N               C                 C             C                C             N
 C               C               C               C                 C             C                C             C
 P               P               C               C                 C             C                C             P
 P               P               C               C                 P             C                C             P
 C               C               P               C                 C             C                C             C
 C               C               C               C                 C             C                C             C
 P               P               C               C                 C             C                C             P
 P               C               C               C                 C             C                C             C
 C               C               C               C                 C             C                C             C
 P               P               P               P                 C             C                C             P
 P               C               C               C                 C             C                C             C
 C               C               C               C                 C             C                C
 C               C               C               C                 C             C                C             C




 C               C               C               C                 C             C                C             C
 N               P               C               N                 N             P                N             P
 C               C               C               C                 C             C                 C            C
 C               C               C               C                 C             C                 C            C
 N               P               C               N                 N             N                 N            N
 C               P               P               C                 C             C               -C             C
 C               P               C               C                 C             P                 C            C
 C               P               C               N                 C             C                 C            P
 C               C               C               C                 C             C                C             C
 P               P               C               C                 C             C                C             C
 P               P               C               C                 C             C                P             P
 P               C               C               N                 C             C                C             C
                                                                                                                    (continued)




                                       Page 67                                        GAO/IKED99-9     Food Stamp Automation
                          Chapter 4
                          Enhanced Funding for Automation Has
                          Achieved Its Objective




                                                                              Reconciliation    Redemption of
                   Generate                                                   of transacted     more than one
                   authorizations     Prevent           Allow for under-      authorization     authorization
State
__-                for benefits       duplicate HlRs    or over-issuance      documents         document
North Dakota       C                  N/A                C                    N/A               N/A
Ohlo” t            N                  N                  N                    N                 N
Oklahomaa          C                  P                  C                    C                 C
Oregona            C                  C                  C                    C                 C
Pennsvlvaniaa      C                  C                  C                    C                 C
Rhode Islanda      C                  C                  P                    c                 C
South Carolinaa    C                  C                  C                    N/A               N/A
South Dakotaa      C                  C                  N                    c                 c
Tennesseea         C                  C                  C                    C                 C
Texasa             C                  C                  P                    C                 C
Utah               C                  P                  C                    N/A               N/A
Vermont            C                  C                  C                    N/A               N/A
VIrgInIaa          P                  C                  C                    C                 C
Vlraln Island9 b   N                  N                  N                    N/A               N/A
Washlnqtor?        C                  C                  P                    C                 N
West VirgIniaa     P                  N                  C                    WA                N/A
Wisconsina         C                  C                  C                    N/A               N/A
Wvombna            C                  C                  C                    N/A               N/A




                          Page 58                                          GAO/RCED-9&9 Food Stamp Automation
                                       Chapter 4
                                       lkhamed Fuuding for Automation Has
                                       Achieved Ita Objective




 Generate data   Generate data                       Program-wide                          Participation                         Tracking
 to meet fed.    to meet other   Sample              reduction and        Expedited        history                               collection of
 reporting       repotting       selection for       restoration of       issuance of      covering 3             Cutoff of      recipient
 requirements    requirements    QC reviews          benefits             benefits         years                  benefits       claims
 P               C               C                   C                    C                C                      C              c
 N               N               P                   N                    N                N                      N              P
 C               P               C                   C                    C                C                      C              C




  C
__~              C               C                   C                    C                 C                     C              C
  C
___~-            C               C                   C                    C                 C                     C              C
-~-C             P               C                   C                    C                 C                     C              C
  C              P               C                   C                    C                 C                     C              P
  C              C               C                   C                    C                 P                     C              C
  C              C               C                   C                    C                 C                     C              C
  P              P               C                   C                    C                 C                     C              N
  N              P               P                   N                    N                 N                     N              C
  P              P               C                   C                    P                 C                     C              C
  P              P               C                   C                    C                 N                     C              C
  P              C               C                   C                    C                 C                     C              C
 P               P               C                   C                    C                 C                     C              P

                                       Note: The program functlonal standards presented here are in abbreviated    form. For complete verston,
                                       see Appendix IV

                                       %tate is in the process of planning or developing a new system or additional automated system capa-
                                       bikes

                                       bState does not have an automated system that supports the Food Stamp Program statewide.


                                       k?=
                                       P=
                                         -   Function IS completely automated.
                                             Function is partially automated.
                                       N=    Function IS not automated at all
                                       N/A   = Not applicable or no response.




                                       Page 69                                                   GAO/RCED-9@9 Food Stamp Automation
                                       Chapter 4
                                       Enhanced Funding for Automation Has
                                       Achieved Its Objective




Table 4.3: Status of Automation With
Regard to the Model Plan’s
Requirements for General Standards’                                          Timeliness and     Coordinate with
                                                                             data quality       federal and state
                                       State                                 requirements       programs
                                       Alabamaa                              C                  C
                                       Alaska                                C                  C
                                       Arizonaa                              C                  C
                                       Arkansas=                             P                  C
                                       Californiaa,b                         P                  P
                                       Colorado                              C                  P
                                       Connecticuta                          P                  N
                                       Delawarea                             C                  C
                                       District of Columbiaa                 P                  P
                                       Floridaa                              P                  P
                                       Georqiaa                              C                  C
                                       Guama                                 C                  C
                                       Hawaii                                P                  P
                                       Idahoa                                C                  C
                                       Illinoisa                             C                  C
                                       Indianaa                              P                  P
                                       Iowa8                                 P                  C
                                       Kansasa                               C                  C
                                       Kentuckva                             C                  C
                                       Louisianaa                            C                  C
                                       Mainea                                P                  P
                                       Marylanda                             P                  C
                                       Massachusettsa                        P                  C
                                       Michiqar?                             P                  C
                                       Minnesotaa b                          P                  P
                                       Mississippia                          C                  C
                                       MissourIa                             C                  C
                                       Montanaa                              N                  P
                                       Nebraskaa                             C                  C
                                       Nevadaa                               C                  P
                                       New Hampshtre                         C                  C
                                       New Jerseya                           C                  C
                                       New Mexicoa                           P                  C
                                       New Yorka                             C         ___-     C
                                       North Carolinaa                       C                  C
                                       North Dakota                          C                  C
                                       Ohio” b                               N                  N




                                       Page 60                                   GAO/WED-9@9 Food Stamp Automation
                                          Chapter 4
                                          Enhanced Funding for Automation Haa
                                          Achieved Ita Objective




                   Maintain
 Maintain          security of   Implement          Generate data      support                                Eventual direct
 confidentiality   automated     regulatory and     for management     management          Routine purging    transmission of
 information       systems       other changes      of information     federal of funds    case files         data
 C                 C             C                  C                  C                   C                  N/A
 C
___     ~~~~       C             C                  C                  C                   C                  C
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  P                   N                  N
 C                 C             P                  C                  P                   P                  WA
 C                 C             C                  C                  P                   C                  N/A
 C
__~~      ~~       C             C                  P                  P                   N                  N
 C                 C             C                  C                  C                   C                  C
 P
__~                P             C                  C                  P                   N                  N
 C                 C             C                  C                  P                   P                  c
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  C                   C                  C
 P
___~               P             P                  P                  P                   P                  N/A
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  P                  P                   C                  P
 C                 C             P                  C                  P                   C                  P
 C
___~               C             C                  C                  C                   C                  C
 C                 C             C                  C                  C                   P                  N/A
 C                 C             C                  C                  C                   C                  N/A
 C                 C             C                  C                  N                   C                  WA
 C                 C             C                  C                  P                   C                  C
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  P                   C                  P
 C                 C             N                  P                  P                   N                  N/A
 C                 C             C                  C                  C                   C                  C’
 C                 C             C                  C                  C                   C                  N/A
 P                 P             N                  P                  P                   N                  N
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  P                   C                  C
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  P                   C                  P
 P                 C             C                  C                  C                   C                  WA
 C                 C             C                  C                  C                   C                  C
 C                 C             C                  C                  C                   C                  C
 N                 N             P                  N                  N                   N                  N
                                                                                                                    (continued)




                                          Page 61                                         GAO/RCELMW9 Food Stamp Automation
Chapter 4
Enhanced Funding for Automation Has
Achieved Ita Objective




                                      Timeliness and     Coordinate with
                                      data quality       federal and state
State                                 reauirements       Droarams
Oklahoma”                             C                  C
Oregona                               C                  C
Pennsylvaniaa                         P                  C
Rhode Islanda                         P                  P
South Carolinaa                       C                  C
South Dakotaa                         C                  C
Tennesseea                            C                  C
Texasa                                C                  C
Utah                                  C                  C
Vermont                               C                  C
Virginiaa                             C                  C
Virgin Islandsa,b                     N                  N
Washinatona                           P                  C
West Virainiaa                        C                  C
Wisconsina                            C                  C
Wvomina                               C                  C




Page 62                               GAO/BCEB9O-9 Food Stamp Automation
                                          Chapter 4
                                          Enhanced Funding for Automation         Has
                                          Achieved Its Objective




                   Maintain
 Maintain          security of   Implement          Generate data             support                                          Eventual direct
 confidentiality   automated     regulatory and     for management            management              Routine purging          transmission of
 information       systems       other changes      of information            federal of funds        case files               data
 C                 C             C                  C                         C                       C                        N
 n                 r-            P                  r.                        r-                      m                        rr


                                                                                                                               “I’

                   C             C                   C                        P                       C                        N/A
                   C             C                   C                        C                       C                        C
                   C             C                   P                        C                       C                        C
 C                 C             C                   C                        C                       C                        P
 C                 C             C                   C                        C                       C                        C
 C
~~~                C             C                   C                        C                       C                        C
 C
___          .-    C             C                   C                        P                       P                        C
 N                 N             N                   N                        N                       N                        N
___       -. ~.
 C
__~.               C             P                   C                        C                       C                        N
 C                 C             C                   C                        C                       C                        P
 C                 C             C                   C                        C                       C                        WA
 C                 C             C                   C                        P                       C                        C

                                          Note: The program functronal standards presented here are In abbreviated   form. For complete version,
                                          see appendix IV

                                          ?State is in the process of planning or developing a new system or addrtronal automated system capa-
                                          bilities.

                                          bState does not have an automated system that supports the Food Stamp Program statewide


                                                        IS completely automated
                                          P = Function is partially automated.
                                          N = Function is not automated at all.
                                          N/A = Not applicable or no response.



                                          Section 129 of the Food Stamp Program Act Amendments of 1980 pro-
Funding at 75 Percent                     vides that certain state agencies can obtain 75-percent federal funding
Encouraged                                to plan, design, develop, or install ADP and information retrieval systems
Development of                            for administering the Food Stamp Program. According to the 1980
                                          House Agriculture Committee report, the increase to 75-percent funding
Automation                                for ADP development was a necessary incentive to encourage states not
                                          in the process of computerizing their programs to automate. According
                                          to responses to our questionnaires, the increased funding did encourage
                                          those states to automate and thus appears to have achieved its objec-
                                          tive. In fact, state agencies receiving the 75-percent funding went
                                          beyond the originally intended purpose of initial automation efforts and


                                          Page 63                                                   GAO/RCED9@9 Food Stamp Automation
                         Chapter 4
                         Enhanced Funding for Automation Has
                         Achieved Ita Objective




                         used the funding to modify, upgrade, and even replace existing auto-
                         mated systems. Furthermore, the 75-percent funding rate had a major
                         impact on the type of automated capabilities developed.


Funding Incentive to     According to the results of our questionnaire, for the majority of the
Automate Was Important   state agencies the increased rate of funding to 75 percent was the most
                         important incentive to automate their Food Stamp Programs. We devel-
to Most State Agencies   oped a list of automation incentives, shown in table 4.4, from informa-
                         tion obtained from (1) state agency requests to the Service since fiscal
                         year 1981 to develop automated Food Stamp Programs, (2) discussions
                         with Service headquarters and regional officials, and (3) discussions
                         with state agency and local office program administrators in each state
                         we visited. The incentives mentioned most often in our questionnaire
                         principally concerned the rate of federal funding. Because most states’
                         automated systems serve other public assistance programs as well as the
                         Food Stamp Program, the funding incentives included funding from the
                         Service for the Food Stamp Program and the Department of Health and
                         Human Services (HHS) for AFDC and Medicaid Programs.

                         According to Service headquarters officials, any particular use or avail-
                         ability of the 75-percent rate of federal funding should be considered in
                         the context of the funding incentives provided by other federal agencies
                         such as HHS which is go-percent. They told us that the 75-percent fund-
                         ing rate provides the Service regions some leverage in encouraging the
                         state agencies to develop systems to meet the needs of the Food Stamp
                         Program. Since the states normally receive 50-percent funding to admin-
                         ister the Food Stamp Program or to develop ADP systems, without the
                         75-percent funding there would be more incentive to design their auto-
                         mated systems to specifically meet the requirements for the HHS’ go-per-
                         cent rate of ADP development funding. However, according to state
                         officials in Texas and Kentucky, two of the four states we reviewed that
                         had developed systems since 1981 with only 50percent funding from
                         the Service, not having the 75-percent funding did not affect their sys-
                         tems’ capabilities.

                         Specifically, 21 state agencies reported that Service funding at the 75-
                         percent level was the most important incentive for automation. Accord-
                         ing to state and federal regional program officials, the availability of 75-
                         percent funding, along with the projected benefits of automation, helped
                         in getting state legislatures to approve the state’s share of ADP develop-
                         ment costs. They told us that while benefits attributed to automation
                         may greatly increase the efficiency and effectiveness of the program,


                         Page 64                                    GAO/RCED-96-9 Food Stamp Autmuation
Chapter 4
Enhanced F’unding for Automation Haa
Achieved Ita Objective




the return is usually several years into the future, and the high initial
development costs discourage state approval. As a result, the higher 75-
percent federal share of development costs reduced the states’ immedi-
ate outlay, thus encouraging state support for the automation effort.

Generally state agencies integrate their AFDCProgram with the Food
Stamp Program through automation. Because of this the second most
important incentive in encouraging Food Stamp Program automation
was HHS' funding rate of 90 percent to develop automated systems for
the AFDCprogram, according to 20 state agencies. HHS' funding at the 90-
percent rate for the operations costs of the AFDCsystem was also an
important incentive in encouraging Food Stamp Program automation,
ranking third in our list of incentives. State and Service regional officials
told us that over half of the Food Stamp Program households also par-
ticipate in the AFBCprogram. As a result, state agencies were encouraged
to design systems that would serve both programs if they could charge
HHSfor 90 percent of the cost to develop and operate the AFDCportion of
the system. For these integrated systems, state agencies generally
obtained either the normal 50percent or 75percent federal funding
from the Service to develop the Food Stamp Program portion of the
systems.

Table 4.4 shows that the normal 50-percent funding rate provided by
the Service for ADP development ranked, along with HHS' funding of
automated Medicaid programs, at the bottom of our list of most impor-
tant incentives. Only seven state agencies reported that the Service’s 50-
percent funding rate was the most important incentive. However, not all
states responding to our questionnaire considered it as an incentive. In
fact, 10 state agencies reported that the 50percent rate was the least
important incentive for their program automation. HHS' funding incen-
tives, across the board, for the development and operation of systems to
serve the Medicaid program were the least important incentives to Food
Stamp Program automation. Although our questionnaire showed that
the benefits of automation were extremely important to the majority of
the state agencies, it placed third in the ranking of most important
incentives to encourage automation. Thus, despite the benefits projected
for program automation discussed in chapter 2, access to the increased
rate of federal funding played a greater role in encouraging the automa-
tion effort.     ”




Page 65                                    GAO/RCED-9@9 Food Stamp Automation
-    .-
                                        Chapter 4
                                        Enhanced Funding for Automation Has
                                        Achieved Its Objective




Table 4.4: Importance Placed on
Incentives to Automate the Food Stamp                                                                            No. of state agencies0
Program Statewide                                                                                                   Most              Least
                                                                                                               important         important
                                                                                                                incentive         incentive
                                        Service fundma at 75 oercent                                                    21                    6
                                        Service funding at 50 percent                                                    7                   IO
                                        Proiected benefits of automationb                                               15                    1
                                        HHS funding at 90 percent for AFDC ADP development                              20                   12
                                        HHS fundina at 90 bercent for AFDC ADP operations                               15                   11
                                        HHS funding at 90 percent for Medicaid ADP
                                        operations                                                                      10                  28
                                        HHS funding at 75 percent for Medicaid ADP
                                        operations                                                                       7                   28
                                        Other                                                                            1                    2
                                        aThe two columns may not add up to the 50 state agencies that reported having statewlde automated
                                        systems because some states selected more than one Incentive for each category

                                        bThe projected benefits, as dlscussed in chapter 2, mclude reducmg program errors, staffing levels, and
                                        case processmg time




Seventy-Five Percent                    As discussed in our April 1988 report, the drafters of the funding provi-
                                        sion in the 1980 amendments to the Food Stamp Act expected the boost
Funding Used to Initiate,               in federal cost-sharing to 75-percent to be a one-time incentive to
Upgrade, Modify, or                     encourage state agencies not in the process of computerizing their pro-
Replace Existing Systems                grams to automate.3 Specifically, section 129 of the Food Stamp Act
                                        Amendments of 1980 provides that state agencies can obtain 75-percent
                                        federal funding to plan, design, develop, or install ADP and information
                                        retrieval systems for administering the Food Stamp Program. The Ser-
                                        vice, however, approved 75-percent funding to some state agencies,
                                        sometimes more than once, to upgrade, modify, or even replace existing
                                        automated systems. We concluded that these approvals represented a
                                        broader interpretation of the act than the drafters of the 75-percent pro-
                                        vision expected as set forth in the legislative history of the act. The Ser-
                                        vice disagreed with our position that Service policy and approval of
                                        some requests differed from what the drafters of the 75-percent provi-
                                        sion expected. Given the difference in views, we brought this issue to
                                        the attention of the Congress for its consideration and any additional
                                         direction it wished to provide. The Congress has not yet given any addi-
                                        tional guidance, and the service has continued to approve 75-percent
                                         funding to upgrade, modify, and replace existing automated systems.


                                        3Food Stamp Program Progress and Problems in Using 75-Percent Funding for Automation (GAO/
                                        RCED-88-58, Apr. 28, 1987).



                                        Page 66                                                   GAO/RCED-90-9 Food Stamp Automation
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Table 4.5 shows that 37 state agencies were approved for 75-percent
funding and, as of December 1988, the Service had approved more than
one request for 29 state agencies for 75-percent funding to automate
their programs. In fact, the Service approved from 2 to 11 different
requests each for these state agencies at the 75-percent funding rate to
upgrade, modify, or replace existing automated systems. Also, only four
state agencies, Michigan, Nebraska, North Dakota, and Virginia,
reported using the 75-percent funding as the House Agriculture Commit-
tee expected, that is, to initiate program automation efforts. The rest of
the state agencies reported using the increased funding to improve or
replace existing automated systems.

Of the four state agencies receiving 75-percent funding for initial ADP
development, one state agency received another approved request to
replace an existing system. Thirteen state agencies received approval
for requests to modify or upgrade automated systems-six of these
states also received approved requests to replace an existing system.
Nine additional state agencies obtained approval for 75-percent funding
to completely replace existing automated systems. The remaining 11
state agencies receiving 75-percent funding received approval to revise
previous requests for planned systems development or additional ADP
equipment.




Page 67                                   GAO/RCED-96-9 Food Stamp Automation
                                          Chapter 4
                                          Enhanced Funding for Automation Has
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Table 4.5: Purposes of Three Most Recent State Agencies’ Requests for 75Percent Funding
                                                               Purposes of requests
                                              Modify/upgrade      Completely replace                     Update/revise            Added ADP
State agency             Initial ADP effort   existing system         existing system                previous requests             equipment
Alabama                                                        X
Alaska                                                         X                            X
Anzona                                                                                      X                             X
Arkansas                                                       X
Colorado                                                                                                                  X
Connectfcut                                                                                 X
Hawaii                                                                                                                    X
Idaho                                                                                                                     X
lll~no~s                                                       X
lndlana                                                        X
Iowa                                                           X                            X
Kansas                                                                                                                    X
LouIslana                                                                                   X
Maryland                                                                                                                  X               X
Michigan                              X                                                     X
MISSISSIPPI                                                                                 X                                             X
Mtssourl                                                       X
Montana                                                                                     X
Nebraska                              X                                                                                   X
Nevada                                                                                                                                    X
New Jersey                                                                                                                X
New Mexico                                                                                                                X
New York                                                       X
North Dakota                          X
Oklahoma                                                       X                            X
Oreclon                                                                                     X                             X
Pennsylvania-
Rhode Island                                                                                X                             X
South Carolina                                                 X          -                 X                                             X
South Dakota                                                                                X
Tennessee                                                                                                                 X
Utah                                                                                                                      X
Vermont                                                        X                            X
Virginia                              X                                                                                   X
Washtnqton                                                                                                                X
Wisconsin                                                      X
Wyoming                                                                                     X                             X
Totals 37 States                      4                       13                           16                           16                4
                                          Legend X = The Servtce approved at least one 75percent   fundmg request for this purpose.


                                          Page 68                                                  GAO/RCEMM-9 Food Stamp Automation
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                                          Enhnnced Funding for Automation Has
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Seventy-Five Percent                      Responses to our questionnaire showed that the use of 75percent fund-
                                          ing had a significant impact on the capability of the automated systems
Funding Had Major Impact                  developed by the state agencies. The 1980 act identified a system war-
on Automated Systems’                     ranting 75-percent funding as one that enabled state agencies to more
Capabilities                              efficiently and effectively administer the Food Stamp Program as
                                          defined by USDA regulations. USDA regulations defined such systems as
                                          those automated systems that are (1) statewide, (2) integrated with the
                                          AFDCprogram, and (3) designed to have the capability to perform cer-
                                          tain functions necessary to process Food Stamp Program cases.

                                          Table 4.6 shows that the majority of the 37 state agencies receiving 75-
                                          percent funding reported that the increased rate of funding encouraged
                                          them to design systems to meet the requirements listed above. Specifi-
                                          cally, 20 of 37 state agencies receiving 75percent funding reported that
                                          the funding has had a great to very great impact on their developing a
                                          statewide system. Twenty-eight of the 37 state agencies reported that
                                          75-percent funding had a great to very great impact on their developing
                                          a system that was integrated with the AFLE program. And 29 state agen-
                                          cies reported that the 75percent funding had a great to very great
                                          impact on selecting the types of automated functions that they included
                                          in their ADP systems.


Table 4.6: Impact of 75Percent Funding on State Agency Systems’ Characteristics
                                                                          No. of state agencies
                                                 Very great                         Moderate                     Little or no
Characteristics                                     impact    Great impact             impact   Some impact            impact
Use of one automated food stamp system                   13                7              6               2                   9
throughout the state
Integrated with other public assistance                  20                8              6               1                   2
programs’ automated systems (to include
AFDC program)
Tvoe of automated functions                              17               12              5               3                   0


                                          While most of the state agencies reported that the 75-percent funding
                                          had a major impact on their automated systems, (1) some of the states
                                          approved for 75-percent funding did not develop statewide, integrated
                                          systems and (2) most of the states receiving 75-percent funding did not
                                          design systems to perform all of the requirements listed as part of the
                                          model plan (shown in app. IV). Specifically, table 4.7 shows that 24 of
                                          the 37 state agencies receiving 75percent funding reported developing
                                          statewide automated systems that were integrated with the AFDC pro-
                                          gram. Five state agency systems were partially integrated statewide,



                                          Page 69                                       GAO/RCED-So-9 Faod Stamp Automation
                                    Chapter 4
                                    Enhanced Funding for Automation Haa
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                                    while eight state agencies developed systems that served only the Food
                                    Stamp Program.

Table 4.7: States’ Integration of
Automated Food Stamp and AFDC       Level of integrated system                                            Number of state agencies
Programs With 75-Percent Funding    Totally integrated statewide                                                                24
                                    Partially integrated statewide                                                               5
                                    Not inteqrated at alla                                                                       8
                                    %cludes   the Virgin Islands, which does not have a statewide AFDC System


                                    Table 4.8 shows that the systems approved for 75percent funding are
                                    capable of performing many of the model plan requirements. In fact, as
                                    compared to tables 4.1,4.2, and 4.3, all of the states have developed
                                    automated systems that are capable of performing many of the model
                                    plan requirements, whether or not they received 75percent funding.
                                    However, as discussed in our April 1988 report, the 75-percent funding
                                    made available in fiscal year 1981 was not intended to be used in the
                                    same manner as the normal 50percent funding rate to develop ADP sys-
                                    tems According to the report of the House Agriculture Committee, in
                                    which the legislation originated, once an initial automation effort was
                                    completed with a one-time funding rate of 75 percent, future develop-
                                    ment and operation of the automated system could receive Service
                                    approval at only 50-percent federal funding.




                                    Page 70                                                  GAO/RCED-90-S Food Stamp Automation
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                                               Enhanced Funding for Automation Has
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Table 4.8: Extent of State Agencies’ Automation, by Function, With 7bPercent Funding
                                                          Number and status of automated functions
                                                                 Issuance, reconciliation, and
                                       Certification                       reporting                         General
State agency                       C        P        N    N/A        C        P         N    WA      C        P        N       WA
Alabama                          10       2              l            12        l     1      l       8         l        .          1
Alaska                           12       l              l            13        l     l      l       g         .        .          .


Arizona                          12       l              l            11        l     l      2       g         .        .          .
Arkansas                          9       1              2             8        2     1      2       5         2        2          ’
Colorado                         10       2              l             9        2     l      2       6         2        l          1
Connecticut
___~~
                                  4       4              4             7        3    3       l       3         3        3          l


Hawall                           10       2              l             9        4     l      l       8         .        .          1
Idaho                            12       l              l            11        2     l      l       g         .        .          .


llllnols                          8       4              l            12        l     1      l       g         .        .          .


lndlana                           4       7              1             7        6     l      .       4         5        ’          l


Iowa                              8       3              1             8        3     9      2       5         4        l          l


Kansas                           11       1              l            13        l     l      l       g         .        .          .


Louisiana                        11       1              l            13        l     l      l       8         l        l          1
Maryland                          5       6              1            11        2     l      l       7         2        l          l


Mlchlgan                          9       3              l            13        l     l      l       6         3        l          l


MISSISSIDDI                      10       2              l            1         l     .      l       g         .        .          .


MIssour                           9       3              l             9        2     2      l       8         l        .          1
Montana                           4       1              7             2        1    10      l           l     5        4          l


Nebraska                         12       l              l             9        2     l      2       g         .        .          .
Nevada                            4       7              1            11        2     l      l       7         2        l          l


NewJersey                        12       ’              l            13        l     l      l       g         .        .          .


New Mexico                       11       1              ’             8        3     l      2       6         3        l          l


New York                          9       3              9             9        4     ’      l       7         1        l          1
North Dakota                     12       ’              l             9        1     l      3       g         .        .          .
Oklahoma                          9       3              ’            11        2     l      l       8         l        1          l


Oregon                           11       1              9            13        l     l      l       g         .        .          .


Pennsylvania                      8       4              8            11        1     1      l       7         2        l          l


Rhode Island                      5       7              l             6        6     1      l       4         4        l          1
South Carolina                   12        l             l            11        l     *      2       7         1        l          1
South Dakota                     12        l             l            12        l     1      l       g         .        .          .


Tennessee                         6       6              l            12        1     ’      l       8         1        l          l


Utah                             11       1              l             9        2     l      2       g         .        .          .
Vermont                          12        l             l            11        l     l      2       g         .        .          .
Vlrglnla                          9       2              1             9        3     1      l       7         2        l          l


Washington                        7       4              1             8        4     1      l       6         2        1          l


                                                                                                                       (continued)




                                               Page 71                                     GAO/RCED9@9 Food Stamp Automation
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                          Enhanced Funding for Automation Haa
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                                            Number and status of automated functions
                                                  Issuance, reconciliation, and
                     Certification                          reporting                                     General
State agency    C         P        N        WA        C        P         N    N/A                 C        P         N   N/A
Wisconsin       11       1             l                10          1          l        2         8          l       l     1
Wvominq         11       1             l                  8         3          l        2         8          1       l     l




                             k%%npletely       automated
                             P = Partially automated
                             N = Not automated at all.
                             N/A = Not appkable or no response
                             Note, State agencies also may have been funded at the normal 50-percent funding level



                             The 75-percent funding provided by Section 129 of the Food Stamp Pro-
Conclusions                  gram Act Amendments of 1980 has served its purpose to encourage
                             Food Stamp Program automation. As intended by the House Agriculture
                             Committee, which originated the 75percent provision, since fiscal year
                             1981 all 53 state agencies administering the Food Stamp Program have
                             begun automating their programs. According to the responses to our
                             questionnaire, the increased rate of federal funding provided since fiscal
                             year 1981 to specifically encourage initial ADP development played a
                             major role in Food Stamp Program automation. The majority of 37 state
                             agencies receiving this increased funding rate reported that it was the
                             most important incentive not only to automate, but to develop systems
                             capable of performing on a statewide basis and of serving AIW Program
                             participants as well. Other than the few state agencies receiving 75-per-
                             cent funding to initiate first-time program automation efforts, many of
                             the state agencies were approved for 75percent funding to develop
                             automated capabilities similar to those the Service approved for other
                             state agencies at the 50-percent funding rate. As a result, the 75percent
                             funding is becoming an incentive to ensure that program needs are con-
                             sidered in continued ADP development, especially in light of higher rates
                             of federal funding offered by other agencies such as HHSto encourage
                             ADP development. However, the drafters of the 75-percent funding pro-
                             vision did not intend for this to be a continuing incentive for ADP devel-
                             opment. They expected that once the initial ADP development with the
                             75-percent funding had been achieved, future ADP development would
                             be at the 50-percent rate of funding.


                             Since all of the state agencies have automated to some extent, thereby
Recommendation to            accomplishing the objective set forth by the originating committee (the
the Congress                 legislation for 75-percent funding), we recommend that the Congress



                             Page 72                                                  GAO/RCEMJO-9 Food Stamp Automation
                   Chapter 4
                   Enhanced Funding for Automation Has
                   Achieved Its Objective




                   amend the 1980 Amendments to the Food Stamp Act of 1977 to discon-
                   tinue the 75-percent level of federal funding to plan, design, develop,
                   and install automatic data processing and information retrieval systems
                   to administer the Food Stamp Program.


                   The Food and Nutrition Service indicated that in the past it has pro-
Agency and State   posed an end to the Food Stamp Act’s provisions for 75-percent funding
Comments and Our   for automation. Nevertheless, the Service continues to disagree with our
Evaluation         interpretation of the legislative history on the 75-percent funding provi-
                   sion in the Food Stamp Act Amendments of 1980.

                   As we explained in the 1988 report, “Food Stamp Program: Progress and
                   Problems in Using 75Percent Funding for Automation (GAO/RCED-88-58),
                   and in this report, the House Agriculture Committee that originated the
                   increase in ADP funding to 75 percent intended the increase to be an
                   incentive to encourage Food Stamp Program automation. This was
                   expressed in the House Committee Report 96-788 as follows: “The boost
                   in cost-sharing is intended to be a one-shot infusion of Federal funds
                   strictly limited to initial developmental costs assuming the fullest possi-
                   ble computerization consistent with cost effectiveness.“

                   The House Committee Report also explained that at the time, many of
                   the states were computerizing their Food Stamp Programs with the nor-
                   mal 50percent federal funding. Although this level of funding would
                   continue to be available according to the report, an additional incentive
                   was needed to encourage states that were not computerizing their pro-
                   grams to automate. The Committee believed that the increase in federal
                   funding from 50 to 75 percent was more than enough to encourage the
                   needed automation.

                   The Service’s reference in the House Committee Report 96-788 to the
                   Congress recognizing that 75-percent funding was for any state to
                   upgrade existing automation was taken from the report section that was
                   addressing the exceptions for the first year of the 75-percent funding
                   only. So that states in the process of computerizing the program would
                   not be affected adversely by the October 1, 1980, trigger date for the
                   enhanced funding, the Committee Report noted that such states could
                   also apply for 75-percent funding to complete the system’s development.
                   Aside from the fiscal year 1981 exception for states in the process of
                   computerizing their programs to complete development, the Committee
                   specified that the 75-percent funding would not apply to the “ongoing



                   Page 73                                   GAO/RCEDSO-9 Food Stamp Automation
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Enhanced Fuuding for Automation Has
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utilization of ADPequipment, services or systems or to any post-installa-
tion modification“ due to “changes subsequently made in the food stamp
program by virtue of laws or regulations. “ “Ongoing system utilization
or upgrading expenses would continue to be shared at the 50percent
rate....”

As we reported in 1988, the Service has provided 75-percent funding for
upgrades or replacements of the complete systems as well as 75percent
funding for upgrades or replacements of systems previously funded at
the 50-percent rate of funding. Although as we stated in the previous
report, the language of the law permits the broad interpretation and
action providing 75-percent funding taken by the Service, we are
pleased that the Service has ended the use of 75-percent funding for
continuing systems development once a state has achieved a sufficiently
high level of automation.

The Service indicates that the results of our survey questionnaire on the
status of states’ automation “must be interpreted cautiously, rather
than boldly“ as it states we have done in the report. While the survey
questionnaire received extensive pre-testing before we sent it to the
state agencies, we realized that states may interpret questions differ-
ently-especially    when considering such a broad term as “automation. “
Therefore, in order to avoid arbitrarily restricting any state to one defi-
nition of what we or the Service believe automation is or should be, we
left that decision to the individual states. We believe that the states are
in the best position to determine the extent of automation in their Food
Stamp Programs in conformance to their definition of what “automa-
tion“ is.

The Service indicates that our report makes little distinction regarding
the degree to which states reported the automation of program func-
tional requirements. As our report indicates, all 53 of the state agencies
have automated at least portions (partially or completely) of their pro-
gram, of which 50 have automated systems that support their program
statewide. Tables 4.1,4.2, and 4.3 indicate the status of automation-
completely, partially, or not automated-with      regard to the program
functional requirements for each state. Finally, the Service indicated
that 70 percent of the 50 states identified in table 4.1, certification func-
tional requirements, were completely automated. With the exception of
references to the partially automated systems for Ohio, Florida, Michi-
gan, and California, the Service did not provide additional documenta-
tion identifying specific states it considered to the completely



Page 74                                     GAO/BcED-So-9 Food Stamp Automation
Chapter 4
Enhanced Funding for Automation Ha.8
Achieved Its Objective




automated. As stated earlier, the extent of determining the level of auto-
mation was left to the individual states. As indicated in table 4.1, 28 of
the 37 responding state agencies approved for 75-percent funding have
partially or completely automated each of the functions critical to deter-
mine program eligibility. Eight of the 16 responding state agencies
receiving 50-percent funding for ADP development are partially or com-
pletely automated with regard to the certification functions. Thus we
believe the report makes the necessary distinction regarding the degree
to which states reported the functional requirements as being
automated.

The State of Vermont indicates that it objects to our recommendation
asking the Congress to discontinue the 75-percent federal funding
because the law and regulations require states to automate. While the
requirement to automate exists, it pertains to all states receiving federal
funding at the 50-percent and/or 75-percent level.




Page 76                                    GAO/RCED-90-9 Food Stamp Automation
Estimating the Effects of Automation on the
Operations of State/Local Food Stamp Programs

               Using multiple regression analysis, we tried to isolate the effects of
               automation on various measures of food stamp program operations,
               such as issuance error rates, government claims against overissued ben-
               efits and collections of those claims, staffing levels, and the proportion
               of cases processed in a timely manner. Our analysis accounts for a
               number of explanatory program factors, such as program caseload and
               policy changes affecting workload, in order not to attribute more effects
               to automation than are warranted.

               We examined the effects of automation using data from four different
               state/local food stamp program offices, including the states of Vermont
               and North Dakota, and local offices in Dallas and San Antonio. These
               four locations do not, however, constitute a representative sample of
               food stamp programs nationwide. Consequently, the results of our anal-
               ysis cannot be used to draw inferences about how automation likely
               affects program measures nationwide. Further, the type of automated
               system adopted and the characteristics of both the program operation
               and participants vary among locations.1 Because our analysis does not
               control for all unique aspects of the food stamp program at each loca-
               tion, our results are not comparable across locations.

               The results also lack comparability across locations because, despite
               obtaining all available data from these locations, we did not have data
               on the identical set of program measures and factors for each location.
               Further, we had relatively few observations (data points) for analysis.
               One consequence of having few observations is that the chances are
               reduced of finding statistical significance for a relationship that actually
               does exist. Therefore, in some cases or locations, our results may under-
               state the effects of automation and other factors on the different pro-
               gram measures. In contrast, the estimated relationships of automation to
               the various program factors may be misspecified, where important fac-
               tors are not adequately controlled for in estimating the relationships.
               This could mean that our results might overstate or understate the
               effects of automation or other factors on program factors.

               In view of these considerations, it is not surprising that the results of
               our analysis suggest that the effects of automation on the various pro-
               gram measures are different in the various locations. Additionally,

               ‘In addition to socioeconomic factors that vary by location, the automated systems may be different
               in several ways. For example, the systems in Vermont and North Dakota are capable of comparing
               different pieces of information for consistency, such as age and status as student or retired, whereas
               the systems in Dallas and San Antonio do not make such comparisons. Also, Vermont and Earth
               Dakota feature on-line systems while Dallas and San Antonio have batch systems.



               Page 76                                                   GAO/RCED909        Food Stamp Automation
                       Appendix I
                       Estimating the Effects of Automation on the
                       Operations of State/Local Food
                       stamp Programs




                       because of data limitations, not all program measures were examined in
                       each location. Specifically, our results suggest automation was statisti-
                       cally significant in contributing to reductions in error rates in North
                       Dakota and to reductions in one category of staffing in both San Antonio
                       and Dallas. We found that automation was not statistically significant in
                       affecting claims, collections, error rates, or staffing in Vermont; the
                       average time spent processing cases in North Dakota; and some catego-
                       ries of staffing or the more timely processing of cases in both Dallas and
                       San Antonio. In two cases, one staffing category for Vermont and a dif-
                       ferent staffing category for San Antonio, our results suggest that auto-
                       mation is statistically significant in affecting a program measure in a
                       direction that is not consistent with the expected effects of automation.

                       This appendix provides a discussion of (1) the concepts of program
                       “measures” and “factors,” (2) the rationale for the use of our models on
                       program operations, (3) the nature and quality of the data used in the
                       analysis, (4) the estimation methodology, and (5) the estimation results.


                       For the purpose of this report, we define a program measure as any
Program Measures and   measure of program performance or operations that is likely to be
Factors                affected by the introduction of automation. For example, automation
                       could affect the different measures of issuance error rates and the pur-
                       suit of government claims and collections of overissued benefits. Auto-
                       mation also could affect the average time it takes to process a food
                       stamp case or affect the proportion of food stamp cases that are
                       processed in a timely manner. Moreover, since automation is typically
                       considered a labor saving improvement, it might affect program staffing
                       levels.

                       We define a program factor as any aspect of the program that could
                       affect the different program measures. Automation is only one of many
                       program factors that could affect program measures. Other program
                       factors include caseloads for food stamps, AFDC,and Medicaid (since all
                       three of these programs may be processed by the same eligibility
                       worker), and changes in government policy affecting participant eligibil-
                       ity or program reporting. All program measures, particularly staffing
                       levels, can serve dual roles as program factors. For example, as a pro-
                       gram measure, staffing may be altered in response to changes in factors
                       such as automation or caseloads, while as a program factor, staffing
                       changes may affect program measures such as error rates and claims.




                       Page 77                                       GAO/RCED+O-9 Food Stamp Automation
                       Appendix I
                       Estimating the Effects of Automation on the
                       Operations of State/Local Food
                       St=uP Fwzra-




                       In modeling the role of automation in program operations, we assume
Modeling the Role of   that all program measures are determined jointly, conditional upon al1
Automation in Food     program factors. Each program measure is represented by an equation,
Stamp Program          in which the program measure is expressed as a function of some or all
                       of the different program factors. The equations we estimate are prop-
Operations             erly considered “reduced form” equations in that all program measures
                       are expressed as functions of program factors only, to the extent that no
                       program measures appear on the right-hand side of any equations. This
                       means that any program measure in a dual role as a program factor
                       (determining some other measure) has been replaced (substituted for) in
                       that role as a factor by other program factors.

                       Nonetheless, staffing, in its role as both a program measure and factor,
                       is treated differently in our model from other program measures that
                       serve dual roles as program factors. Specifically, the model has equa-
                       tions to explain staffing as a program measure, while equations for
                       other measures include staffing as a program factor. Because we assume
                       staffing (in the current year) is determined by the status of program
                       factors in the previous year, staffing is not jointly determined with
                       other program measures, and therefore it can be a program factor in the
                       reduced form equations for other measures.

                       We consider staffing to be determined by program factors in the previ-
                       ous year because current year staffing is primarily dependent on budget
                       decisions made in the previous year. Because reliable budget data were
                       not available, we could not use budget as a factor explaining staffing.
                       Instead, we assumed that previous-year program factors are key deter-
                       minants of the current year budget and we therefore substituted the
                       previous-year status of program factors for the budget data we had
                       hoped to use.:!

                       The relationships over time of the program measures and factors in our
                       model are displayed in figure 1.1. It describes, for example, how staffing
                       can be viewed in the current year as both a program measure and factor,

                       ‘Ideallv the budget (and staffing) for the current year should reflect the current-year status of pre
                       gram f&or-s (automation, caseload, etc.j. However. the current-year status of factors cannot be
                       known with certainty during the previous year, when the current-year budget is formulated. There-
                       fore, in the previous year, predictions (expected values) of the status of program factors for the
                       current year must be used to decide on the current-year budget (and staffing). The equations for
                       staffing, then, are based on the assumption that as of the previous year, the best predictors for the
                       current year’s status of program factors are the status of those factors in the previous year. We allow
                       one exception to this assumption, however, in that we consider the status of automation in the future
                       to be known with certaimy; therefore, as of the previous year, the predicted and actual status of
                       automation for the current year is the same. Thus, our staffing equations show all determining fac-
                       tors as of the previous year except automation, which is shown as of the current year.



                       Page 78                                                   GAO/RCED+O-9 Food Stamp Automation
                                                  Appendix I
                                                  Estimating the Effects of Automation on the
                                                  Operations of State/Local Food
                                                  Stamp Prom




                                                  because as a measure it is determined by program factors in the previ-
                                                  ous year.



    Figure 1.1:Structure of Model of Food Stamp Program Operations

Previous   Year                  I                                  Current      Year


     Program Factors (t-4)   ’
     1. Caseloads                       Program    Factors   (1)
     2. Policy Changes
     3. Automation                                                                       Program Measures     (1)
                                                                                         1. Error Rates
\
                                                                                         2. Claims
                                                                                         3. Collections
                                                                                         4. Timeliness
                                                                                 b       5. Time Spent/Lost

                                                                                         Also - some measures
                                                                                                may interact


                                                                                     \




                                                  Note (t) is time In quarters




    Expected Effects of                           Each program measure is expressed as an equation to show what fac-
                                                  tors are likely to affect that measure. The estimated parameters of each
    Automation on Various                         equation will suggest the direction of each effect. We can use economic
    Program Factors                               reasoning as a basis for developing expectations concerning the direc-
                                                  tion of an effect, and then evaluate the estimation results with regard to
                                                  their consistency with these expectations.

                                                  Economic reasoning suggests that program measures should improve,
                                                  e.g., issuance error rates fall, when program-related resources, such as
                                                  staffing and automation, are enhanced. Similarly, program measures
                                                  should worsen when demands on program resources, such as caseload,
                                                  are enhanced. Accordingly, an increase in food stamp program caseload
                                                  is likely to result in an increase in issuance error rates, given that all
                                                  else, including staffing, remains unchanged. Thus, food stamp program
                                                  caseload is likely to be positively related to program error rates. This


                                                  Page 79                                                           GAO/RCED-So-9 Food Stamp Automation
                                                         Appendix I
                                                         Estimating the Effects of Automation on the
                                                         Operations of State/Local Food
                                                         S-P    prognuns




                                                         and other expected relationships based on economic reasoning are sum-
                                                         marized in table 1.1.


Table 1.1: Expectations of Automation and Other Key Factors Affecting Efficiency Measures
                                                                           Key factors
                                                                                    Caseload
                                                    Automation                     Food        AFDC/                                   Staffing,           Polk!
Equation/program measure
___~                                         DevelopmeW      Operation0         stamps       Medicaid                                         all       changes
Error rates                                                                +c             -c                   +                 +                             +1-d
Claims                                                                                    +              +/-               +/-                  +              -f-l-
Collecttons        -                                                                      +              +/-               +1-                  +              +/-
Timeliness      [percent   cases processed    on time)                                    +                                                     +
Average       time spent processing    each   case                         +                                                                    +                      +
Stafflno                                                             +/-                                       +                 +            N/A                      +
                                                         aWe anticipate the development phase of automation will affect program measures in the opposite
                                                         dIrectIon of automation during the operation phase. This is because the development phase represents
                                                         a pertod when the normal actlvlty of program resources is disrupted because of training and other
                                                         requirements In developing the automated system.

                                                         bT~o policy changes, monthly reporting and computer matching, are accounted for in the model. Both
                                                         are Intended to positively affect program measures (error rates, claims), but they also may increase
                                                         workload and that may negatively affect these measures.
                                                         ‘A plus indicates a positive relationship, meaning an increase (decrease) in the value of a factor should
                                                         result in an increase (decrease) in the value of the measure. A minus indicates a negative relatlonship,
                                                         meaning an increase (decrease) in the value of a factor results in a decrease (increase) in the value of
                                                         the measure.

                                                         dA plus/minus Indicates that the factor may have opposing effects on the program measure. For exam-
                                                         ple, the factor “food stamp caseload” may be positively related to the measure “claims” because more
                                                         cases implies more opportunities that can result in a claim, and therefore more claims; or food stamp
                                                         caseload may be negattvely related to claims because more cases tmplles less time that the staff can
                                                         spend pursuing claims, and therefore fewer claims.

                                                         N/A means not applicable



                                                         Appendix II presents a description of the Food Stamp Program auto-
Nature of the Data                                       mated systems for each of the seven states/local offices we reviewed.
Used in the Empirical                                    Although we collected data from all seven locations, only the four loca-
Analysis                                                 tions of Vermont, North Dakota, Dallas, and San Antonio were able to
                                                         provide data sufficient to allow us to empirically estimate the effects of
                                                         automation on different program measures.

                                                         Data for each of the four locations consist of quarterly observations
                                                         extending (on average) from about 1982 to mid-1987. We did not obtain
                                                         data on all measures and factors for each location. However, we did
                                                         obtain data on three different measures of error rates for Vermont and



                                                         Page 80                                                     GAO/RCEIM@9 Food Stamp Automation
Appendix I
Estimating the Effects of Automation on the
Operations of State/Local Food
stamp Programs




North Dakota and on more than one category of staffing for Vermont,
Dallas, and San Antonio.

For two program measures/factors (variables), error rates and staffing,
only annual observations were available. We transformed annual obser-
vations to quarterly by assigning the value of each annual observation
to all four quarters in the corresponding year, and then calculating a
five-quarter (centered) moving average to replace the annual observa-
tions Transforming the annual data to quarterly observations was nec-
essary to obtain sufficient observations to estimate the parameters of
the equations. Nonetheless, in reality, we have only about six observa-
tions for these “transformed” variables. Table I.2 lists all of the vari-
ables used in the estimation of the different equations.




Page 81                                       GAO/BCED90-9 Food Stamp Automation
                                      Appendix I
                                      E&imating the Effesta of Automation on the
                                      Operationa of State/Local Food
                                      Stamp prorpgms




Table 1.2 List of Variables Used in
Empirical Analysis
                                      1. Caseload Variables
                                      All Iccations.
                                         FI ICASE= Number of food stamp cases per quarter.
                                         AFDCCASE= Number of AFDC cases oer auarter.
                                         MEDCCASE= Number of Med&dca&          p&q&rter.
                                         AFMED=Number of AFDC + Medicaid cases per quarter.

                                      2. Staffing Variables
                                      Vermont:
                                        INTKSPEC= Number of intake specialists.
                                        REVSPEC= Number of review specialists.

                                      Dallas and San Antonio:
                                        SUPERV= Number of supervisors,
                                        ELGWORK- Number of eligibility workers.
                                        CLERK= Number of clerks.

                                      3. Program Operations Measures Variables
                                      Error Rates’, Vermont and North Dakota:
                                         LlSSERR= State estimated issue error rate.
                                         LFISSERR= Federally estimated issue error rates for state.
                                         LCASERR= State estimated case error rate.
                                      lThese error rates are positively related to “more” errors.

                                      Clarms and Collections, Vermont:
                                        CLAIMS= Government claims for over-issuance of food stamps, in constant (1982) dollars.
                                        COLLECTIONS= Government collection of CLAIMS, in constant (1982) dollars.
                                      Avera e Minutes per case, North Dakota:
                                        MINt SCAS= Number of staff minutes devoted to food stamp cases per nonpublic
                                        assrstance case (FSCASE).
                                      Timeliness of Eligibility Determination, Dallas and San Antonio:
                                        !-CATGTIM= Trmeliness in terms of the proportion of eligibility determinations completed
                                        within the thrrty-day time period established by federal and state program regulations
                                        (category 6 on the form), where the estimates are positively related to determinations
                                         being more timely.

                                      4. Other Variables
                                      North Dakota, Dallas and San Antonio:
                                        POLYl = Dummy variable equal to 1 begrnning when compliance with federally mandated
                                        monthly reporting started, and zero prior to that time.a
                                      Vermont:
                                        POLY 1 = Variable equal to the number of food stamp cases subject to federally mandated
                                        monthly reporting, since cases were phased in over time.
                                        POLY2= Dummy variable equal to 1 beginning when compliance with Vermont mandated
                                        computer matching of case files across human service agencies started, and zero prior to
                                        that time.
                                      San Antonto:
                                        DCATGCATS= Dummy variable to account for period durin which manner of
                                        accumulating trmeltness data changed, equal to 1 in FYs 18 85-86, zero otherwise.
                                                                                                                       (continued)




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              FMimating the Effects of Automation on the
              Operations of State/Local Food
              stamp Programs




              5. Automation Variables
              All locations have both automation development (AUTODEV) and automatlon operations
              (AUTOOP) which are dummy variables that take on the value of 1 during the developmental
              and operational phases of the automation, respectively, and zero otherwise. The
              developmental phase of automation is that period when, according to the food stamp
              program officials at each location, the normal duties of the program staff were affected
              (interrupted) by training and other tasks associated with making the automated system
              operational. Often the developmental period overlaps with the beginning of the operational
                enod These specific periods are listed below (by fiscal year and by quarter) for each
              I&at&.
              Dallas:
              AUTODEV 04.3 - 85 4
              AUTOOP 86 1 ON
              San Antonio:
              AUTODEV 83.3 - 83.4
              AUTOOP 83.4 - ON
              North Dakota:
              AUTODEV 84.1 - 85.1
              AUTOOP 85.1 ON
              Vermont:
              AUTODEV 84.1 - 84.4
              AUTOOP 84 1 - ON
                Clatms and Collections only:
                AUTODEV 85.2 - 85.2
                AUTOOP 85.3 - ON
              aDummy variables are used to represent the changing status of a factor which, perhaps for lack of data,
              cannot be quantlfled



              We estimated the different equations using ordinary least squares.” This
Estimation    and other regression methods of analysis are designed to isolate the
Methodology   effects of automation on the different measures of program operations
              while simultaneously controlling for the possible influence of other pro-
              gram-related factors on these same program measures. In principle, this
              kind of analysis minimizes the probability of attributing changes in pro-
              gram measures to automation when in fact they may have been caused
              by other program-related factors.

              All equations are assumed linear in functional form. In those models in
              which the program measure (dependent variable) is expressed as a per-
              centage, including all measures of issuance error rates and timeliness
              rates, it is appropriate from a theoretical (statistical) standpoint to
              transform the dependent variable so that it is not constrained to lie


              “Since we assume the different program measures, for each location, are Jointly determined, a simul-
              taneous estimation technique would seem appropriate. However, simultaneous estimation Improves
              results only if there are many observations, and since we had relatively few observations. ordinary
              least squares was the best technique to use in this case.



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                     Estimating the Effects of Automation on the
                     Operations of State/Local Food
                     stamp Programs




                     between 0 and 1 (or 100 percent). This was done using the                                standard
                     logit transformation.4 As a check on the correctness of the                              logit
                     transformation, we also estimated these equations without                                 the logit
                     transformation,    and the results were, as expected, similar                            to those
                     with the logit transformation.


                     As discussed in chapter 2, the estimation results suggest that in some,
Estimation Results   but not all, offices we examined, automation has affected the various
                     program measures in accordance with expectations based on economic
                     reasoning. Nonetheless, according to Food Stamp Program officials at all
                     four locations, many improvements in program measures, such as lower
                     error rates, have occurred as a result of automation, but have remained
                     unseen, or have been negated, because of the onset of many policy
                     changes during the operation period of automation. Although we include
                     at least one program factor in each equation to control for the effects of
                     the major policy changes, to the extent that these variables do not ade-
                     quately account for the effects of all policy changes, our results can
                     understate the effects of automation on the different program measures.

                     Estimation results are presented in tables I.3 through I.1 1. For each
                     equation (program measure) estimated, the tables show all program fac-
                     tors included in the equation, the estimated parameter for each factor
                     and the associated t-statistic. The t-statistic is used to test the hypothe-
                     sis that the estimated parameter is different from zero, or that the pro-
                     gram factor significantly affects the program measure (dependent
                     variable) of the equation.

                     For each equation, the results tables also show the sample period, in
                     quarters, and the R-Square. The R-Square measures the goodness of fit
                     or, more specifically, the proportion of the variation in the dependent
                     variable (program measure) that is explained by the estimated equation
                     (the different program factors).




                     ‘The standard logit transformation for some percentage P = log(P/(l-P)). and the corresponding vari-
                     ance of this term V = n/r(n-r), where n is the number of observations (e.g., cases sampled) and r is the
                     number of observations in which one of two alternatives occurs (e.g., a case either is found in error or
                     it is not). When using a logit transformed dependent variable, heteroskedasticity (violation of the
                     assumption that the estimated residuals have constant variance) ls a concern but can be corrected by
                     weighting all data by the inverse of the variance of the (logit transformed) dependent variable. Con-
                     sequently, the results presented in tables 1.3,1.6,1.8, and 1.10,all of which are for logit equations, are
                     based on weighted data.



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                       Appendix I
                       Edmating the Effecta of Automation on the
                       Operations of State/Local Food
                       stamp Programs




Estimation Results:    Data available on program measures for Vermont include three different
                       measures of error rates, measures of claims and collections, and two cat-
Vermont                egories of staffing. The three measures of error rates include the state-
                       determined case and issue error rates, and a federally determined issue
                       error rate. The two categories of staffing are intake and review special-
                       ists. We estimated equations to explain each of these program measures
                       in terms of other program-related factors.

Error Rate Equations   We present estimation results for the three error rate equations in table
                       1.3. The operational phase of automation is not a significant factor in
                       reducing error rates according to the results for the state-measured case
                       and issue error rates, equations 1 and 2. In equation 3, for federally
                       measured issue error rates, automation does appear significant and con-
                       sistent with expectations. However, as discussed in the table, there is
                       reason to believe equation 3 results are misleading.




                        Page 86                                    GAO/RCED9O-9 Foad Stamp Automation
                                        Appendix I
                                        Estimating the Effects of Automation on the
                                        Operations of State/Local Food
                                        Stamp prom




Table 1.3:Vermont Estimation Results-
Error Rates                             Equation/dependent                    Independent                             Parameter
                                        variable                              variables                                estimate             t-Statistic
                                        1,   LCASERR(t)
                                                                              CONSTANT                                       -5.41                    -.92
                                                                              FSCASE(t)                                     00002                      2.13”
                                                                              AFDCCASE(t)                               - .000003                     -.17
                                                                              MEDCCASE(t)                                 .000003                        .23
                                             R-SQUARE= .92                    INTKSPEC(t)                                  -.0016                     -.03
                                                                              REVSPEC(t)                                        ,032                     .54
                                                                              POLY 1(t)                                     .00004                       .68
                                                                              POLY2(t)                                        .0017                      .02
                                             sample: 81.3-87.2                AUTODEV(t)                                      .2228                    3.01”
                                                                              AUTOOPER(t)                                      -.14                  -1.26
                                        2.   LISSERR(t)
                                                                              CONSTANT                                       -1.27                    -.25
                                                                              FSCASE(t)                                  -.00001                     -1 .67b
                                                                              AFDCCASE(t)                                   .00001                        .41
                                                                              MEDCCASE(t)                                   .00002                      2.03”
                                             RSOUARE=.94                      INTKSPEW                                       -.049                   -1.17
                                                                              REVSPEC(t)                                   -.0086                      -.16
                                                                              POLYl(t)                                   - .00003                         64
                                                                              POLY2(t)                                       - ,053                    -.55
                                             sample. 81 3-87.2                AUTODEV( t)                                       ,159                    2.39”
                                                                              AUTOOPER(t)                                      -.03                    -.32
                                        3.   LFISSERR(t)
                                                                              CONSTANT                                         8.65                    2.72”
                                                                              FSCASE(t)                                     .00004                     9.24a
                                                                              AFDCCASE(t)                                   .00001                     1.34b
                                                                              MEDCCASE(t)                                 - .00002                   -3.08”
                                         -
                                             R-SQUARE= 99                     INTKSPEC(t)                                      ,013                      .51
                                                                              REVSPEW)                                        -.14                   -4.36”
                                                                              POLYl(t)                                    .000006                        .19
                                                                              POLY2(t)                                      -.073                    -1.20
                                             sample: 81 3-87 2                AUTODEV(t)                                       ,096                    2.15a
                                                                              AUTOOPER(t)                                   -.252                    -4.14a
                                        aThe coefflctent IS slgnlftcantly different from zero (plus or minus) at a 90 percent confidence     level

                                        bThe coefficient   IS slgnlflcantly   different from zero (plus or menus) at an 80 percent conftdence level


                                        The results for equation 1, the state-measured case error rate, show that
                                        two program factors, food stamp caseload and the development phase of




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Estimating the Effects of Automation on the
Operations of State/Local Food
stamp Programs




automation, are significant in affecting the case error rate. For both fac-
tors, the direction of their effect on the case error rate is consistent with
expectations. Specifically, food stamp caseload is positively related to
the error rate, which suggests that more cases, all else including staff
held constant, will result in greater error rates. The development phase
of automation is also positively related to the error rate, suggesting that
development is disruptive and may, even if only temporarily, result in
increasing error rates. Although the operation phase of automation is
not a statistically significant factor, at least it is nearly significant and
negatively related to error rates, which is consistent with the expecta-
tion that automation results in lower error rates, all else equal.

The results for equation 2, the state-measured issue error rate, are
somewhat consistent with those for equation 1. Program factors that
significantly affect the issue error rate include both food stamp and
Medicaid caseloads, and the development phase of automation. For all
significant factors, with the exception of the food stamp caseload, the
direction of their effect on the issue error rate is consistent with expec-
tations Specifically, Medicaid caseload is positively related to the issue
error rate, suggesting that more cases, for a given staff, result in greater
error rates. The development phase of automation is positively related
to issue error rates, suggesting that development is disruptive and will
result in greater error rates. The operation phase of automation is not
significant, but the direction of its effect on issue error rates is consis-
tent with the expectation that automation reduces error rates.

We expected that equation 3 results, for the federally measured issue
error rate, would be comparable to those of equation 2, for the compar-
able state measure of the issue error rate. The results for equations 2
and 3 are, however, appreciably different in both the significance and
direction of effects for some program factors. In particular, the opera-
tion phase of automation is not significant in equation 2, but is signifi-
cant in equation 3 and suggests that automation results in lowering
error rates which is consistent with expectations. This difference in
results likely reflects a substantial difference in the state and federal
estimates of issue error rates for a period of time just prior to the opera-
tion phase of automation. The different estimates of issue error rates
occurred because of a disagreement between Vermont and the Food and
Nutrition Service officials on a rule interpretation for determining bene-
fits Since the state issue error rate reflects the rule interpretation
understood by the state caseworkers, it is the more appropriate measure
for our analysis. Therefore we believe equation 2 results are more accu-
rate than those of equation 3.


Page 87                                       GAO/RCEDW-9 Food Stamp Automation
                                   Appendix I
                                   JZstimating the Effects of Automation on the
                                   Operations of State/Local Food
                                   stamp ~lpluns




Claims and Collections Equations   The results for the claims and collections equations are presented in
                                   table 1.4. The results suggest that automation (operation phase) has not
                                   significantly affected claims or collections.

                                   The results for equation 4, government claims for over-issued food stamp
                                   program benefits, suggest that the estimated parameter for only the pol-
                                   icy change requiring computer matches of case files, POLYZ, is signifi-
                                   cant in affecting claims. Since POLYZ is negative, the results indicate
                                   that this policy change has led to a decrease in claims. Computer match-
                                   ing can result in fewer claims because it means more frequent monitor-
                                   ing of changes in each participant’s income status, which often is the
                                   reason for an issue error.

                                   Equation 5 results, government collections of claims, should be reasona-
                                   bly consistent with the results for equation 4 since claims and collec-
                                   tions are clearly related. However, there are some differences in the
                                   results for 4 and 5, including that POLYB is no longer significant and
                                   that review specialists are significant. The parameter for review special-
                                   ists is positive, which is consistent with our expectation that collections
                                   should increase with additions to staff levels.

                                   The results for 5 suggest that only review specialists and the develop-
                                   ment phase of automation are significant factors affecting collections.
                                   Review specialists are positively related to collections, which is consis-
                                   tent with expectations that additions to staff should result in more col-
                                   lections, all else equal. The development phase of automation also is
                                   positively related to collections. This is not consistent with expectations
                                   that development is disruptive to both claims and collection efforts of
                                   the staff.




                                   Page 88                                        GAO/RcED90-9 Food Stamp Automation
                                        Appendix I
                                        Estimating the Effecta of Automation on the
                                        Operations of State/Local Food
                                        S-P     pro-




Table 1.4:Vermont Estimation Results-
Claims and Collections                  Equation/dependent                    Independent                              Parameter
                                        variable                              variables                                 estimate               t-Statistic
                                        4.   CLAIMS(t)
                                                                              CONSTANT                                   - 173939                       -.21
                                                                              FSCASE(t)                                        -.30                     -.19
                                                                              AFDCCASE(t)                                    -3.82                     -1.00
                                                                              MEDCCASE(t)                                    -2.05                     -1.26
                                              R-SQUARE= .79                   INTKSPEC(t)                                        701                       .09
                                                                              REVSPEC(t)                                      6548                         .83
                                                                              POLYl(t)                                        11.15                      1.08
                                                                              POLY2(t)                                     -48080                      -2.04”
                                                                              AUTODEV(t)                                    -9331                       -.48
                                              sample: 81.3-87.2               AUTOOPER(t)                                     5622                        .28
                                        5.    COLLECTIONS(t)
                                                                              CONSTANT                                   -452386                       -1.90a
                                                                              FSCASE(t)                                           .28                       .65
                                                                              AFDCCASE(t)                                    -2.21                     -2.03a
                                                                              MEDCCASE(t)                                      -.68                    -1.48
                                              R-SQUARE=.83                    INTKSPEC(t)                                      2760                       1.21
                                                                              REVSPEW)                                         5403                       2.42a
                                                                              POLYl(t)                                         -.52                      -.18
                                                                              POLY2(t)                                      -5309                        -.80
                                              sample: 81.3-87.2               AUTODEV(t)                                     10006                        1.84a
                                                                              AUTOOPER(t)                                     -616                       -.ll
                                        aThe coefflclent   IS slgnlficantly   different from zero (plus or minus) at a 90 percent confidence   level


Staffing Equations                      We present results for the staffing equations in table 1.5. The results
                                        suggest that the operation phase of automation is not significantly
                                        related to intake specialists, but is significantly and positively related to
                                        review specialists, which is not consistent with expectations that auto-
                                        mation should lead to a reduction in staff, all else equal.

                                        Equation 6 results, for intake specialists, suggest that food stamp
                                        caseload and the development phase of automation are significant fac-
                                        tors affecting the number of intake specialists. Food stamp caseload is
                                        negatively related to intake specialists, suggesting that as caseload
                                        increases the number of intake specialists declines. This is not consistent
                                        with expectations that greater caseload should lead to larger staff
                                        levels. The development phase of automation is positively related to
                                        intake specialists, which is consistent with expectations that develop-
                                        ment is disruptive and may require additional staff.



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                                        Eldmating the Effects of Automation on the
                                        Operations of State/Local Food
                                        stamp Programs




                                        Equation 7 results, for review specialists, suggest that the operation
                                        phase of automation is the only significant factor affecting review spe-
                                        cialists. The operation phase of automation is positively related to the
                                        number of review specialists, suggesting that automation resulted in
                                        increasing the review staff, which is not consistent with expectations.
__- -.
Table 1.5:Vermont Estimation Results-
Staffing                                Equation/dependent                    Independent                             Parameter
                                        variable                              variables                                estimate                 t-Statistic
                                        6.   INTKSPEC(t)
                                                                              CONSTANT                                       74.12                       10.05”
                                                                              FSCASE(t-4)                                  - .0002                      -1.41b
                                                                              AFDCCASE(t-4)                                -.OOOl                         -.46
                                                                              MEDCCASE(t-4)                                -.OOOl                       -1.17
                                             R-SQUARE=.83                     POLY 10-4)                                     .OOOl                            .30
                                                                              POLY2(b4)                                         ,592                          .79
                                                                              AUTODEV(t)                                      1.430                         2.07a
                                                                              AUTOOPER(t)                                    -.501                         -.41
                                             samde: 82.1-87.2
                                        7.   REVSPEC(t)
                                                                              CONSTANT                                         76.2                     20.33”
                                                                              FSCASE(t-41                                   .00006                          .a9
                                                                              AFDCCASE(t-4)                               - .00007                       -.95
                                                                              MEDCCASE(t-4)                                 .00004                        1.14
                                              R-SQUARE= .72                   POLY 1(t-4)                                   .00006                          .27
                                                                              POLY2(t-41                                         .03                        .09
                                                                              AUTODEVW                                        -.I3                       -.36
                                                                              AUTOOPER(t)                                        .a7                      1.39b
                                              sample. 82.1-87.2
                                        aThe coefficient   IS signrficantiy   different from zero (plus or minus) at a 90 percent confidence    level

                                        bThe coefflctent   is significantly   different from zero (plus or minus) at an 80 percent confidence     level.




Estimation Results: North               Data on program measures for North Dakota include the same three dif-
                                        ferent measures of error rates used for Vermont, and a variable measur-
Dakota                                  ing the average time spent in processing a food stamp case. An equation
                                        for each of these program measures was estimated. There were not suf-
                                        ficient data on staffing in North Dakota to explicitly include it as a pro-
                                        gram factor in any equation. However, a state official described staffing
                                        levels as constant over our sample period, so the effects of staffing,
                                        along with other unknown factors, are represented by the constant term
                                        and/or subsumed in the error term in each equation estimated.



                                         Page 90                                                           GAO/RCED-90-S Food Stamp Automation
                       Appendix I
                       Estimating the Effects of Automation on the
                       Operations of State/Lo&    Food
                       stamp Programs




Error Rate Equations   Table I.6 presents the estimation results for the three error rate equa-
                       tions. The results for the state-determined case and issue error rates,
                       equations 1 and 2, suggest that the operation phase of automation has
                       led to statistically significant reductions in those two error rates. Equa-
                       tion 3 results, for the federally determined issue error rate, suggest that
                       automation has not had a significant effect on issue error rates.

                       The results for equation 1 suggest that, in addition to the operation
                       phase of automation, both food stamp and AFDCcaseloads and the policy
                       change to monthly reporting are all significant factors affecting the case
                       error rate. AFDCcaseload is positively related to case error rate, and that
                       is consistent with expectations that more cases per staff should result in
                       less time processing each case and, therefore, greater error rates. Food
                       stamp caseload is negatively related to case error rate. This result is not
                       consistent with expectations or the results for AFDCcaseload, although it
                       has been argued that the staff can become more proficient in processing
                       cases when caseload increases. The policy change to monthly reporting
                       is negatively related to the case error rate. This is consistent with the
                       purpose of monthly reporting, that it should result in fewer case errors,
                       although monthly reporting also increases the workload of the staff and
                       that could result in more case errors.

                       Equation 2 results suggest that, in addition to the operation phase of
                       automation, the development phase of automation and the food stamp
                       caseload are significant factors affecting the issue error rate. The devel-
                       opment phase of automation is positively related to the issue error rate,
                       and that is consistent with expectations that development is disruptive
                       to normal operations. As in equation 1, the food stamp caseload is nega-
                       tively related to the error rate, and that is not consistent with
                       expectations.

                       The results for equation 3, the federally determined issue error rate, are
                       the same in sign and similar, though not identical, in significance for all
                       factors, including automation, from the results for equation 2, the com-
                       parable state-determined issue error rate. Since these two measures of
                       the issue error rate are reasonably close over time, we can only point to
                       the greater frequency of observation in the earlier periods of the sample
                       for the state-determined rate as the reason for the small differences in
                       results for equations 2 and 3.




                       Page 91                                       GAO/RCED90-9 Food Stamp Automation
                                     Appendix I
                                     EstImatIng the Effecta of Automation on the
                                     Operations of State/Local Food
                                     S-P    pro(gams




Table 1.6: North Dakota Estimation
Results-Error Rates                  Equation/dependent                    Independent                              Parameter
                                     variable                              variables                                 estimate                t-Statistic
                                     1.   LCASERR(t)
                                                                           CONSTANT                                          -.75                     -1.64b
                                                                           FSCASEW                                    - .000020                       -1 .72b
                                                                           AFDCCASE(t)                                    00002                          2.74a
                                                                           MEDCCASE(;)                                 -.00001                          -.82
                                          R-SQUARE=.93                     POLYl(t)                                          -.21                     -1.89”
                                                                           AUTODEW                                          .0477                          .56
                                          sample: 81.3-87.1                AUTOOPER(t)                                   -.1614                       -1.85”
                                     2.   LISSERR(t)
                                                                           CONSTANT                                          -.69                      -.81
                                                                           FSCASE(t)                                   -.00004                        -1.85”
                                                                           AFDCCASE(t)                                    .00002                        1.09
                                                                           MEDCCASE;;)                                 - .00003                       -1.07
                                          R-SQUARE=.91                     POLY 1(t)                                       -.197                      -1.02
                                                                           AUTODEWt)                                      .21956                        1.476
                                          sample: 81.3-87.1                AUTOOPEW)                                   - .22879                       -1 .56b
                                     3.   LFISSERR(t)
                                                                           CONSTANT                                         -.06                        -.12
                                                                           FSCASE(t)                                   - .00002                       -1.43b
                                                                           AFDCCASE(t)                                    -.ooo                         -.03
                                                                           MEDCCASE(t)                                 - .00007                       -3.73”
                                           R-SQUARE=.96                    POLYl(1)                                       -.039                         -.31
                                                                           AUTODEV(t)                                         164                       1.59b
                                           sample: 81.3-87.1               AUTOOPER(t)                                    -.112                       -1.16
                                     aThe coefficient   IS slgnlflcantly   different from zero (plus or minus) at a 90 percent confidence    level.

                                     bThe coefflctent   IS slgniflcantly   different from zero (plus or minus) at an 80 percent confidence     level.


Minutes of Staff Time Per Case       Table I.7 presents results for the program measure of average time
Equation                             spent processing nonpublic assistance food stamp cases. The results sug-
                                     gest that the operation phase of automation has not had the expected
                                     effect of reducing time spent per case. A North Dakota state official pro-
                                     vided two possible explanations for this result. First, automation has
                                     been of most help in saving time for upper management, and our data on
                                     time spent account only for the time of caseworkers and not that of
                                     upper management. Second, time spent per case has risen during the
                                     operation phase of automation because of numerous policy changes;
                                     therefore, if we do not adequately control for the effects of policy
                                     changes (and it is possible we do not), we may understate the contribu-
                                     tion of automation to reducing the average time spent processing cases.



                                     Page 92                                                            GAO/RCED90-9 Food Stamp Automation
                                         Appendix I
                                         Estimating the Effects of Automation on the
                                         Operations of State/Local Food
                                         stamp Programs




                                         The results for equation 4 suggest that both the development and opera-
                                         tion phase of automation are contrary in sign to expectations and not
                                         significant. However, both the AFDCcaseload and the policy change to
                                         monthly reporting are significant and consistent with expectations. Spe-
                                         cifically, AFDCcaseload is negatively related to average minutes, all else
                                         equal, which is consistent with the simple fact that a given caseworker
                                         with more cases to process must spend less time per case to complete the
                                         task. The policy change of monthly reporting is positively related to
                                         average minutes, which suggests monthly reporting takes more time per
                                         case.

Table 1.7: North Dakota Estimation
Results-Minutes    of Staff Time Spent   Equation/dependent                    Independent                              Parameter
Per Food Stamp Case                      variable                              variables                                 estimate               t-Statistic
                                         4.    MINFSCAS(t)
                                                                               CONSTANT                                         43.7                      1.18
                                                                               FSCASE(t)                                      -.OOl                      -.84
                                                                               AFDCCASE(t)                                    -.002                     -2.32a
                                               R-SQUARE=        .86            MEDCCASE(t)                                      ,001                      1.03
                                                                               POLYl(t)                                        18.05                      1.88”
                                                                               AUTODEV(t)                                     -5.72                      -.76
                                               samDIe: 82.4-87        1        AUTOOPER(t)                                      3.62                       .45

                                         aThe coefflclent   IS slgniflcantly   different from zero (plus or minus) at a 90 percent confidence   level




Estimation Results: San                  Data available on program measures for San Antonio included a mea-
                                         sure of timeliness of eligibility determination and three categories of
Antonio                                  staffing: supervisors, eligibility workers, and clerks. Equations to
                                         explain each of these program measures were estimated.

Timeliness                               Table I.8 presents the estimation results for the timeliness equation.
                                         Timeliness measures the proportion of cases processed within the 30-
                                         day time constraint established by federal and state program regula-
                                         tions; therefore, a positive value added to a given measure of timeliness
                                         indicates that more cases are processed on time and would be considered
                                         an improvement in this program measure. Automation in the operation
                                         phase is negatively related to timeliness, and not significant, which is
                                         not consistent with expectations that automation should improve the
                                         timely processing of cases. A possible explanation for this result, pro-
                                         vided by an official at the San Antonio office, is that prior to automation
                                         the caseworkers specialized on only one type of case, either food stamps
                                         or AFDc/Medicaid. Beginning with the operation phase of automation,
                                         however, caseworkers were considered generic, meaning they were


                                         Page 93                                                            GAO/RCED-90-S Food Stamp Automation
                                          Appendix I
                                          Estimating the Effects of Automation on the
                                          Operations of State/Local Food
                                          stamp Programs




                                          expected to handle any type of case. Since the generic worker must
                                          invest more time to understand several programs rather than just one,
                                          the switch to generic workers, at the time automation became opera-
                                          tional, may have been responsible for a reduction in timeliness. There-
                                          fore, the operation phase of automation factor may be capturing the
                                          effect of both automation (expected positive relationship) and the
                                          switch to generic workers (expected negative relationship), to the extent
                                          that the combination of both effects may negate the apparent statistical
                                          significance of either.

                                          The other estimated relationships are mostly consistent with expecta-
                                          tions and many are significant. Specifically, the food stamp caseload and
                                          the Mm/Medicaid caseload are not significant. All three staffing vari-
                                          ables are significant, and two of the three staffing variables, supervisors
                                          and clerks, are positively related to timeliness, suggesting that more
                                          workers improve the timely processing of cases. The dummy variable,
                                          DCATGCATS, simply reflects the consequences of a change in the manner
                                          in which timeliness was measured. Finally, the policy change to monthly
                                          reporting is positive and significant, suggesting that timeliness was
                                          improved because of monthly reporting, which is not consistent with our
                                          expectations.

Table 1.8:San Antonio Estimation
Results-Timeliness   in Processing Food   Equation/dependent                    Independent                              Parameter
Stamp Cases                               variable                              variables                                 estimate                t-Statistic
                                          1    LCATGTIM(t)
                                                                                CONSTANT                                           5.94                      4.20”
                                                                                FSCASE(t)                                     -.OOOl                      -1.29
                                                                                AFMEDH                                           .0002                         .97
                                                                                SUPERV(t)                                        .6687                       2.90”
                                                R-SQUARE= .81                   ELGWORK(t)                                    - .3958                     -2.60”
                                                                                CLERK(t)                                         .2457                       1.49b
                                                                                DCATGCATS(t)                                    -1.36                     -4.99a
                                                                                POLY 1(t)                                          2.35                      1.92”
                                                sample 82.1-87.2                AUTODEV(t)                                           .69                       .73
                                                                                AUTOOPER(t1                                       -.48                      -.64
                                          aThe coefflclent   IS slgnrflcantly   different from zero (plus or minus) at a 90 percent confidence    level
                                          “The coefflclent   IS slgnlflcantly   drfferent from zero (plus or minus) at an 80 percent confidence     level


Staffing Equations                        Table I.9 presents the estimation results for the three staffing equations.
                                          In all three equations, automation in the operation phase is negatively




                                          Page 94                                                            GAO/RCED-90-9 Food Stamp Automation
Appendix   I
Estimating the Effects of Automation on the
Operationa of State/‘Local Food
St.=nP pro-




related to staffing, which is consistent with expectations that automa-
tion should permit reductions in staff, all else equal. However, only in
equation 3, for clerks, is the relationship significant. These results are
consistent with an explanation provided by a San Antonio program offi-
cial, who stated that so far only clerks have been affected by automa-
tion since supervisors and eligibility workers have been understaffed
(even with automation) for some time.

Other significant relationships include m/Medicaid         caseload, in all
three equations, and the policy change to monthly reporting, in equa-
tions 1 and 2 only. The m/Medicaid         caseload variable is positively
related to staffing in all three equations, which suggests that greater
caseloads lead to more staffing, and that is consistent with our expecta-
tions. However, the policy change to monthly reporting is negatively
related to staffing, which suggests that monthly reporting is responsible
for reductions in staff levels, all else equal, and that is not consistent
with expectations.




Page 95                                       GAO/RCED-9@9 Food Stamp Automation
                                    Appendix I
                                    Estimating the Effects of Automation on the
                                    Operations of State/Loud Food
                                    stamp Programs




Table 1.9: San Antonio Estimation
Results-Staffing                    Equation/dependent                    Independent                              Parameter
                                    variable                              variables                                 estimate               t-Statistic
                                    2.   SUPERV(t)
                                                                          CONSTANT                                          3.59                      2.63a
                                                                          FSCASE(t-4)                                 -.00001                        -.12
                                                                          AFMED(t-4)                                     .00029                       2.71a
                                         R-SQUARE=.63                     POLY 1(t-4)                                     -.906                    -2.55”
                                                                          AUTODEV(t)                                       .2468                        53
                                                                          AUTOOPER(t)                                   -.3880                       -.81
                                         samole: 83.1-87.2
                                    3    ELG’WORK(t)
                                                                          CONSTANT                                       28.61                        5.78”
                                                                          FSCASE(t-4)                                 - .00008                       -.25
                                                                          AFMED(t-4)                                      .0026                       6.62”
                                         R-SQUARE= .89                    POLY 1(t-4)                                  -2.014                      -1 .56b
                                                                          AUTODEV(t)                                        ,106                        .06
                                                                          AUTOOPER(t)                                  -1.045                        -.60
                                         sample: 83.1-87.2
                                    4.   CLERK(t)
                                                                          CONSTANT                                        26.34                      3.90”
                                                                          FSCASE(t-4)                                   do05                       -1.16
                                                                          AFMED(t-4)                                      .0031                      5.84a
                                          R-SQUARE= .87                   POLY 1(1-4)                                     1.686                        .96
                                                                          AUTODEV(t)                                       -.41                     -.18
                                                                          AUTOOPER(t)                                   -6.719                     -2.83”
                                          samde: 83.1-87.2
                                    aThe coefflclent   IS slgniflcantly   different from zero (plus or minus) at a 90 percent confidence   level

                                    bThe coefflclent   IS slgnlflcantly   different from zero (plus or mmus) at an 80 percent confidence     level.




Estimation Results: Dallas          Data available on program measures for Dallas included a measure of
                                    timeliness of eligibility determination and three categories of staffing:
                                    supervisors, eligibility workers, and clerks. Equations to explain each of
                                    these program measures were estimated.

Timeliness                          Table 1.10 presents the estimation results for the timeliness equation.
                                    Similar to the results for the same equation for San Antonio, automation
                                    in the operation phase is negatively related to timeliness and not signifi-
                                    cant, which is not consistent with expectations that automation should
                                    improve the timely processing of cases. The possible explanation for this
                                    result, discussed above for San Antonio, applies here as well-that    the



                                    Page 96                                                            GAO/RCEIMO-9 Fad Stamp Automation
                                         Appendix I
                                         JMimatiug the Effects of Automation on the
                                         Operations of State/Local Food
                                         S-P    ~lpams




                                         switch to generic workers at the time automation became operational
                                         may cause our results to be misleading as to the true effect of automa-
                                         tion on timeliness.

                                         Most of the other estimated relationships are significant and consistent
                                         with expectations. One exception is the staffing category of eligibility
                                         workers, which is significant but negatively related to timeliness, This
                                         result suggests, contrary to expectations, that additional staff reduces
                                         timeliness. However, the staffing category of supervisors is positively
                                         related to timeliness, which is consistent with the expectation that addi-
                                         tional staff enhances timeliness. Both m/Medicaid       caseloads and the
                                         development phase of automation are significant and negatively related
                                         to timeliness, and that is consistent with expectations that both addi-
                                         tions to caseload and the disruptive nature of development should
                                         reduce the proportion of cases processed on time.

Table 1.10: Dallas Estimation Results-
Timeliness in Processing Food Stamp      Equation/dependent                    Independent                              Parameter
Cases                                    variable                              variables                                 estimate                t-Statistic
                                         1.   LCATGTIM(t)
                                                                               CONSTANT                                        13.16                        3.54a
                                                                               FSCASE(t)                                   - .00008                        -.96
                                                                               AFMED(t)                                     - .0004                      -2.02”
                                                                               SUPERV(t)                                       .2787                        1.52b
                                                                               ELGWORK(t)                                   -.2160                       -1.87”
                                                                               CLERK(t)                                     -.1220                       -1.14
                                              R-SQUARE= .68                    POLYl(t)                                       -.681                        -.86
                                                                               AUTODEV(t)                                     -.958                      -1.33b
                                                                               AUTOOPER(t)                                  -.3091                         -.40
                                               sample: 82.1-87.2
                                         aThe coefficient   IS slgnlflcantly   dtfferent from zero (plus or minus) at a 90 percent confidence    level
                                         bThe coefflclent   IS slgnlflcantly   different from zero (plus or minus) at an 80 percent confidence     level


Staffing Equations                       Table I. 11 presents the estimation results for the three staffing equa-
                                         tions. Only in equation 3, for eligibility workers, is automation in the
                                         operation phase both significant and negatively related to staffing,
                                         which is consistent with our expectation that automation should permit
                                         reductions in staff, all else equal.

                                         In general, the results presented in table I. 11 are mostly inconsistent
                                         with expectations, and equation 4, clerks, has a very poor fit (low R-
                                         Square), Besides automation in equation 3, only one other estimated



                                         Page 97                                                            GAO/RCED90-9 Food Stamp Automation
                                         Appendix I
                                         F%imating the Effecta of Automation on the
                                         Operationa of State/Local Food
                                         stamp Programs




                                         relationship in table I. 11, the policy change of monthly reporting in
                                         equation 2, is both statistically significant and consistent with expecta-
                                         tions. These results for the Dallas staffing equations may be a conse-
                                         quence of the fact that our data for Dallas actually reflect several
                                         suboffices. These several suboffices were merged into one office during
                                         the sample period, and this may mean that the nature of the operation
                                         and the staffing requirements were affected by these mergers during
                                         our sample period.

Table 1.11: Dallas Estimation ReSUltS-
Staffing                                 Equation/dependent              Independent                               Parameter
                                         variable                        variables                                  estimate                t-Statistic
                                         2.   SUPERV(t)
                                                                         CONSTANT                                           6.17                      554a
                                                                         FSCASE(t-4)                                   - .00003                       -.54
                                                                         AFMED(t-4)                                     -.Oool                        -.45
                                                                         POLY 1 (t-4)                                       1 .Ol                     3.15a
                                              R-SQUARE=.83               AUTODEV(t)                                         .I62                         .59
                                                                         AUTOOPER(t)                                        ,187                         .50
                                              sample:    83.1-87.2
                                         3.   ELGWORK(t)
                                                                         CONSTANT                                         31.66                        5.83”
                                                                         FSCASE(t-4)                                   - .00003                       -.ll
                                                                         AFMED(t-4)                                       .0009                        1.03
                                              R-SQUARE=       .90         PoLYl(t-4)                                      -1.66                      -1.06
                                                                         AUTODEV(t)                                     -7.002                       -5.20a
                                              sample:    83.1-87.2        AUTOOPER(t)                                   -6.220                       -3.42a
                                         4.   CLERK(t)
                                                                          CONSTANT                                        32.19                        7.92a
                                                                          FSCASE(t-4)                                    ,00004                              .18
                                                                          AFMED(t-4)                                    -.0006                        -.94
                                              R-SQUARE=.44                POLY 1 (t-4)                                    -3.27                      -2.79”
                                                                          AUTODEV(t)                                       1.448                       1.44b
                                                                          AUTOOPER(t)                                     3.624                        2.66”
                                              sample:    83.1-87.2

                                         aThe coefftcrent IS slgnifrcantly different from zero (plus or minus) at a 90 percent confidence    level

                                         bThe coefficrent IS signrficantly different from zero (plus or minus) at an 80 percent confidence    level




                                          Page 98                                                      GAO/RCED-99-Q Food Stamp Automation
Appendix II

Description of the Automated Food Stamp
Programs GAO Reviewed

                       Following are brief descriptions of the automated Food Stamp Program
                       systems developed and operated by the state agencies of Vermont,
                       North Dakota, Kentucky, Texas, and California.



Vermont State Agency

Background             Vermont’s request for federal funding described an automated system
                       called “ACCESS” as an on-line computer system for administering social
                       welfare programs. Using a fully integrated data base, the system, devel-
                       oped for the most part in fiscal year 1983, handles data collection, eligi-
                       bility determination, caseload management, administrative decision
                       support, and child support collections for such welfare programs as the
                       Food Stamp, Aid to Families with Dependent Children, and Child Sup-
                       port Programs. It uses a fully integrated data base to support all func-
                       tions to offer financial management.


Overview               The system consists of two main components, an on-line component and
                       batch component. The on-line component provides for data entry, edit-
                       ing, and correction; eligibility and notice determination; and data
                       inquiry. The system operates on-line via remote cathode ray tubes in
                       district offices attached to the central site computer with leased lines.
                       The batch component provides periodic functions such as disbursements
                       and reports. The on-line system runs during the normal working hours
                       with minimal operator intervention. It is menu driven. A user signs onto
                       the system and is presented with a menu of functions from which to
                       select. Each function operates in three modes (entry, correction, display)
                       which, for security reasons, users are allowed to use or prohibited from
                       using depending on their functional roles. The batch system runs daily
                       in the evening when the on-line system is not operational. The major
                       batch functions include (1) notices of decision; (2) AF’DCchecks; (3) food
                       stamp mailing labels, cash out checks, and benefits list; (4) Medicaid
                       cards; (5) interface to other systems (Medicaid claim processing, Social
                       Security Administration, etc.); (6) correction request notices; (7) auto-
                       matic discontinuation for failure to correct; (8) mailing labels for case
                       reviews; (9) periodic operational reports; and (10) periodic management
                        reports.

                       The ACCESS intake process is capable of accepting new applicant data
                       and all changes to data. The information collected on the application


                       Page 99                                    GAO/RCEDSO-9 Food Stamp Automation
-
                     Appendix II
                     Description of the Automated Food Stamp
                     Programa GAO Reviewed




                     form (name, address, date of birth, program applied for and date, social
                     security number, sex) is entered into the system. All raw data necessary
                     for eligibility determination are transmitted into the system in an effi-
                     cient, integrated operation. Both financial and nonfinancial tests are
                     included. The system prompts the eligibility worker to ensure that all
                     information has been gathered. If it is not entered, error messages will
                     appear in association with the case, and on the worker’s daily report.



North Dakota State
Agency

Background           On October 1, 1984, the North Dakota Department of Human Services
                     implemented a statewide on-line system to assist with the administra-
                     tion of the public assistance eligibility determination process for appli-
                     cants. The statewide on-line system is referred to as the Technical
                     Eligibility Computer System (TECS)with capabilities of determining eligi-
                     bility, calculating benefits for food stamps and AF’DC,and providing man-
                     agement with a tool to maintain state-supervised and county-
                     administered welfare programs. The Service approved the state’s
                     request for the development of the TEC system for about $1.1 million,
                     The Food Stamp and AFDCPrograms are administered by the Depart-
                     ment of Food Services, which is within the state’s Human Services
                     Department.


Overview             TECSwas developed and designed as primarily an on-line system that
                     creates, edits, and updates application, case, and recipient data on a
                     statewide data base. Using on-line data entry techniques, transactions
                     are edited at the terminal and not accepted on the data base until all
                     edits are complete and accurate. TECSalso has components that produce
                     notices, listings and case status documents and benefits, and reports on
                     either an on-line or batch process made on a regular cycle for program
                     management purposes.

                     Specifically, TFXS'conceptual design is divided into five major sections :
                     (1) client certification, (2) financial information and control, (3) manage-
                     ment information and control, (4) TECSdata base, and (5) control
                     requirements.




                     Page 100                                   GAO/RCEMW9   Food Stamp Automation
                   Dmuiption of the Automated Food Stamp
                   w        GAO Reviewed




                 e The client certification system is primarily an on-line system which cre-
                   ates, edits, and updates application, case, and recipient data on a data
                   base.
                 l The financial information and control and the management information
                   and control systems are primarily batch systems to produce benefits and
                   reports on a regular cycle.
                 . The TEX!sdata base contains the information necessary to identify the
                   eligible recipients for the public welfare programs administered by the
                   system and the services for which they are eligible.
                 . The control requirements incorporate both manual and automated meas-
                   ures to ensure that client data are accurately captured at the local office
                   level and processed and reported at the central state office.



Kentucky State
Agency

Background         To adequately serve the needs of its citizens, simplify and decrease the
                   workload of its caseworkers, and lower case error rates and related fed-
                   eral penalties, the Commonwealth of Kentucky developed and imple-
                   mented the Kentucky Automated Management and Eligibility System-
                   Food Stamps (KAMESFS).The state started implementing the system in
                   March 1987, with three pilot counties. Initially, in 1983, the Kentucky
                   state agency requested funding to develop the Kentucky Automated
                   Certification and Issuance System (K.&IS). KACISwas intended to auto-
                   mate the certification and issuance process of the Kentucky Food Stamp
                   Program. However, during the course of the development and implemen-
                   tation of KACIS,the Commonwealth terminated the contract with the
                   company developing the system. In December 1985 the Commonwealth,
                   through a court settlement, purchased the KACISsoftware from the con-
                   tractor and submitted a new ADP development plan (KAMES) to the
                   Food and Nutrition Service, which was approved in July 1986.

--~   ~~~   ~
Overview           The December 1985 KAMES planning document explained that the plan
                   was for the KAMESFSto be a stand-alone system. KAMES will later be
                   integrated with a separate system, known as the KAMES Income Main-
                   tenance, being developed to support the AFDC,Medical Assistance, Refu-
                   gee, and State Supplementation programs. KAMES is to replace
                   Kentucky’s current computer system with a system that will meet the



                    Page 101                                  GAO/BcED-W9   Food Stamp Automation
                            Appendix II
                            Description of the Automated Food Stamp
                            Progmma GAO Reviewed




                            increased needs of administering the Food Stamp Program. KAMBF-Sis
                            an on-line, menu-driven system that provides for the on-line collection,
                            update, and inquiry of food stamp information. The system supports an
                            interactive client interview through use of an on-line application.

                            The systems includes such features as the capability to (1) determine
                            eligibility and compute allotments through an automated process, (2)
                            detect and control eligibility errors prior to issuance of benefits, (3)
                            determine and calculate financial eligibility computations, and (4) gener-
                            ate notices to clients.



Texas State Agency

Background                  The Texas state agency has developed four different automated systems
                            that service the Food Stamp Program statewide and in selected local
                            offices throughout the state. The statewide automated system is called
                            the System for Application, Verification, Eligibility, Referral, and
                            Reporting (SAVERR).For the local office level, the state agency developed
                            a network of automated systems distributed across the state to interact
                            with the statewide system. This system is referred to as WELNET(Wel-
                            fare Network).


Overview -The   Statewide   The system began development in 1977 as an integrated database for
                            application, eligibility determination and case maintenance, referral, and
System                      reporting processes. It is designed to process application data via on-line
                            data entry for Food Stamp Program, AFDC,and Medical Assistance only
                            programs and to store the information on an application area of the data
                            base.

                            An integrated client data file is the central feature of the SAVERRdata
                            base design. The SAVERRdatabase includes a single master client file for
                            Food Stamps, AFBC,Supplemental Security Income, and Medical Assis-
                            tance only clients. The client area of the SA~ERRdatabase contains only
                            one client record for each client, regardless of the number of cases in
                            which the client is (or has been) active.

                            The unique client identifier number is a randomly generated nine-digit
                            number which identifies each client in the data base. The SAVERRclient
                            number remains with a client through time, so that if he or she leaves


                            Page 102                                  GAO/RCED-9@9 Food Stamp Automation
                                Appendix II
                                Description of the Automated Food Stamp
                                Programs GAO Reviewed




                                the state’s rolls and later reapplies for benefits, the same client number
                                is used each time he or she reapplies. Remote data entry processes
                                notices of applications, certification forms, and case update forms.

                                The on-line Case/Client Inquiry to the WERR data base includes the
                                following:

                                A. Applicant cross-reference by name, Social Security Number or Histor-
                                ical Information in Casefile.

                                B. Application File, by application number.

                                C. Client cross-reference by name, Social Security Number, Historical
                                Information in Casefile, or alias.

                                D. Client file, by client number.

                                E. Public Assistance, m/Medical           Assistance only case, by case
                                number.

                                F. Food Stamp case, by case number.

                                G. State Data Exchange and Supplemental Security Income cases, by
                                case number.

                                H. Additionally warrant or Authorization To Participate (ATP) informa-
                                tion can be called up by warrant or ATP number.


Overview-The     Local          Because of the monumental task foreseen by state officials in auto-
Off ice Automated Systems       mating all 202 local food stamp offices in Texas, state officials devel-
                                oped a phased approach for WELNETto automate the certification
                                process at the local offices. The Service approved the state’s request to
                                develop Welnet I and II for $1 million and $21 million, respectively.
                                Welnet, which initially consisted of two phases, now consists of three:

                            l   WELNETPhase I, at a program cost of about $1 million, consisted of
                                microcomputers capable of performing only required program house-
                                hold budget calculations. The first phase of WEWET specifically entailed
                                the installation of 611 Sanyo microcomputers that were used to support
                                the eligibility intake process at the case determination level in large
                                offices in major metropolitan areas of the state. The Phase I implemen-
                                tation is principally a local data processing function installed on small


                                Page 103                                       GAO/RCED9&9   Food Stamp Automation
     -
                               Appendix II
                               Description of the Automated Food Stamp
                               Programs GAO Reviewed




                             microcomputers. It automates the client information intake process, per-
                             forms the budget calculation associated with eligibility determination,
                             calculates the food stamp allotment and the AFDCbenefit amount, and
                             prints documents designed for both case folders and SAVERReligibility
                             system data entry.
                           l WELNETPhase II was planned as a system providing terminals at each
                             worker’s desk to interact with the applicant and participant during the
                             application and eligibility determination process through benefit calcu-
                             lation and, eventually, on-line issuance and reporting. The network was
                             to consist of a principal network node directly linked with the central
                             site mainframe computer installed in Austin, Texas. Phase II, however,
                             ran into unexpected equipment limitations, causing the state to abandon
                             this $26 million expenditure and move into WELNETPhase III.
                           0 WELNETPhase III has an estimated cost of about $28.7 million. Because
                             of the problems in Phase II, this phase was essentially planned to accom-
                             plish the Phase II objectives. Phase III consists of the implementation of
                             an additional 60 offices and the retrofit of the original 36 offices from
                             Phase II. The equipment consists of personal computers with substan-
                             tially more capability than those to be used in Phase II.

                               The local offices will have a network which includes hardware and soft-
                               ware that performs the print, file storage, and communication functions.
                               The implementation of this strategy requires four tiers of networked
                               automation support: central site mainframe, regional node, local office
                               network, and individual work station.



California State
Agency

Local Office Automation-
Welfare Case Data System
(WCDS)

Background                     WCDSis designed to improve the administration  of public assistance pro-
                               grams for 19 California counties. The system, designed by Santa Clara
                               County, was first implemented in that county in April 1967 and is made
                               available at no charge to other governmental entities.




                               Page 104                                  GAO/RCED-90-9 Food Stamp Automation
                               Appendix II
                               Description of the Autmded   Food Stamp
                               Programs GAO Reviewed




Overview                       The WCDSprovides automated support for all functions in which a Cali-
                               fornia county welfare department is involved. The system includes such
                               features as

                               automatic error detection;
                               exception and “reminder” information for each worker;
                               automatically produced statistical data to meet county, state, and fed-
                               eral reporting requirements;
                             . automatic communication between eligibility and service workers and
                               between the welfare department and other federal and state agencies;
                               automatic updating of Central Index Systems;
                               automatic notice to recipients of action taken;
                               an automated “reminder” system which allows the eligibility workers to
                               enter free-form or coded reminders; and
                             . automatic computer-generated mail transmittals to be mailed with case
                               renewals, recertification, and income report forms,


San Francisco Local Office
Food Stamp Automated
On-Line Issuance System

Background                     San Francisco County was among the first counties to develop a Food
                               Stamp Automated Issuance and Reporting System. The state of Califor-
                               nia assumed ownership and called it a Food Stamp Automated On-Line
                               Issuance System (FOSOLIS).San Francisco County is one of the pilot test
                               counties of a 16-county consortium using the Case Data System to
                               administer the program, which will use the FOSOLISsystem to issue
                               benefits.


Overview                       IQOLIS is an automated system that uses an on-line computer network to
                               facilitate the issuance of food stamps accessed on-line using a plastic
                               magnetic card at food stamp outlets. This system replaces paper ATPS
                               with electronic communications to food stamp outlets to distribute food
                               stamp coupon books to clients.

                               Food stamp benefits are authorized through the state’s Case Data Sys-
                               tem’s daily process and transmitted to FOSOLISon the morning of the day
                               following the date printed on the form used for the appropriate transac-
                               tion. The Case Data System, which maintains limited statewide food



                               Page 106                                  GAO/RCED-So-9 Food Stamp Automation
Appendix II
Description of the Automated Food Stamp
Pmgrams GAO Reviewed




stamp caseload information, produces an ATP register for local office use
to reconcile the on-line benefits issued by F’SOLIS.




Page 106                                  GAO,‘TtCED9O-B FoodStampAutomation
U.S. General Accounting Office Survey of State
Food Stamp Programs

          r
                                                 U.S. General Acccunting Office
                                               Survey of State Food Stanp Programs
              Thz United States General Accounting Office, an agency responsible                  for evaluating
              federal prapmns, iscar'luztinga           reviewofthelevelofauWnat.ionofthe                   food
              stanp prqrams in the Unitad States.            Specifically    we are interested       in the
              pccqress the states havemadeindevelcping               statewide systansanl       ths rolesof
               federal financial   particiption      inthatdevelopnent.          lhisreviewwasrequestedby
              Senators rtichard Sugar ati Jesse Hslms of the Ccmnittee on Agriculture,                  Nutrition,
              ati Fonastry, U.S. Swate.         Collecting    informatia3     fmn ea& state or territnry           is
              thz mxt impxtantmrt          of this investigation.         Pleasa help us fulfill       the
              Carmittea's   request w ccnpleting this qusstiaxnaire.


                -   Please return the caqleted    questionnaire in th? enclose3 self-addressed
                    business reply envelope within cneweek of receipt,    if possible.   It sluuld
                    take nomore than 30 mimrtes to cxqalete.
                -   If you have any questions about +&z questionnaire please call                       collect   Mr.
                    Michael Rives or m. Linda Lohrke at 214-767-2020.
                -   If the envelope has been misplacfz3 please mail thz canpleted                      questionnaire    to:
                           U.S.      General Accounting Office
                           Attn:      W. Michael Rives
                           Suite      607
                           1114      Camwxe St.
                           Dallas,      TX   75242

              Thank you for your help.



              ~:meansby            which the faxl stamp program is swrtd          in a state.   'Ihis cculd
              imlu3ecanpuker       hwdware am3 softwareormanualmeanstoperform             case record
              storage, eligibility      determination,  benefit calculation,   frant-en3 verification,
              verification    natchirq,   notice generation,   claims tracking ard reccxtery. issuance,
              ardprqramrepxtirq.
              Enhanced Furding: any furding wer the standard SOpercent federal financial
              larticiption   for foal stamppF&mADP      developnent, operations,    ard/or
              ahinistrative   costs. (mnced    furding does mt inclulegrantsormney-in-kid.1
              Statewide:    in use in -all      local   offices   in tk   state.
              Lncal Offices:          includes any office     that caxlucts    intake,   eligibility
              determination,         ard ca= mgement.



                                                                    138




          L



                        Page107                                                     GAO/RCED9@9FoadStampAutomation
          U.S. General Accounting Office Survey of
          StateFoodStampFrograma




1.   Does yzur       state     have   an a-ted                   4.    t3irrooctl#1980,kwmny
     sy5ten1thatt3qprts               its     fad                      raqusstahssyur        etatemdeof
     ~tarrpprqramstatewide?                   (Check                   19aBforalh5ncdfalelral~ing
     me)                                                               forth3puqul5ofdevelopingor
                                                                       lmprwing the ADP SyiJtedS) use3
     1.    c     1   Yes                                               tOnflxnttbdoadS~pICgZ7UTl
                                                                       in your state? P1easelnclule
     2.    [     1 No-->        SKLPTOQ.         28                    r4qinatsmde~AtMmced
                                                                       Pluming mcunmlt* hms) ad
                                                                       anmbmts     or revida     mTi&s.
2.   Whatisthnanean3acronymof                                          (Enter runbar of requests; if
     ynxstatewidesystem?                                               tKTu, enter 0)

                                                                                        rmbsr      requests

                                                                 1).   HWmmnyofthserequsstsfor
3.   FYan what       federal     agency(s),      if                    dmtbcd  furding hsve ban
     any,hasyurstateraquested                                          epwd?      (Entsr txder appuvcd:
     enhaxedfLpdingforfuxIstsrp                                        if tam, enter 0)
     ADPdevelqmmt?       (Checkme)
     1.    I     3 F@quested sn?L?ul&                                                  runbedr aWed
                   furrlingfranbthFt=S
                   an3 i-ES
                                                                 6.    I%w m5ny     of tinme mquests     have
     2.    t     1 Requested etlhaxd                                   radtadin        straluirmcml
                   furdingcnlyfran~                                    furxlingdirbmsments          for put-
                                                                       atate?      bmrrmbrof
     3.    c     1 Rquestsdenhanced                                    diatruraamts:       if rime, enter 0)
                   fixding a-dy frcxn HB



     4.    C 1 Havenotrequested
               enhanced furding fran
               eithzr FNS or HITS
               lFxxJ(BxxEDamuiz4
               AFSYB, RSA6EsIcfPlOQ.                  21




                                                           139




          Page108
          Appendix ID
          U.S. General Accounting Of’fice Survey of
          State Food Stamp pro(pluns




7.   In your crpinion, hm much of an @act,        if any, did cbtainirq                     emhamxd fmxling
     haveanthadevelcpnsntofthe           fillwing   syatencharacteristics                     inorder to
     mtsetcahanced~ingreqrdranents~rtkfoad5tanpADPsyetaninyour
     state?   (Check cm for each)
                                           0 Very (I       (I                   II        'II Little   ll
                                           ll areatq GreatWeratcll                   Sons 'Porno       n
                                           nilkactlrimpnctP~ctO~ctIIimractII
                                           (I   1.     Q       2.   q    3.     (r    4.    w     5.   1
     1. Typeof     functims                a           (I           q           q           (I         'II

                                           T           ‘I           (I          (I          (1         n
     2. Integratimwithother                (r           n           B           n           a          n
        public assistance         sub-ll               'II          (I          ll          ll         n
        mstial systsns                     n           T            q           11          P          P

          fcodstaItpsystml                 n            n           a           n           P          a
          througl-cut the state            (I           n           n           1           (I         P
                                           (I           (I          n           n           n          (I




8.   In your Cpinicn, hzw important,                 if stall, was=    enhanced firndirqlmpur
     state's ability  to autmmte its                 focd stMp 8~etetQ f-k     ad
     1.    C 3 Extrenelyimportant

     2.    c   3 veryilyxxtmt
     3.    [   1 Mxlerately       important
     4.    c   3 -t             iJqwtant
     5.    C I    Little   or m importance




                                                             140




          Page 109                                                            GAO/RCEDW9         Food Stamp Automation
             Appendix III
             U.S. General Accounting Office Survey of
             State Food Stamp Programs




Please ccnsider requests your state bsmsde for enhanced federal finamial
participation  for ths puq?oseofdevelcpingcr~ingthsADPeystemusedto
supgm-t pur state's food stmpprqram.           Specificallycmsideranl          thee requests
made in Advanced Planning Docunents or zane&nents/revisio             to AaG?n=edPlenning
Docmmts for system iqmvmmts         8inceDctober       1, 1980.
Fbr the mxtre2mtenhanced            Ming       request:
9.    When did your state make its meet                   11.   What was thz name axI acronym of
      recentrequestfor       enhanced                           the systembeing  develop&?
      fLlndingt.odevelcporexperdits
      food st.aq~ADP system(s)? (Rx
      example if ths date was January                     12.   What level     of federal financial
      1982, enter Olb2.1                                        participation,       if any, was
                                                                apprcved bDr this request thrcqh
                                                                eachofthe       following prcgmns?
      +/A-                                                      (Qleckcmeforeachpqram)
                        Yr.
                                                                Fixd stmps
10.   Which of the follming      categories,                    1.     c    1 75%
      basedmthzFNSguideto
      preparingAdvanc&      Planning                            2.     c 1 50%
      Docunmts, best describes the
      basic purpoZZf     this automstion                        3.     C I      Request denied
      effort?    (Qleckcme)
                                                                4.     I:   1 Requsst still      perding
      1.     [    1 First time automtion
                     (i.e., automating a                        5.     [    I   Other (Please specify)
                    trenual system)
                                                                AFIX
      2.     C I    Completely replacing an
                    existinq automted                           1.     I: 1 90%
                    system&th    a newsystem
                                                                2.     c 1 50%
      3.     [:   'J Making additions to an
                     existing automated                         3.     C 1 Request denied
                     syS-
                                                                4.     [    I   Request still    perding
      4.     C ]    Making deletions  fran an
                    existing automted                           5.     C I      Othsr (Please specify)
                    systm
                                                                6.     [:   I   Not applicable
      5.     ! 1 Makingchangestian
                    existing   automated                        Medicaid
                    syS-
                                                                1.     c 1 90%
      6.     C I    Other (Please explain)
                                                                2.     c 1 50%
                                                                3.     C 1 Request denied
                                                                4.     I:   I   Request still    perding
                                                                5.     [    I   Other (Please specify)
                                                                6.     [    I   Not applicable

                                                    141




             Page 110                                                       GAO/RCJZD-90-9 Food Stamp Automation
           Appendix III
           U.S. General Accounting Office Survey of
           State Food Stamp Programs




Fbr the seccndmstrecmtenhanced             funding request:

13.   Whendidyour      statemakeits                   15.   W-c&was tk nme 51~3acronyn of
      5eccn3 nwstrecentrquestfor                            ths systsm being dwelopad?
      enhanced fm.Iingtodevelcpor
      exparditsfoodstampADP
      system(s)?     (Fbr exanple if the
      datewasJaruwv1962.          enter
                                                      16.   what level of federal financial
                                                            participation,      if any, ws
                                                            agp-vvsd     5x this request ttraqh
                                                            enchofthe       follwingpxq?ams?
                                                             @mckcneSxeachpqram)
                                                            FbalstJmlpe
                                                            1.   c     1   75%

14. Which of the follting       categories,                 2.   c 1 50%
      knsedonthemguideto
      prepuing    Advanced Planning                         3.   [     ]   Requestdenied
      D~~unmts,bestdescribesthz
      basic purpGZf      this automation                    4.   [     I   RJF.quest still     perxling
      effirt?   1t-k    a-4
                                                            5.   C ]       Other (Please specify)
      1. c 1 Firsttimeautolnation
                   (i.e., automatinga                       AFEC
                   m~1syEltem)
                                                            1.   c     1 90%
      2. I      1 Ccnpletely   replacing an
                   existing automated                       2.   c 1 50%
                   8yetemGitha     newsystan
                                                            3.   [     I   &quest     denied
      3.     t 1 Maki!qadditionsto         an
                   existing    automted                     4.   [     I   Rqlsst     still    perding
                   systell
                                                            5.   C I       Other (Please specify)
      4.     c 1 Makirq deletions       fran an
                    existing   automated                    6.   [     ] Not applicable
                    5ys*
                                                            Medicaid
      5.     c 1 Making changes toan
                    exieting   automated                    1.   c     1 90%
                    SW+=
                                                            2.   c 1 50%
      6.     C 1 Other (Pleaee explain)
                                                            3.   I:    1 Request      denied
                                                            4.   I:    ]   Request still       perding
                                                            5.   [     1 Other (Pleasespacify)
                                                            6.   [     ] Not applicable




                                                  142

                                                                                                          J


           Page 111                                                   GAO/BCEWO-9 Food Stamp Automation
               Appendix Ill
               US. General Accounting Office Survey of
               State Food Stamp Programa




-

    Fbr th?thirdmstrecenterihanced                 fun3ing request:
    17.   When did your state make its third                19.   What was ths nme ti                acronym of
          mostrecentrequest       for enhanced                    the systembeingdevel~i
          furxling tideveloporexp~tiits
          fmd stmpADPsystfms?           (For
          exmmleif     ths date was Janllary
          1982; enter 01/82. If m          at&              20.   what level of federal financial
          didmtnmke~~~                                            prticipation,     if any, was
          pleamsenter~m"intheYo5r                                 appruved~rthisrequestthrough
          4psceadskiptoQ.           21)                           each of the follavirq     prqms?
                                                                   (Checkcneforeechprogxam)

          +‘+.                                                    F-cd sttsnrx
                                                                  1. c 1 75%
    18.   Which of the follcwing categories,                      2.     [: 1 50%
          basedcntheEtSguideto
          premring Advmcxz! Planning                              3.     [    ]   Request denied
          Lkxunmts, best describes the
          basic purpoGf      this automtion                       4.     [    1 Request still          perding
          effort?   (Checkme)
                                                                  5.     [    ]   Other (Please specify)
          1.     c   1 First      time automtion
                           (i.e.,   automting  a                  AErc
                          Il5nual systen)
                                                                  1.     c 1 90%
          2. c 1 Completely          replacing an
                          existing automated                      2.     I: 1 50%
                          systemwith a new system
                                                                  3.     [    I   Request denied
          3.     c 1 Making additions           to an
                          existing   automted                     4.     f    I   Requsst still        perding
                          syst-n
                                                                  5.     I:   I   Other (Plea5especify)
          4.     c 1 Making deletions           franan
                          existing   autmnted                     6.     [    I   Not   applicable
                          SyStgn
                                                                  Medicaid
          5. I: 1 Making changes to an
                          existing   automated                    1.     c 1 90%
                          sySm
                                                                  2.     I: 1 50%
          6.     [   I    Othzr (Please explain)
                                                                  3.     C 1 Request denied
                                                                  4.     [    I   Reqmst     still     petiing
                                                                  5.     C 1 Other (Please specify)
                                                                  6.     C    1 Not applicable



                                                         143




               Page 112                                                  GAO/RCED-90-9 Food Stamp Automation
          Appendix III
          U.S. General Accounting Office Survey of
          State Food Stamp Programs




21.   In your opinion, how important,        if atall, were each of ths follcwitlg in your
      state's pnqress in autmmting         its fad stmp prugrzun? (Umk cne for &h of
      thz follarinq   incentives)
                                     n      ll           II   l-ear-    ‘II         (I Little   lJ
                              lEIxtrmely    P v=Y        (I ately       lTSaxwhst(I    orno     T
                              lbnportant    ~important   Whpotiant      rlmportant Timport-
                              'II    1.     a    2.      a     3.       W     4.    W      5.   W
      l.mharCd75%fd-          W             B            n              n           n           9l
         era1 financial   Far-W             n
          ticipatim   for f&W               a
          stmpp~mnauto-W                    Ti
         mtion dcvalaamt      ‘II           11
                                     a      n            n              ll           ll         ll
      2.stardaNl50%         fed- W          n            n              n            ll         n
          era1 financial      pxr-ll        n            n              n            n          n
          ticipsticn    for fadW            n            (I             B            n          n
          stmp program auto-~               II           n              II           n          n
          nation dcvclqmmt           T      11           11             n            ‘1[        n
                                     11     11           n              n            n          ll
      3. Projected benefits ll              n            n              n            n          n
          ofautmmtion                a      D            n              II           q          n
                                     n      ?!           n              3l           n          n
      4. Incentives offered W               (I           a              n            n          n
          by HI-IS for AFDZ          V      n            n              11           11         ll
          autmation&i+-W                    n            n              (I           0          1T
          umt thet was IrkedI               n            n              n            n          n
          grated into mr             W      W            II             W            W          W
          fodsaqY5ystPn              n      W            11             n            n          n
                                     n      11           n              (1           ll         '1[
      5. Incentives offered W               n            (I             n            W          n
          bfHHSforAFD2               ll     n            P              n            n          W
          autortatione               W      ll           n              W            W          W
          stiamthatwas               n      n            n              (I           (I         ll
          integrated    intD         W      n            (I             W            a          W
          your fax3 stanp            n      n            n              W            W          W
          sytm                       W      W            W              n            W          W
                                     W      n            n              (I           W          W
      6. Incentives offered W               n            n              W            W          n
          b HHS for t4dicaidW               (I           II             n            W          W
          autoration develq-W               W            n              n            n          W
          smtthetwssinte-W                  n            Q              ll           W          11
          grated into yxr            W      W            n              (I           W          W
          food stimP- sv5tm
                          -          n      ll           II             W            n          W
                                     n      W            n              3l           W          11
      7. Jncentiwx offered W                n            T              n            n          W
          by HHS for t4dicaidW              W            W              T            n          ll
          automtion opus              ll    n            W              a            W          n
          tiam that was              q      n            n              11           W          W
           integrated into           W      q            W              n            11         W
          )mlr fed stmp              n      n            W              n            W          n
          SP-                        T      ll           n              W            W          II
                                      n     n            W              n            W          n
      8. Other (please               (I     ll           n              W            W          W
             specify)                ll     W            W              11           W          W
                                     n      (I           q              W            W          W
                                                 144




          Page 113                                                 GAO/RCED-90-9 Food Stamp Automation
           Appendix III
           U.S. General Accounting Office Survey of
           State Food Stamp Programa




22.   Which, if any, of thz items listed              23.   Which, if any, of the items listed
      in question 21 was most important                     in question 21 was least important
      forammsting      the food stmp                        inautwnatingthsfoodstmp
      system(s) in your state? Please                       systm(s)    inyour state? Please
      explain briefly.                                      explain briefly.




24.   In your cqinion, how much, if at all, has the level of fo~A stamp sysm
      auwtion     in your state increased or decreased the level of each of the
      following food stamp prcqram chsracteristics?    (Fleck cne for each)
                                 W            w          WNsither      W          W              n
                                 n            n          Wincreaeed    W          n              W
                                 WGreatly     WMo3eratelyW    nX       WModeratelyW Greatly      W
                                 Wincreaeed   Winzreaeed~Wdecreased    Wdezreased Wdecreas&      W
                                 w    1.      W     2.   W     3.      W     4.   W    5.        W
      1.    Nunber   of eligi-   W            n          W             n          w              W
            bility   mrkers      W            w          n             W          n              n
                                 W            W          W             W          W              W
      2.    Nunbfx- of clients   W            w          l!            w          n              n
            serve3               W            W          W             w          W              W
                                 W            W          W             w          w              W
      3.    Prqram               W            n          n             (T         W              n
            acccxmtability       W            n          W             W          W              w
                                 W            n          w             w          W              11
      4.    Food staqprcgramW                 n          n             n          W              W
            errors            n               n          (I            n          W              11
                                 W            w          W             W          W              W
      5.    Timeliness of pro-W               w          W             W          W              n
            ceasing fmd stat-@                W          n             n          w              W
            awlications
             -_               W               n          w             W          W              W
                                 w            w             w          w           W             w
      6.    Other (Please        W            w             n          (I          n             w
            explain)             n            W             n          w           n             n
                                 W            n             w          n           n             n
                                 n            w             n          n           n             n




                                                  145




           Page 114                                               GAO/RcED90-9 Food Stamp Automation
          AppendixIIl
          U.S. General Accounting Off&            Survey of
          State Food Stamp Programs




25. For Ve autnmsted %yatm that -w=-'s--=PIfog~
     statewide, which ax or more of tlrc tillwing   shtemdsbestdgclfbeethe
     level of autmmtion used to perform csh of tha 6allowixq fmctions?                               Please
     fill  In the blankswithcneletter   frrJpthckey   pwidsd:
    A: lie furtim     is anpletdy                   a-.
    62 ‘Iht furtiarr  is prtially                  a-.
    CI TttefuxMcmisnota~atau.



     1.                StoragaofczJseracordinBnmtial
     2.                Maintemmeofiesmncehietnry
     3.                Eligibility         det ermimtimatinitialapplication
    4.                 EIigibilfty         detemination              at rexrtifioaticm
     5.                Eligibility         determination             with changce in a@icant     statue
     6.                Em&it          calculatim
     7.                hont-end         verifkatim
     8.                Checkirq~ici~tim                        inotkrpublicassistameprqrms
     9.                Claims trac?drg:              calculating          cwetynywints
    10.                Claim3 tracking:              deductimsaticalculatium&rree
     11.               Claim         tracking:       tracking         xmm.pmlt      QMta
     12.               ISSUSlre
    13.                Recuwiliatim
    14.                Tenninatim          aterf3oftxrtificaticm                 pcriad
     15.               GenerationofanyfaadstaqFrogrimmtices
     16.               Gmerationofany                 focd stampprqramrepats
     17.               Generationofany               IPlXrev
     18.               Any elerttonicmail                  capnbilities
     19.               Sampling forquality                  ontrol
     20.               other,        pleasa      specify




                                                             146




          Page 116                                                               GAO/BC~9@9    Food Stamp Automation
           AppendixID
           U.S.GeneralAccountingOffkeSurveyof
           StateFoodStampPrograms




26.   Fbr *hich of the functions listed                       27.     Fbr tich    of the functions listad
      m t?kz preceding page that are                                  cm the prexding      page that are
      either mtaubmatad        or mrtially                            either mt automted or partially
      aubreted,ifany,areautmeted                                      autmated,ifany,areautomated
      txqabilitiesbeingplanrM         in your                         cap~Mlities     actually    being
      state? (List all that apply)                                    developsd inyour       state? (List all
                                                                      that apply)




28.   What level, if any, of         sood stanp offices in your state                 are capable of
      matching reported wags         alld resource infomatimagainstthe                     follauingother
      data basesbefbe      bmd       stanpeligibilityis     detenninad?                Pleasa fill     in the
      blankswithcne    letter        frcmthekeyprcwided:
                  c--stabe1errcloEficc
                  L-rfxmloffice                 (allarae)
                  B-m-                        dlmrloffia?ss
                  El--XItRftd

      1.             sO=ial Sennity          Mninistratimwagedata
      2.             Social    8ecurity      Mninistration          validation     of social   security   nunber
      3.             AEm
      4.             kdicaid
      5.             Supplemental      SecurityIncane
      6.             hergy      assistanze
      7.             State memploynmtccnpenmtion                     agmcywqdata
      8.             Statedepwtmmtofmimrvehicledata
      9.             state     assistame      prqRm6
      10. -          Other.plaasesp135fy.




                                                        147




           Page116                                                               GAO/RCED9@9FoodStampAutomation
             Appendix III
             U.S. General Accounting Of&e Survey of
             State Food Stamp Programs




29.   IsU-~~automatedsystem(s)that                        32.   In FLU opinion, when do you
      supportsthefoodstamppKgramin                              expect the nextmajor       functio~l
      your state titally       integrated    with               alterationor      imprcwemmt inyour
      th? AFCC databass statewide,                              state's food stanpautonation
      partially   integrated      statewide,                    S~S~BCI?     (Fbr exeqle,   for Jan,
      cmlyintegratedin~rtsoftk                                  1999 enter 01/89.       If no state
      state, or mtintegrated           at all?                  edministered system enter N/A.)
       (Qleckone)
                                                                          /
      1.     [:   1 Totallyintegrated                           -yr.m.
                     statewide
      2.     C    1 Partially    integrated           33.       Please explain the nature of the
                     statewide                                  nextexpectedmsjor     fwwtional
                                                                alteration  or impruvement in your
      3.     C 3 Onlyintegrated           inprts                states foal stzn7ip system.
                 of the state
      4.     1 1 Not integrated         atall
      5.I:        ]~ostatewide&xx~stamp

      6.     [:   1 No statewide AFDc system

30.   Whatpercentofyour      state's feed
      stanp caseload is processed umk-
      thz current state administered                  Please give the name, title,  an3
      aubm3tion system? (If no state                  tele@one nunbr of ths pera3nb?-u
      atministered   system enter N/A)                anpletedthissurv~incaseweneed
                                                      to clarify   any ana4ers.


31.   What was the average nunber of                  Title:
      foal stempcases permonthinyour
      state ticfnO3.  1, 1986 to Sept.                Phone nut-bar:
      30, 19877 (Enter nunber belcw)                                      (Area cede)

                                                      Thank you for mur assistance




                                                    148




             Page 117                                                    GAO/RCEIMO-9 Food Stamp Automation
Model Plan Requirements for Certification;
Issuance,Reconciliation, and Reporting; And
General Standards
                1. Determine eligibility and calculate benefits or validate the eligibility
Certification   worker’s calculations by processing and storing all casefile information
                necessary for the eligibility determination and benefit computation
                (including but not limited to all household members’ names, addresses,
                dates of birth, social security numbers, individual household members’
                income by source: earned and unearned, deductions, resources, and
                household size). Redetermine or revalidate eligibility and benefits based
                on notices of change in households’ circumstances.

                2. Identify other elements that affect the eligibility of household mem-
                bers such as alien status, presence of an elderly person in the household,
                or status of periodic work registration, disqualification actions, categori-
                cal eligibility, and employment and training status.

                3. Provide for an automatic cutoff of participation for households that
                have not been recertified at the end of their certification period.

                4. Notify the certification unit (or generate notices to households) of
                cases requiring Notices of

                (a) Case Disposition,
                (b) Adverse Action and Mass Change, and
                (c) Expiration.

                5. Prior to certification, cross-check for duplicate cases for all household
                members by means of comparison with food stamp records within the
                relevant jurisdiction.

                6. Meet the requirements of the IEVSsystem. Generate information, as
                appropriate, to other programs.

                7. Provide the capability to effect mass changes: those initiated at the
                state level, as well as those resulting from changes at the federal level
                (eligibility standards, allotments, deductions, utility standards, Supple-
                mental Security Income, AFDC,Social Security Administration benefits).

                8. Identify cases for which action is pending or followup must be pur-
                sued; for example, households with verification pending or households
                containing disqualified individuals.

                9. Calculate or validate benefits based on restored benefits or claims col-
                lection, and maintain a record of the changes made.



                Page 118                                   GAO/RCED-9Cb9 Food Stamp Automation
                      Appendix N
                      Model Plan Requirements for Certifkation;
                      Issuance, Reconciliation, and Reporting, And
                      General Standards




                      10. Store information concerning characteristics of all household
                      members.

                      11. Provide for appropriate Social Security enumeration for all required
                      household members.

                      12. Provide for monthly reporting and retrospective budgeting, as
                      required.


                      1. Generate authorizations for benefits in issuance systems employing
Issuance,             ATPS,direct mail, or on-line issuance and store all Household Issuance
Reconciliation, and   Record information including: Name and address of household, house-
Reporting             hold size, period of certification, amount of allotment, case type (Public
                      Assistance or Nonpublic Assistance), name and address of authorized
                      representative, and racial/ethnic data.

                      2. Prevent a duplicate Household Issuance Record from being estab-
                      lished for participating or disqualified
                                                    ,          households.

                      3. Allow for authorized under- or overissuance due to claims collection
                      or restored benefits.

                      4. Provide for reconciliation of all transacted ATPSto the Household Issu-
                      ance Record masterfile. This process must incorporate any manually
                      issued ATPS,account for any replacement or supplemental ATPSissued to
                      a household, and identify cases of unauthorized and duplicate
                      participation.

                      5. Provide a mechanism allowing for a household’s redemption of more
                      than one valid ATPin a given month.

                      6. Generate data necessary to meet federal issuance and reconcilation
                      reporting requirements, including:

                      (A) Issuance

                      (1) Food and Nutrition Service (FM)-259-Summary              of mail issuance
                      and replacements, and

                      (2) Fhs-250-Reconciliation           of redeemed ATPSwith reported authorized
                      coupon issuance.



                      Page 119                                         GAO/RCEWO-9 Food Stamp Automation
          Appendix IV
          Model plan Requirements for Certification;
          Issuance, Reconciliation, and Report@ And
          General Standard6




          (B) Reconciliation: FNS~~-ATP Reconciliation Report.

          7. Generate data necessary to meet other reporting requirements,
          including

          (A) ms-10 1-Program          participation   by race,

          (B) ms-388-[State]       Coupon issuance and participation       estimates, and

          (C) ms-209-Status        of claims against households.

          8. Allow for sample selection for quality control reviews of casefiles,
          and for management evaluation reviews.

          9. Provide for program-wide reduction or suspension of benefits and res-
          toration of benefits if funds later become available, and store informa-
          tion concerning the benefit amounts actually issued.

          10. Provide for expedited issuance of benefits within designated time
          frames.

          11. Produce and store a participation        history covering 3 years for each
          household receiving benefits.

          12. Provide for cutoff of benefits for households which have not been
          recertified timely.

          13. Provide for the tracking, aging, and collection of recipient claims and
          preparation of the ms-209, Status of Claims Against Households report.


          The following standards apply to all proposed systems.
General
          1. Perform all activities necessary to meet the various timeliness
          requirements established by the Service.

          2. Allow for reprogramming to implement regulatory and other changes,
          including a testing phase to meet implementation deadlines, generally
          within 90 days.

          3. Generate whatever data are necessary to provide management infor-
          mation for the state agency’s own use, such as caseload, participation,
          and case actions data.


          Page 120                                         GAO/RCED-90-9 Food Stamp Automation
Appendix IV
Model Flan Requirements for Certification;
Issuance, l&conciliation, and Reporting, And
General Standards




4. Provide support as necessary for the state agency’s management of
federal funds relative to Food Stamp Program administration, and gen-
erate information necessary to meet federal financial reporting
requirements.

5. Provide for routine purging of casefiles and file maintenance.

6. Perform all activities necessary to coordinate with other appropriate
federal and state programs, such as AF’DCor Supplemental Security
Income.

7. Perform all activities necessary to maintain the appropriate level of
confidentiality of information obtained from applicant and recipient
households.

8. Perform all activities necessary to maintain the security of automated
systems to operate the Food Stamp Program.

9. Provide for the eventual direct transmission of data necessary to meet
federal financial reporting requirements.




Page 121                                       GAO/RCED9@9 Food Stamp Automation
Comments From the U.S. Department of
Agriculture’s Food and Nutrition Service

Note GAO comments
supplementtng those in the
report text appear at the
end of this appendix.                      Jnlted States                                    Food and                   3101 Park Center Drive
                                           Department    of                                 Nutrition                  Alexandria. VA 22302
                                           Agriculture                                      Service



                             Mr. John W. Harman                                                         September   5, 1989
                             Director,    Food and Agriculture                     Issues
                             Resources.    Community, and
                                 Economic Development Division
                             U.S. General Accounting      Office
                             441 G Street,    N.W.
                             Washington,    D.C.   20548

                             Dear Mr. Harman:

                             We have received your official      draft report,    number RCED-89-172. entitled
                             "Food Stamp Program Automation:        Some Benefits   Achieved;     Federal Incentive
                             Funding No Longer Needed."      We appreciate     the opportunity     to comment on
                             this draft,   and we anticipate   that this process will        improve the final
                             product.

                             In this report the General Accounting              Office    (GAO) addressed the complex
                             subject     of the costs and benefits         of automation     in the Food Stamp Program
                             and concluded that 75 percent funding for State automation                   is no longer
                             needed.      The Food and Nutrition       Service     (FNS) has in the past proposed an
                             end to the Food Stamp Act's provisions              for enhanced funding for automation
                              (virtually    all other State administrative            costs are matched at the rate of
                             50 percent).       Nevertheless,      in spite of our coocerns about 75 percent
                             funding for automation.          we must urge caution in using the GAO data to reach
                             the conclusions      contained      in the report.       The methodologies     employed by GAO
                             to measure the effects         of automation     and the exteot of State automation
                             have serious limitations          that are not adequately         emphasized in the report.

                             Policy      on Automation             Fuodioq

                             In its response to an earlier     GAO report on this subject,     number
See comment 1                RCED-88-58. FNS questioned      GAO’s interpretation   that 75 percent funding
                             was available   only where no automated systems existed.       We still  differ on
                             this matter.    The legislative    history   in the House Committee Report 96-788
                              (page 113) says:

                                        . . . although the great majority      of States now have systems. those
                                      systems canoot perform more sophisticated         computer functions.     such as
                                      computing eligibility   or integrating       with AFDC files.     The planning
                                      necessary to transform    and upgrade those systems would necessarily
                                      result   in most States incurring    significant    developmental    and
                                      installation         costs      .   .   .”

                             Clearly  there was recognition      by the Congress that States had some degree
                             of automation   in place, however rudimentary      or unsophisticated,       and the 75
                             percent funding was an incentive       for any State to achieve a more effective
                             level of automation.     Therefore,    we differ  with GAO's position      that
                             Congress intended enhanced funding only for States without             any automation.




                                              Page 122                                                   GAO/WED-9&9     Food Stamp Automation
                            Appendix V
                            Comments From the U.S. Department of
                            Agriculture’s Food and Nutrition Service




                                                                                                                L




                  Nevertheless.  FNS believes   that Congress may not have intended to provide
                  ongoing support at the enhanced rate for continuing       system development    once
                  a State has achieved a sufficiently      high level of automation.    Aa a result,
                  FNS does not provide 75 percent     funding for upgrades or replacements     of
                  complete systems which meet existing      standards and which were funded at the
                  enhanced rate.


                  Effects    of Automation

See conmen’   2   In this report GAO attempted            to measure the effectiveness       of State
                  automation       of the Food Stamp Program.         While the regression      models
                  developed to determine this effectiveness               do include    a number of relevant
                  variables,      a number of equally       important   factors    are left  out.    For
                  example, no consideration           is given to the economic health of State and
                  local governments.         changes in State priorities        regarding   social   service
                  funding,     differences      in the types of households served. varying
                  capabilities       of different     automated systems, adequacy of central          computer
                  servicing      resources,    and proficiency      of State ADP staffs.      These and other
                  characteristics        of the local operating       environment     can be expected to
                  influence      the outcomes of automation         examined by GAO.

                  GAO indicates      its awareness of this limitation.        although  relegating     the
                  acknowledgment       to a footnote    unduly downplays its significance.         Given the
                  complexities      of the issue, it is unlikely       that any of the relatively
                  simple models      presented in the report can provide a definitive            answer to
                  the question      of automation's     effectiveness.     We believe,  therefore.     that
                  it is prudent      to interpret    these findings    with great caution.

                  Status    of State   Automation

See commen! 3     Similarly,      the results       of GAO's survey questionnaire           also must be
                  interpreted       cautiously.      rather   than boldly      as is done in the report.          FNS
                  believes     there are problems of definition              in the questionnaire;        the States
                  have not always interpreted              the questions     in the same way vhich makes it
                  unwise to compare one State to another unless qualifying                       statements    are
                  added.      Further.     in interpreting        the questionnaire.      the report makes little
                  distinction       regarding     the degree to which States reported             the program
                  functional      requirements       as being automated.         GAO says that 50 States are
                  automated.        In fact, many large States such as Ohio, Florida.                  Michigan and
                  California      still    are only partially         automated.     Based on our own reading
                  of the GAO survey findings,              it appears that of those 50 States that GAO
                  describes      as automated. only about 70 percent have completely                   automated all
                  of the certification          functional      requirements     and about 30 percent of the
                  States have partially           completed or not automated the certification
                  requirements.




                            Page 123                                                GAO/RCEMJO-9 Food Stamp Automation
-       -
                           Appendix V
                           Comments From the U.S. Department of
                           Agriculture’s Food end Nutrition Service




                                                                                                                 3
                 Federal     Cversight      of Automation

See comment 4.   Another aspect of automation            revleved     by GAO was the cost accounting             for
                 the development of State automated systems.                      Although GAO did not question
                 any specific       costs charged to FNS by any of the States audited,                    GAO
                 nevertheless       asserts that     greater     controls      are needed over ADP-related
                 charges to the Food Stamp Program.                The controls         recommended by GAO center
                 on FNS collecting.        recording     and reconciling          State expenditures      for
                 specific     I&P-related     costs.     However, the revised Office              of Management and
                 Budget (OMB) Circular         A-102. published         March 11. 1988. prohibits           Federal
                 grantor agencies from requiring              grantees to report at this ievel of detail.
                 Thus. FNS cannot collect          the information          recommended by GAO, and FN8'
                 accounting      records cannot be considered             inadequate       for not containing      such
                 information.

                 We do agree with GAO that additional          emphasis should be placed on ADP
                 equipment inventory     management.     iiowever. we do not agree that FNS should
                 reconcile   State agency equipment acquisition         with funding draws and State
                 agency property    records;  this 1s clearly      the responsibility    of the State
                 agency.    FNS will be revising     current    handbooks to strengthen    equipment
                 inventory   management and control      upon publication    of the final   FNS revised
                 regulation   for ADP system deveiopment and funding.

                 To conclude,    we acknwledge    that this report tackles        a complicated        area of
                 Food Stamp Program administration.        It attempts      to bring some new
                 understanding    to the subject,    but additional     analysis     and interpretation
                 are required.      The Food and Nutrition     Service intends to continue           its
                 pursuit   of both knowledge and improved management in this area.                 Specific
                 technical    comments follov   in an enclosure      to this letter.




                 Acting     Administrator

                 Enclosure




                           Page 124                                                 GAO/WED-9@9       Food Stamp Automation
                                     Appendix V
                                     Comments From the U.S. Department of
                                     Agriculture’s Food and Nutrition Service




                                                                                                              ENCLOSURE
                                                                                                              Page 1


                                              FNS   RESPONSE     TO GAO REPORT RCED-89-172


                             Page     Paragraph        Comments


Now on p 3                   4            1            Delete the Last phrase of the iast line:          ",raising  the
                                                       possibility     of fraud. waste and abuse."       This 1s an
See comment 5                                          unsubstantiated      allegation against    the States when the
                                                       problem appears to be inadequate        recordkeeping.


Now on p 4   See comment 6   6            1            Line    1 delete      the phrase        ".     . .   and     Service   .   .   .‘I

Now on p 6 See comment 6     a                         Chapter     3. line     1 delete        ".     . .   and     the Service       . . .I'

Now on p 38 See comment E    48                        Line    1 of title      delete     ".        . . and the Service           . . ."

Now on p 38 See comment E    48                        Line    5 delete       ".   . . and Service                . . ."

Now on p 39 See comment 7    49                        Lines S-13 are misleading.        FNS does monitor project
                                                       development   and costs as indicated      in the audit
                                                       report.    However, FNS is not permitted        to require
                                                       reporting   of actual operational     expenditures     by
                                                       approved ADP project.     Such project-specific        data can
                                                       be obtained   from State administering      agencies.

Now on pp 38-49              48-60                     In Chapter 3. GAO indicates        that FNS' accounting       for
                                                       approved ADP projects       is inadequate    because specific
See comment 8.
                                                       data relating     to cost object expenditures        by State
                                                       agencies for ADP developmental         and operational     costs
                                                       are not maintained      in E?JS' accounting     system.   GAO
                                                       further   concludes that controls       vould be improved by
                                                       FNS' collection     and recording     of State expenditures
                                                       for specific    ADP related    costs in FNS' accounting
                                                       records.     These findings    and subsequent
                                                       recommendations     are based on the premise that grantee
                                                       object class expenditure       data should be reported        to
                                                       FNS and recorded in FNS' accounting          records.

                                                       GAO's finding      and recommendation are inconsistent
                                                       vith governmentwide       rules and regulations     for the
                                                       reporting     of grant related   expenditures.      OMB has
                                                       prohibited     Federal grantor agencies from requiring
                                                       grantees to report by object class category or
                                                       expenditure.       This policy  of OMB was clearly     stated
                                                       in zts March 11. 1988. publication          of the revised
                                                       Circular     A-102. Grants and Cooperative      Agreements with
                                                       State and Local Governments.         The rule specifies:




                                     Page 125                                                          GAOIRCED-909 Food Stamp Automation
                         Appendix V
                         Comments From the U.S. Department of
                         Agriculture’s Food and Nutrition Service




                                                                                               ENCLOSURE
                                                                                               Page 2


                                                "Federal    agencies shall not require   grantees to
                                                report   on the status of funds by object class
                                                category or expenditure    (e.g..  personnel,    travel,
                                                equipment)."

                                         FNS. as the grantor agency. is permitted                     to require
                                         financial       reporting    on program functions            or
                                         activities.         Because of the two differing              rates of
                                         reimbursement         for ADP developmental          and operational
                                         co6ts (i.e..        75 percent and 50 percent,             respectively),
                                         FNS is permitted          to require     States to report
                                         expenditures        for ADP developmental           and operational
                                         costs as separate categories              on the SF-269. Financial
                                         Status Report.           FNS is not permitted         to require       the
                                         reporting       of object class expenditures             related     to
                                         specific      ADP development        projects      as recommended by
                                         GAO. Thus, FNS cannot collect                 the information
                                         recommended by GAO, and FNS' accounting                    records
                                         cannot be considered           inadequate       for not containing
                                         such information.           The sections        of the draft report
                                         listed      above should be revised           to delete reference          to
                                         Service accounting          records,     and the recommendations
                                         should be revised accordingly.

                                         Further,    the specific  validation     and reconciliation    by
                                         FNS of all such charges to the grant may be
                                         duplicative    of the cost audits required       by the Single
                                         Audit Act and OMB Circular        A-128.

Now on   p.   43.   54       3           The last sentence is misleading                 in that it implies
                                         that FWS retroactively             approved the total          cost and not
See comment 9.                           just the $270.000.            Aleo. the report gives the
                                         impression       that FNS approved the ovarrun with no
                                         explanation        or justification        from North Dakota.           The
                                         report on the post-installation                 review. which was
                                         made available         to GAO during their          audit.     says in
                                         part.      "Costs examined during the review are in
                                         compliance vith the appropriate                 regulations       and
                                         planning      documents governing          their    allowability.
                                         Project      costs allocated         to the Food Stamp Program
                                         through October 31. 1984 exceeded the project                       budget
                                         of $843.877 by $185.574.               With the addition          of late
                                         billings      the final      project    overrun may approach
                                          $300.000.       Although an overrun of this magnitude is of
                                         obvious concern, it does not appear to be the result
                                         of wasteful        spending.      as the project       was completed in
                                         a satisfactory        and timely manner.            In retrospect,        it
                                         is clear the project            budget was inadequate.
                                         especially       in the area of central            data processing
                                         charges for data base software operation                     and system




                         Page 126                                                   GAO/RCED-9@9 Food Stamp Automation
                          Appendix V
                          Comments From the U.S. Department of
                          Agriculture’s Food and Nutrition Service




                                                                                           ENCLOSURE
                                                                                           Page 3


                                           cceununication    during the test:ng     period."
                                           North Dakota was required       to provide FNS with a
                                           report explaining       Its Cost overruns.      Retroactive
                                           approval     was granted after receipt     and review of that
                                           information.

Now on pp 44-46   55-58                    With regard to ADP equipment inventory       management. OMD
                                           Circular  A-102 Attachment G requires      grantees to
                                           maintain  effective    controls over and accountability
                                           for all property     and other assets.   and to ensure that
                                           such property     is used for authorized   purposes.    Such
                                           controls  are a component of the annual audits
                                           performed by States under OMB Circular       A-128.

Nowonp   71       70           Table       Table 4.8 leaves open a number of possibilities                   for
See comment 10                 4.8         interpretation,       some of which would be misleading.
                                           One erroneous      interpretation       that could result        can be
                                           exemplified      by Montana:       A casual reader could
                                           believe     that Montana received         75 percent funding to
                                           automate, but failed         to complete the project,          since
                                           many Program functions          are not fully       automated.
                                           Howwer. the truth is that Montana received                  75 percent
                                           funding only for planning           and feasibility      analysis.
                                           and not for development.




                          Page 127                                               GAO /RCED-90-9 Food Stamp Automation
               Appendix V
               Comments From the U.S.Department of
               Agriculture’s Food and Nutrition Service




               The following are GAO’Scomments on the Food and Nutrition Service’s
               letter dated September 5, 1989.


               1. Our response to the Service’s comments is discussed at the end of
GAO Comments   chapter 4.

               2. Our response to the Service’s comments is discussed at the end of
               chapter 2.

               3. Our response to the Service’s comments is discussed at the end of
               chapter 4.

               4. Our response to the Service’s comments is discussed at the end of
               chapter 3.

               5. We are not alleging that the states we visited have contributed to
               fraud, waste, and abuse of federally funded automated systems equip-
               ment. We maintain that, because of inadequate accounting and adminis-
               trative controls, the states have no reasonable assurance that the
               equipment is safeguarded against waste, loss, and unauthorized use.

               6. We have not deleted “Service” from the pages indicated by the Ser-
               vice because we have sufficient evidence to support our position in the
               report that the Service did not maintain adequate accounting records
               and monitor ADP costs to oversee the states’ ADP expenditures.

               7. We do not believe that the report’s discussion on the Service not being
               required to monitor or determine the actual expenditures for the ADP
               systems is misleading. In fact, while not required to do so, the Service
               currently asks all state agencies to report ADP operational costs. More
               specifically, the Service should require that state agencies account for
               expenditures related to specific funding approvals, which are approved
               to develop specific systems, in addition to general ADP operations costs
               incurred to operate the Food Stamp Program. As noted in the report,
               project-specific data could not be obtained from state administering
               agencies, as claimed by the Service.

               8. In neither the report draft nor the final report do we suggest or rec-
               ommend that the Service account for or require state agencies to
               account for specific object costs expenditures for ADP development or
               operation costs. We state that the Service and the state agencies should



               Page 128                                   GAO/RCEMO-9   Food Stamp Automation
-
    Appendix V
    Comments From the U.S. Department of
    Agriculture’s Food and Nutrition !&s-vice




    account for the total actual costs to develop systems that required spe-
    cific Service approval. As explained in the report, the Service requests
    specific approval of ADP expenditures that equal or exceed $200,000 or
    more over a 12-month period, or a total of $300,000 or more at the
    regional office level. For estimated ADP costs of over $1 million, regional
    office approval also must have concurrence with Service headquarters.
    While this elaborate system for approval is in place to ensure that eco-
    nomic, efficient, and effective ADP systems are developed, no corre-
    sponding requirement exists for the state agencies to report that they
    spent the specific amount approved. Our recommendation merely states
    that the Service require that the states report the total amount spent to
    develop the approved system for which the Service approved a specific
    amount. Currently, the Service’s Southeast Regional Office requires that
    state agency claims to federal reimbursement be reconciled to approved
    ADP funding requests.

    Finally, our report neither makes reference to nor recommends any
    action by the Service to validate or reconcile any charges to the grant
    which could be construed as duplicative of the cost audits required by
    the Single Audit Act and Office of Management and Budget Circular A-
    128. Our recommendation pertains to amending the Service post-instal-
    lation and budget review process. Specifically, we found that many of
    the Service’s regional offices did not routinely monitor or account for
    state expenditures reported against the specific ADP approved amounts.
    Thus, our recommendations request that the Service routinely account
    for state-reported expenditures against the total Service-approved
    amount to ensure that states do not exceed the approved amount-as
    was done in North Dakota. During the time of our review, the Service’s
    Southeast and Southwest Regions were already doing this.

    9. As stated in the report, we were not able to obtain any information to
    show that the Service approved only the $270,000 overrun. According
    to Service Mountain Plains regional officials, based on the post-installa-
    tion review, the Service approved the total system, which inadvertently
    meant that they retroactively approved the overrun. According to a
    post-installation review, covering October 1, 1983, through October 1,
    1984, the overrun stood at $185,574 but was estimated to eventually
    approach $300,000. At the time of our review, the overrun amounted to
    about $270,000. According to a Service regional official, the North
    Dakota state agency never requested approval of this overrun from the
    Service. The agency did request approval from the Department of
    Health and Human Services for that agency’s share of the cost overrun.



    Page 129                                    GAO/RCED-90-9 Food Stamp Automation
Appendix V
Commenta From the U.S. Department of
Agrkubm’s  Food and Nutrition Service




It should not be inferred from the report that we believe the overrun
represented wasteful spending. Rather, our point is that spending ADP
funds prior to Service approval is prohibited by Service regulations [7
CFR 277.18 (d) 61.

10. Table 4.8 makes no reference to or attempt to indicate anything
about the plans, progress, or extent of automation in any state that
received 75-percent funding. It merely states that the listed states
received 75-percent funding and each state has certain functions
automated.




Page 130                                  GAO/RCEDW9   Food Stamp Automation
Amcndls       \.I

Comments From the State of Kentucky


Note GAO comments
supplementing those In the
report text appear at the
end of this appendix                                                  CABINET FOR HUMAN        RESOURCES
                                                                         COMMONWEALTH    OF KENTUCKY
                                                                               FRANKFORT 40621




                             DEPARTMENTFORSOCIALINSURANCE
                              -AnEqualcJ~“Y”#,” Employer
                                                      WFl”




                             August   23,1989




                             Mr. John W. Harmon, Director
                             Food and A riculture Issues
                             U.S. Genera PAccounting    Office
                             441 G Street, NW
                             Room 4075
                             Washington,   DC     20548



                             Dear Mr. Harmon:

                             The Commonwealth     of Kentucky appreciates      the opportunity    to review the draft repot-t entitled
                             Food Stamp Proqram Automation:            Some Benefits Achieved;        Federal Incentive  Fundinq      No
                             lonqer Needed and provide comments prior to finalization          of the report.  For the most part, our
                             comments are directed      to the portions   of the re art dealing      with the Kentucky    Automated
                             Management   and Eligibility   System - Food Stamps PKAMES - FS).

See comment   1              As an general observation,     it is noted the General           Accounting     Office   evaluated     the following
                             criteria to determine benefits of program automation:

                                                current costs I benefits to federal,   state, and local administrators;       and
                                              -effectiveness   for error reduction.

                             The following      criteria   should   also have been included    in the evaluations:

                                              - advantagesaccrulng    to the client as a result of automation; and
                                              - future savings in admlnistrative    costs as a result of lower costs to process                cases
                                              automatically    when compared to the costs to process cases manually.

                             It IS also noted that portions    of the report addresses major areas with a narrow approach,           eg
Now on pp 3435               pages 44 - 47 compares         case processing     costs between     one automated     county     and one
                             nonautomated     county.   The comparison      excludes all factor except the worker to caseioad ratio.
See comment 2                The excluded factors include office organization,        staff tenure and training,  salary scales, office
                             overhead costs, case characteristics,   accurac of case processing, and efficiencies   of the automated
                             system. It would appear that this narrow J rawmg of data would not be indicative             of the actual
                             situation.




                                             Page 131                                                      GAO/RCED9@9 Food Stamp Automation
                       Comments    F’rom the Suite of Kentucky




See comment 3.   It is further noted that the varying socio - economic conditions     in each state and the
                 varying degree of automation      in each state affects the accuracy of the results.

                 The reports presents       no strong   statrstical  data to support      the conclusion       that
See comment 4.   automated    systems have not been cost effective        in case processing and that further
                 accomplishments      cannot be made. The conclusion         that federal incentive    funding      is
                 no longer needed to encourage        automated     systems is at variance with the statement
Now on Q 50      on page 61 - “According        to responses to our questionnaire          - - - all of the state
                 agencies stated that the increased         funding   was very important        to either   begin
                 automation    efforts or to modify, upgrade, and replace existing automated            systems.”



                 Our specific   comments    are:

Nowon p 16.            Page 19, Paragraph     2, lines 1 - 3: “However,      we did not examine          each
                       automated    system to determine   if design flaws and I or operational     problems
                       may have prevented    the automated     system from achieving   its specific goals or
                       objectives.”

See comment 5.             Comment.      If operational    problems    are not considered,       the results   of the
                           study is biased.

Nowonp    18.          Page 21, Paragraph      2, Line 3: “In fact, Kentucky       achieved one of the objectives
                       of its automated        system - to reduce      errors       - before  the program      was
                       automated.”

See comment 6.             Comment:      This statement  does not address further       error reduction    or
                           prevention   resulting from KAMES - FS implementation       but makes it appear
                           that the objective was achieved in toto prior to KAMES - FS implementation.
                           It also does not address the part automation     played in keeping the error
                           rate low. Were there any further error reductions/prevention        as a result of
                           automation?

Now on p 20            Page 23, Table 2.1, Line 5 from bottom under “Direct On - Line”:                Indication     is
                       that Kentucky does not match other automated   files on - line.

See comment 7.              Comment:        Thus table does not reflect the KAMES - FS on - line matching
                            with other automated         system files that occurs during the application  I
                            recertification    process.

Now on p. 21           Page 25, Paragraph    2, Line 4: “- - -and          enter    notes   to the worker      of any
                       additional action needed on the case.”

See comment 8.             Comment:     KAMES - FS does not allow the supervisor to enter noteson                - line
                           to the worker indicating  additional  actions needed in the case.

Now on p 24            Page 29, Last 2 lines; Page 30, Line 1: “For example,                 as a result         of a
                       nonautomated,    concerted   effort,  Kentucky     experienced     a large drop          in its
                       program error rates prior to its automated     system’s operation.”

See comment 9              Comment:      Further error reduction    or prevention  resulting from KAMES - FS
                           implementation       is not addressed.   The error rate reduction   could not have
                           been sustained without       the support of automation.


                                                                  2




                       Page 132                                                       GAO/RcEDW9        Food Stamp Automation
                      Appendix VI
                      Comments From the State of Kentucky




Now on pc 25 26     Page 32, Line 1 through        Page 33, Line 10, and Page 33 - 34, footnote 4: “The
                     Kentucky program - - - are not included in the list because the information    was
                    not available   - - - It should enable workers to avoid making certain errors.    In
                    turn, error rates should decrease even further.”

See cornmen!   10      Comment:        While specifying        that data is not available       to support    a
                       conclusion    regarding     the impact of KAMES - FS implementation         upon error
                       rates, this section implies that Kentucky         had already achieved      its limit in
                       error reductions     / preventions    and there was only a “belief”    that the system
                       should enable workers           to avoid certain errors.   If this is to be asserted,
                       statistics should be presented to support the position.

Now on p 26         Page 32, Last 2 Lines through     Page 33, First 2 Lines: “For example, the state
                    shortened  the time period      between   caseworker     reviews of the recipient
                    household  circumstances    from the once - per - year requirement     to at least
                    once every 6 months.”

See commen: 11         Comment:     Certification     perrods of “once - per - year” were never assigned
                       to cases across the board but only to specific types of cases, eg. all 551 or RSDI
                       households    and cases with income only from annualized                 farm income.
                       These households        are still given a year certification   period.     Certification
                       periods were shortened           for other specific types of cases, eg. earnings         /
                       earnings hrstory cases whose certification         period was set at three months.

Now on p 28         Page 35, Last 4 Lines: “Other errors, such as those           resulting   from   arithmetic
                    calculations, - -to be manor after automation.”

See comment    12      Comment:    One result of automation     should be the virtual elimination   of
                       calculation errors.  Though the rates both before automation         and after
                       automation   are “minor”,  are there statistics to show there was no change
                       orthat the change was insignificant?

Now on D 34         Page 43, Paragraph     3, Lines 4 - 8: “Even though the on - line systems - - - permit
                    paperless,   direct   entry    - - - paperwork     accompanied     the automated
                    operations.”

                       Comment:     A primary cost of paperwork,     is the costs involved in completing
                       the paper.     Before automation,       paper was produced        as a result of the
                       worker   hand completing    various forms.       After automation      most of this
                       paper is system generated,    eg application       , request for information,   etc.
                       Were there any verifiable  savings/costs    as a result of automation?

                       Another costs of paperwork        is the handlrng and storage of paper. Prior to
                       automation     certain paper files were required        to meet federal guidelines.
                       Under KAMES - FS paper files of each case are still marntained           for the same
                       reason , e      client’s  statement     at application.     Kentucky    continues    to
                       negotrate   ?or paper reduction      to decrease these costs. It is anticipated   that
                       an abbreviated     printed applicatron    will be approved which will significantly
                       reduce handling and storage of paper files.

Now on p 34         Page 44, Paragraph   1, Lanes 1 - 4: “For example, the number of forms needed
                    to process food stamp cases rn Kentucky       remained   about the same.     - - -
                     reduced the need for 11 forms - - - required 9 new forms to process the case.”


                                                              3




                       Page 133                                                     GAO/lWEDWB         Food Stamp Automation
                     Appendix VI
                     Comments From the State of Kentucky




See comment 13        Comment:      Were the 11 eliminated   forms hand completed           and are the
                      required 9 new forms system generated?      If so is there a verifiable   savings /
                      cost in worker   time and a possible decrease I increase in errors due to
                      incorrect forms.

                      Were the required 9 new forms mandated       simply because of automation;
                      or are they system back-up forms such as a hard copy application;    or would
                      they have also been required    under the manual system due to program /
                      policy changes? If some or all are back-up forms for the automated      system,
                      they will not be used except when the system is down.     If some or all would
                      have been required   under the nonautomated      system, there is no savings I
                      costs difference.

Now on P 50        Page 61, Paragraph 1, Lines8- 10: “ - - -all of the state agencies stated that the
                   increased funding was very important   to either begin automation      efforts or to
                   modify, upgrade, and replace existing automated      systems.”

See comment 14.       Comment:     Kentucky did not receive enhanced         (75%) funding    for design,
                      development,    and implementation    of the automated       system KAMES - FS).
                      Incentive funding was not an inducement        to automate.       Other factors eg,
                      client advantages,   case accuracy,  staff utilization     and costs, etc.     were
                      factors.

Now on p 57        ‘ac$ 65, Takle:   “Generate   Data to Meet         Other      Reporting       Requirements”          is
                   m rcated as Partially”  automated.

See comment 15.       Comment:      Without citation of instances when KAMES - FS does not meet
                      reporting   requirements,    we are unable to verify or question this indicator.

Now on p 57.       Page 65, Table: “Tracking     Collection   of Recipient    Claims”        is indicated     as
                   “Partially” automated.

See comment 16.       Comment:      Prior to the development      and implementation      of KAMES - FS,
                      the claims collection     system was in operation    in Kentucky.   This system
                      automaticall     tracked claims collections    that were not in recoupment      status.
                      KAMES - FS d Id not incorporate     the functions of that system but does
                      automatically    reduce benefits and track collection      for cases under
                      recoupment.      Between the two systems, all claims collections       are
                      automatically    tracked.
Now on pp 76-98.   Page 81 - 113 Appendix        1: The general Indication       of Appendix  1 is that due to
                   variables and factors that cannot yet be measured,            the study cannot arrive at
                   statistically valid conclusions.

See comment 17.       Comment:    Before decisions     are made regarding terms of funding, the
                      study should be re-designed      and repeated when more valid data is
                      available.

Nowonp   101       Page 117, Paragraph     4, Line 6: ” - - -June   1987, with     3 pilot counties         - - -over   a
                   9 months period.”

See comment 18.       Comment:       KAMES - FS pilot was begun      in March      1987.




                     Page 134                                                        GAO/RCEDfB@B Foad Stamp Automation
                     Appendix VI
                     Comments From the State of Kentucky




Nowon   P   101         Page 118, Paragraph 2, Line 2 4: KAMES will later be integrated            with   a
                        separate system known as KAMES Income Maintenance         -State
                        Supplementation    programs.”

                              Comment:      Though KAMES - FS IS currently a stand -alone system, the
                              Intention  is for it to be the basis for a larger, rnte rated system (KAMES)
                              currently bein developed       to support AFDC, Me 3 rcal Assistance, Refugee
                              Assistance, an 3 State Supplementation        as well as Food Stamps.


                  We hope these comments         are of benefit

                  If you have further questions, please contact James E. Randall,       Director   of the
                  Divisionof  Management     & Development,   at (502) 564-3556.


                  Sincerely   yours,



                  Mike Robinson, CornmIssIoner
                  Department   for Social Insurance
                  275 East Main Street
                  Frankfort, Kentucky       40621




                                                                  5




                     Page 135                                                   GAO/RCED9@9 Food Stamp Automation
               Appendix VI
               Commenta From the State of Kentucky




               The following are GAO'Scomments on Kentucky’s letter dated August 23,
               1989.


               1. In response to Kentucky’s comments pertaining to the criteria used or
GAO Comments   not used in the report to determine benefits of program automation, we
               did not include the “current costs” as a criterion for evaluating or deter-
               mining the benefits of automation. Our report does not present a cost/
               benefit analysis to determine whether automation is cost effective. The
               analyses presented in the report show that automation has achieved
               many of the expected benefits, such as enhancing the eligibility workers’
               ability to prevent or detect program errors. It also shows that automa-
               tion has not always made the expected changes in the results of Food
               Stamp Program operations, such as reducing the program error rates.

               In addition, although Kentucky stated that our analysis does not include
               the “advantages accruing to the client as a result of automation” our
               analysis does include many of the advantages accruing to the client. For
               example, more timely application processing, which we tested for in San
               Antonio and Dallas, Texas, benefits the client through more timely
               receipt of benefits. As stated in chapter 2, more accurate benefit eligibil-
               ity determination, complete coverage of the application process, quicker
               implementation of program changes, and more accurate determination
               of household income all benefit the clients through accurate food stamp
               allotments.

               Although we did not perform an administrative cost comparison
               between manual versus automated case processing, results of our
               regression models suggest that future savings may have been achieved.
               For example, we tested for the change in program staffing in Vermont,
               and Dallas and San Antonio, Texas. In each situation, our regression
               models considered numerous factors over a period of time before and
               after each of the systems were automated, as shown in appendix I.

               2. We realize that in some situations, such as the comparison between
               the two California counties, our review is very narrowly focused.
               Accordingly, we recognize this in the report to ensure that the reader
               makes the proper judgment pertaining to our observations.

               3. Because we recognized that varying socioeconomic conditions in each
               state and the varying degree of automation in each state affect the accu-
               racy of the results, we purposely do not compare the results of the auto-
               mated systems for each of the programs we reviewed. However, the


               Page 136                                   GAO/RCED-9@9 Food Stamp Automation
Appendix Vl
Comments From the State of Kentucky




report does consider other factors, such as the number of food stamp
and AFDCcases, claims collection, and eligibility determination timeli-
ness, that are needed to determine possible changes resulting from
automation.

4. The report does not conclude that automated systems have not been
cost effective in case processing or that further accomplishments cannot
be made. We did not attempt to determine the cost effectiveness of any
of the automated systems we reviewed. As stated above, our report
determines only whether some of the benefits attributed to automation
have been achieved. As a result, we showed that the automated systems
we reviewed achieved many of the expected benefits, such as enhancing
the eligibility workers’ ability to prevent or detect program errors. We
also showed statistically that the same automated systems did not
always make the expected changes in the results of their respective
Food Stamp Program operations, such as reducing the program error
rates. We concluded that additional time may be needed to determine
whether the benefits achieved by automation will eventually cause more
of the expected changes in the results of program operations and that to
date-after     9 years of the program administrators’ special emphasis on
automation-the      expected results have not been fully achieved.

Finally, we do not agree with Kentucky that the report’s conclusion that
federal incentive funding is no longer needed to encourage automated
systems is at variance with the statement that the increased funding
was very important to states to either begin automation efforts or to
modify, upgrade, and replace existing automated systems. The first
statement pertains to the legislative intent for enhanced funding. As
stated in our report, according to the 1980 House Agriculture Committee
report, the increase to 75-percent funding for ADP development was a
necessary incentive to encourage states not in the process of computeriz-
ing their programs to automate. According to the states’ responses to
our questionnaire, all of the states are at least in the process of com-
puterizing; thus, enhanced funding is no longer needed to meet that
intended objective. We also found from the questionnaire responses that
the enhanced funding had been very important in meeting not only the
objective for which it was intended but also assisted the states to
upgrade, modify, or replace existing ADP systems.

5. We have appropriately identified and recognized limitations in the
data and analysis presented in the report to allow the reader to place
the results in proper perspective.



Page 137                                  GAO/RCED90-9 Food Stamp Automation
Appendix VI
Chnmenta From the State of Kentucky




6. We do not believe this introductory statement makes it appear that
the objective was achieved in total prior to KAMES. The report accu-
rately portrays Kentucky’s automated system. The report states that we
could not and did not evaluate the impact of Kentucky’s automated sys-
tem because it had only become operational during the period of our
review and data were not available for a before-and-after comparison.
Further, we state that Kentucky has had success in reducing its program
error rates-success which could not be attributed to the automated
system because the reduction occurred before the system was imple-
mented. In addition, we stated that Kentucky officials said that
although they do not expect the system to automatically decrease error
rates, they believe that as the automated system becomes more of a rou-
tine part of the program operation, it should enable workers to avoid
making certain errors. In turn, error rates should decrease even further.

7. We corrected table 2.1 to indicate the system’s on-line matching
capability.

8. We deleted the statement from the report based on Kentucky’s
comment.

9. We did not evaluate error reduction or prevention resulting from
KAMES-FS because the automated system was not operational until 1988,
subsequent to our field work. Thus, appropriate data on the automated
system were not available for our review.

10. The report does not imply that Kentucky achieved its limit in error
reductions nor does it draw any conclusions regarding the impact of
KAMESFSimplementation. Moreover, the “belief” that the system should
enable workers to avoid certain errors is based on systems documenta-
tion, demonstration of systems operational capabilities, and discussion
with state agency personnel.

11. The report has been revised to reflect this new information supplied
by Kentucky’s responses.

12. We agree that one result of automation should be the virtual elimina-
tion of calculation errors. As indicated in our report, each automated
system we reviewed ensures accurate arithmetic calculations in the area
of household income and resources calculations. For example, our
review of obtained state program error data for North Dakota and Ver-
mont revealed that arithmetic errors had minimal impact on overall pro-
gram error rates before and after automation. However, we believe


Page 138                                  GAO/RCED-90-9 Food Stamp Automation
Appendix VI
Commenta From the State of Kentucky




virtual elimination of such errors may never occur when viewed in con-
text to invalid entries entered by the eligibility worker or the integrity
of client-supplied information.

13. We did not obtain information to determine whether there were veri-
fiable savings/costs in worker time or decrease/increase in errors due to
incorrect forms. Also, we did not make the determination nor does the
report state that the change in the number of forms resulted from auto-
mation. We merely showed that the number of forms had not been
noticeably reduced after the system was automated.

14. The sentence has been revised to limit the discussion to those state
agencies receiving 75-percent funding.

15. Table 4.2 has been revised based on a Kentucky state official’s clari-
fication of KAMES’ ability to meet federal reporting requirements such
as reconciliation and status of claims against household reports.

16. Table 4.2 has been revised to reflect the new information presented
in Kentucky’s letter.

17. The report makes no relationship between benefits achieved or not
achieved and ADP funding. We make no recommendation concerning the
funding of individual ADP systems per se. Our recommendation to dis-
continue the 75-percent funding pertains only to the original purpose of
the enhanced funding- that of being an incentive to encourage those
not in the process of computerizing to begin automating the Food Stamp
Program. Since this purpose has been met, enhanced funding is no
longer needed.

18. The report has been revised to reflect this change.




Page 139                                   GAO/RCED-90-z) Food Stamp Automation
Amendix        VII

Comments From the State of North Dakota


Note GAO comments
supplementing those in the
                                     1     --.        -=_
report text appear at the      ;
                                   -c
                                      --
                                                            =_
                                                                               NORTH      DAKOTA      DEPARTMENT         OF HUMAN           SERVICES
end of this appendix.                                                                              600 E BOULEVARD AVENUE
                                                                                                STATE CAPITOL    JUDICIAL WING
                                                                                               BISMARCK. NORTH DAKOTA      54505




                             Cohn A Graham Executive                Chrector



                                                 August            21,     1989


                                                 Mr. John W. Harman,       Director
                                                 Food and Agriculture        Issues
                                                 U.S. General   Accounting        Office
                                                 441 G Street   NW
                                                 Room 4075
                                                 Washington,   DC 20548

                                                 Dear        Mr.         Harman:

                                                 Your letter     of July   28, 1989,   to Mr. John Graham,     Executive     Director                                       of
                                                 the North     Dakota  Department    of Human Services,   regarding      Food Stamp
                                                 Program    Automation   was forwarded    to me for technical       comment.

Now on p 23.                                     Page 27, the last   sentence        of                  the      first paragraph      is       in error.         In
                                                 compliance  with  7 CFR 273.9(a),                             our automated     system         does the      following
See comment 1                                    in regards  to income   eligibility                            tests:

                                                 1.         For categorically              eligible       households,           the   gross     and    net   income
                                                            tests  are not           applied.

                                                 2.         For households               containing    a food stamp defined                   elderly    or disabled
                                                            member,  both          the      gross   and the net income   tests                  are applied.

                                                 3.         For     households           containing      an elderly        or    disabled       member,      only     the
                                                            net     income    test         is applied.

                                                 We do not concur          with     GAO's conclusion         as indicated       by Table                      2.2 on
Now on p. 25                                     page 31 that        appropriate       data to evaluate           the system's     effect                       on
                                                 overissuance        claims      and collections          was unavailable.        Even                    though
See comment 2                                    quarterly      claims      reports     (FNS-209)      were available        only back                      to October
                                                 1983,     the increase        of newly established           claims     from 283 in                      the Ol-03/84
                                                 quarter      to 633 in the 03-06/89             quarter,     and the increase         in                   collections




                                                      Page 140                                                                              GAO/RCED-90-9 Food Stamp Automation
Appendix VII
Gmunents From the State of North Dakota




Mr. John W. Harman,         Director
Page 2
August  21, 1989



from $19,939        in the lo-12/83        quarter   to $61,279     in the 03-06/89      quarter,
are highly      indicative        that   as workers    began to familiarize       themselves
with    the capabilities          of the new system,       both newly established        claims
and collections          increased     dramatically.       See Attachment    1 for individual
quarterly     amounts.

Sincerely,



L&.kb
Administrator       of   Food   Services

CJM/mj

Enclosure




 Page 141                                                            GAO/RCED-90-9 Food Stamp Automation
Appendix M
Commenta From the State of North Dakota




                                         ATTACHMENT      1



      QUARTER                         NEW CLAIMS                      DOLLARS     COLLECTED

  lo/83   -   12/83                         740*                               19.939
  01/84   -   03/84                         283                                22,970
  04/84   -   06/84                         255                                261896
  07/84   -   09/84                         191                                20,691
  lo/84   -   12/84                         104                                15,284
  01/85   -   03/85                         300                                22,000
  04/85   -   06/85                         335                                25,292
  07/85   -   09/85                         303                                33,364
  lo/85   -   12/85                         352                                33,600
  01/86   -   03/86                         386                                23,702
  04/86   -   06/86                         425                                38,707
  07/86   -   09/86                         270                                27,527
  lo/86   -   12/86                         380                                31,852
  01/87   -   03/87                         979                                32,891
  04/87   -   06/87                         887                                43,486
  07/87   -   09/87                         480                                47,848
  lo/87   -   12/87                         486                                42,652
  01/88   -   03/88                         331                                37,206
  04/88   -   06/88                         445                                38,686
  07/88   -   09/88                         450                                38,916
  lo/88   -   12/88                         450                                44,410
  04/89
  01/89    1 ;;I;;                          586                                46,986
                                            633                                61,279

 *This    Form 209 was not indicative           of new claims         actually     establlshed
   in the quarter       because    all claims     loaded      from   a previous     manual
   system    registered     as new claims     this     quarter.       It is estimated          that
   about   200 new claims       were actually       established       during    the quarter.




Page 142                                                             GAO/RCED-9@9 Food stamp Automation
               Appendix W
               Comments Fkom the State of North Dakota




               The following are     GAO’S   comments on North Dakota’s letter dated
               August 21,1989.


               1. We revised this report to more accurately reflect the specificity
GAO Comments   required when discussing income eligibility tests.

               2. North Dakota states that it disagrees with our conclusion that appro-
               priate data were not available to evaluate the automated system’s effect
               on overissuance claims and collections. As evidence the state provided
               quarterly claims and collections data from October 1983 through June
               1989.

               Although the state provided additional data, the data were not suffi-
               cient to allow us to estimate the system’s effect on overissuance claims
               and collections by using our regression model. Additional “points in
               time” would be needed to perform a viable regression model. While the
               data in North Dakota’s letter show a marked improvement in collections
               since fiscal year 1984, the data alone do not show that automation
               caused this increase. For example, according to Service and state offi-
               cials, increased emphasis was placed on collecting overissuances around
               the fiscal year 1984 time frame, which coincides with the implementa-
               tion of North Dakota’s automated system. Thus, without the aid of a
               regression model, we were unable to distinguish among the impact of
               either the new program emphasis, the automated system, or other
               events that could have caused a change in the amounts of collections.




               Page 143                                      GAO/RCEDsO-9 Food Stamp Automation
Comments From the State of Texas


Note ;A3 comments
suppie-nen~lng those in the
repor text appear at the
                                                                                                                                                                1
end o’ lh~s appendix.



                              COMMISSIONER                                                                                              lOAltO MEMIERS
                              Ron Llndrey

                               August          18,   1989




                               Mr. John W. Harman, Director
                               Food and Agriculture      Issues
                               United   States   General   Accounting                              Off ice
                               441 G. Street,      NW
                               Room 4075
                               Washington,     D.C. 20548
                               Dear Mr.           Harman:
                               Attached    are the department's                           comments pertaining      to your report
                               entitled    Food Stamo Prosram                          Automation  : s        Benefits   Achieved;
                               g.
                               F                                                                                  The department
                               appreciates    the opportunity                         to comment on the document.
                               In developing      conclusions      and recommendations     from the   data
                               gathered   in this    study,     two factors   are apparent    which should
                               receive  serious   consideration      in that process.
                               0            Relevance          to current           automation             systems       in Texas
                                            Texas currently          has implemented            the Phase III WelNet system.
                                            This system is in             operational         use by 80% of the eligibility
                                            staff     in Texas.        Since it was in the developmental                           stage at
                                            the     time     of this        study,       data       on that          system        was not
                                            appropriate        for inclusion.           Consideration            should be given to
                                            the fact that users generally                     prefer       this   system to the two
                                            systems       which      are    included         in      the     study.      Also,        it   is
                                            apparent       to developmental           staff       that       many requirements             of
                                            the     Family      Security      Act would           be     extremely        difficult        to
                                            satisfy      without        an integrated             automated         system       like    the
                                            Phase III        system.     That system is scheduled                    to be in use by
                                             100% of the eligibility              staff     by the end of July 1990.
                                0           Lack of sufficient                data      to       justify         the   generalization         of
                                            results
See comment 1                               The study        frequently               cites     lack    of data         and conflicting
                                            results      in the various                sections      of this      report.        In order to
                                            successfully         develop              a funding         strategy          for      automation
                                            efforts     in the states,                further     study designed            to gather      more
                                            comprehensive           and              generally         applicable             evidence        is
                                            advised.


                                                        John H. Winters     Human     Services      Center   l   XIl West 51st Street
                                                            Mailing   Addressp.0. Box 149030l Austin, Texas             787l69030
                                                                               Telephone         (512) 45G3Oll




                                    Page 144                                                                        GAO/IKEIM@9         Food Stamp Automation
         Appendix VIII
         Comments From the State of Texas




r

    Mr. John W. Harman
    August 18, 1989
    Page 2

    If  you      require   additiona   1   information,   please   cal 1 Ms.   Nancy
    Vaughan      at (512)450-3063.
    Sincerely,



    Ron Lindsey
    RL:ma
    Attachment




         Page 146                                           GAO/RCED-9@9 Food Stamp Automation
                     Appendix VJII
                     Comments From the State of Texan




                                        IAS COMMENTS ON GAO DRAFT REPORT
                                          FOOD STAMP PROGRAMAUTOMATION
                                   Some Benefits   Achieved: Federal Incentive
                                            Funding No Longer Needed

Nowonp    12.    CHAPTER 1:       Objectives,  Scope, and Methodology,        Page 15
                                  We selected  for review      the Texas . . . automated                     Food
                                  Stamp Program operations..."
See comment 2.   It is explained       that Texas was chosen to provide               geographic
                 balance.    The statement        that the statewide         system (SAVERR) could
                 not be reviewed       "because      pre-automation       program operation       data
                 were   not available”      is unclear       as to the reason for and the
                 nature    of the unavailability.           Clarification       of this   statement    is
                 requested.
                 Additionally,   the fact that Texas did not receive             the enhanced
                 75% funding   and the justification          for the inclusion      of the Texas
                 systems in this    study of the effects          of that funding     would be
                 more clearly   explained       in this  section.    Currently,    this
                 information   appears    later    in the report.

Now on p. 20.    CHAPTER 2:       Table   2.1, Page 2
                                  Major   Manual Tasks Assumed by the Seven Automated
                                  Systems    GAO Reviewed to Improve Application
                                  Processing    and Make Policy  Changes
                 The table     omits  several   tasks that          Texas      did     assume      in both    the
                 Statewide     and Local Office     systems.

See comment 3.   The Statewide          system   capabilities      that     exist      but   are    not   marked
                 on Table 2.1          are:
                 0      Compute calculations:      The Statewide    system (SAVERR) checks
                        all ongoing    Food Stamp budgets     for accuracy.       If the budget
                        run on SAVERR is not identical        to the locally        generated
                        figures,   an error   message is generated      rejecting       the
                        attempted   update.
                 0      Consistent     policy       application:        A multitude      of edits       are run
                        by SAVERR that check various                 codes against       other data
                        elements    to minimize          possible     errors     or local    variations       in
                        the application         of policy.        Examples include         checking       for
                        eligibility       for medical         deductions     from income,        invalid
                        exemptions      from the employment             component of the program,             and
                        premature     recertification            of clients      sanctioned     due to
                        intentional       program violations.




                     Page 146                                                        GAO/RCED9@9 Food Stamp Automation
                    Appendix VIII
                    Comments FremtheStateofTexaa




                     Compare information         for consistency:           Again,   SAVERR has
                     numerous comparative          edits.     There are approximately          1700
                     possible    error   messages that SAVERR may generate                  to avoid
                     various   errors.      Examples of those using comparison                 of data
                     include,    comparison      of demographic          data of applicants
                     against   the active      client     file    to avoid duplication         of
                     benefits,     comparisons       of authorizations          for benefits    against
                     those already     issued      for the same        reason,     and comparison     of
                     program code indicators            against     the parameters      of age or
                     location    to ensure validity.
                     Determine    whether  eligibility    criteria   are met for Gross
                     Income:     SAVERS checks gross income for every case in which
                     that limit    applies   against   the household    size to ensure
                     eligibilit.
                     Determine     whether   eligibility     criteria   are met for Net
                     Income:      Similarly,     SAVERS always edits     against  net income
                     limits    for the authorized        number of recipients.    This is true
                     of benefit      amount as well as program eligibility.
See comment 4   The Local Office       systems      capabilities        that   exist     but   are   not
                marked on Table       2.1 are:
                0    Compare information               for consistency:       Eligibility        is
                     determined        in the Local Office            system by utilizing            the
                     Generic      Worksheet(GWS).            One of the features          of the GWS is to
                     perform      edits     that match those performed                on  SAVERR. This is
                     desirable       in that        inconsistencies       are discovered         while     the
                     worker     is still        actively      communicating       with the client,
                     thereby      easing      resolution        of the discrepancy.          Additionally,
                     the GWS performs            edits     that are beyond the scope of those
                     possible      on the SAVERFZmainframe.               An example is to compare
                     previous      GWS information            to the information          being entered        in
                     the current         interview.        This detailed       information       was
                     previously        unavailable         in automated      records.
                0    Alert     caseworkers    to supervisory     notes:  The offices   studied
                     have Office      Automation     systems that allow communication        with
                     the caseworkers.        This can be in the form of memorandum like
                     instructions       or in the form of *8immediate18 messages       that
                     will    appear on the caseworkers'         screen regardless    of the
                     application      being utilized      at the time.




                                                               2




                     Page 147                                                     GAO/RCED-90-I) Food Stamp Automation
                    Appendix VIII
                    Comments From the State of Texas




Now on p 21      CHAPTER 2:       Administrative     Improvements    From Automation,    Page 24
                                  "Following     the initial  applicant   screening,    each of
                                  the automated     systems,  can guide the eligibility
                                  worker..."
See comment 5.   This section        later   states:      "Further,      the Vermont and Kentucky
                 systems will        not permit      the worker to bypass any of the
                 information       requested      on the application."             The Texas Generic
                 Worksheet     also requires         the worker to address            all of the
                 information       requested      on the application.             Our system for
                 processing      reported      changes after        certification        will allow
                 information      not normally         required     for such adjustments          to be
                 bypassed    in order to enhance the efficiency                     of that process.
See comment 6.   At the end of this paragraph         appears the statement:      "For example,
                 for household    members    reported   as students   or elderly,     Kentucky's
                 system compares their       reported   ages to insure   that the program-
                 required   age limits    are met. " This is also true of both the local
                 and statewide    systems in Texas.

                 CHAPTER 2:       Automated         Systems Are Designed            to Enable Eligibility
                                  Workers       to Prevent,        Detect,     and Correct       Certain
Now on D. 22                      Errors,       Page 26
                                  "Each of        the seven       automated     systems we reviewed
                                  improved        the eligibility          workers'      ability   to
                                  accurately         determine       applicant      eligibility      to
                                  participate          in the Food Stamp Program."
                 The last       sentence   of this paragraph       in summarizing       the I'....
                 general      improvements      brought     about by the automated        systems...."
                 lists:     "the process      of appropriately       determining     the applicant's
                 household       income, household        related  deductions,     other household
                 resources,        and whether     non-financial     requirements      are met."
                 The Texas system also has a well documented history                      of avoiding,
                 detecting,        and recovering      duplicated    benefits.    This will      be
                 covered      in more detail       in subsequent     comments.

                 CHAPTER 2:       Automated       Systems Help Determine            Household      Income,
Now on p. 22                      Page 27
                                  "The automated        systems increased           the likelihood     of
                                  the eligibility        worker's  accurate           use of household
                                  income...."
See comment 7    The sentence,      "The systems in Kentucky       and North Dakota convert
                 income reported       on a weekly basis into a monthly        figure    as
                 required     by the program.",    would correctly     include    a reference     to
                 the Texas system,       since the local  Generic     Worksheet     also performs
                 this   function.
                                                                 3




                     Page 148                                                    GAO/RCED-so-9 Foad Stamp Automation
                     Appendix VIII
                     Comments From the State of Texas




Now on p 23     In the description           of IEVS on pages 27-28, the word
                "discrepancies"         may be misleading.            Not all IEVS notices         reflect
                discrepancies       in the comparison          of case income with IEVS income
                as the text     implies.         In the case of Internal           Revenue Service
See comment 8   data (unearned        income),      there    is no automated       comparison      of case
                income to IEVS income.              The IEVS system simply           reports    Internal
                Revenue Service         data to the caseworker           if the interest        income
                exceeds a certain          threshold:      therefore,      it is not a discrepancy
                at the time     it is reported           to the worker.        Furthermore,       not all
                discrepancies       in the comparison          of case income with IEVS income
                are reported      to the worker as the text              implies.       Discrepant
                income must exceed a certain               threshold     before    it is reported         to
                the worker.

                CHAPTER 2:         BENEFITS NOT ALWAYS ACHIEVED THROUGH PROGRAM
Now on p 24                        AUTOMATION, Page 30
                                   "AS just described, the seven automated systems...."
                This section           addresses        expected          benefits     of automated        systems
                that were not determined                     statistically           to have been achieved
                during       the performance            of this         study.     The end of the referenced
                paragraph         states:        "For example,            preventing      major types of errors
                such as those involving                   household          income was often beyond each
                automated         systems capability                because the system did not always
                have access to the necessary                         information."        It should be noted
                that the Texas Department                      of Human Services            is currently       testing
                on-line        access with the Texas Employment                         Commission.       They have
                on-line        data regarding            applicants'           wage and unemployment
                compensation            history.      The limitation              of unavailability          of data is
See comment 9   being rapidly             reduced      in importance            by on-going       developmental
                 activities.          Other examples             of this       enhanced ability         to use
                 interagency          information          include        systems currently          being developed
                 to obtain        birth      records       from the Texas Department                 of Health       and
                 an automated           child      support       referral       system to the Texas Office               of
                 the Attorney           General.
                The last       sentence     of this paragraph           states:        "Also,     improvements
                such as reducing          the number of program forms needed to process
                applications         were countered        by new automated              system-required
                forms to process          applications.       'I Although       still       in development
                during      this   study,     implementation         of the WelNet Phase III              system
                has reduced orders            for client      notification           forms     by 20%. The
                Automated        Data Entry system         used in Phase III               has also reduced
                utilization        of data entry        forms associated            with most automated
                systems.        For these reasons,         caution       is advised         regarding
                generalized        conclusions       in the area of reduction                  of program
                forms.




                      Page 149                                                          GAO,, RCED-90-S Food Stamp Automation
                      Appendix VIII
                      Chnments From the State of Texas




Now on p 25      CHAPTER 2:         Table 2.2,       Page 31
                                    "Locations       Where Appropriate              Data Was Available...."
                 This table        indicates       that there was no appropriate                 data for the
                 two offices         studied     in Texas to determine              the effect        of
Nowon p 27       automation       on program         error    rates.    Later,      in footnote         4 on page
                 33, it is explained             "Reported       state   agency quality          control      error
                 rates     are statistically            valid    estimates       only for the total
See comment 10   statewide       food stamp        caseloads."        Due to this        characteristic         of
                 the quality         control     system,      an attempt       to determine         trends    in
                 Texas on a statewide              basis would have been advisable.                     The quality
                 control      sample could         have been reviewed            for determination          of
                 whether      the actions        sampled were processed               using an automated
                 application         or not.     Comparison        of automated        and non-automated
                 results      on a statewide          basis might have resulted                in significantly
                 different       rates.

                 The table        further        indicates       that data was not available                  in Texas
                 for the areas of claims                   for overissuances           and amount of
Now on p 30      collections         for overissuances.               Subsequently,        in Table 2.3 on page
                 38, data is represented                   indicating        that between 1982 and 1987,
                 claims      increased         from $8,047,000           to $12,480,000.          Similarly,
                 collections         rose from $1,184,000                to $5,744,000.         Most recent         data
                 for 1988 indicates                that claims        are up to $13,560,000              and
                 collections         have risen          to $6,196,000.           Specialized         recovery      units
                 exist     in Texas that were automated                      beginning     in 1986. All claims
                 and recovery          activity        does not, however take place in the
                 automated        units.       It is encouraging             that during      this period         of
                 increasing         results        in these areas,           the automated        system’s       share
                 of the statewide              total     has been increasing.             Their     share has gone
                 from 22% in 1986, to 27% in 1987, and to 37% in 1988. For the
                 first     three quarters             of 1989, automated            recovery      units      are
                 producing        40% of the statewide                total.     Since the results            obtained
                 in Vermont were not found to be significant,                              conclusions          and
                 recommendations             in this        area might best address             the need for
                  further      study.
                 Finally,    in the area of the               amount of time spent on Food Stamp
                 cases,   Texas is implementing                 the capability    to gather this type
                 of data.   That capability     is            targeted   for availability   in November
                 1989.

Now on p. 28     CHAPTER 2:          Same Errors  Occurring After Automation,      Page 35
                                     "We found that the types of errors    occurring...."
                 One characteristic            of the      errors        referred    to in the sentence,
See comment 11   "Thus automation            does not      appear        to have    affected  the major types
                                                                     5




                       Page 150                                                         GAO/RCED9@9 Food Stamp Automation
                         Appendix VIII
                         Comments From the State of Texas




                     of errors    that are being made.",        is that the majority           are
                     attributable     to clients'   failure     to report      accurate     information.
                     The next section      of the report     addresses     this    problem.      It is
                     suggested    that these sections       be combined,       or that the nature                          of
                     these errors     be introduced     in this   section.

                     CHAPTER 2:         Error     Prevention       Often Beyond Systems Capabilities,
Now on p 28                             Page 36
                                        "Further,      even though the automated            systems data
                                        matching      capabilities       have enhanced....."
                     This paragraph         refers    to wage data available           from the Texas
See comment 12       Employment      Commission being 3 to 6 months old. It is notable                      that
                     this valid      limitation       does not apply to Unemployment            Compensation
                     income data that will            be available      upon the implementation          of the
                     on-line    interface        with that agency that was described             earlier      in
                     these comments. The data on this                type of income is current           and
                     can potentially          enhance the detection         of failing     to report     the
                     receipt    of income from this           source.


                     CHAPTER 2:         Automation's  Effect    on Program Staffing       Varied   in
Now on p 31                             Vermont and Dallas    and San Antonio,       Texas,    Page 40
                                        "Texas program officials      expected....*'
                     Beginning       with this paragraph,              the positive            relationship        between
See comment 13       automation        and reduction         of staff         in Texas is documented.                 It is
                     suggested       that the magnitude             of this         reduction        be depicted.       For
                     example,      based on caseload             increase         alone during         this   period,       the
                     increase      of ten staff         members in Dallas               would have been an
                     increase      of 13.       Also,     in San Antonio,              a Local Office         Practices
                     tracking      system      was used that involved                  extensive       monitoring        of
                     changes reported           to the office.           This system was designed                  to
                     reduce agency errors             in acting        on changes.           It was not an
                     automated       system and required              a high volume of staff                activity.
                     The monitoring          systems currently             being tested            in Texas are
                     largely     free of recordkeeping                activities          by staff       members. As
                     work is assigned           and completed,           the automated            system produces
                     feedback      as requested         locally.       This system is designed                  for use in
                     the Phase III         network      which is currently                in use by 80% of the
                     eligibility        staff.     The positive          results        which Texas has already
                     experienced        in increasing          productivity            should continue          to
                     increase      as development           progresses          in the area of automation.




                                                                          6




                 -



                          Page 151                                                         GAO/RCELMO-9 Food Stamp Automation
                       Appendix VIII
                       Comments From the State of Texas




                   CHAPTER 2:        Automated     Systems Had Little           Effect  on Eligibility
Now on p 33                          Determination      Timeliness          in Texas,  Pages 42-43
                                     "In both offices,.....,              the automated    system was not
                                     statistically      significant.....'@
                   This section         states   that in the first          year of using the automated
                   system,      the percentage        of cases processed           timely     in Dallas
                   increased       by 24%. The inability          to find a significant
                   relationship         to automation     is probably         due primarily         to the
See comment 14     limited      scope of the study in Texas. The level                     of improvement
                   cited     for Dallas       has been experienced          throughout        the state     as
                   automation        has been implemented.            Additionally,         a wider study
                   might have increased            the ability      to differentiate            between
                   competing       factors     such as the Local Office              Practices      techniques
                   mentioned       in the preceding       section      of these comments.

                   CHAPTER 2:        Automated       Systems Have Not Always Reduced                Paperwork
Now on p. 34                         In The States        We Reviewed,    Pages 43-44
                                     "In comparing        automated    and nonautomated
                                     operations,..."
                   The first       paragraph        concludes        with this       sentence:    "Paperwork
                   increased       for the batch-process                systems in the Texas and
                   California        local     offices      mostly      because of the need to duplicate
                   the paper file           information         for entry       into the automated         systems.
                   "This appears          to reference          the San Antonio          practice     of entering
                   data regarding           the Local Office            Practices       system into an
                   automated       format.       It should be clarified               that this was not a
                   requirement         but a voluntary            practice      chosen at that location.
                   This tracking          system was not designed                 as an automated       system and
                   was therefore          labor     intensive        as a result        of trying     to add an
                   automated       component.         The WelNet system is designed                 to reduce
                   paperwork,        eliminate        duplication          of tasks,     and provide      automated
                   tracking      without       batch data entry             processes.      The selection      of
                   this    location       of study probably              hindered     the potential       for
                   significant         findings       in this       area.

                   CHAPTER 2:        Comparison         Shows That an Automated           Office    Processes
                                     Fewer Cases Per Worker at a Greater                   Cost    Than a
Now on pp 34-35.                     Nonautomated         Office,    Pages 44-45
                                     "Our comparison           of two local  office        operations       in
                                     California......."
See comment 15     This finding     is not applicable      to Texas. The number of cases
                   processed    per worker has been increasing          throughout  the period
                   between 1981 and 1987. While caseloads            have increased    about 28%,
                   eligibility     worker staff   levels     have only been increased       by about
                   16%. Due to staff      funding   procedures,    these figures    include    Aid
                                                                   7




                       Page 152                                                    GAO/‘RCED-9&S Food Stamp Automation
                      Appendix VIII
                      Commenta F’romtheState         ofTexas




                 to Families    With Dependent      Children     and Medicaid cases.    The
                 generic   casework     approach  being adopted      in Texas complicates
                 efforts   to differentiate      savings     between programs   within    the
                 scope of Income Assistance         Services.

                 CHAPTER 3:        State Agencies'       ADP Equipment Inventory    Records Were
Now on p 44                        Not Accurate,       Page 55
                                   "In Texas.. .we could not determine        which equipment
                                   belonged     to which system because the inventory       did
                                   not identify      the name of the system or the approved
                                   federal    funding    account..."
                 The department          can identify        the number of workstations,                file
                 servers,       etc. that were purchased               in support      of a project         and can
See comment 16   determine       that amount of equipment                is used to support         the
                 project.       Whether or not an inventory                 tag number can be directly
                 related      to a specific        project's        procurement      seems an unnecessary
                 requirement        and could result           in unnecessary        delays     in the
                 implementation          of necessary        systems.         For instance,       a large
                 system,      such as WelNet Phase III,                may require      the installation          of
                 equipment       over a period        of several         months,    and the equipment          is
                 stored     in the warehouse          until      it is scheduled         for installation.
                 During that time,            another    federally         approved    system,     such as an
                 accounting        system,     may require         equipment      at once.      WelNet
                 equipment       that would not be installed                  for several      months is used
                 in support        of the accounting           system,      and, once the accounting
                 system equipment           is received,         it is used to replenish            the WelNet
                 stock.       In doing so, the amount of equipment                     approved     for a
                 project      is the same as the amount of equipment                       used in support        of
                 the project:         the department         has not exceeded approval              thresholds
                 nor delayed        system installation.               In fact,     system installation           is
                 expedited.
                 The above clarifies   the statement                  attributed  to the assistant
Now on p   46    deputy commissioner   at the bottom                  of page 58 that equipment
                 "cannot  be traced  to the specific                  automated  system developed."

                 CHAPTER 4:        All State Agencies     Have Automated     to Some Extent,
Now on p 50                        Page 61
                                   "All    of the state agencies   administering     the Food
                                   Stamps Program have automated       at least   portions     of
                                   their    Food Stamp Program using 75 percent        federal
                                   fundins    as well as the normal 50 percent       federal
                                   funding.
See comment 17   The department    has not cla imed the                75% fund ing for        any WelNet
                 development    or procurement  costs.                 The only system         for which the

                                                                  8




                       Page 153                                                     GAO/RCED9O-9 Food Stamp Automation
-
                     Appendix VIII
                     CommentsFromtheStateofTexas




                 department    receives 75% funding is a case management                system   which
                 supports   the management of fraud investigation   cases.

                 APPENDIX II:        Overview      - The Local Office      Automated   Systems,
NOW on p   103                       Page 120
                                     "Phase II, however,        ran into unexpected      equipment
                                     limitations,       causing   the state    to abandon this     $26
                                     million      expenditure   and move into WelNet Phase
                                     III."
                 The Phase II equipment        has been used in Income Assistance
                 Services    offices     since 1984 in support          of the Generic     Worksheet.
                 It is presently        being deinstalled        in favor of the more flexible
                 and powerful       PC/LAN equipment.        It should be noted that the
                 equipment     has served a useful        purpose      for five years and that it
                 is fully    depreciated.      Furthermore,         approximately    half of the
                 equipment     will   be used to support         other application      systems,    at
                 no additional       expense to federal        agencies,     while the remainder
                 will   be used for maintenance          spares.




                                                            9




                      Page 154                                              GAO/RCED90-9 Food Stamp Automation
               Appendix VIII
               Comments From the State of Texas




               The following are GAO'Scomments to Texas’ letter dated August 18,
               1989.


                1. Although the report frequently cites lack of data, it correspondingly
GAO Comments   cautions the reader as necessary about the conclusions reached. More-
               over, the report is very careful not to generalize the data beyond the
               scope of their applicability since the data sets pertain only to each indi-
               vidual automated system’s Food Stamp Program. Furthermore, the
               funding issue as presented in the report pertains only to the issue of
               whether the increased 75-percent ADP funding has achieved its original
               purpose of encouraging states not in the process of computerizing to
               automate. We believe that our evidence and analyses in the report are
               sufficient to recommend that the Congress discontinue 75-percent fund-
               ing for Food Stamp Program automation.

               2. According to Texas’ comments the report’s statement that its state-
               wide system could not be reviewed because pre-automation program
               operation data were not available is unclear as to the reason for and the
               nature of the unavailability. Our statement is based on interviews with
               Texas ADP management personnel who indicated that information about
               Food Stamp Program operations, including error rates, personnel, and
               timeliness, was not available because there is no requirement to main-
               tain those data. Moreover, the Advance Planning Documents prepared
               for the SAVERRsystem, which was developed in fiscal years 1977 and
               1979, were not available.

               The fact that Texas did not receive 75-percent funding to automate its
               Food Stamp Program was not a major consideration for including it in
               our review. As stated in the report’s objective, scope, and methodology
               section, because there is no typical type of automated system, we
               selected the locations, Texas being one, to obtain a broad view of differ-
               ent systems with different automated capabilities in different parts of
               the country. Texas’ local offices were selected because they represented
               both types of automated systems in use in the state and each had availa-
               ble for review program information for several years before and after
               the systems were automated. Finally, our review of these systems
               focused on only the system’s operations and did not address the rate of
               funding to develop the systems.

               3. The report has been revised where appropriate.

               4. The report has been revised where appropriate.


               Page 156                                   GAO/RCEI.HO-9 Food Stamp Automation
Appendtv VIII
Comments From the State of Texas




5. The report has been revised based on Texas’ clarification of the initial
and recertification process. We concur with the state’s contention that
the automated system will not permit the worker to bypass any of the
information requested on the client’s application.

6. Kentucky is cited as an example of the prior statement, “Further, the
automated systems apply program policy as appropriate to each appli-
cation.” This statement includes the Texas systems.

7. The report has been revised to include the Texas systems in discus-
sion on the conversion of reported income.

8. The report’s text that describes Texas’ use of   IEVS   has been changed.

9. The report now includes a discussion of Texas’ testing of on-line
access to income and unemployment data with its employment agency.

10. In order to compare error rates for nonautomated and automated
Food Stamp Programs, the error rates must be established for each par-
ticular program. Texas statewide program error rates are established
from the composite of the numerous local office operations that process
the caseload using varying degrees of automation from essential manual
systems to the current on-line operations in locations such as Galveston.
Thus, the statewide error rates cannot give a true picture of automa-
tion’s relationship to program error rates. And as stated in the report,
there is no statistically valid error rate established for less-than-state-
wide programs.

In response to Texas’ comment that table 2.2 indicates that data on
claims and collections were not available in Texas, we state that the
table indicates only that the data pertaining to claims and collections
identified for the specific local operations we reviewed in Dallas and San
Antonio were not available. Thus, for these locations we could not sta-
tistically determine the existence of a relationship between the auto-
mated system and claims or collections.

11. In response to Texas’ comment we have revised the report.

12. The report has been revised to indicate that unearned income data
are current and can potentially detect the reporting of unearned income.

13. We are unable to validate Texas’ statement that the Dallas office
would have increased by 13 staff members instead of the actual 10 staff


Page 166                                   GAO/RCED9@9 Food Stamp Automation
Appendix VIII
Comments From the State of Texas




members without automation. As stated in the report, the regression
models indicated that the automated system was statistically significant
in decreasing the number of eligibility workers. However, the model can-
not provide the actual number of workers that decreased.

14. We disagree with Texas’ comment that the inability of the model to
find a significant relationship between the cases processed in a timely
manner in Dallas and automation was “probably” due primarily to the
limited scope of the study in Texas. Since each location has to be viewed
on its own merits, a wider study in Texas, which would also include
different types of automated systems would likely show varying rela-
tionships between automation and case processing time. Moreover, in
each location we considered in fact included “dummy” variables, as
shown in the appendix I, to account for the impact that local office prac-
tices and techniques may have on case processing time.

15. The report’s discussion comparing an automated office to a nonauto-
mated office pertains only to the two local offices in California, not
Texas.

16. The report has been revised to reflect Texas’ comment.

17. The report has been revised in response to Texas’ comment.




Page 167                                  GAO/RCEJHO-9 Faod Stamp Automation
Anrwndix     IX

Comments From the State of Vermont


Note GAO comments
supplementing those in the
report text appear at the
end of this appendix.
                             Comniesioner's Office
                             Tel:   (802) 241-2852




                                                                                                                                        Qugust                 10,      1989




                             Mr.         John    W.  Harmon,                                 Director
                             Food          and Agriculture                                      Issues
                             U.S.         General      Accounting                                    Office
                             441     G Street,                           N.W.
                             Room        4075
                             Washington,                          D.C.                 20548

                             Dear          Mr.          Harmon:

                                           Thank              you         for          the          opportunity                    to         review the       draft           report,           FM
                             Stamp          Prooram                             Automation:                               Some      Benefits                   Achieved;                  Federal
                             Incentive             Fundina                             No        Lonaer            Needed.                  Al though                 cone      lusions            and
                             recommendations                                     have         been        excluded             from           the       draft,             it       is    obvious
                             from        the        title                          of       the         report            that           one         recommendation                           is      to
                             eliminate              enhanced                              funding               for        automation.                          Since             Vermont             is
                             already                   completely                        automated,                  we     would             be       unaffected                    by     such         a
                             decision;                       however,                    we strongly                   object             to      this          recommendation                       on
                             behalf               of         other                states            who        are      In       the        process               of       automating                 in
                             order    to comply                                 with         the      requirements                    of       Section               1537        of     the      Food
                             Security     Act   of                              1985        (P.L.          99-198).               The        regulations                     issued          by    FNS
See comment 1                to implement       the                               above               law       do      allow            for          some           exceptions                if       a State
                             can         demontirate                            that           it         1s    not      cost-effective                                to   automate                  specific
                             functions                       of
                                                           the        Food          Stamp            program,                but        it       is                               clear         that         the
                             intent           of     Congress                 was          to        require                automation;                                           therefore,                   an
                             assumption             was        made         that         automation                   was        beneficial.                                             If      Congress
                             now       wishes          to        conclude                 on        the         basis            of        the                                 GAO        study           that
                             automation            is not            cost-effective                         and      therefore                 not                                beneficial,                      and
                             that     enhanced               fundlng              should             be      eliminated,                     then                                  it      should               also
                             remove       the        requirement                    to        automate.                    Were         Congress                                          to        eliminate
                             enhanced         funding             without             also         removing               the     mandate                                      to      automate,                   one
                             would       have          to       conclude                 that           their            purpose               WdS                                  to      shift               this
                             admlnlstrative                                cost              burden              back         to        the          States.

                                          In           addltlon                 to     this                     fundamental                          concern,              we        also          question
                             whether                    or           not         it       1s                   even         possible                        to         measur-e                the         cort-
See comment 2                effectiveness                              of    automation,                             given          the             large             numberof                  variables
                             involved                   and          the     resulting                          dlfflcultv                      of    isolating          the    variable                            of
                             the       effects                    of     automation.                              Although                    the     GAO study         does      attempt                           to
                             dccoun           t        for          some               of           the         variables,                       such        as   caseload           size                          and
                             program               changes,                       it         cannot              adequately                      account        for     all     variables.




                                     Page 158                                                                                                                    GAO/RCEpWO                     Food Stamp Automation
                         Appendix IX
                         Comments From the State of Vermont




                Page 2
                Mr. John                 W.         Harmon
                August             10,         1989


                           For       example,               the       report             concludes                  that          automation                    in      Vermont
                increased               the       number         of       review            specialists                       by      15      between              the      years
                1981       and       1987,          though            Vermont              had      estimated                     that         numbers               of     staff
See comment 3   would         decrease.                  One       factor            not        taken            into           account              in      drawing              the
                conclusion                  that        the       increase                 was        attributable                          to      automation                    was
                that        the       Department                 of       Social            Welfare                assumed               responsibilitv                           for
                the       administration                      of      the       Fuel          Assistance                     Program              (LIHEQP)                during
                this        same        timeframe.                 and        also         signiflcantlv                          expanded                its        Medicaid
                program.                      It      was        for           these             reasons                   that           additional                      review
                specialists                   were       added:            in      fact,           without                automation                    we     would           have
                needed           to      add       an     even         greater              number             of        staff.                This          Increase                in
                staff,          therefore,                 had       nothing              whatsoever                     to      do     with          the       Food        Stamp
                Program.

                             We      question                      the       validity               of     the    model used          in      the        regression
                analysis                  based                    on        the          results                 in   two other             at-eas,              claims
Now on p 30     establishment                     and        collection,                            and    the     Food
                                                                                                                      Stamp            error           rate.          Page
                30      of    the       report           shows          that       in                 1982     Vermont   established                     S63,OOO           in
                claims             and       collected                 S12,OOO;             in      1983,          claims               established                   were
See comment 4   S101.000              and       collections                  were       SZB,OOO.               In 1984,                the       year        In     which
                out-       automated                claims             system           was       installed,                   claims              increased               to
                S233.000                 and       collections                   to S69.000.                   Taking              an      average              for      the
                years          1984         through              1907,        the       dollar-         amount            of       claims             established
                is      1237,000              per      year,           and      collections                 are      S77,OOO                per       year.           This
                shows         that        both       claims            and     collections                 have       more          than         doubled            since
                automation                 was       installed.                   We do         not      understand                   how        this        dramatic
                increase               could         be      seen        as statisticallv                       insignificant.

Now on p 27                  Page         34         of      the         draft          report           states              that          automation              has        not   had
                a    statistlcallv                             significant                   effect                on     Vermont’s                     error      rate.            The
                following                     tale             presents                 Vermont’s                  reported                  case          error      rates         for
                the       period               of         1981         through             1987:

                                                            Fiscal               Year                             Error             Rate
                                                                     1981                                                 10.89%
                                                                     1902                                                 12.00
                                                                     1983                                                   9.7s
                                                                     1984                                                 10.45
                                                                     1985                                                   8.55
                                                                     1986                                                   8.40
                                                                     1987                                                   6.BO

                Using          a    statewlde            Implementation                      date          of      9/03          for        the    automated
See comment 5   system,            the       years      1981        -   1993        represent                 pre-automation                     years.           and
                the      years          1984       - 1987       are      post-automation.                             The       average error                  rate
                for      the     pre-automatron                   years        1s     lO.EE%.           and        the      post-lmplementatron
                average            15      8.55%.         There         has       been,         therefore,                   an      overall          decrease
                of     more       than         2% In     the      error         rate.            We belleve                  that        it     1s probably
                 lmposslble               to    draw    meanlnqful                conclusions                   about         the      causes          for     this




                         Page 159                                                                                                          GAO/RCED-90-9 Food Stamp Automation
                          Appendix IX
                          Comments From the State of Vermont




                 Page        3
                 Mr.       John         W.      Harmon
                 CIugust          10,          1989


                 decrease,                given       the             many          changes             in     Food      Stamp        Program              rules             over
                 the       last         few     years               that           probably               have      had     an     impact             on        the        error
                 rate:          for           example,               changes                in      household            comoosition,                     one       of         the
                 highest              error         categories.                     have            made       the     determination                     of     who          must
                 be   included                  in    the          Food        Stamp             household           more      complex               and      therefore
                 more       error-prone.                             We     also      question                     the         rssumption              made            in     the
                 regression          analysis                         model      that        higher                  caseloads              result              in      higher
                 error      rates.         To                    our     knowledge,             there                 is    no        evidence             to         support
                 this     assumption.

                             To       summarize,                    we        do      not          believe           it        is     possible             to         measure
                 accurately                    the       cost-effectiveness                                  of   automation                 due      to         large
                                                                                                                                                                the
                 number           of    variables                involved               and      the    lack        of     reliable               data        on     how
See comment 6.   these          variables               affect            the        components             chosen            in       this         study.             We
                 wish        also        to    point           out       the         obvious         omission             of      other           lere        easily
                 quantifiable                   benefits               of         automation,              such          as       better             management
                 information,                  more          consistent                  application               of      policy            and       therefore
                 more        equitable             treatment                  of      recipients,              and       improved              notification
                 to        recipients                of        case           actions.                 We       certainly                  recognize                 the
                 difficulties                  experienced                     by      GCIO    staff       in      attempting                  to      carry         out
                 the        charge          given            them          by       the      Senate          Committee                 on      IAgriculture,
                 Nutrition,               and     Forestry,                 and        we urge       you      to      make       our       comments              known
                 to     the       recipients                of     this          study.

                             Thank          you       once          again            for         inviting           our        comments.             which            we     hope
                 will        be       helpful               to     you.

                                                                                                              Sincerely.



                                                                                                              Veronica              Celani
                                                                                                              Commissioner




                 VHC:bfb




                           Page 160                                                                                                 GAO/BCEMJO-9 Food Stamp Automation
               Appendix IX
               Comments From the State of Vermont




               The following are GAO'Scomments on Vermont’s letter dated August 10,
               1989.


               1. In response to Vermont’s disagreement with our recommendation to
GAO Comments   eliminate enhanced funding for automation because the law and regula-
               tions make it clear that automation is required, we disagree that the
               mandate to automate was directly timed only to the use of enhanced
               funding. The requirement to automate exists for all states receiving 50-
               percent and/or 75-percent funding. Also, the 50-percent actual funding
               for developing and operating automated systems in the Food Stamp Pro-
               gram will continue to be available. Our report does not state that auto-
               mation is not cost effective as stated in Vermont’s letter. The report
               does state that states and the Service did not maintain adequate records
               of automated systems costs. In addition, our analyses show that auto-
               mation has achieved many of the expected benefits, such as enhancing
               the eligibility workers’ ability to prevent or detect program errors. How-
               ever, our analyses also show that automation has not always expe-
               rienced the expected changes in the results of Food Stamp Program
               operations, such as reducing the program error rates.

               2. As discussed above, our report does not measure the cost effective-
               ness of automation in the Food Stamp Program. The report does discuss
               automation’s effect on program operations including reducing program
               error and streamlining administrative procedures and costs associated
               with automation.

               As stated earlier in the report, we did not include all the variables
               affecting automation, such as quality of staff and socioeconomic factors
               within the community served by the program, because of the lack of
               adequate data. However, the variables that are included in our regres-
               sion models enabled us to determine the statistical significance of possi-
               ble relationships between automation and each of the different
               measures of program benefits, while controlling for the effects of other
               program-related factors, such as changes in staffing or caseload.

               3. After receiving Vermont’s comments, we contacted Vermont officials
               and clarified the consequences of the Fuel Assistance Program on our
               data set. The result of our discussions was to adjust data on staff levels.
               The Vermont models were all rerun with the new data, and the results
               indicate that the automated system was a statistically significant factor
               in increasing staff levels.



               Page 161                                  GAO/RCEDSO-9 Food Stamp Automation
Appendix IX
Comments Prom the State of Vermont




4. Comparing the raw data, dollar amounts of claims, and collections,
before-and-after automation is not a valid method for examining causal
relationships because it does not account (control) for the influence of
other factors, such as policy changes, caseload, or staffing levels. That is
the reason for doing an analysis with the regression models instead of
simple comparisons. Our model results suggest that automation has not
significantly affected claims or collections.

5. Similarly, comparing the raw data on error rates before and after
automation is not a valid method for determining the effect of automa-
tion on error rates. Vermont is correct to point out that we did not
account for all rule changes in the program (although in discussions
with Vermont officials, we did identify and account for major rule
changes), and we did not account for other factors such as household
composition. Concerning these other factors, we did not have sufficient
data on them to include them in the models.

Regarding Vermont’s comment questioning our assumption that higher
caseloads result in higher error rates (all else including staff levels held
constant), we have no specific data to support this assumption. Rather,
we believe it is a rational assumption that increased workloads are
likely to result in greater error rates. In any event, the assumption is not
a binding constraint on the analysis, but rather a testable hypothesis.

6. Given the available data at the states we reviewed, we believe we
conducted the analysis using the best available data and methodology.
The model results we present are not presented as conclusive evidence,
and are only one of several types of evidence presented concerning the
issues of the report.




Page 162                                   GAO/RCEDSo-9 Food Stamp Automation
Append1 \ S

Major Contributors to This Report


                         Gerald E. Killian, Assistant Director
Resources,               Ned L. Smith, Assignment Manager
Community, and           Scott L. Smith, Technical Specialist
Economic
Development Division,
Washington, D.C.

                         Michael E. Rives, Evaluator-in-Charge
Dallas Regional Office   Martin B. Fortner, Site Senior


                         Martin F. Lobo, Site Senior
Boston Regional Office

                         Edward W. Joseph, Site Senior
Cincinnati Regional
Office

                         Susan S. Mak, Site Senior
San Francisco
Regional Office




(023277)                 Page 163                                GAO/RCED-90-9 Food Stamp Automation
    ,
.




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