EXAMINING THE POTENTIAL TO EXPAND DATA MATCHING IN 

THE NATIONAL SCHOOL LUNCH AND BREAKFAST PROGRAMS' 

ELIGIBILITY AND VERIFICATION PROCESSES 

WHITE PAPER 

Food and Nutrition Service · Office of Policy Support · United States Department of Agriculture 

Prepared by: 

2M Research Services, LLC 

500 E Border St, Suite 680 

Arlington, TX 76010 

Submitted to: 

Office of Policy Support 

Food and Nutrition Service 

3101 Park Center Drive 

Alexandria, VA 22302-1500 
USDA is an Equal Opportunity Provider 
October 2016 

Examining the Potential to Expand Data Matching in the National 

School Lunch and Breakfast Programs' Eligibility and Verification 

Processes 

Authors: 

Steven Garasky 

Roderick Harrison 

Angela Campbell 

Molly Matthews-Ewald 

Paul Ruggiere 

Submitted by:
 

2M Research Services, LLC
 

500 E Border St, Suite 680
 

Arlington, TX 76010
 

Submitted to: 

Office of Policy Support 

Food and Nutrition Service 

3101 Park Center Drive 

Alexandria, VA 22302-1500 

Project Director: 

Paul Ruggiere 

Project Officer: 

John Endahl 

This study was conducted under contract number AG-3198-C-15-0021 with the 

Food and Nutrition Service. This report is available on the Food and Nutrition Service web site: 

http://www.fns.usda.gov/research-and-analysis. 

Suggested Citation: 

U.S. Department of Agriculture, Food and Nutrition Service, Examining the Potential to Expand Data 

Matching in the National School Lunch and Breakfast Programs' Eligibility and Verification Processes, 

by Steven Garasky, Roderick Harrison, Angela Campbell, Molly Matthews-Ewald, Paul Ruggiere. Project 

Officer, John Endahl, Alexandria, VA: October 2016. 
 

 

 

 

 

 

Contents
 

Glossary of Acronyms ................................................................................................................................... iii
 

Chapter 1: Introduction .................................................................................................................................1
 

NSLP and SBP Overview................................................................................................................................2
 

Certification......................................................................................................................................................3
 

Verification .......................................................................................................................................................4
 

Research Design ...........................................................................................................................................5
 

Approach to Study Objectives .........................................................................................................................6
 

Objective 1: Summarize Current Data Matching Practices.......................................................................6
 

Objective 2: Identify Additional Data Sources............................................................................................6
 

Objective 3: Obtain insights on candidate data sources from interviews with State agencies..............7
 

Organization of the White Paper.................................................................................................................7
 

Chapter 2: Literature Review.........................................................................................................................8
 

Introduction......................................................................................................................................................8
 

Historical Perspective on Data Matching .......................................................................................................8
 

Data Matching by FNS.................................................................................................................................. 10
 

Benefits of Data Matching ....................................................................................................................... 11
 

Challenges in Data Matching................................................................................................................... 12
 

Data Sharing Among Agencies and Privacy ............................................................................................ 13
 

Characteristics of Data Matching in the NSLP........................................................................................ 14
 

Feasibility of Using Additional Programs for Data Matching in the NSLP ................................................. 15
 

Income Eligibility Thresholds.................................................................................................................... 16
 

Identifiers Used for Matching................................................................................................................... 16
 

Program Eligibility Period and Frequency of Updating ........................................................................... 17
 

Privacy Protection and Data Sharing Policies......................................................................................... 17
 

Lessons Learned from Other Federal Agencies.......................................................................................... 17
 

Chapter 3: Expert Interviews .......................................................................................................................18
 

Findings on Candidate Data Sources .......................................................................................................... 21
 

Recommendations on Candidate Data Sources......................................................................................... 26
 

Chapter 4: State Agency Interviews.............................................................................................................26
 

Findings ....................................................................................................................................................... 26
 

Programs Used for Data Matching .......................................................................................................... 26
 

Updating Program Files............................................................................................................................ 29
 

Updating Enrollment Files........................................................................................................................ 29
 

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Identifiers Used for Data Matching ......................................................................................................... 30
 

Data Transfer and Matching Processes for Direct Certification ............................................................ 32
 

Direct Verification Processes ................................................................................................................... 33
 

Challenges to Data Matching................................................................................................................... 34
 

Overcoming Identified Challenges........................................................................................................... 35
 

Candidate Means-Tested Programs ............................................................................................................ 36
 

State Perceptions ..................................................................................................................................... 36
 

Perceived Challenges to Adding Means-Tested Programs..................................................................... 36
 

Suggestions from Interviewed States for Integrating Additional Data Files.......................................... 37
 

Chapter 5: Conclusions and Recommendations.........................................................................................37
 

Appendix A. Domains of Inquiry by Research Method...........................................................................A-1
 

Appendix B. Programs Reviewed, But Not Selected, in Objective 2 ......................................................B-1
 

Appendix C. Findings on Candidate Programs.......................................................................................C-1
 

Appendix D. Expert Interview Guide...................................................................................................... D-1
 

Appendix E. Study Methodology ............................................................................................................E-1
 

Appendix F. State Interview Guide.........................................................................................................F-1
 

List of Tables 

Table 1: Key Findings for Each Program ......................................................................................................... 22
 

Table 2: Programs Used for NSLP Direct Certification Data Matching: Regulation-Mandated and
 

Discretionary Authority........................................................................................................................ 28
 

Table 3: Frequency with which Program and Enrollment Files are Updated ................................................ 30
 

Table 4: Student Identifiers Used for Matching .............................................................................................. 31
 

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Glossary of Acronyms
 

ACA Patient Protection and Affordable Care Act (2010) 

ACF Administration for Children and Families 

AFDC Aid to Families with Dependent Children 

AGI Adjusted Gross Income 

APTC Advanced Premium Tax Credit 

CEP Community Eligibility Provision 

CHIP Children's Health Insurance Program 

CMA Computer Matching Agreements 

CMS Centers for Medicare and Medicaid Services 

CN Child Nutrition 

CSR Cost Sharing Reductions 

DC Direct Certification 

DC-M Direct Certification-Medicaid 

DEFRA Deficit Reduction Act of 1984 

DHFS Department of Healthcare and Family Services (Illinois) 

DOB Date of Birth 

DOE Department of Energy 

DOL Department of Labor 

EITC Earned Income Tax Credit 

eREP Electronic Resource and Eligibility Product 

FDPIR Food Distribution Program on Indian Reservations 

FNS Food and Nutrition Service 

FPL Federal Poverty Level 

F/RP Free or Reduced Price 

HHFKA Healthy, Hunger-Free Kids Act of 2010 

HHS Department of Health and Human Services 

HUD Housing and Urban Development 

IEA Information Exchange Agreements 

IEVS Income Eligibility Verification System 

IHS Indian Health Service 

IRS Internal Revenue Service 

KDOE Kentucky Department of Education 

LEA Local Education Agency 

LIHEAP Low Income Home Energy Assistance Program 

MAGI Modified Adjusted Gross Income 

MOU Memorandum of Understanding 

NDHHS Nebraska Department of Health and Human Services 

NHSIA National Human Services Interoperability Architecture 

NSLP National School Lunch Program 

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OMB Office of Management and Budget 

PHA Public Housing Authority 

PII Personally Identifiable Information 

QHP Qualified Health Insurance Plan 

SA State Agency 

SBP School Breakfast Program 

SFA School Food Authority 

SIPP Survey of Income and Program Participation 

SNAP Supplemental Nutrition Assistance Program 

SSI Supplemental Security Income 

SSIS Statewide Student Information System 

SSN Social Security Number 

SWICA State Wage Information Collection Agency 

SY School Year 

TANF Temporary Assistance for Needy Families 

UI Unemployment Insurance 

USDA United States Department of Agriculture 

WAP Weatherization Assistance Program 

WAPTAC Weatherization Assistance Program Technical Assistance Center 

WIC Special Supplemental Nutrition Program for Women, Infants, and Children 

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Chapter 1: Introduction
 

The U.S. Department of Agriculture (USDA) Food and Nutrition Service (FNS) contracted with 2M Research 

Services (2M) to (1) examine participant data maintained for means-tested programs, and (2) to assess 

the data’s potential for directly certifying children for free or reduced price (F/RP) school meals in the 

National School Lunch Program (NSLP) and for directly verifying household income for families that 

submit applications for school meal benefits.1 This study builds on U.S. Government Accountability Office 

(GAO) recommendations derived from its 2-year study of NSLP certification and verification processes 

released in 2014.2 

GAO assessed steps taken by the NSLP to prevent ineligible households from receiving benefits, and to 

strengthen program oversight. GAO reviewed NSLP policies, interviewed program officials, and performed 

limited eligibility testing of a randomly drawn, non-generalizable sample of 25 approved household 

applications. Among these 25 NSLP applications, 19 had been approved based on self-reported 

household size and income. Of these 19, GAO determined that 9 were not eligible for benefits because 

their income exceeded eligibility guidelines for their household size. The other six approved applications 

in the sample indicated categorical eligibility (i.e., they were eligible for the NSLP based on participating in 

another means-tested program such as the Supplemental Nutrition Assistance Program (SNAP), or 

through meeting an approved designation such as being homeless or a foster child). GAO found that two 

of these six households were also ineligible for benefits, and an additional household was not eligible for 

free meals, but may have been eligible for reduced price meals. 

GAO concluded that USDA had taken steps to strengthen controls and to increase access to program 

benefits for eligible individuals. However, GAO also highlighted deficiencies with current controls, and the 

need for additional corrective actions. To better identify ineligible NSLP applicants, GAO recommended 

that USDA “explore the feasibility of computer matching school meal participants with other sources of 

household income, such as State income databases, to identify potentially ineligible households—those 

with income exceeding program eligibility thresholds—for verification.”3 

GAO’s recommendation drew from experiences in other Federal programs. The origins of computer 

matching information on public assistance recipients against other income and program files are firmly 

rooted in concerns with identifying ineligible participants and reducing improper payments. The Tax 

Reform Act of 1976 (P.L. 94-455), the Social Security Amendments of 1977 (P.L. 95-216 and 96-580), 

the Food Stamp Act Amendments of 1980 (P.L. 96-249), and the Food Stamp and Commodity 

Distribution Amendments of 1981 (P.L. 97-98) all require data matching between two or more agencies 

for the purpose of computer matching records on participants to evaluate eligibility. Section 2651 of the 

Deficit Reduction Act (DEFRA) of 1984 (P.L. 98-369) requires State agencies (SAs) administering SNAP 

(formerly the Food Stamp Program), Temporary Assistance to Needy Families (TANF, which replaced Aid to 

Families with Dependent Children [AFDC]), Medicaid, and Unemployment Compensation to contribute 

participant and income data to a State Income Eligibility Verification System (IEVS), and to use the system 

to verify eligibility. Each of these efforts preceded NSLP data matching. 

1 The School Meals Program includes several programs, such as National School Lunch Program (NSLP), School Breakfast 

Program (SBP), and Special Milk Program. Because eligibility determinations established for NSLP also result in eligibility for 

other School Meal Programs, NSLP as used throughout this White Paper is synonymous to all School Meals Programs. 

2	 Government Accountability Office. (2014, May). School-meals programs: USDA has enhanced controls, but additional 

verification could help ensure legitimate program access. Report to Congressional Requesters (GAO-14-262). Retrieved from: 

http://www.gao.gov/assets/670/663258.pdf 

3 Ibid. 

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Computer matching to certify (rather than verify) eligibility was introduced to the NSLP in the Child 

Nutrition and WIC Reauthorization Act of 1989 (P.L. 101-147), which authorized direct certification of 

children in households receiving either Food Stamps or AFDC. The concept of using participation in one 

assistance program to confer categorical eligibility for another program grew even more important with 

the Patient Protection and Affordable Care Act (ACA) in 2010. Section 1561 of the ACA requires the 

Department of Health and Human Services (HHS) to “develop interoperable and secure standards and 

protocols that facilitate enrollment of individuals in Federal and State health and human services 

programs.”4 

A key part of HHS’s response to the ACA mandate to facilitate program enrollment through data sharing 

was to have its Administration for Children and Families (ACF) form a Tri-Agency Committee along with the 

Center for Medicare and Medicaid Services (CMS) and USDA FNS to create opportunities to improve 

information sharing, system integration, and program coordination. A goal of the Tri-Agency Committee is 

to expand access and improve outcomes for government and the populations served. In particular, ACF is 

funding the development of a National Human Services Interoperability Architecture (NHSIA), which would 

support the sharing of information and eligibility determinations across programs, agencies, and 

departments. The NHSIA hopes to improve the efficiency and effectiveness with which human services 

are delivered, reduce fraud, and improve outcomes for children and families.5 

For this study, 2M explored whether there are additional files, such as the wage records collected by 

State Unemployment Insurance (UI) agencies, or data on household income and size collected through 

applications for other means-tested programs, which might enhance computer matching for NSLP direct 

certification and direct verification. 2M placed the effort to improve NSLP computer matching in the 

broader context of initiatives such as computer matching by other Federal agencies and the HHS-USDA 

Tri-Agency Committee. These initiatives will be discussed in greater detail in the concluding chapter. 

NSLP and SBP Overview 

In 2014, the NSLP served nutritionally balanced meals each school day to more than 28 million children 

in almost 100,000 participating public schools, nonprofit private schools, and residential child-care 

institutions.6 The School Breakfast Program (SBP) operates in more than 89,000 schools and institutions, 

serving school breakfasts to more than 12 million children each day.7 

All children enrolled in a school participating in the NSLP and SBP have access to free, reduced price, or 

paid meals. Children whose family income falls at or below 130 percent of the Federal poverty level (FPL) 

($31,005 for a family of four in school year [SY] 2014–15) qualify for free meals; children with family 

incomes between 130 percent and 185 percent of the FPL ($44,123 for a family of four in SY 2014–15) 

4 Patient Protection and Affordable Care Act. P.L. 111-148, 124 STAT 119 (2010). 5	 Administration for Children and Families, 2012. Your essential interoperability toolkit: An ACF/HHH resource guide. ACF 

interoperability overview, p.10. Retrieved from: 

http://www.acf.hhs.gov/sites/default/files/assets/acf_toolkit_july_2012_final.pdf 

6	 Moore, Q., Conway, K., Kyler, B., & Gothro, A. (2015, May). Direct certification in the National School Lunch Program: State 

implementation progress, school year 2013–2014. (Report No. CN-14-DC). Alexandria, VA: U.S. Department of Agriculture, Food 

and Nutrition Service, Office of Policy Support. Retrieved from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertification2014.pdf 

7 U.S. Department of Agriculture, Food and Nutrition Service. (2013, September). The School Breakfast Program. Retrieved from: 

http://www.fns.usda.gov/sites/default/files/sbp/SBPfactsheet.pdf 

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are sold meals at a reduced price. Children whose family income exceeds 185 percent of the FPL pay the 

full price established by their local school food authority (SFA).8 

Recurring payments from any source are considered as income to determine eligibility for the NSLP and 

SBP.9 This includes, but is not limited to, gross earnings from work, public assistance, child support, 

alimony, and retirement and disability benefits. Income excluded from eligibility determination includes 

any cash income or value of benefits excluded by statute, such as the value of benefits under SNAP or 

Food Distribution on Indian Reservations (FDPIR), and some Federal educational benefits; payments 

received from a foster care agency or court for the care of foster children; student financial assistance; 

loans; and infrequent earnings received on an irregular basis. 

Students may also qualify for free meals on the basis of categorical eligibility (i.e., automatically eligible 

based on receipt/participation in certain assistance programs). Students in households that receive 

benefits from participation in SNAP, TANF, or FDPIR; those participating in a Federal Head Start program; 

and certain homeless, runaway, migrant, and foster care children, are automatically eligible for NSLP and 

SBP benefits.10 

This White Paper reports on whether computer matching in the NSLP and SBP may be expanded to 

include additional data sources. Results will inform FNS objectives to increase participation in the NSLP 

and SBP among eligible students, reduce burden placed on families and administrative staff in 

processing applications and other documentation, and to mitigate program error. 

Certification 

There are three ways in which households can be certified for free or reduced price meals: 

1.	 Households submit applications with information on family composition and income; this can 

confer income eligibility; 

2.	 Households submit applications with a case number demonstrating their participation in SNAP, 

TANF, or FDPIR; this confers categorical eligibility for free meals to all children in the household; 

and 

3.	 Households are directly certified through data matching due to participation in a program or due 

to special status; this confers categorical eligibility (with the exception of direct certification with 

Medicaid, as described later in this section). 

In the NSLP, computer matching has provided an effective alternative to household applications and 

manual checking of case numbers against program participation lists to certify a student’s eligibility. In 

direct certification, student enrollment records are electronically matched against eligibility records of 

children in households participating in other programs to certify applicants as categorically eligible for 

free meals. States must directly certify children in households enrolled in SNAP, and have the discretion 

to directly certify children participating in TANF, FDPIR, Federally funded Head Start programs, and certain 

migrant, homeless, runaway, and foster child programs.11,12 

8 U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals: Determining and 

verifying eligibility. Retrieved from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-2015a.pdf 9 Ibid. 10 Ibid. 11 Ibid. 12 State-funded pre-kindergarten programs with eligibility requirements identical or more stringent than Federally-funded Head 

Start also confer other source categorical eligibility. 

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Households with students directly certified as categorically eligible for free meals in the NSLP and SBP do 

not need to submit an application. This decreases the administrative burden that households and local 

educational agencies (LEAs) bear in completing and processing paper applications. Directly certifying 

children also increases the likelihood that students in households participating in programs that confer 

categorical eligibility for free meals will receive these benefits.13 FNS is currently examining the use of 

Medicaid data to directly certify students for F/RP meals.14 Students certified on the basis of Medicaid 

data are not considered categorically eligible for free school meals since they must meet an income 

standard to be eligible. 

Verification 

All information submitted on household applications is subject to subsequent verification to assure that 

determinations of eligibility and benefit levels are correct.15 Each year, LEAs must review a sample of 

household applications approved for F/RP meal benefits to verify that the household was certified to 

receive the correct level of benefits. In this “standard verification” process, school districts must verify a 

random 3 percent sample of “error-prone” applications, or 3,000 error-prone applications, whichever is 

smaller.16 Error-prone is defined as applications indicating income within $100 per month ($1,200 per 

year) of the applicable income eligibility limit. If LEAs qualify to use one of the alternate sample sizes, 

applications with case numbers are also verified. LEAs can also verify applications for cause. That is, they 

can verify an application if there is reason to question the information that was provided. In contrast, 

eligibility determinations established through direct certification are not subject to subsequent 

verification of income or household composition. 

Standard or for-cause verification requires that households submit documentation supporting income or 

case numbers that were reported on the application. Depending on the household’s sources of income, 

supporting documents might include pay stubs, Social Security or Supplemental Security Income (SSI) 

benefit award letters, and court support payment decrees or documentation from a homeless shelter. 

Section 105(a) of the Child Nutrition and WIC Reauthorization Act of 2004 (P.L. 108-265) requires LEAs 

to make at least one follow-up attempt to contact households that fail to provide the supporting 

documents after the initial request. Based on the documentation, the LEA can verify that the household is 

eligible for F/RP meals, or can take any corrective actions that are needed. 

States are authorized to use SNAP, TANF, FDPIR, Medicaid, and State Children’s Health Insurance 

Program (CHIP) for direct verification.17 Other means-tested programs with income criteria similar to the 

thresholds for F/RP meals are also permitted for use in direct verification.18 Similar to direct certification, 

direct verification enables LEAs to verify household income or program participation using records from 

other agencies.19 

13 U.S. Department of Agriculture, Food and Nutrition Service. (2014, August). The National School Lunch Program direct 

certification improvement study: Main report. Retrieved April 29, 2016 from 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 

14 U.S. Department of Agriculture, Food and Nutrition Service. (2016, January 27). Request for Applications to Participate in 

Demonstration Projects to Evaluate Direct Certification with Medicaid. Retrieved May 4, 2016 from: 15 U.S. Department of Agriculture, Food and Nutrition Service. (2014, August). The National School Lunch Program direct 

certification improvement study: Main report. Retrieved April 29, 2016 from 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 16 National School Lunch Program; Determining Eligibility for Free and Reduced Price Meals and Free Milk in Schools, 7 C.F.R. §§ 

210 and 245 (December 2008). 

17 The Child Nutrition and WIC Reauthorization Act of 2004, P.L. 108-265, 118 STAT 729 (2004). 18 Section 9(a)(3)(F) of the Richard B. Russell National School Lunch Act. 19 U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals determining and 

verifying eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-

2015a.pdf 

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Direct verification can: 

•	 decrease the burden placed on households to document their income; 

•	 prevent the loss of benefits for students in non-responsive households; 

•	 decrease the administrative burden placed on LEAs processing household income
 

documentation; and
 

•	 increase overall program integrity. 

Current statutes give LEAs the discretionary authority to initiate the direct verification process. State 

agencies administering the school meals programs are charged with assisting LEAs in developing and 

conducting direct verification and with facilitating data sharing arrangements between LEAs and State 

agencies administering SNAP, TANF, FDPIR, Medicaid, and other means-tested programs.20 

Research Design 

USDA FNS commissioned 2M to produce this White Paper to determine whether any additional meanstested programs might prove feasible for use in the direct certification of school-age children participating 

in the NSLP. The White Paper also discusses whether additional data sources may be used to directly 

verify the income disclosed on NSLP applications. Given GAO’s recommendation to explore expanding the 

use of computer matching for verification of household applications, 21 as well as the continued desire of 

FNS to improve tools provided to States for direct certification, and well-known advances in computerbased data exchanges and matching, FNS sought to extend its previous research on data matching to 

determine if additional datasets can be added for direct certification and direct verification efforts in costeffective ways. 

This White Paper was informed by results from three research activities: 

1.	 A literature review; 

2.	 Interviews conducted with experts familiar with the data files from candidate means-tested 

programs, and State earnings and employment files; and 

3.	 Semi-structured interviews with eight SA officials who are responsible for data matching 

operations 

The study’s objectives and 2M’s analytic approaches are outlined in the section below. Study activities 

were designed to provide FNS with recommendations on the potential utility of each of the candidate 

means-tested program files examined. Appendix A details the domains of inquiry by method. 

20 Verification of Eligibility for Free and Reduced Price Meals in the National School Lunch and School Breakfast Programs, F.R. 

Vol. 73, No. 244 Thursday, December 18, 2008, page 76847. Retrieved from: https://www.gpo.gov/fdsys/pkg/FR-2008-12-

18/pdf/FR-2008-12-18.pdf 21 U.S. Government Accountability Office. (2014, May 25). School meals programs: USDA has enhanced controls, but additional 

verification could help ensure legitimate program access. (Report No. GAO-14-262). Retrieved from: 

http://www.gao.gov/products/GAO-14-262 

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Approach to Study Objectives 

Objective 1: Summarize Current Data Matching Practices 

Summarize the experiences, benefits, challenges, and barriers of current 

data matching practices in State agencies and Local Education 

Agencies. 

State and LEA data matching practices were summarized using information available in recent 

publications, including several FNS reports and studies on data matching in school meals programs, 

direct certification technical assistance site visit reports, annual reports to Congress, and consultations 

with FNS experts. 2M reviewed the legislative and regulatory progression of data matching in the NSLP, 

the data sources most widely used for direct certification and direct verification, and trends in the 

processes used to match student enrollment records to external program data. 

Objective 2: Identify Additional Data Sources 

Identify additional data sources that have potential for improving future 

matching processes in the school meals programs. 

2M evaluated the eligibility requirements for 70 means-tested assistance programs. Programs that award 

funds directly to families or households were of primary interest. Programs that award funds to localities 

or organizations were not relevant to the study’s objectives, and were thus eliminated. 2M also excluded 

programs that: 

•	 primarily serve populations other than households with school-age children (e.g., people with 

disabilities, the elderly, and students attending college); 

•	 do not collect data needed to calculate income as a percent of the FPL; or 

•	 utilize income sources and eligibility thresholds that are not compatible with those used in the 

NSLP. 

A list of all programs that were reviewed but not selected is provided in Appendix B. 

Based on these criteria, six programs were selected as having potential utility in direct certification or 

direct verification in the NSLP: 

•	 Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) 

•	 Affordable Care Act (ACA) Health Insurance Marketplace (Marketplace)22 

•	 Public Housing Program and Housing Choice Voucher Program (HUD Programs) 

•	 Low Income Home Energy Assistance Program (LIHEAP) 

•	 Weatherization Assistance Program (WAP) 

•	 Earned Income Tax Credit (EITC) 

22 Affordable Care Act Health Insurance Marketplace is not a program. It is organizations set up to facilitate the purchase of health 

insurance in accordance with the Patient Protection and Affordable Care Act (P.L. 111-148). Nevertheless, to enhance 

readability of the paper, the Marketplace is referred to as a “program” and the data potentially available from the Marketplace 

are referred to as “program data.” 

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2M also identified State Income and IEVS as potential data sources for use in NSLP direct verification. 

The IEVS includes UI and wage records and that could reduce household and administrative burden 

resulting from providing and processing income documentation. 

Interviews were conducted with experts in the Federal agencies that administer these programs. 

Information collected on each program from published material and the expert interviews was used to 

determine which, if any, of these programs collect and maintain the data needed to potentially add to 

direct certification and direct verification processes in the NSLP. 

Objective 3: Obtain insights on candidate data sources from 

interviews with State agencies 

Obtain insights from a sample of State agencies or Local Education 

Agencies to evaluate the practicality of implementing findings from 

Objective 2. 

Using the information gained from the literature review and expert interviews, 2M interviewed eight State 

agencies to assess whether the candidate programs offered some potential for data matching.23 These 

interviews were designed to solicit SA insights on the potential utility of the data files from the candidate 

programs to improve direct certification rates, and to assess any barriers and challenges that might 

impede their use, or render their use cost-ineffective. 

Organization of the White Paper 

This White Paper continues with Chapter 2, which presents the results from the literature review on data 

matching across Federal agencies. Chapter 3 presents research on the means-tested programs that were 

reviewed and the characteristics of the program files that suggested which programs’ data files merited 

further investigation for potential use in data matching. This third chapter concludes by identifying the 

specific files that were advanced for review by expert consultants. Chapter 4 presents findings from the 

expert interviews, which indicated that data files from the HUD programs and LIHEAP were worth 

considering for NSLP data matching in an effort to increase direct certification, simplify direct verification, 

and/or reduce program errors. 

Chapter 5 reports the results from interviews with eight State agencies on their perceptions of the 

potential value or cost-effectiveness of adding the HUD and LIHEAP program data files to those with 

which they currently conduct data matching. The final chapter (Chapter 6) integrates study findings into 

recommendations on whether the HUD and LIHEAP program files might merit further consideration for 

being added to the data files that SAs and LEAs currently use for NSLP data matching. 

Several appendices are included to supplement the content of the main text. The appendices present: 

• domains of inquiry by research method (Appendix A); 

• a list of the means-tested assistance programs reviewed for this study (Appendix B); 

• summary descriptions of the candidate programs (Appendix C); 

• expert interviews: semi-structured interview guide (Appendix D); 

• State agency selection methodology (Appendix E); and 

• State agency interviews: semi-structured interview guide (Appendix F). 

23 Nine interviews were planned. However, due to scheduling conflicts with one interviewee, only eight interviews were conducted. 

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Chapter 2: Literature Review
 

Introduction 

This chapter summarizes USDA’s recent research on data matching in the NSLP, recent annual reports to 

Congress on NSLP direct certification, FNS reports on direct certification technical assistance site visits 

conducted in 2015, and best practices summarized in the FNS Match to Meal newsletter. Special 

attention is devoted to the findings reported in the NSLP Direct Certification Improvement Study on 

program data used by SAs in SY 2012–13, as this is the most comprehensive report on direct 

certification practices published to date. The report provided SY 2012–13 profiles for each State and 

detailed matching practices, processes, and systems, as well as the barriers and challenges that States 

and LEAs encountered in data matching. 

This chapter identifies the datasets that States and LEAs most frequently used for matching in SY 2012– 

13, the identifiers used in matching these datasets, and how frequently the datasets were updated. 

Requisite characteristics of candidate data sets that may facilitate NSLP data matching, and that might 

raise challenges similar to those that many SAs and LEAs have encountered in the past, are also 

identified. 

The chapter begins with a brief historical look at data matching, followed by a review of data matching 

efforts undertaken by FNS and a discussion of the perceived benefits and challenges of data matching. 

Data sharing among agencies, privacy protections, and characteristics of State data matching systems in 

the NSLP are also discussed. The review outlines the trajectory of data matching in the NSLP certification 

and verification processes and concludes with a discussion of the feasibility of using additional programs 

for data matching in the NSLP. 

An evolving technological environment has generated substantial improvements in data exchanges 

among Federal, State, and local agencies. Many practices presented in this chapter, while based on the 

most recently available publications, have changed. Findings from the literature review will nevertheless 

help to establish how data matching has been used for NSLP direct certification and verification. 

Historical Perspective on Data Matching 

Federal computer matching of public assistance program databases began in the 1970s. At this time, a 

few States began to computer match recipients of benefits from AFDC24 against wage data in State 

Employment Security files to reduce overpayments and payments to ineligible families.25 The Privacy Act 

of 1974 (P.L. 93-579) introduced protective measures for the exchange of records maintained on 

individuals: “No agency shall disclose any record which is contained in a system of records by any means 

of communication to any person, or to another agency, except pursuant to a written request by, or with 

the prior written consent of, the individual to whom the record pertains.”26 However, the Act permitted 

disclosures for specific purposes, including routine administrative uses within a Federal agency. 

These “matching programs” were defined as: 

24 AFDC was ended by the Personal Responsibility and Work Reconciliation Act of 1996 (PRWORA) and replaced with the 

Temporary Assistance for Needy Families (TANF) program. 

25 0ffice of Management and Budget, “Privacy Act of 1974; Revised Supplemental Guidance for Conducting Matching Programs,” 

47 Fed. Reg. 97 (May 19, 1982), p. 21657. Cited in: Office of Technology Assessment: Computer matching to detect fraud, 

waste, and abuse, p. 47. 26 The Privacy Act of 1974 (5 U.S.C. § 552a). Retrieved May 14, 2016 from: http://www.archives.gov/about/laws/privacy-act1974.html 

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. . . any computerized comparison of two or more automated systems of records or a system of 

records with non-Federal records for the purpose of (a) establishing or verifying the eligibility of, or 

continuing compliance with statutory and regulatory requirements by, applicants for, recipients or 

beneficiaries of, participants in, or providers of services with respect to, cash or in-kind 

assistance or payments under Federal benefit programs; or (b) recouping payments or delinquent 

debts under such Federal benefit programs.27 

In 1977, the Department of Health, Education, and Welfare launched the first major Federal computer 

matching program, Project Match, to match AFDC rolls with Federal payroll files in several States and 

localities in order to identify government workers who were also receiving AFDC, despite earnings that 

should have disqualified them from the program.28 

Along with these specific efforts, a series of legislative acts allowed or required Federal and State 

agencies to build data matching programs:29 

•	 The Tax Reform Act of 1976 (P.L. 94-455) allowed the Department of Health, Education, and 

Welfare HEW to search the databases of other Federal agencies to locate parents who failed to 

pay child support. 

•	 The Social Security Amendments of 1977 (P.L. 95-216 and 96-580) required States to access 

and use wage data maintained by the Social Security Administration (SSA) and State Employment 

Security agencies in determining eligibility for AFDC and SSI benefits. 

•	 The Food Stamp Act Amendments of 1980 (P.L. 96-249) amended the Internal Revenue Code 

and the Social Security Act to allow State Food Stamp agencies to obtain and use wage, benefit, 

and other information in files of the SSA and State unemployment compensation agencies. 

•	 The Food Stamp and Commodity Distribution Amendments of 1981 (P.L. 97-98) required States 

to obtain and use earnings information acquired from employers to verify income. 

•	 The Deficit Reduction Act of 1984 (P.L. 98-369) required the Internal Revenue Service (IRS) to 

disclose data on an individual’s unearned income to State welfare agencies and the SSA to verify 

the income of an applicant or beneficiary of the AFDC, SSI, and Food Stamp Program. 

The Computer Matching and Privacy Protection Act of 1988 (P.L. 100-503) sought to address ambiguities 

in interpreting and implementing the Privacy Act of 1974. The Act prohibits agencies from sharing records 

for computer matching without written agreements establishing the purpose and legal authority for the 

data exchange. These written agreements describe the records and data elements used, and specify how 

individuals will be notified at the time of application that their data might be used in matching. The 

agreements also establish procedures for verifying the results of the match and for record administration 

and maintenance.30 This Act also prohibits the suspension, termination, reduction, or denial of benefits 

based on data matches without independent verification of the information and mandates that 

individuals be informed about their right to contest data matching results.31 

Several subsequent legislative acts authorized or mandated data matching for direct certification in the 

NSLP: 

27 The Privacy Act of 1974 (5 U.S.C. § 552a). Retrieved May 14, 2016 from: http://www.archives.gov/about/laws/privacy-act1974.html 

28 Office of Technology Assessment. (1986, June). Electronic records systems and individual privacy: 1986 Computer matching to 

detect fraud, waste, and abuse, p. 41-42. (NTIS order #PB87-100355). Retrieved from: http://ota.fas.org/reports/8606.pdf 29 0ffice of Management and Budget, “Privacy Act of 1974; Revised Supplemental Guidance for Conducting Matching Programs,” 

47 Fed. Reg. 97 (May 19, 1982), p. 21657. Cited in: Office of Technology Assessment: Computer matching to detect fraud, 

waste, and abuse, p. 47. 30 The Privacy Act of 1974, as amended. Matching Agreements, 5 U.S.C. § 552a(o) (2010).
 31 The Privacy Act of 1974, as amended. Verifying and Opportunity to Contest Findings, 5 U.S.C. § 552a(p) (2010).
 

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•	 The Child Nutrition and WIC Reauthorization Act of 1989 (P.L. 101-147) authorized direct
 

certification of children in households that received either SNAP or TANF.
 

•	 The Child Nutrition and WIC Reauthorization Act of 2004 mandated that all States and districts 

directly certify children in SNAP households in SY 2008–2009 and subsequent years.32 

•	 The Healthy, Hunger-Free Kids Act of 2010 (HHFKA) introduced the Direct Certification 

Performance Rate (DC rate), and required that States directly certify at least 80 percent of 

children in SNAP households in SY 2011–12, 90 percent in SY 2012–13, and 95 percent in 

subsequent school years. States with DC rates that fall below these thresholds must adopt a 

Continuous Improvement Plan.33 

Data Matching by FNS 

In 2007, FNS released a report that evaluated the feasibility of expanding NSLP computer matching, 

using identifiers such as a student’s first and last name to determine whether that student’s household 

participated in means-tested benefits, including the Food Stamp Program, TANF, Medicaid/CHIP and 

FDPIR.34 These programs were found to have household income eligibility requirements similar to the 

NSLP. Before computer matching was introduced to provide direct certification to establish a student’s 

eligibility for free meals in the NSLP and SBP, certification for these programs relied entirely upon 

households submitting applications providing information on family size and income or providing case 

numbers demonstrating their participation in the food stamp or other qualifying programs. 

Since the 2007 study, FNS has built an extensive body of research and evaluations chronicling the 

progressive use of data matching to expand student access to meal benefits in the NSLP and SBP. Since 

SY 2008–09, FNS has reported annually to Congress on the progress States have made in implementing 

direct certification, including the use of computer matching to compare student enrollment data against 

program participant data. 

In 2014, the National School Lunch Program Direct Certification Improvement Study: Main Report 

provided a comprehensive review of the different computer matching processes that States and districts 

employed in SY 2012–13 to conduct direct certification.35 The study also identified several practices that 

may be beneficial to direct certification performance, and challenges commonly encountered in these 

efforts.36 For example, States that reported they used other programs in addition to SNAP had higher 

average DC rates than States that only used SNAP (required by regulation).37 States that used TANF and 

foster care data also reported higher DC rates, on average, than States that did not use these programs. 

Central matching States that performed direct certification more frequently than monthly had DC rates 

higher than States that matched less than monthly. The use of probabilistic matching, where matched 

records are scored for similarity, was also associated with higher average DC rates. States with individual 

32 U.S. Department of Agriculture. (2015, May). Direct certification in the National School Lunch Program: State implementation 

progress, school year 2013–14, Report to Congress. 33 States submit Continuous Improvement Plans to FNS, detailing the following: specific measures the State will use to identify 

more children eligible for SNAP direct certification, a multiyear timeline for the State to implement these measures, and goals for 

the State to improve direct certification results. 

34 U.S. Department of Agriculture, Food and Nutrition Service. (2007, February). Data matching in the National School Lunch 

Program: 2005. Retrieved May 19, 2016 from http://www.fns.usda.gov/sites/default/files/DataMatching-V1.pdf 35 U.S. Department of Agriculture. (2014, August). The National School Lunch Program direct certification improvement study: 

Main report. Retrieved May 19, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 

36 U.S. Department of Agriculture. (2015, January). The National School Lunch Program direct certification improvement study: 

Practices and performance report. Retrieved June 30, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationStatePractices.pdfTeh 

37 Analysis on direct certification performance rates was limited to States using central matching processes because in local 

matching States, practices can vary by district. 

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lookup systems had higher DC rates as well, as did States using secondary matching processes relative to 

other States. 

FNS also publishes guidance to aid States in their direct certification efforts. The quarterly Match to Meal 

newsletter highlights best practices for attaining and/or maintaining high DC rates. Many best practices 

from the newsletter are summarized in the following sections, along with evidence from recent research 

and Federal reports. 

In a continued effort to enhance the use of data matching in the NSLP, FNS is examining the use of 

additional program data for direct certification and is evaluating the use of Medicaid for direct 

certification as part of demonstration projects conducted over the last 4 school years.38,39 In SY 2012– 

13, FNS conducted initial Direct Certification-Medicaid (DC-M) demonstrations in five states—Florida, 

Illinois, Kentucky, New York (New York City), and Pennsylvania.40 Participant States were authorized to 

data match to Medicaid files containing income and household size information to directly certify for free 

meals those students who were both enrolled in Medicaid and resided in households with gross 

household income below 133 percent of the FPL. The DC-M demonstrations also extended free meal 

benefits to all children within such households. 

The evaluation of the initial demonstrations indicated that only districts in New York City experienced 

significant increases in the number of students directly certified and in the total number of students 

certified for free meals as a result of the demonstration. New York City districts increased the number of 

students directly certified by 7.1 percentage points in the first year of the demonstration, and by 6.9 

percentage points in the second. They also increased the total number of students certified for free meals 

by 5.6 percentage points in the first year, and by 5.9 percentage points in the second. Other 

demonstration sites either did not experience statistically significant outcome differences or did not 

implement DC-M in time to assess certification outcomes (October of the first year of the demonstration 

for evaluation of first year impacts). 

In SY 2013–14, Massachusetts and New York State were added as Cohort 2 of the DC-M 

demonstrations, but were not included in the certification outcome analysis because implementation 

occurred after impact measures were assessed. DC-M impacts for Year 3 (SY 2014–15) and Year 4 (SY 

2015–16) have not yet been published. Most recently, several additional States have applied to 

participate in the DC-M demonstrations, which have been expanded to include direct certification for both 

free and reduced price meals.41 

Benefits of Data Matching 

Improves Participation Rates 

Direct certification can increase participation rates in the NSLP during times of increased economic 

hardship. A 2015 study noted that during recessions, many households move from income eligibility 

38 U.S. Department of Agriculture, Food and Nutrition Service. (2015, January). Evaluation of demonstrations of National School 

Lunch Program and School Breakfast Program direct certification of children receiving Medicaid benefits: Year 1 report. 

Retrieved May 19, 2016 from: http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationMedicaidYr1.pdf 39 U.S. Department of Agriculture, Food and Nutrition Service. (2016, June). Year 2 demonstration impacts of using Medicaid data 

to directly certify students for free school meals. Retrieved June 28, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/DirectCertwithMedicaidYear2.pdf 

40 Kentucky and Pennsylvania implemented DC-M statewide, while districts were randomly assigned to either a treatment or 

control group in Florida, Illinois, and New York City. 

41 U.S. Department of Agriculture, Food and Nutrition Service. (2016, January 27). Request for applications to participate in 

demonstration projects to evaluate direct certification with Medicaid. Retrieved May 4, 2016 from: 

http://www.fns.usda.gov/sites/default/files/cn/SP23-2016a.pdf 

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levels for paid meals to income levels within F/RP eligibility guidelines. Often these families seek benefits 

from assistance programs that also confer categorical eligibility for free meals in the NSLP (e.g., SNAP, 

TANF).42 Direct certification can identify eligible children in these households and provide free meals to 

them when their families need assistance the most. 

The Community Eligibility Provision (CEP) has also increased participation in the NSLP.43 The provision 

allows districts and schools that can directly certify 40 percent or more of their students as eligible for 

free meals to provide free meals to all their students.44 From October 2010 to October 2012, for the 10 

States and the District of Columbia where CEP was available, average daily NSLP participation increased 

by 13 percent in schools that implemented CEP.45 

Reduces Certification Error Rates 

Using data matching to directly certify students as eligible for meal benefits has the potential to reduce 

improper certifications in the NSLP. Recent evidence suggests that NSLP direct certification provides 

more accurate determinations of eligibility for free meals than household applications.46 In FNS’ most 

recent study of improper payments, 96 percent of students who were directly certified for free meals were 

correctly identified as eligible. In contrast, only 79 percent of students were correctly certified based on 

household applications. 

Reduces Burden on School Districts and Households 

Direct certification also reduces the administrative burden for LEAs and households.47 Directly certified 

households no longer need to complete and submit applications. In addition, processing of these 

applications by LEAs is eliminated. 

Challenges in Data Matching 

Although computer matching has enhanced direct certification and direct verification efforts in the NSLP, 

States encounter several common challenges. For example, many States report that enrollment data 

were not updated for matching in time for direct certification in the fall. States also report delays in 

obtaining enrollment data from all of their LEAs. These problems make it less likely that newly enrolled 

students will appear in the enrollment files used for data matching, or that matched records of students 

who recently transferred will be allocated to the LEA of the student’s new school. States also cited 

difficulty in identifying eligible children enrolled in non-public schools, as well as migrants and homeless 

children. Some States also expressed concerns that matching processes might compromise student 

confidentiality.48 

42 Food Research and Action Center. (2015, January). National School Lunch Program: Trends and factors affecting student 

participation. Retrieved July 1, 2016 from: http://frac.org/pdf/national_school_lunch_report_2015.pdf 43 The Community Eligibility Provision allows LEAs to provide free school meals to all students, regardless of individual eligibility. To 

participate, LEAs must establish through direct certification that at least 40 percent of the students enrolled in their district are 

eligible for free meals. For more information about the CEP, see http://www.fns.usda.gov/school-meals/community-eligibilityprovision 

44 Ibid. 45 These States could operate CEP prior to nationwide availability in SY 2013–14. 46 U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. (2015). Program error in the National 

School Lunch Program and School Breakfast Program: Findings from the second access, participation, eligibility and 

certification study (APEC) II, Volume I: Findings. Retrieved May 19, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/APECII-Vol1.pdf 47 Food Research and Action Center, Center on Budget and Policy Priorities. (2014, July 25). Improving direct certification will help 

more low-income children receive school meals. Retrieved July 1, 2016 from: http://www.cbpp.org/research/improving-directcertification-will-help-more-low-income-children-receive-school-meals 48 U.S. Department of Agriculture. (2014). The National School Lunch Program direct certification improvement study: Main report. 

Retrieved May 19, 2016 from: http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 

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States also identified challenges in directly certifying 95 percent of students participating in SNAP as 

mandated by HHFKA. Their enrollment records do not include SNAP participants who are homeschooled 

(and not attending classes at a school around the time of meal service), who are virtually schooled, or 

dropouts. Also, there are some public schools that do not participate in the NSLP, so even though the 

children appear in the State’s school enrollment records, they cannot be directly certified for school 

meals at the local level. As a result, these circumstances artificially reduce DC rates for SNAP 

participants. State DC rates, which are currently determined using all children who receive SNAP as the 

base population (the denominator in the rate calculation), reflect not only how effective the State is in 

directly certifying SNAP participants, but also the size of student populations that do not appear in 

student enrollment records. 

States also noted difficulties in matching data between different sources. File layout and data entry 

protocols can vary between program participation and school enrollment files, and the agency responsible 

for performing the match does not always control the format in which the matching data is received.49 In 

interviews with State agencies, Kentucky indicated that they can address this challenge to some extent by 

requesting program files to include specified fields and information. They noted, however, that they could 

not validate the business rules used to provide the information. For instance, a request for all participants 

under 20 years of age could be interpreted to include those aged 20 years and under or individuals age 

19 years and under. 

Data Sharing Among Agencies and Privacy 

Data matching requires cooperation between different SAs that administer the NSLP and SBP, and those 

that administer SNAP or other assistance programs. Memoranda of Understanding (MOUs) must be 

developed between the SA administering the NSLP (e.g., Department of Education or Agriculture), and the 

SAs (e.g., the Department of Social Services) administering other programs such as SNAP and TANF. 

MOUs establish a data sharing agreement between the agencies, and define the roles that each agency 

will fulfill in the data matching process, including how data from each agency are transferred or accessed 

and which agency is responsible for conducting the match. 

The agencies involved in NSLP-related data matching vary by State. For example, the California 

Department of Education sends the student enrollment data maintained in its statewide student 

information system (SSIS) to the California Department of Social Services, which then matches this 

enrollment data against a SNAP eligibility data file that is extracted from the mainframe of the California 

Department of Health Care Services Medicaid Eligibility Data System (MEDS).50 In other States, the 

agency administering SNAP or other assistance programs may simply provide an electronic file to the 

education agency listing program participants. 

Establishing MOUs and data matching processes between SAs can present challenges. Differences in the 

privacy and confidentiality policies of each SA can make the process time-consuming. For example, the 

MOU process for Medicaid took more than 1 year in Nebraska. In West Virginia, the sensitive nature of 

foster care data made it difficult to establish a data sharing agreement.51 

However, leveraging existing relationships with other agencies and stakeholders can potentially help 

mitigate the MOU process for direct certification. One State that has leveraged its existing relationships is 

Nevada. The Nevada Department of Agriculture (NDA) used its existing relationships with Tribes and 

49 Ibid.
 50 U.S. Department of Agriculture, Food and Nutrition Service, Training and Technical Assistance Team. (2015). California direct
 

certification process information questionnaire. 51 U.S. Department of Agriculture. (2014). The National School Lunch Program direct certification improvement study: Main report. 

Retrieved May 19, 2016 from: http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 

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Bureau of Indian Education partners to add FDPIR participant data to the State’s direct certification 

efforts. The NDA both administers the FDPIR program and performs direct certification in Nevada.52 

Another challenge that States face with data sharing is maintaining confidentiality. Nearly one-third of the 

States that completed 2015 FNS Direct Certification Training and Technical Assistance Team site visits 

indicated they faced challenges involving the confidentiality and security of program and student 

enrollment data as they were developing their direct certification data matching processes.53 However, 

other States have successfully developed strong and innovative data sharing relationships that may 

reduce the likelihood of a confidentiality breach. For example, Delaware consolidated student data and 

built interoperability between systems used by the State’s Department of Education and Department of 

Health and Social Services. As a result, SNAP agency records that have been matched with student 

enrollment records are permanently linked. Whenever any record having this link established is updated 

by one agency, the other agency’s records are automatically updated to reflect this change. This 

decreases the need to transfer student information using multiple systems or external methods, 

potentially decreasing the instances where student confidentiality may be breached. In 2013, over 90 

percent of students in Delaware were linked using this method.54 

Although some States may have ongoing relationships between NSLP SAs and other program 

administering agencies, others may have yet to establish relationships needed to ease the process of 

developing direct certification partnerships. 

Characteristics of Data Matching in the NSLP 

State computer matching processes for the NSLP are heavily influenced by whether matching is 

conducted centrally or locally.55 Central matching occurs at the State level, where the NSLP SA matches 

State student enrollment records, usually maintained in an SSIS, with a State list of children from 

households participating in SNAP and other assistance programs. The NSLP SA then sends a list of the 

“matched” students to the districts (or other local level recipient), which are responsible for processing 

the match records and directly certifying students in their point-of-service system. 

Districts receive lists of exact and, in many instances, potential matches. In some States these lists are 

separate, whereas in others they are combined. Districts may conduct further review on potential 

matches based on additional information they obtain or may conduct additional types of secondary 

matching. In other States, districts initiate the process via an upload of enrollment data into a State 

computer or web-based system for matching against the most recently available list of school-aged SNAP 

(or other benefit program) recipients. 

52 U.S. Department of Agriculture, Food and Nutrition Service. (2015, Summer). Match to meal newsletter, issue 7. State Systems 

Support Branch, Child Nutrition Division. (Document provided by FNS). 53 During TTA site visits each year, a group of States complete questionnaires detailing the processes used in direct certification. 

The site visits focus on States not meeting the 95 percent standard for direct certification rates with SNAP. The States are also 

asked about any challenges and barriers faced to achieving higher direct certification rates. In 2015, site visits were conducted 

in 17 States. 

54 U.S. Department of Agriculture, Food and Nutrition Service. (2013, July). Match to meal newsletter, issue 1, vol. 1. State 

Systems Support Branch, Child Nutrition Division. (Document provided by FNS). 

55 U.S. Department of Agriculture. (2015). The National School Lunch Program direct certification improvement study: State 

practices and performance report. Retrieved May 19, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationStatePractices.pdf 

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In local matching States, SAs forward or place on web portals lists of children in households participating 

in SNAP or other means-tested programs to districts. The districts then match these students to their 

enrollment data using their own computer systems.56 

Identifiers collected in both student enrollment and program records are used to perform computer 

matches. All States match on a student’s first name, last name, and date of birth (DOB). In some States, 

matching elements include middle names; student Social Security Number (SSN); gender; address 

elements such as street address, city, and zip code; school name; SNAP or other program IDs; and 

parent/guardian data elements such as first, middle, and last names. In addition, phonetic matching of 

first and last names is a tool used in some States. The elements used for matching in a State typically 

reflect specific features in their matching processes and systems. That is, the rationale for the elements 

used lies in the role each plays in improving the matching process and system specific to each State, 

rather than “best practices” that could be recommended for adaptation across States. Consistent with 

this, a recent study found that the data elements used for matching were not significant factors in 

improving direct certification performance.57 Rather, the seemingly universal use of first name, last 

name, and DOB suggest that these are the essential building blocks for matching processes. 

SAs and LEAs are required to match with SNAP at least three times per school year—at or near the 

beginning of the school year, then 3 and 6 months later.58 Frequent updating of enrollment records 

improves the data matching process by ensuring that new and transfer students are included in the 

SSIS.59 Program data also need to be frequently updated and matched with enrollment data for States to 

accurately identify students in households that begin receiving benefits from assistance programs that 

confer categorical eligibility later in the school year.60 

Feasibility of Using Additional Programs for Data Matching 

in the NSLP 

Although the use of computer matching in the NSLP has improved since its inception, efforts could 

perhaps be expanded to include additional programs. Currently, SNAP, TANF, and Foster Care are among 

the program data files that States match against for direct certification. Moreover, some States have 

taken advantage of current regulations that permit matching against FDPIR, Head Start, and data on 

migrant, homeless, or runaway children. 

An additional source of data for the NSLP matching comes from IEVS. IEVS was established by Section 

2651 of DEFRA to link data files for direct verification of household income. State agencies administering 

SNAP, TANF, Medicaid, and Unemployment Compensation are required to contribute participation and 

income data to IEVS, and to use the system for verification. However, over time the potential of IEVS has 

been reduced. The Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (P.L. 104-

193) made verification using IEVS optional for State SNAP agencies; the Family Educational Records 

Privacy Act (P.L. 93-380) prevents schools from requiring the collection of student SSNs. Even when 

56 U.S. Department of Agriculture. (2015). The National School Lunch Program direct certification improvement study: State 

practices and performance report. Retrieved May 19, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationStatePractices.pdf 

57 Ibid. 58 Determining Eligibility for Free and Reduced Price Meals and Free Milk in Schools. 7 C.F.R. § 245.6. 59 Statewide Student Information Systems (SSIS) maintain enrollment data and are most commonly used in the direct certification 

processes of central matching States. SSIS was used as the source of enrollment data by 80 percent of central matching and 29 

percent of local matching States in SY 2012–13. 60 U.S. Department of Agriculture. (2015, May). Direct certification in the National School Lunch Program: State implementation 

progress, school year 2013–14, report to Congress. Retrieved May 19, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertification2014.pdf 

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student SSNs are available in enrollment records, districts are not required to share this information. 

These limitations make it difficult to link data on a household’s income back to the student. Use of IEVS 

for direct verification is also complicated by variation in the timeliness of IEVS data source updates.61 

Other programs with income eligibility criteria similar to the NSLP might capture unduplicated, school-age 

children in participant households and prove feasible for data matching and increasing NSLP direct 

certification rates. A review of recent State practices in NSLP computer matching for direct certification 

indicates that data from other means-tested programs must possess the following information to be 

feasible for NSLP data matching: 

1.	 Income or poverty thresholds, and/or the household income and composition data needed to 

calculate FPL thresholds for comparison against NSLP guidelines; 

2.	 Identifiers for matching to student enrollment records; 

3.	 Recertification periods and participant data updates that can provide reasonably current data to 

the NSLP at the beginning and during the school year; and 

4.	 Participant data privacy protections and data sharing policies that are compatible with NSLP 

policies and regulations, and that would permit data exchanges. 

Income Eligibility Thresholds 

Data matching would be most feasible and straightforward using data from means-tested programs that 

have income guidelines and eligibility thresholds identical or similar to those used in the NSLP. These 

programs could be used to confer categorical eligibility, allowing students to be directly certified based 

solely on participation in the program. 

Some means-tested programs, such as State Medicaid programs, may have income guidelines and 

eligibility thresholds that differ from the NSLP. Data matching would remain feasible, however, if the 

income and household composition data needed to calculate household income as a percent of FPL were 

available.62 These programs could not provide categorical eligibility, but could be used to directly certify 

students based on income eligibility determinations calculated from income data available from the 

program. 

Identifiers Used for Matching 

SAs and LEAs in all States currently match on at least a student’s first name, last name, and DOB. These 

data elements are available in SNAP data for all States, and would also be needed in data files from any 

means-tested program that would be used for data matching. A range of other data elements, with 

considerable variability across States, are used for matching, such as gender, SSN, and home address. 

Many States also use phonetic name matching, while others do not. This variability across States reflects 

differences in State data matching systems and processes. 

For direct verification and for direct certification based on income eligibility, candidate data files would 

also need to include some combination of the child’s and the parent/guardian’s street address, city, zip 

code, and county, and the SSN of all household members. These identifiers will allow States to match 

program files for participants residing in the student’s household. In particular, SSNs would facilitate 

matching with several sources of income data for all household earners. 

61 Social Security Administration. (2014, June 12). Model CMPPA (Computer Matching and Privacy Protection Act) agreement. 

Retrieved from: https://www.ssa.gov/dataexchange/documents/2013%20CMPPA%20State%20Model.pdf 62 U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. (2015, 

September 3). U.S. Federal poverty guidelines used to determine financial eligibility for certain Federal programs. Retrieved 

from: https://aspe.hhs.gov/2015-poverty-guidelines#guidelines 

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Program Eligibility Period and Frequency of Updating 

States are required to conduct matches for SNAP direct certification three times each year, but most 

States perform direct certification matches more frequently.63,64 The eligibility periods for other meanstested programs can affect the feasibility of using program data for direct certification. Eligibility periods 

that exceed 6 months or 1 year may not capture changes in household income or composition that would 

place a household above or below NSLP thresholds, resulting in incorrect certifications, or failure to 

certify children now eligible for NSLP benefits. 

Privacy Protection and Data Sharing Policies 

Many States have privacy protection and data sharing policies that may preclude data sharing between 

SAs administering means-tested programs and the NSLP SA. State policies governing the use of student 

information and participant data from other means-tested programs must be reviewed to determine 

whether data matching would be feasible. The time and resources required to establish an MOU might 

exceed the expected benefits from matching to any additional program data files. 

Lessons Learned from Other Federal Agencies 

NSLP data matching may be improved by incorporating lessons learned across other Federal agencies. 

For example, in an effort to decrease improper payments, the IRS conducted a case study in Florida, 

matching tax return data with SNAP administrative data to verify eligibility for the EITC.65 This study found 

that SNAP data could help the IRS identify tax returns for audit because of discrepancies in the residency 

of qualifying children. In addition, data matching with SNAP could also help the IRS increase the number 

of EITC recipients through outreach to eligible households who fail to file a tax return. 

HHS evaluated the overlap between health programs and human services programs in a 2013 study, and 

estimated that with the expansions of health coverage implemented through the ACA, between 85 and 95 

percent of SNAP, WIC, TANF, and LIHEAP recipients would also be eligible for healthcare benefits through 

either the ACA Marketplace or Medicaid.66 Conversely, the study estimated that among people who would 

qualify for Medicaid if all States expand eligibility, 49 percent receive SNAP, 18 percent receive LIHEAP, 

and 9 percent receive housing subsidies. The much lower overlap in expected eligibility between 

recipients of LIHEAP and of housing subsidies, relative to SNAP recipients, with Medicaid suggests that 

LIHEAP and the housing programs may be better positioned than Medicaid to identify SNAP recipients 

eligible for free meals in the NSLP that are not already matched to the NSLP. The report also proposed 

innovative methods by which applicant information might be directly transferred to agencies 

administering other benefit programs. 

CMS exchanges data with several State programs, insurers, employers, and other payers of prescription 

drug benefits through its maintenance of Computer Matching Agreements (CMA) and Information 

Exchange Agreements (IEA).67,68 These data sharing agreements support CMS efforts to facilitate and 

63 Application, Eligibility and Certification of Children for Free and Reduced Price Meals and Free Milk. 7 C.F.R. 245.6(b)(3) 64 Food Research and Action Center. (n.d.) Roadmap for ending hunger: Increase data-match frequency to improve NSLP direct 

certification rates. Retrieved May 20, 2016 from: http://www.frac.org/pdf/roadmap_nslp_direct_certification.pdf 65 Urban Institute. (2015, September). Using Supplemental Nutrition Assistance Program data in earned income tax credit 

administration: A case study of Florida SNAP data linked to IRS tax return data. Retrieved May 24, 2016 from: 

http://www.urban.org/sites/default/files/alfresco/publication-pdfs/2000438-using-snap-data-in-eitc-administration.pdf 66 Urban Institute. (2013, December 23). Overlapping eligibility and enrollment: Human services and health programs under the 

Affordable Care Act. Prepared for Office for the Assistant Secretary of Planning and Evaluation, Department of Health and 

Human Services (Under Task Order HHSP23337026T). Retrieved May 24, 2016 from: 

https://aspe.hhs.gov/sites/default/files/pdf/76961/rpt_integrationproject.pdf 

67 Including State Pharmaceutical Assistance Programs (SPAPs), AIDS Drug Assistance Programs (ADAPs), and Patient Assistance 

Programs (PAPs). 

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coordinate individuals’ coverage under Medicare Part D and other prescription drug programs. Records 

on beneficiaries receiving coverage under these programs are provided to CMS, and in return CMS 

provides Medicare Part D coverage information.69 Additionally, CMS and SSA share data regarding 

enrollment in Medicare Savings Programs that help beneficiaries with their Medicare expenses and the 

Part D Low-Income Subsidy administered by SSA.70 Enrollment in a Medicare Savings Program triggers 

auto-enrollment in the SSA-administered subsidy program. Low-Income Subsidy enrollment data, 

however, is only shared through data leads to State Medicaid agencies, who use it as they deem 

appropriate. Additionally, SSA provides similar leads for SSI to state Medicaid agencies. 

In 2009, the Departments of Energy (DOE) and Housing and Urban Development (HUD) partnered 

together to lower existing barriers to the use of weatherization funds in public and assisted multi-family 

housing.71 Under DOE regulations implemented in 2010, HUD Qualified Assisted Housing units are 

automatically eligible for weatherization assistance based on income eligibility and verification 

determinations made by HUD.72,73 HUD publishes a list of all eligible properties on the DOE website, and 

DOE may provide weatherization assistance to residents living in these properties having income below 

200 percent of the FPL, without further evaluation. 

In summary, NSLP data matching for eligibility and verification determinations may be improved by 

incorporating lessons learned across other Federal agencies. States continue to take measures to 

improve NSLP-related data matching, for example, by adding more data elements and sending data more 

frequently. Other States are implementing advanced matching processes to resolve unmatched records, 

increase the frequency of data matching, and incorporate new data sources.74 These advances, and 

recent FNS research, provide a strong case for the continued expansion of NSLP data matching. Adopting 

additional sources for and approaches to data matching that support direct certification and direct 

verification can increase access to benefits and decrease administrative burden on both families and 

administrative resources. These considerations are discussed in more detail in the concluding section. 

Chapter 3: Expert Interviews 

This chapter presents information compiled on several means-tested assistance programs in an effort to 

identify additional data sources that have potential for improving future matching processes in the school 

meals programs (Objective 2). To accomplish this, 2M reviewed the eligibility requirements for 70 means68 Centers for Medicare and Medicaid Services. (2014, March). Prescription drug assistance programs. Retrieved May 25, 2016 

from: https://www.cms.gov/Medicare/Coordination-of-Benefits-and-Recovery/Prescription-Drug-AssistancePrograms/Overview.html 

69 Centers for Medicare and Medicaid Services. (2016, April). Coordinating prescription drug benefits. Retrieved May 25, 2016 

from: https://www.cms.gov/Medicare/Coordination-of-Benefits-and-Recovery/Prescription-Drug-AssistancePrograms/Coordinating-Prescription-Drug-Benefits/Coordinating-Prescription-Drug-Benefits-Page.html 70 Social Security Administration. (2016). Program operations manual system (POMS): HI00815-025 SSA outreach to low-income 

Medicare beneficiaries – extra help and Medicare savings programs. Retrieved from: 

https://secure.ssa.gov/poms.nsf/lnx/0600815025 71 U.S. Department of Housing and Urban Development. (2010, March). Fact sheet: HUD-DOE weatherization memorandum of 

understanding: Streamlining weatherization assistance in affordable housing. Retrieved May 25, 2016 from: 

http://portal.hud.gov/hudportal/documents/huddoc?id=DOC_4269.pdf 

72 HUD Qualified Assisted Housing includes the following properties; Public Housing, Project-Based Section 8 Assisted Housing, 

Section 202 Supportive Housing for the Elderly, Section 811 Supportive Housing for Persons with Disabilities, and Certain Low 

Income Housing Tax Credit (LIHTC) properties. 

73 U.S. Department of Housing and Urban Development. (n.d.) HUD-DOE partnership: Multifamily weatherization of public and 

assisted housing. Retrieved May 25, 2016 from: http://portal.hud.gov/hudportal/HUD?src=/recovery/partnerships/HUD_DOE 74 U.S. Department of Agriculture. (2014). The National School Lunch Program direct certification improvement study: Main report. 

Retrieved May 19, 2016 from: http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 

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tested programs. Programs that award funds directly to families or households were of primary interest. 

Conversely, programs that award funds directly to localities or organizations were not relevant to the 

study’s objectives, and were removed from further consideration. 2M also excluded programs that: 

1.	 primarily serve populations other than households with school-age children (e.g., people with 

disabilities, the elderly, and students attending college); 

2.	 do not collect data necessary for calculating income as a percent of the FPL, which is needed to 

determine NSLP eligibility; or 

3.	 include income sources and eligibility thresholds that are incompatible with those used for the 

NSLP. 

Six programs were chosen for further evaluation, because they had income eligibility criteria and 

certification periods similar to those for the NSLP, and also included school-age children in participant 

households. (Complete profiles of the six chosen programs are provided in Appendix C). While not a 

means-tested program, IEVS databases were also considered for use. IEVS includes employer reports of 

earnings data that may be useful in data matching to directly verify income eligibility determinations 

made from self-reported income on household applications. 

2M conducted interviews between February and May of 2016 with knowledgeable experts from the 

Federal agencies administering each selected program. The selected programs were: 

•	 Special Supplemental Nutrition Program for Women, Infants, and Children 

FNS administers WIC through grants provided to the States. The program provides lowincome women, infants, and children in households who are at nutritional risk with 

supplemental nutritious foods, nutrition education and counseling, and referrals to 

healthcare services. States have discretion to set income eligibility thresholds for WIC 

between 100 percent and 185 percent of the FPL.75,76 

•	 Affordable Care Act Health Insurance Marketplace77 

CMS manages HealthCare.gov, the Federal Marketplace developed under the ACA, while some 

States have elected to manage their own health insurance Marketplaces. The Marketplace 

utilizes household income and composition information provided by consumers, to determine 

eligibility for Qualified Health Insurance Plans (QHPs), Medicaid, and CHIP.78,79 Applicant 

information provided in the Marketplaces are matched and verified against data provided by the 

IRS, SSA, and several other Federal agencies.80,81 

75 U.S. Department of Agriculture, Food and Nutrition Service. (n.d.) Women, Infants, and Children (WIC): About WIC-WIC’s mission. 

Retrieved on April 29, 2016 from: http://www.fns.usda.gov/wic/about-wic-wics-mission 76 Applicants eligible for WIC include pregnant women (from pregnancy and up to 6 weeks after birth, or after pregnancy ends); 

breastfeeding women (up to the infant’s 1st birthday); non-breastfeeding postpartum women (up to 6 months after the birth of 

an infant or after pregnancy ends); infants (up to 1st birthday); and children up to their 5th birthday. 

77 Affordable Care Act Health Insurance Marketplace is not a program. It is organizations set up to facilitate the purchase of health 

insurance in accordance with the Patient Protection and Affordable Care Act (P.L. 111-148). Nevertheless, to enhance 

readability of the paper, the Marketplace is referred to as a “program” and the data potentially available from the Marketplace 

are referred to as “program data.” 

78 Qualified Health Plans (QHPs) provide essential health benefits, follow established limits on cost-sharing (deductibles, 

copayments, and out-of-pocket maximum amounts), and meet other requirements. QHPs are certified by each Marketplace in 

which they are sold. 

79 U.S. Centers for Medicare and Medicaid Services. (n.d.). Medicaid and CHIP coverage. Retrieved April 21, 2016 from 

Healthcare.gov website: https://www.healthcare.gov/medicaid-chip/getting-medicaid-chip/ 80 Privacy Act of 1974, Supplementary Information: Health Insurance Exchanges Program; Notice to establish a new system of 

records, 78 Fed. Reg. 25 (February 6, 2013), pp. 8539–8542. 

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• Public Housing and Housing Choice Voucher Programs 

HUD administers the Public Housing program to provide rental housing for eligible, lowincome families, the elderly, and persons with disabilities.82 The Housing Choice Voucher 

program, also administered by HUD, provides rental subsidies for housing chosen by 

eligible tenants.83 

• Low Income Home Energy Assistance Program 

HHS administers LIHEAP through block grants made to State and local agencies for the 

provision of home energy assistance to low-income families at or below 150 percent of 

the poverty level except where 60 percent of the State median income is higher.84,85 

• Weatherization Assistance Program 

WAP is administered by DOE and provides weatherization assistance to low-income 

households at or below 200 percent of the FPL through State agencies.86,87 

81 The Department of Homeland Security (DHS), Department of Veteran Affairs (VA), Department of Defense (DoD), Peace Corps, 

and Office of Personnel Management (OPM) provide data that is maintained in the Federally facilitated Exchange (FFE). 82 U.S. Department of Housing and Urban Development. (n.d.). HUD’s Public Housing Program. Retrieved April 26, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/topics/rental_assistance/phprog 

83 U.S. Department of Housing and Urban Development. (n.d.). Housing choice vouchers fact sheet. Retrieved April 30, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/topics/housing_choice_voucher_program_section_8 

84 U.S. Department of Health and Human Services. (n.d.). LIHEAP and WAP funding. Retrieved April 21, 2016 from: 

https://liheapch.acf.hhs.gov/Funding/funding.htm 

85 U.S. Department of Health and Human Services, Administration for Children and Families. (2016, February). About LIHEAP. 

Retrieved on April 21, 2016 from: http://www.acf.hhs.gov/programs/ocs/programs/liheap/about 86 D.C. Department of Energy and Environment. (n.d.). Weatherization Assistance Program. Retrieved April 29, 2016 from: 

http://doee.dc.gov/service/weatherization-assistance-program 87 The Energy Conservation and Production Act of 1976, 42 U.S.C. 6865(c)1 § 415(c). 

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•	 Earned Income Tax Credit 

The EITC reduces the amount of taxes for, or provides refunds to, working people with 

low to moderate income.88 Income eligibility requirements are based on tax filing status 

and the presence or absence of qualifying children.89 

•	 Income Eligibility Verification Systems 

IEVS was established under DEFRA to reduce errors in eligibility and benefit-level 

determinations.90 States are required to implement an IEVS, and verify income and other 

applicant data for certain assistance programs.91 

A general discussion guide was developed for the expert interviews and approved by FNS. The discussion 

topics were designed to obtain information about the data elements available in each program data file, 

how the program data is exchanged with external agencies, and policy and regulatory provisions that 

govern administration of program data (see Appendix D). 

Findings from the expert interviews and published information about the selected programs are provided 

in Appendix C. Each program profile includes 

•	 an overview of the purpose of the program, 

•	 a description of personally identifiable information (PII) collected on participants and applicants, 

•	 a description of income and poverty data collected, 

•	 a description of recertification periods, 

•	 a summary of relevant data sharing and privacy protection policies, and 

•	 a discussion of the compatibility of key data elements needed for data matching with the NSLP. 

Findings on Candidate Data Sources 

The expert interviews assisted 2M in assessing the feasibility of expanding data matching in the NSLP to 

include the use of each of the selected programs. Specific barriers and challenges associated with each 

program were identified, including: 

1.	 the data elements available for matching; 

2.	 the accuracy of matching those elements to school enrollment data; 

3.	 legislative and regulatory issues with the transmission of student, parent, and/or household data 

between agencies; and 

4.	 additional burdens upon staff and IT resources. 

Findings from this analysis are summarized in Table 1 (below). 

88 Internal Revenue Service. (2016, March). Earned income tax credit (EITC). Retrieved May 24, 2016 from: 

https://www.irs.gov/credits-deductions/individuals/earned-income-tax-credit 89 Internal Revenue Service. (2016, March). Do I qualify for EITC? Retrieved May 24, 2016 from: https://www.irs.gov/creditsdeductions/individuals/earned-income-tax-credit/do-i-qualify-for-earned-income-tax-credit-eitc 90 U.S. Department of Health and Human Services, Office of Inspector General. (1994, October). State income and eligibility 

verification systems (IEVS): State profiles, part I, Alabama through Montana. Retrieved May 4, 2016 from: 

http://oig.hhs.gov/oei/reports/oei-06-92-00081.pdf 91 This includes Medicaid, unemployment compensation, SNAP, TANF, and child welfare services, and in such State programs as 

elderly assistance, SSI, and other assistance programs for the blind and disabled. See Income and Eligibility Verification system, 

Applicable Programs, 42 U.S.C. 1320b—7(b). Use of the IEVS for SNAP was made optional by the Personal Responsibility and 

Work Opportunity Act of 1996 (PRWORA) ((PL 104-193). See 

http://www.fns.usda.gov/sites/default/files/ComputerMatching_summary.pdf 

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Table 1: Key Findings for Each Program 

Program Description Key Findings Income Eligibility Thresholds Include in SA Interviews 

Special Supplemental Nutrition 

Program for Women, Infants, and 

Children 

(WIC) 

WIC provides Federal grants to 

States for supplemental foods, 

healthcare referrals, and 

nutrition education to lowincome women who are 

pregnant, breastfeeding, or nonbreastfeeding postpartum, and 

to infants and children who are 

at nutritional risk. 

PII—not collected on children ≥ 5 

years old in the household, or on 

other household members. Few 

school-age children in the 

household (e.g., pregnant, 

breastfeeding, or postpartum 

women under 18 years old) 

would be identified in WIC 

records. 

<185% FPL NO 

Affordable Care Act (ACA) Health 

Insurance Marketplace 

The ACA raised the eligibility limit 

for Medicaid from 100 percent 

to 133 percent of the FPL in 32 

participating States, and 

provides tax units between 100 

percent and 400 percent of the 

FPL with Advanced Premium Tax 

Previous year’s Modified 

Adjusted Gross Income (MAGI), 

used to determine Medicaid 

eligibility, could be used for 

direct certification and 

verification in the NSLP. APTC 

and CSR eligibility 

<133% FPL (Medicaid) 

100-400% FPL (APTC) NO 

Credit (APTC) to subsidize the 

purchase of qualified health 

plans, and Cost Sharing 

Reductions (CSRs) to reduce 

deductibles. 

determinations utilize projected 

income and would not be 

feasible for data matching in the 

NSLP. 

Public Housing and Housing 

Choice Voucher Programs 

These HUD programs provide 

public housing or help very lowincome families, the elderly, and 

the disabled afford housing in 

the private market. Public 

housing authorities (PHAs) 

receive Federal funds from HUD 

to administer the housing choice 

vouchers. 

Income eligibility is based on 

annual income from income 

sources similar to the NSLP’s. 

Since matching to Medicaid’s 

past-year income data seems 

viable, it may also be possible to 

conduct data matching with 

Form-50058 for income 

determination and verification. 

<80% Median Income of 

Locale (Low limit) 

<50% Median Income of 

Locale (Very-low limit) 

YES 

Low Income Home Energy 

Program (LIHEAP) 

LIHEAP assists low-income 

households with home energy 

costs, energy crises, 

weatherization assistance, and 

LIHEAP has the PII, the FPL 

information, and the sources of 

income needed to make data 

matching with NSLP feasible. 

<150% FPL 

or 

<60% State Median Income 

(whichever is higher) 

YES 

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Program Description Key Findings Income Eligibility Thresholds Include in SA Interviews 

minor energy-related home 

repairs.92 

Weatherization Assistance 

Program (WAP) 

DOE provides grants to States, 

territories, and some Indian 

Tribes to improve the energy 

efficiency of the homes of lowincome families. The States 

contract with local governments 

and nonprofit agencies to 

provide weatherization services 

to those in need, using the latest 

technologies for home energy 

upgrades. 

Many WAP participants are 

directly certified based on 

LIHEAP. It seems unlikely that 

WAP would capture additional 

households beyond those 

identified through LIHEAP. <200% FPL NO 

Earned Income Tax Credit (EITC) 

The credit is a benefit for 

working people with low to 

moderate income. Eligibility is 

determined by tax filing status, 

earned income and adjusted 

gross income (AGI), and the 

number of qualifying children 

claimed. 

EITC income calculations are 

based on tax units rather than 

the economic units used in the 

NSLP, and may include children 

residing in a separate household. 

EITC data could not be shared for 

data matching under current IRS 

regulations. 

$14,820 - $47,747 (single) 

$20,330 - $53,267 

(married) for annual earned 

income and adjusted gross 

income 

NO 

Income and Employment 

Verification System (IEVS) and 

Unemployment Insurance (UI) 

IEVS systems are established to 

permit data sharing of UI 

earnings data, and other income 

sources with means-tested 

assistance programs to 

establish eligibility and benefit 

levels, and to verify income. 

Matching to IEVS requires the full 

SSN of all members of the 

household. FNS now collects only 

the last four digits of the SSN of 

one household member. 

N/A NO 

92 Administration for Children and Families, Office of Community Services. (2016, February 4). About LIHEAP. Retrieved April 21, 2016 from: 

http://www.acf.hhs.gov/programs/ocs/programs/liheap/about 

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Findings from the interviews and published information on the selected programs indicated that several 

of these programs have little potential for NSLP data matching. The evaluated programs are organized 

into those that do not appear feasible and those that appear feasible for NSLP data matching. 

Not Feasible for NSLP Data Matching 

WIC is currently inadequate for use in data matching because PII is collected only for the women, infants, 

and children under 5 years of age who receive benefits. Because WIC does not collect or maintain 

information on nonparticipant household members, WIC files include very few school-age children 

(excepting qualifying women under 18 years of age), and would not be useful for matching to student 

enrollment records. 

Similarly, WAP does not seem to be a useful data source to consider further because most participants in 

WAP have categorical eligibility conferred by LIHEAP. The WAP data expert indicated that the “vast 

majority” of recipients also receive benefits in the LIHEAP program.93 This finding makes it unlikely that 

WAP would identify additional students eligible for benefits in the NSLP who were not already identified by 

LIHEAP. Data collection and verification processes are also more robust in LIHEAP than WAP, and provide 

a higher quality source of information. Data matching with LIHEAP instead of WAP records is likely to 

provide a more cost-effective means of directly certifying school-age students in households receiving 

WAP benefits. 

EITC is also likely to be unsuitable for NSLP data matching. Eligibility for the credit is assessed for tax 

units rather than for the economic units used in the NSLP. Children claimed for EITC may reside in a 

separate household, and spouses residing in the same household might file separate returns. Further 

research would be needed to assess the differences in estimated income based on tax units and 

economic units before EITC could be used for NSLP direct certification or direct verification. 

The Marketplace uses a single application to determine eligibility for Medicaid and CHIP, and for the APTC 

and other Cost Sharing Reductions (CSRs) for QHPs.94,95 Consumers estimate their expected AGI for the 

current tax year, including their spouse’s income and tax dependents, to determine eligibility for the APTC 

and CSR.96 Medicaid, on the other hand, uses the income reported on the previous year’s tax return to 

determine eligibility. 

It does not seem feasible to use projected income data collected in the Marketplace for direct 

certification or direct verification of income in the NSLP. The APTC is calculated based on expected 

income, and is adjusted if the income projected on the application differs from the income reported on 

the tax return at the end of the year.97 For the NSLP, the household is defined to include individuals 

within the same residence; in the Marketplace, the household includes the applicant, the spouse, and 

any tax dependents, whether or not they reside together. Income eligibility determinations would vary 

93 Expert Interview, March 4, 2016, DOE Weatherization and Intergovernmental Programs Office. 94 Qualified health plans (QHPs) provide essential health benefits, follow established limits on cost-sharing (deductibles, 

copayments, and out-of-pocket maximum amounts), and meet other requirements. QHPs are certified by each Marketplace in 

which they are sold. 95 The APTC provides an advanced subsidy on monthly QHP premiums. Internal Revenue Service. (n.d.). Eligibility for the premium 

tax credit. Retrieved April 30, 2016 from: https://www.irs.gov/Affordable-Care-Act/Individuals-and-Families/Eligibility-for-thePremium-Tax-Credit 96 U.S. Centers for Medicaid and Medicaid Services. (n.d.) How to count income and household members. Retrieved May 21, 2016 

from: https://www.healthcare.gov/income-and-household-information/ 97 Insureds must pay back any portion of the APTC that exceeds the actual APTC calculated on income reported on tax returns at 

the end of the year. Insureds may also receive a refund if they received a lower APTC than they were entitled to, based on their 

actual income for the year. 

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between the NSLP and the Marketplace. In addition, several income sources considered in the NSLP are 

not used in the Marketplace.98 

Feasible for NSLP Data Matching 

HUD collects data on the annual income of each family member through Form-50058.99 The income 

sources included on the form align well with those collected on NSLP household applications. However, 

income eligibility for HUD programs is based on the median income of the county or metropolitan area in 

which a participant household chooses to live. Income qualifications therefore vary for each local housing 

agency and PHA, making it infeasible to add HUD program data to the existing list of programs that confer 

categorical eligibility in the NSLP (e.g., SNAP, TANF, and FDPIR).100 Nevertheless, data from Form-50058 

on a household’s annual income and size could be used for direct certification based on income eligibility 

determinations. 

LIHEAP appears to be feasible for use in direct certification based on income eligibility and direct 

verification of income. LIHEAP’s income eligibility limits (110 percent to 150 percent of FPL) fall within 

those of the NSLP (130 percent of FPL for free meals, and 185 percent of FPL for reduced price meals). It 

should be noted, however, that most LIHEAP funds are block grant funds. As a result, being eligible for 

LIHEAP does not assure receipt of LIHEAP benefits. While participation rates vary by State, the 

Congressional Research Service report found that only 21 and 22 percent of households eligible for the 

program in fiscal years 2009 and 2010, respectively, participated even though most of the awarded 

funds were utilized. 101 

LIHEAP income sources closely align with those used to determine NSLP income eligibility. Similar to the 

HUD programs, LIHEAP could be used for direct certification based on income eligibility determinations, 

but not for categorical eligibility. The applicant’s FPL would have to be calculated, however, to use LIHEAP 

to determine whether a student should receive free meals or reduced price meals. 

FNS currently permits the use of State Medicaid and CHIP data for NSLP direct verification. Because the 

use of State Medicaid data for the direct certification of students for F/RP meal benefits is already being 

explored in ongoing demonstrations, 2M did not examine Medicaid program data in further 

detail.102,103,104 

98 Military pay, veteran’s disability, worker’s compensation, Supplemental Security Income (SSI), and State or local cash 

assistance are not included in the Marketplace, but can be included in the NSLP. 99 U.S. Department of Housing and Urban Development. (n.d.). Form 50058. Retrieved from: 

http://portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/systems/pic/50058 100 TANF is not used to confer categorical eligibility in States where income eligibility guidelines exceed those used in the NSLP. In 

these States, the program may be used for direct certification based on income eligibility determinations. 101 Perl, L., Congressional Research Service. (2015, July). LIHEAP: Program and funding. CRS Report for Congress (7-5700 

RL31865). Retrieved from National Energy Assistant Director’s Association website at: 

http://neada.org/wp-content/uploads/2013/08/CRSLIHEAPProgramRL318651.pdf 102 U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. (2015, January). Evaluation of 

demonstrations of National School Lunch Program and School Breakfast Program direct certification of children receiving 

Medicaid benefits: Year 1 report. Retrieved December 11, 2015, from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationMedicaidYr1.pdf 103 U.S. Department of Agriculture, Food and Nutrition Service. (2016, January). Request for application to participate in new 

demonstrations to evaluate direct certification with Medicaid. Retrieved June 5, 2016 from: 

http://www.fns.usda.gov/sites/default/files/cn/SP23-2016os.pdf 104 U.S. Department of Agriculture, Food and Nutrition Service (2015, June). Year 2 demonstration impacts of using Medicaid data 

to directly certify students for free school meals. Retrieved from: 

http://www.fns.usda.gov/year-2-demonstration-impacts-using-medicaid-data-directly-certify-students-free-school-meals 

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Recommendations on Candidate Data Sources 

After exploring the six identified means-tested programs in more detail, 2M concluded that only two 

programs have potential for use in NSLP data matching: 

1. HUD’s Public Housing and Housing Choice Voucher Programs 

2. LIHEAP 

These programs could potentially be used for both direct certification of income eligibility and direct 

verification of income. However, because income eligibility thresholds differ from those used in the NSLP, 

2M does not recommend that LIHEAP and HUD program data be used to confer categorical eligibility at 

this time, as this would also require a statutory change. 

Chapter 4: State Agency Interviews 

This chapter presents findings from the semi-structured interviews conducted with eight State agencies 

and the officials responsible for their NSLP data matching operations.105 As discussed in detail in the 

Methodology appendix (Appendix E), 2M selected States for interview based on several State NSLP 

characteristics drawn from the most recent published information available. These characteristics 

included the programs used for data matching and State data matching processes. 

Interview questions were designed to obtain insights on the potential utility of, and foreseeable barriers 

and challenges to, adding the candidate data sets to the interviewed States’ data matching programs. 2M 

also asked about the steps that States or districts would have to take to incorporate any of the candidate 

data sets, whether adding these data sets seemed cost-effective, and strategies that might mitigate 

anticipated barriers and challenges. A copy of the State agency interview guide can be found in Appendix 

F. 

Findings 

Programs Used for Data Matching 

Most States that were interviewed used a similar combination of program files that are currently 

permitted for NSLP data matching for direct certification (see Table 2). California, Illinois, Kentucky, 

Louisiana, and Nebraska match against SNAP, TANF, and Foster Care files for direct certification. Florida 

matches on SNAP and TANF. Colorado’s SNAP file includes TANF data, but the SA cannot identify which of 

the two programs produced the match. To overcome this, Colorado is attempting to add identifiers that 

would distinguish matches from each program, but indicated that the process is costly. Colorado is 

currently exploring adding Foster Care to the programs it matches for the NSLP. Wyoming data matches 

with both SNAP and Foster Care for NSLP direct certification. 

California, Florida, Illinois, and Kentucky were among the five States that participated in the initial 

Medicaid demonstration that evaluated using Medicaid records for direct certification.106,107 In California, 

105 2M originally planned interviews with nine State agencies, but was unable to schedule one of the interviews. 106 U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. (2015, January). Evaluation of 

demonstrations of National School Lunch Program and School Breakfast Program direct certification of children receiving 

Medicaid benefits: Year 1 report. Prepared by Mathematica Policy Research, Contract No. AG -3198-B-12-0006. Alexandria, VA: 

U.S. Retrieved December 11, 2015, from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationMedicaidYr1.pdf 107 FNS has issued a request for application to participate in new demonstrations to evaluate the use of Medicaid for direct 

certification for both free and reduced price meals. 

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14 districts data matched with Medicaid records as part of the initial FNS demonstration project; the 

State has applied to participate in new demonstrations to expand this effort.108 Kentucky continues to 

match against Medicaid. Florida continues to match against Medicaid as well, and has been selected to 

participate in the new Medicaid demonstrations. In the 3 school years prior to July 2015, Illinois matched 

against an income-eligible Medicaid file received from the State Department of Healthcare and Family 

Services (DHFS). Data matching with Medicaid was suspended as of July 2015 in Illinois, as DHFS 

develops a new data system which will incorporate SNAP and TANF records received from the Illinois 

Department of Human Services and Medicaid records from DHFS into one file cleaned for duplicates. 

Nebraska has used Medicaid for direct verification, but the Nebraska Department of Health and Human 

Services (NDHHS) recently suspended providing these data for NSLP data matching while changing their 

administrative processes. MOUs remain in effect and planning is underway to restore the use of Medicaid 

records for direct verification. Nebraska has been selected to participate in a Medicaid direct certification 

demonstration during SY 2016–17. Louisiana indicated that it plans to apply in September 2016 for the 

Medicaid demonstration project. Colorado and Wyoming also plan to apply for a future Medicaid direct 

certification demonstration. 

Several States indicated that it is difficult to identify which agency in their State maintains data on 

homeless, runaway, and migrant children that would support statewide NSLP data matching. In States 

that were interviewed, if direct certification with these programs occurs, it is at the local level, with 

program coordinators or liaisons working directly with district food service directors. 

LEAs in California, Colorado, Florida, and Wyoming are currently matching against data from homeless, 

runaway, and migrant children programs, while LEAs in Louisiana match to program files on only 

homeless and migrant children. Nebraska has an indicator to identify homeless and runaway children in 

their SSIS; however, it is only updated at the end of the school year, and is therefore not usable for direct 

certification matching. Nebraska plans to update its student and staff record system to allow districts to 

provide real-time information to the State. Migrant data in Nebraska are collected in its MIS2000 system 

and is only received twice a year. Talks are in progress between the Nebraska Migrant Education Program 

coordinator and the State’s vendor that maintains the system to establish real-time data matching 

procedures.109 Florida is the only State among the eight interviewed to use Head Start program files for 

data matching. 

108 U.S. Department of Agriculture, Food and Nutrition Service. (2016, January). Request for applications to participate in new 

demonstrations to evaluate direct certification with Medicaid. Retrieved June 3, 2016 from: 

http://www.fns.usda.gov/sites/default/files/cn/SP23-2016os.pdf 109 The MIS200 Database Management System is a solution for States serving migrant children and is used to store, retrieve and 

report student information electronically without dependency on a national database. The Nebraska Migrant Education 

Program uses MIS2000 for the inter-and-intrastate transfer of information when children move from one school to another. 

Nebraska Department of Education. (n.d.). Data collection and reporting: MIS2000 database management system. Retrieved 

June 5, 2016 from: https://www.education.ne.gov/migrant/data%20collection%20and%20reporting.html 

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Table 2: Programs Used for NSLP Direct Certification Data Matching: Regulation-Mandated and Discretionary Authority 

SNAP TANF FDPIR Medicaid Head Start Foster Care Homeless Migrant Runaway 

California   Locally 

14 districts are 

currently 

participating; 

applying for the 

new 

demonstrations 

--  Via local liaison 

Colorado 

SNAP file includes 

two forms of 

TANF; working on 

adding an 

identifier to 

distinguish 

programs 

May add if 

awarded Direct 

Certification grant 

-- -- Working on MOU 

with CDHS to 

establish use 

Via local liaison 

Florida   --   -- Via local liaison 

Illinois   --

Matched prior to 

SY 2015-16, 

working to 

reestablish use--

--  -- -- --

Kentucky   --  --  -- -- --

Louisiana   --

Plans to apply in 

September for 

participation in 

2nd 

demonstration 

--    --

Nebraska  

Plans to explore 

in the future at 

the State level 

Applied to 

participate in the 

new 

demonstrations 

--  -- -- --

Wyoming  

Currently 

reaching out to 

tribal contacts 

-- --  Via local liaison 

Source: 2M interviews with State agencies conducted May 2016. 

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With the exception of California, where some local schools use FDPIR for matching, none of the States 

interviewed currently match against this program for direct certification. California is trying to implement 

statewide matching with FDPIR, and is attempting to promote relationships with tribal organizations in the 

State to navigate concerns regarding confidentiality. Colorado plans to incorporate the program into their 

data matching if they received a direct certification grant. Other States indicated that they did not use 

FDPIR because they either did not have a large tribal population or reservations did not operate the 

program. Florida and Louisiana stated that they do not have FDPIR in their States.110 Tribal populations in 

Florida and Louisiana receive benefits from assistance programs available to the general population, 

rather than from FDPIR. Wyoming has been in contact with tribal officials in efforts to incorporate the 

Tribal Foster Care program and FDPIR, but have found that tribal offices are often hesitant to share data. 

Nebraska has not pursued FDPIR data in the past due to the perceived overlap between FDPIR and SNAP; 

however, in recent consultations with FDPIR coordinators and FNS staff, Nebraska has plans to explore 

using the data in the future at the State level to identify additional children to support LEA participation in 

CEP. 

Updating Program Files 

The frequency with which program files are updated varied among the States that were interviewed (Table 

3). The agencies administering the NSLP in Florida and Nebraska receive updated program files on a 

nightly basis. Program files in Colorado, Illinois, Kentucky, and Wyoming are updated monthly; however, 

Colorado is planning to change to a weekly schedule. Louisiana program files are updated only five times 

per year, but plans are underway to transition to monthly updates. 

Updating Enrollment Files 

Florida, Illinois, and Louisiana update public school student enrollment files either daily or in real time. 

Colorado advises its LEAs to update daily, but only requires districts to update enrollment files at least 

monthly. Therefore, the frequency with which districts update enrollment files in Colorado varies. 

Kentucky also updates its student enrollment files monthly. Nebraska requires updates to the entire 

student enrollment file only at the beginning and end of the school year. Non-public schools in Nebraska 

are only required to upload new files once a year, at the beginning of the school year. A secondary system 

is in place in Nebraska where both public and non-public schools can upload current enrollment data, 

including new and transfer students, for a nightly match. 

110 U.S. Department of Agriculture, Food and Nutrition Service. (2015, August). Food Distribution on Indian Reservations (FDPIR) 

FDPIR contacts. Retrieved June 3, 2016 from: http://www.fns.usda.gov/fdpir/fdpir-contacts 

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Table 3: Frequency with which Program and Enrollment Files are Updated 

Program Files Enrollment Files 

California Varies by program Monthly or when a student transfers, but advised 

to update daily 

Colorado Monthly; changing to weekly Advised to be daily 

Florida Nightly Real time 

Illinois Monthly Monthly 

Kentucky Monthly Real time, but closes after April 30 

Louisiana Five times per year; in discussion to change to 

monthly 

Major updates three times per year, but can be 

updated throughout the school year 

Nebraska Nightly Two major updates, but can be updated 

throughout the school year 

Wyoming Monthly Monthly 

Source: 2M interviews with State agencies conducted May 2016. 

Identifiers Used for Data Matching 

Most States that were interviewed use a similar set of core identifiers for data matching (see Table 4). 

With the exception of Wyoming, they matched on the student’s first name, last name, and date of birth. 

Colorado also matched on gender; Louisiana and Florida also matched on self-identified race and 

ethnicity. Colorado and Kentucky use street addresses for matching. Louisiana uses zip codes. Florida, 

Kentucky, and Louisiana match on the student’s SSN; Colorado does so if it is available. 

Wyoming was unique among the States that were interviewed. It places lists of SNAP and Foster Care 

participants on a direct certification website, organized by zip code and township. LEAs login to a secure, 

online portal and retrieve a link to access the direct certification records for students in their county. Most 

LEAs in Wyoming download the list and perform direct certification using a variety of different software 

packages, including Horizon, Nutrikids, RocketSCAN, and Infinite Campus. The identifiers used to match 

enrollment data also vary by LEA. While this system is unconventional in many respects, it is conducive to 

the geographical character of the State and was designed to accommodate a sparse population. 

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Table 4: Student Identifiers Used for Matching 

First 

Name 

Last 

Name 

Phonetic 

First, Last 

Name 

Date of 

Birth 

Middle 

Name or 

Initial 

Gender Race, 

Ethnicity Address Phone Number Zip Code SSN Student ID 

California   --  --  -- 1 -- -- -- --

Colorado   --  --  -- -- --  2 --

Florida   --  --   -- -- --  --

Illinois3   4  --  -- -- -- -- -- 

Kentucky5   --   -- --   --  --

Louisiana   --  --   -- --  --

Nebraska6   --  --  -- -- -- -- -- 

Wyoming7 

1 Discard if no match 

2 If available 3 Illinois also uses city. 4Soundex uses a four-character code to compare the similarity of two names (i.e. strings) based on how the words sound when spoken. Microsoft. (n.d.) SOUNDEX (Transact-SQL). Retrieved June 

5, 2016 from: https://msdn.microsoft.com/en-us/library/ms187384.aspx 5 Kentucky also uses guardian name and SSN. The State is working on adding address history, phone number. 6 Nebraska also uses alias, address, address, and zip code to increase the probability percentage. These are only used in addition to first name, last name, gender, and date of birth. 7 Identifiers used vary by LEA. 

Source: 2M interviews with State agencies conducted May 2016. 

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Kentucky is the only State among those interviewed that collects parent identifiers (first and last name, 

middle initial, last four digits of the SSN, and address). Louisiana noted that the collection of parental 

identifiers is prohibited by State law.111 Kentucky and Wyoming collect data on other household 

members, but for the sole purpose of extending benefits to other eligible children in the household. 

Data Transfer and Matching Processes for Direct Certification 

Data transfer and matching processes varied by State. In California, LEAs can use one of two methods to 

data match for direct certification. LEAs, county offices of education, and private schools have direct 

access for matching purposes to the SSIS where SNAP, TANF, Foster Care, and Medicaid (in the 14 

participating districts) records are maintained. Districts can also contact their local county welfare 

departments to obtain lists of children participating in SNAP, TANF, and homeless, migrant, and runaway 

programs. The first method matches at the State level, with district administrators accessing the SSIS and 

requesting a downloadable report. 

In Wyoming, program files are sent to the Department of Education and placed on a secure website, 

where LEAs log in and access a matched list of students organized by zip code and township. 

Kentucky’s Department of Education (KDOE) receives two files from the Cabinet for Health Family 

Services: a combined SNAP and TANF file and a separate Foster Care file. These files are cleaned and 

uploaded by KDOE, along with student enrollment data, to the State Commonwealth Office of Technology, 

which performs a probabilistic match.112 The matched file is sent back to KDOE, with a yes/no indication 

of whether the matching threshold was met. KDOE then uploads it into a custom web application where 

LEAs can download data for their county via a secured login. 

The Illinois Department of Education (IDOE) also receives two separate files: one for Foster Care from the 

Department of Children and Family Services, and another for SNAP and TANF from the Department of 

Human Services. IDOE then performs a probabilistic match with student enrollment records, and provides 

the list of exact and potential matches to districts. Illinois uses an intricate matching system where SNAP 

records are checked first. If the match fails on SNAP records, TANF is checked, and then Medicaid. 

Illinois is currently building a new system that will merge SNAP and TANF, and eventually Medicaid, into 

one file. The new system will eliminate duplicate records that the current system generates when a child’s 

household participates in two or more programs.113 

Nebraska maintains three systems for direct certification data matching: 

1.	 Public and non-public LEAs upload student enrollment data to receive a Nebraska Department of 

Education Student ID for matching with NDHHS data. This system can also be used for direct 

111 Louisiana Department of Education. (n.d.). Protecting student privacy. Retrieved June 3, 2016 from: 

https://www.louisianabelieves.com/resources/library/data-center/protecting-student-privacy 112 Probabilistic matching is performed using a scoring threshold where probabilities/weights are assigned to identifiers, indicating 

how likely records match. 113 Illinois indicated that it currently receives a file with both SNAP and TANF participants; however, the file does not have an 

identifier delineating each program’s participants. 

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verification.114 Public schools upload to the Nebraska Student and Staff Record System, and nonpublic schools upload enrollment data to the Student Unique Identifier system.115 

2.	 The Child Nutrition Program Electronic Application and Reimbursement System is used by both 

public and non-public schools to enter individual student records or upload a CSV file to the Child 

Nutrition Program website.116,117 

3.	 The Nebraska Department of Education receives a file each night from NDHHS into a secure FTP 

folder. The dataset is cleaned before conducting a probabilistic match with student enrollment 

records. 

In Louisiana, the data matching process for direct certification is carried out by a third-party vendor due to 

State confidentiality laws surrounding student data.118 As of SY 2015–16, the Louisiana Department of 

Education is no longer permitted to collect PII on students and currently only receives a student’s unique 

ID, zip code, initials, and DOB. Student enrollment and program data are instead submitted directly to a 

vendor for matching. The vendor then provides the matched data to the LEAs. 

Florida LEAs upload enrollment files to the SSIS at the beginning of the school year. The Department of 

Children and Families uploads a cumulative file containing information on children participating in SNAP 

and TANF, and children participating in Medicaid in households with incomes at or below 133 of FPL, to 

the SSIS each day throughout the school year. In the new DC-M demonstration beginning in SY 2016–17, 

income thresholds of 130 percent and 185 percent will be used to determine F/RP meal eligibility. The 

file also contains information on children participating in Medicaid in households with incomes between 

133 and 138 percent of FPL for direct verification. Matches are performed nightly, and districts are 

alerted if any new records are matched through June 30th. Additional students can be uploaded by 

districts in real time for inclusion in a nightly match. 

In Colorado, the Department of Human Services provides a monthly file of all Colorado SNAP recipients 

between the ages of 0 and 22 years. The file is uploaded to an online system that districts can access 

and to upload their student enrollment data for monthly or real-time matching. 

Direct Verification Processes 

Some interviewed States use data from one or more programs for NSLP direct verification. LEAs in Illinois 

and Nebraska use SNAP, TANF, and Foster Care program files. Illinois plans to match against Medicaid 

data to directly verify applications in the future. 

Nebraska LEAs use two methods of data matching for direct verification of applications. LEAs can look up 

students in the State direct verification system using first name, last name, gender, and DOB; this method 

requires an exact match. Alternatively, they can upload a file to identify students on applications for other 

114 Nebraska Department of Education. (2015, October 26). Nutrition services direct certification and direct verification. Retrieved 

June 29, 2016 from: 

https://www.education.ne.gov/NS/forms/nslpforms/DC/DirectCertificationDirectVerification_2015-10-26.pdf 115 The Nebraska Student and Staff Record System (NSSRS) is used by the Nebraska Department of Education to collect data from 

its public schools. Non-public schools use the Student Unique Identifier system to obtain Student IDs for new students and to 

update information for existing students (e.g. grade level, location, and school year). Nebraska Department of Education. (n.d.). 

Nebraska student and staff record system. Retrieved June 5, 2016 from: https://www.education.ne.gov/nssrs/ 116 Nebraska Department of Education. (2015, October 26). Nutrition services direct certification and direct verification. Retrieved 

June 29, 2016 from: 

https://www.education.ne.gov/NS/forms/nslpforms/DC/DirectCertificationDirectVerification_2015-10-26.pdf 117 The Child Nutrition Program Electronic Application and Reimbursement System is a web application that provides application, 

claim, and reporting management for Nebraska LEAs. Nebraska Department of Education, Nutrition Services. (n.d.) Training 

registration user manual. Retrieved June 5, 2016 from: 

https://www.education.ne.gov/ns/documents/Training%20Registration%20User%20Manual.pdf 118 Act No. 837 of 2014, LA Rev Stat § 17:3914. https://www.legis.la.gov/legis/ViewDocument.aspx?d=916157 

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programs and receive a report showing to which programs the student has been matched. Nebraska also 

used Medicaid data at the local level for direct verification until its access to Medicaid files was 

suspended in July 2015. The Nebraska Department of Education is currently working to reestablish use of 

Medicaid data for matching. An MOU with the NDHHS has already been signed. 

LEAs in California can directly verify any questionable program case numbers provided on an NSLP 

application through their local county welfare office, but this process is not performed at the State level. 

Florida LEAs conduct daily matches to Medicaid data for direct verification between October 1 and 

November 15 of each year. 

Wyoming does not currently conduct any direct verification data matches. Given the size of the State 

population, determinations on error-prone applications occur at the State level. Wyoming notifies LEAs 

what action needs to be taken to verify the identified applications. 

Colorado does not data match for direct verification at the State level, but relies exclusively upon 

household documentation. 

Challenges to Data Matching 

Interviewees were asked about the challenges they face in their current data matching practices and 

reasons for not matching to additional programs currently permitted by NSLP regulations. The challenges 

and reasons provided by the interviewed States varied, and included training, staff turnover, working 

with/relying on other agencies, duplicate matches, lack of available data, and difficulties incorporating 

new datasets into the current matching process. These are presented below. 

Training 

California cited challenges in training LEAs to effectively and frequently perform direct certification data 

matches. Colorado also stated that training LEAs to effectively use the State upload system is their 

biggest challenge, noting that errors in this process can cause missed or duplicate matches. 

Staff Turnover 

Staff turnover was also cited as a challenge to NSLP data matching. Colorado specifically noted that staff 

turnover compounds challenges involving training and technical assistance. 

Working with/Relying on Other Agencies 

California indicated having difficulty encouraging LEAs to engage with local county welfare offices, and 

with homeless and migrant local liaisons, to ensure that they are maximizing their direct certification 

counts. California also cited having difficulties with data matching procedures. Data are shared in a blind 

manner to protect student and participant confidentiality, but this also prevents human adjudication. 

California State personnel cannot identify why a match did not occur (e.g., an incorrect birth date, 

hyphenated name) for children who should have been directly certified through SNAP. The quality and 

timeliness of program data once posed challenges for Colorado as well. 

KDOE relies on program-administering agencies to extract program data, and cannot validate the 

business rules that were used for the extraction. KDOE can request data within certain parameters (e.g., 

only children between ages 0 to 21 years), but cannot ensure that these data have been provided 

accurately. A match is generated for each program in which the student or his/her family participates, 

and LEAs are responsible for resolving duplicate matches. 

Nebraska recently faced additional challenges restoring the use of Medicaid data for direct verification. 

NDHHS, the agency administering Medicaid, discontinued providing a program file to the Nebraska 

Department of Education, in part because Nebraska was not part of the ongoing FNS Medicaid direct 

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certification demonstrations. NDHHS was not aware that FNS authorizes the use of Medicaid for NSLP 

direct verification in all States. 

Duplicate Matches 

Kentucky indicated that duplicate matches were a problem. The State and districts receive data in which 

students may appear as participants across multiple programs. Kentucky matches against Medicaid, 

SNAP, TANF, and Foster Care records, but described the matching process as occurring in a “black box” 

over which it has little control. More precisely, Kentucky indicated that agencies administering the 

programs control the business rules used to extract the participant data. Furthermore, matching is 

conducted by a State agency separate from the program administering agency and KDOE. For example, 

the KDOE will submit a request for a data file to an agency administering a program. The request might 

be for participants within a certain age range from households meeting certain income parameters. KDOE 

also directs the agency conducting the matching on how to conduct the match (i.e., the matching 

algorithm to use). Nevertheless, as KDOE does not conduct the matching, they cannot verify the business 

rules used (e.g., was a request for children under 20 years of age interpreted as individuals under 20 

years of age or age 19 years and under) and cannot identify problems that may arise during the process. 

Florida also noted having difficulty at times resolving duplicate matched records, and cited challenges in 

correcting enrollment data errors in such instances where children have highly similar or identical 

identifiers (e.g., twins). 

Lack of Available Data 

Illinois indicated that the absence of a student’s SSN or a unique identifier for data matching decreases 

the efficacy of data matching between program and student enrollment data. Nevertheless, Illinois State 

personnel did not feel that this inhibited their direct certification efforts. 

Difficulties Incorporating New Datasets into Current Matching Process 

Nebraska indicated that new datasets can pose challenges when initially incorporated into the matching 

process, including interpreting how values are defined within the fields provided and establishing the 

correct business rules to inform SFAs on how to utilize the information. Nebraska also cited that data 

cleaning can be a difficult task, especially when the program administering agencies make unexpected 

changes to the dataset. While a rare occurrence, Nebraska stated that new variables and unexpected 

values in program files have caused data systems to crash for extended periods. 

Overcoming Identified Challenges 

Most challenges cited by States involved problems with non-matches and duplicates resulting from 

cleaning and formatting issues with the program and enrollment data used for matching. Several States 

indicated that these challenges were largely mitigated by updating their direct certification systems and 

processes. For example, Colorado indicated that challenges with non-matches, and the quality and 

timeliness of program data, have been improved with the implementation of a new direct certification 

system in the past 3 years. Colorado is also currently working on adding an identifier to indicate with 

which programs students are matched. 

Florida mitigates problems in its data matching processes by using checks and balances to address 

enrollment data errors. The direct certification system generates a detailed report on non-matches for 

human adjudication. This report provides the likely reasons for the occurrence of non-matches, such as 

incorrect DOB, despite matches on first name, last name, and gender. 

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Candidate Means-Tested Programs 

States were asked to consider the potential benefits and challenges of adding HUD and LIHEAP to the 

program files used in their matching processes for direct certification and direct verification. Overall, 

States expressed support for expanding data matching in the NSLP to include these programs. 

State Perceptions 

A prominent theme from the interviews, and explicitly noted by five states, was that in order to assess the 

potential benefits of adding these additional programs to current data matching efforts, information is 

needed on the percentage or number of students who do not appear in the program records they 

currently use for matching (e.g., SNAP, TANF) but who could be certified using HUD and LIHEAP records. 

Nebraska noted that it used HUD program files to identify children eligible for the Summer Food Service 

Program and expressed optimism about the potential benefits of matching against HUD program files for 

the NSLP. It felt that having this prior relationship will greatly facilitate any future efforts related to 

matching against HUD program files in Nebraska. 

Louisiana firmly indicated that matching with HUD and LIHEAP records could work if the respective 

administering agencies provided a file that only included households with income under 130 percent of 

FPL. Similarly, the programs could be used for direct certification of reduced price eligibility if files were 

provided that filtered households within 130 and 185 percent of FPL. Illinois noted that having the 

program agency calculate a household’s income-to-poverty ratio was important to avoid confidentiality 

issues. California, Colorado, Florida, Illinois, and Louisiana argued, however, that the most preferable 

solution would be for FNS to confer categorical eligibility to HUD and LIHEAP participants. This solution, 

however, would require a statutory change. These States indicated that conferring categorical eligibility 

would also reduce the administrative burden placed upon both the program-administering agency and the 

NSLP-administering agency resulting from cleaning and filtering data to identify only households with 

FPLs that meet NSLP thresholds. 

Perceived Challenges to Adding Means-Tested Programs 

States also discussed other challenges to adding these program files to current data matching processes, 

including increased administrative burden and privacy concerns. Identifying and establishing contact with 

the agency that owns or manages the HUD and LIHEAP program files in order to establish an MOU was 

also a concern. Nebraska noted that it took over a year to identify the correct contact in the State 

Medicaid agency for pursuing data matching against Medicaid files. Colorado is still attempting, after 6 

months, to schedule a meeting with its Medicaid agency liaison. 

Several States—California, Colorado, Illinois, Kentucky, and Nebraska—pointed to time and costs involved 

in adding a new file to the data matching process. California estimated that it would take 9 to 15 months 

to establish an MOU because of strict confidentiality requirements and legal negotiations between 

agencies, and even up to 2 years in instances where disagreements occur. California also estimated that 

start-up costs for matching to a new data file would be in the hundreds of thousands of dollars. California, 

Florida, Kentucky, Louisiana, and Wyoming identified IT start-up costs for programming as a challenge; 

Florida estimates it can take up to $300,000 to add a file to its matching processes. In comparison, startup costs for integrating an additional program data file from the Direct Certification demonstrations with 

Medicaid ranged between almost $5,000 to more than $185,000.119 

119 U.S. Department of Agriculture, Food and Nutrition Service. (2016, June). Year 2 demonstration impacts of using Medicaid data 

to directly certify students for free school meals. Retrieved June 29, 2016 from: 

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Colorado indicated that privacy protections for these additional programs would be no different than for 

current programs, but new State legislation strengthening the language surrounding the privacy of 

student information is already driving away vendors concerned about heightened liability costs in the 

event of a breach in student confidentiality. 

Suggestions from Interviewed States for Integrating Additional Data 

Files 

States offered several suggestions for mitigating these challenges. California, Colorado, and Illinois 

suggested that FNS could reduce the administrative burden of adding HUD and LIHEAP by conferring 

categorical eligibility for the NSLP, rather than only using the programs for income eligibility 

determinations as is the case with the Medicaid demonstrations. It is important to note, however, that 

these would require statutory changes. 

Illinois noted that agencies administering other programs have little incentive to take on the burden of 

preparing data to share with the NSLP SAs. Additionally, even if they are willing to do so, they may not be 

able to justify the effort as helping improve the operations of their own programs. Illinois suggested that 

legislatively mandating the use of a new program file, as is the case with SNAP, provides leverage in 

gaining the cooperation of a program-administering agency. 

Colorado also suggested that FNS provide funds and agency contact information needed to establish 

MOUs to incorporate new programs. Kentucky, which shares a border with seven other States, 

recommended FNS help develop data sharing agreements among the program agencies of neighboring 

States to facilitate interstate data sharing. Louisiana suggested that a standard, prototype agreement 

would be helpful, and Wyoming proposed that FNS provide a letter of support. 

Despite challenges, none of the State agency personnel interviewed expressed that the challenges and 

additional costs they identified represented insurmountable barriers to adding HUD, LIHEAP, or other 

program files to their matching programs for NSLP direct certification and/or direct verification. On the 

contrary, most States interviewed expressed optimism in adopting new strategies and implementing 

plans to improve their data matching processes, and seemed willing to explore the addition of HUD or 

LIHEAP files, provided there was evidence from pilot studies or other research that doing so would help 

certify additional children who are eligible for the NSLP. As one interviewee noted, they were in favor of 

any policy solution that will help the kids. 

Chapter 5: Conclusions and Recommendations 

FNS has made strides in utilizing both technological innovations and policy changes to improve access to 

meal benefits to eligible school children. With this in mind, FNS contracted with 2M to examine 

participant data maintained for a range of means-tested programs, and to assess their potential for NSLP 

data matching for direct certification of eligible students and direct verification of household income. 

Expanding data matching to include other programs may identify additional students eligible for the NSLP 

through direct certification. It may also reduce administrative burden on LEAs and households by 

providing a potential additional avenue for both direct certification and direct verification of household 

income. 

http://www.fns.usda.gov/sites/default/files/ops/DirectCertwithMedicaidYear2.pdf 

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Encouraging the NSLP SAs to implement data matching with other means-tested programs has already 

resulted in expanding NSLP participation among eligible children and reducing administrative burden on 

households and LEAs. The CEP allows schools and districts with a percentage of children directly certified 

for the NSLP that is greater than the CEP threshold to provide free meals to all students, regardless of 

individual eligibility and without additional paperwork. CEP relies solely on the percentage of students 

directly certified in a school or LEA to establish CEP eligibility.120 

The findings from the research detailed in this White Paper suggest that LIHEAP and HUD program files 

may be feasible to incorporate into NSLP data matching. These programs meet three important criteria 

for inclusion: (1) they have identifiers needed to conduct matches with student enrollment records, (2) 

they have income eligibility criteria similar to the NSLP, and (3) they include participant households that 

are likely to include school-age children. 

2M makes the following recommendation based on this research: FNS 

should conduct a pilot study with three to five States to more accurately 

assess the number of additional unduplicated children that would be 

directly certified for the NSLP through data matching with HUD and 

LIHEAP program files. 

Findings from the State interviews and examination of available publications indicate that further 

research is needed to determine the number of additional children in HUD and LIHEAP program files that 

do not also appear in SNAP or other program records currently permitted for NSLP data matching. Several 

States interviewed for this study were interested in matching against additional program files, but 

indicated that they would be hesitant to invest the time and money needed to establish MOUs with 

administering agencies, and to incorporate additional program data into their direct certification systems, 

without some knowledge about the number of unduplicated students that could be certified through HUD 

and LIHEAP files. Establishing MOUs could be facilitated by FNS developing templates for use by States. 

A recent analysis of overlapping participation in human services benefit programs focused on Medicaid. 

The study was one of five reports in a project on Integrating HHS Programs and Reaching Eligible 

Individuals under the ACA commissioned by the Office of the Assistant Secretary for Planning and 

Evaluation of HHS.121 The study involved microsimulations using survey data from the 2011 Current 

Population Survey Annual Social and Economic Supplement. One simulation sought to identify 

respondents who received or qualified for SNAP, TANF, housing subsidies, the Child Care and 

Development Fund, LIHEAP, WIC, UI, and EITC, and who would also qualify for insurance affordability 

programs (Marketplace subsidies, Medicaid, and CHIP) if the ACA provisions that were to take effect in 

2014 had already been available. 

Study results suggest substantial overlap in program eligibility and participation. For example, over 90 

percent of the children in the human services programs studied would also qualify for Medicaid, including 

96 percent and 94 percent of children in households receiving housing subsidies and LIHEAP, 

respectively. Conversely, the study estimated that among those under 65 years old who would qualify for 

Medicaid (if all States expanded eligibility), 49 percent would already receive SNAP, 18 percent LIHEAP, 

120 U.S. Department of Agriculture, Food and Nutrition Services. (2015, September). School meals Community Eligibility Provision. 

Retrieved June 5, 2016 from: http://www.fns.usda.gov/school-meals/community-eligibility-provision 121 Urban Institute. (2013, December). Overlapping eligibility and enrollment: Human services and health programs under the 

Affordable Care Act. Prepared for Office of the Assistant Secretary for Planning and Evaluation, DHHS (Task Order 

HHSP23337026T). Retrieved from: https://aspe.hhs.gov/sites/default/files/pdf/76961/rpt_integrationproject.pdf 

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17 percent would live in a WIC household, 9 percent would receive housing subsidies, 6 percent would 

receive TANF, and 3 percent would reside in a household receiving child care subsidies. 

While these findings provide evidence of program overlap, it does not answer the question of how many 

children in HUD or LIHEAP participating households are neither certified for F/RP meal benefits in the 

NSLP nor receiving benefits from other programs used in data matching such as SNAP or TANF. 

Another study used 2003–2007 survey data from the 2004 Survey of Income and Program Participation 

(SIPP) panel study122 to examine families receiving benefits from multiple assistance programs.123 

Researchers examined monthly participation in nine benefit programs, including SNAP, TANF, WIC, NSLP, 

public health insurance (including Medicaid and CHIP), housing assistance (public housing, Section 8 

housing, or subsidized rent), and LIHEAP.124 

This study also provides evidence of program participation overlap. In the 2004 SIPP panel study, 29 

percent of households receiving LIHEAP and 48 percent of households receiving subsidized housing 

assistance also received benefits from two to three other assistance programs. Eight percent of 

households receiving LIHEAP and 18 percent of households receiving housing subsidies also received 

food assistance, which includes school meals. As the study groups school meals with SNAP and WIC 

under food assistance, the exact overlap between LIHEAP or housing benefits and school meals is 

unclear. 

An earlier study used data from the 2001 and 2004 SIPP Panels to evaluate the overlap in 4-year 

average participation among several programs, including SNAP, TANF, WIC, Medicaid, public or subsidized 

rental housing, and the NSLP and SBP.125 Among the nearly 5.4 million households that received housing 

subsidies in 2004, 29 percent received free or reduced priced school meals. The study also indicated 

that 48 percent of housing recipient households received SNAP benefits, 72 percent received Medicaid, 

and 10 percent received TANF (three programs that are currently permitted for direct certification and 

verification).126 

The authors of this study concluded that: 

Human services programs serve many of the same people who will qualify for health 

programs under the Affordable Care Act. The resulting overlap, along with new Federal 

resources for IT development, may create promising opportunities to use one program’s 

data to qualify low-income consumers for other programs. This offers the possibility of 

improving participation levels in uncapped programs, lowering administrative costs, lifting 

122	 SIPP is a household-based, longitudinal survey designed to provide a nationally representative sample. Information related to 

income, labor force participation, social program participation and eligibility, and general demographic characteristics is 

collected at multiple times from a panel of survey participants over the course of a 2.5 to 4-year period, depending on the 

length of the panel. See 122 U.S. Census Bureau. (n.d.). Survey of income and program participation: SIPP introduction and 

history. Retrieved June 1, 2016 from: http://www.census.gov/programs-surveys/sipp/about/sipp-introduction-history.html 123 Urban Institute. (2014, February). Characteristics of families receiving multiple public benefits. Retrieved June 1, 2016 from: 

http://www.urban.org/sites/default/files/alfresco/publication-pdfs/413044-Characteristics-of-Families-Receiving-MultiplePublic-Benefits.PDF 124 SSI benefits and work supports (transportation assistance and/or child care subsidies) were also examined. 125	 U.S. Census Bureau. (n.d.). An analysis of the characteristics of multiple program participation using the Survey of Income and 

Program Participation (SIPP). Retrieved June 5, 2016 from: https://www.census.gov/sipp/workpapr/wp246.pdf 126	 Medicaid is permitted for use in direct certification only in selected States participating in USDA demonstrations. However, all 

States are allowed to use Medicaid data for direct verification. 

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burdens from consumers, and increasing the accuracy of eligibility determinations by 

reducing the potential for manual error.127 

It is also important to note that HUD and LIHEAP differ qualitatively from SNAP. Specifically, unlike SNAP 

where all of those who apply and are eligible receive benefits, simply qualifying for HUD or LIHEAP does 

not guarantee receipt of benefits. Those who apply to receive assistance from HUD are commonly placed 

on long waiting lists, and annually less than one-quarter of those who apply and are eligible for LIHEAP 

benefits receive assistance.128,129 However, information from households that apply for both HUD and 

LIHEAP benefits may still be able to be usable for matching with the NSLP. 

Per this recommendation, research examining administrative program data in a small sample of three to 

five States is needed to assure that the overlap of HUD and LIHEAP with programs currently used in NSLP 

data matching is not so high as to preclude the use of HUD or LIHEAP for certifying additional students. 

This number of States will allow for variability in important aspects of State-level program administration 

such as agency configuration, data systems, and data sharing procedures. 

2M makes the following recommendation based on this research: FNS 

should investigate the feasibility of recommendations made by States in 

this study to enhance data matching in the NSLP. 

The States offered several suggestions for addressing challenges related to data matching with the NSLP. 

Some suggestions would require statutory changes. For example, it was suggested that the administrative 

burden of adding HUD and LIHEAP could be reduced by conferring categorical eligibility for the NSLP 

rather than only using the programs for income eligibility determinations, as is the case with the Medicaid 

demonstrations. States indicated that conferring categorical eligibility would reduce the administrative 

burden placed upon both the program-administering agency and the NSLP-administering agency resulting 

from cleaning and filtering data to identify only households with incomes that meet NSLP thresholds. 

States also noted that agencies administering other programs have little incentive to take on the burden 

of preparing data to share with the SAs. Furthermore, they may be prohibited from doing so unless they 

can justify the effort as helping improve the operations of their own programs. States suggested that 

legislatively mandating the use of a new program file, as is the case with SNAP, provides leverage in 

gaining the cooperation of a program-administering agency. 

Not all recommendations made by States require statutory or legislative changes. States recognized that 

new initiatives come with costs, and suggested that FNS provide funds to support new data matching 

efforts. Other recommendations focused on facilitating within- and cross-state communication. For 

example, agency contact information is needed to establish MOUs to incorporate new programs. States 

that share multiple borders recommended that FNS help develop data sharing agreements among the 

program agencies of neighboring States to facilitate interstate data sharing related to program 

127 Urban Institute. (2013, December). Overlapping eligibility and enrollment: Human services and health programs under the 

Affordable Care Act. Prepared for Office of the Assistant Secretary for Planning and Evaluation, DHHS (Task Order 

HHSP23337026T). Retrieved from: https://aspe.hhs.gov/sites/default/files/pdf/76961/rpt_integrationproject.pdf 128 U.S. Department of Housing and Urban Development. (n.d.). Housing choice vouchers fact sheet. Retrieved April 30, 2016 

from: http://portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/programs/hcv/about/fact_sheet 129 Perl, L., Congressional Research Service. (2015, July). LIHEAP: Program and funding. CRS Report for Congress (7-5700 

RL31865). Retrieved from National Energy Assistant Director’s Association website at: http://neada.org/wpcontent/uploads/2013/08/CRSLIHEAPProgramRL318651.pdf 

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participants who move between States, as developing interstate MOUs can be challenging. A standard 

prototype agreement was recommended, along with FNS providing a letter of support. 

2M makes the following recommendation based on this research: FNS 

should explore how the NSLP can join ongoing efforts to integrate State 

eligibility systems. 

Important current initiatives provide additional approaches to expanding NSLP data matching and merit 

the attention of FNS. FNS is already participating in some of the most promising data exchanges 

developed by Federal, State, and local agencies. For example, as part of the Tri-Agency Interoperability 

Workgroup, FNS has partnered with CMS and ACF to improve information sharing, system integration, and 

coordination among health and human services programs.130 This partnership materialized in direct 

response to ACA regulations that require Medicaid and CHIP to rely upon electronic data from other 

programs to verify eligibility at application and renewal, rather than compelling applicants to provide 

documentation for verification.131 

To support this effort, CMS provided a 90 percent Federal match for investments in modernizing existing 

eligibility systems. The Office of Management and Budget (OMB) issued a cost-allocation waiver allowing 

other administering agencies to integrate their assistance programs into the new health coverage 

system.132,133 The cost-allocation waiver was originally set to expire on December 31, 2015, but a 3-year 

extension has been granted through December 31, 2018.134 

In 2015, HHS reported on several States with successes in integrating health and human services 

programs.135 In Louisiana, 76 percent of children are renewed for Medicaid and CHIP coverage using 

data matches to other human services programs or sources. Both Louisiana and South Carolina use an 

Express Lane Eligibility system to qualify children for Medicaid based on SNAP income determinations. 

Express Lane Eligibility was authorized by Section 203 of the Children’s Health Insurance Program 

Reauthorization Act of 2009, and allows States to use data collected by other public agencies, including 

the NSLP SAs, to establish eligibility requirements for Medicaid and CHIP.136,137 This system insured more 

than 20,000 additional children in Louisiana, and 92,000 in South Carolina. 

130 U.S. Department of Health and Human Services, Administration for Children and Families. (n.d.). Your essential interoperability 

TOOLKIT: An ACF/HHS resource guide. Retrieved May 26, 2016 from: 

http://www.acf.hhs.gov/sites/default/files/assets/acf_toolkit_july_2012_final.pdf 131	 Urban Institute. (2015, February). Integrating health and human services programs and reaching eligible individuals under the 

Affordable Care Act: Final report. Retrieved May 31, 2016 from: 

https://aspe.hhs.gov/sites/default/files/pdf/139271/rpt_IntegrationFinalRpt.pdf 132 U.S. Department of Health and Human Services, U.S. Department of Agriculture. (2015, July). Tri-Agency letter on detail of cost 

allocation waiver. Retrieved May 27, 2016 from: https://www.medicaid.gov/federal-policyguidance/downloads/SMD072015.pdf 133 These requirements are set forth in Office of Management and Budget (OMB) Circular A-87 (Section C.3) and Section 200.405 

of the “Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards” (2 C.F.R. 200). 134 U.S. Department of Health & Human Services, Administration for Children and Families. (n.d.) Guidance on funding and cost 

allocation for health and human services systems. Retrieved June 6, 2016 from: http://www.acf.hhs.gov/cost-allocation 135 Urban Institute. (2015, February). Integrating health and human services programs and reaching eligible individuals under the 

Affordable Care Act: Final report. Retrieved May 31, 2016 from: 

https://aspe.hhs.gov/sites/default/files/pdf/139271/rpt_IntegrationFinalRpt.pdf 136	 U.S. Department of Health and Human Services. (2012, December 1). CHIPRA mandated evaluation of express lane eligibility: 

First year findings. B. What is express lane eligibility (ELE)? Retrieved June 29, 2016 from: 

https://aspe.hhs.gov/report/chipra-mandated-evaluation-express-lane-eligibility-first-year-findings/b-what-express-laneeligibility-ele 137 Other agencies include those administering TANF, child support enforcement, Medicaid, CHIP, SNAP, WIC, housing assistance, 

Head Start, and certain child care programs. 

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Utah has reduced its administrative costs by coordinating the administration of several programs into its 

Electronic Resource and Eligibility Product (eREP) system. The State integrated a system of electronic 

case records, an eligibility rules engine, external data matching, online applications, and benefit 

payments in 2008, resulting in an almost 10 percent reduction in operating costs. As a result, the 

caseload that could be handled by a single worker increased by 53 percent from 2009 to 2010. The 

eREP system was developed over the course of 6 years to perform administrative functions for several 

programs.138 The system also supports a customer directory, allowing real-time data sharing across 

programs. The eREP system reduces administrative burden on applicants. After applicants enter 

information into an online application, eREP assesses eligibility for several programs. The State sends a 

single notice to the applicant identifying all programs for which they are eligible or ineligible. 

Several other States have implemented online systems for health and human services programs, using a 

number of approaches. In Alabama, applicants can use an online client portal (MyAlabama) to complete a 

survey and obtain information about how to apply for various health and human services programs. 

Pennsylvania uses a screening tool to identify assistance programs for which applicants are potentially 

eligible. Pennsylvania also uses a multi-benefit online application (COMPASS), and has incorporated 

eligibility determinations for Medicaid using MAGI, SNAP, TANF, and LIHEAP. Kentucky and New Mexico 

also use multi-benefit online applications. Illinois uses a multi-benefit online application on which 

consumers can apply for SNAP, TANF, and Medicaid using a single application. Kentucky has plans to 

implement an online portal where consumers can use a single online application to apply for Medicaid, 

TANF, SNAP, child care assistance, and LIHEAP. Information provided on the application will also be used 

to prescreen eligibility for WIC. Colorado, New Mexico, California, and Virginia use similar application 

processes. 139 

Many of these States have also integrated self-service case management features into their online 

systems, which allow applicants to check their application status and benefit issuance, and even update 

their information. 

Both New Mexico and Illinois have taken progressive approaches in response to Medicaid regulations 

regarding electronic data matching. New Mexico had plans to implement a State data services hub by fall 

2015 to support all assistance programs administered in the State. Similarly, Illinois is developing an 

Integrated Eligibility System to facilitate verification matches among program-administering agencies. This 

single system uses a business rules engine that supports verification criteria for all participating programs 

in the form of client portals with eligibility screening tools, online applications for several benefit 

programs, and self-service case management features. Eligibility and business rules engines automate 

tasks conducted in the eligibility and enrollment processes, and automate electronic data matching to 

verify eligibility.140 

It is important to note that while these systems are a social good when they work well, at times 

implementation has been problematic. GAO identified several issues that plagued the development and 

rollout of the State and Federal marketplaces. Evaluations conducted in December 2012 and June 2013 

found that Healthcare.gov had problems, including: 

138	 Urban Institute. (2014, July). Examples of promising practices for integrating and coordinating eligibility, enrollment and 

retention: Human services and health programs under the Affordable Care Act. Retrieved May 31, 2016 from: 

https://aspe.hhs.gov/sites/default/files/pdf/77016/rpt_PromissingPractices.pdf 139 Center on Budget and Policy Priorities. (2015, March). State innovations in horizontal integration: Leveraging technology for 

health and human services. Retrieved May 31, 2016 from: http://www.cbpp.org/research/state-innovations-in-horizontalintegration-leveraging-technology-for-health-and-human 140 Ibid. 

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•	 inadequate capacity to accommodate demand, which resulted in error messages, long load 

times, and website outages; 

•	 software coding errors that made it difficult to access and use the site; and 

•	 limited system functionality including the inability to compare and purchase health plans.141 

CMS has since mitigated these logistical problems using system development best practices.142 

Nevertheless, lessons learned from the ACA State and Federal marketplaces rollout suggest that States 

need to be careful when implementing new systems so as not to adversely affect service delivery. 

Thus, 2M recommends that FNS explore how the NSLP can be more involved in ongoing State efforts to 

integrate health and human services. In doing so, SAs administering the NSLP may be able to capitalize 

on the availability of match funds and the OMB cost-allocation waiver. Most States interviewed for this 

White Paper indicated that adding even one additional program to existing NSLP direct certification 

processes could require start-up costs of more than $100,000, an estimate that falls within the range of 

start-up costs found for integrating an additional program data file from the Direct Certification 

demonstrations with Medicaid—between almost $5,000 to more than $185,000.143 

The benefits and goals of developing these State eligibility systems closely align with those underlying 

FNS’s aim to expand NSLP data matching, including: 

•	 expanding coverage to reach additional students, 

•	 reducing administrative burden incurred by staff in processing applications, 

•	 reducing burden on families eligible for multiple programs by decreasing the need to submit 

duplicative information across agencies, and 

•	 reducing error rates and increasing program integrity using electronic data sources from other 

agencies.144 

In addition, FNS involvement in the Tri-Agency Interoperability Workgroup along with CMS may provide an 

opportunity to investigate the prevalence of Medicaid participants that do not participate in the NSLP but 

are eligible based on Medicaid eligibility. Medicaid is a way to directly certify eligible households that do 

not participate in SNAP and other means-tested programs. It would be important for FNS to determine 

changes that would need to occur to facilitate data matching with Medicaid, since this is a goal of the ACA 

and the Tri-Agency Workgroup. FNS should consider examining how working with the Medicaid program 

via the Tri-Agency Interoperability Workgroup may facilitate data matching for the NSLP at the State level. 

2M makes the following recommendation based on this research: FNS 

should become involved with the Commission on Evidence-Based 

Policymaking 

141	 Government Accountability Office. (2015, March). Report to congressional requesters: Healthcare.gov - CMS has taken steps 

to address problems, but needs to further implement systems development best practices. Retrieved July 19, 2016 from 

http://www.gao.gov/assets/670/668834.pdf 142	 Government Accountability Office. (2015, March). Report to congressional requesters: Healthcare.gov - CMS has taken steps 

to address problems, but needs to further implement systems development best practices. Retrieved July 19, 2016 from 

http://www.gao.gov/assets/670/668834.pdf 143	 U.S. Department of Agriculture, Food and Nutrition Service. (2016, June). Year 2 demonstration impacts of using Medicaid 

data to directly certify students for free school meals. Retrieved June 29, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/DirectCertwithMedicaidYear2.pdf 144	 Coalition for Access and Opportunity. (n.d.). The health and human service integration opportunity toolkit five reasons to 

integrate human services into health care reform now. Retrieved May 31, 2016 from: http://www.clasp.org/documents/ACAand-Human-Services-Integration-Toolkit-Five-Reasons-to-Integrate.pdf 

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The Federal Government has a long history of initiatives to support data matching across public 

assistance programs that dates back to the 1970s. FNS involvement in efforts such as the Tri-Agency 

Interoperability Workgroup has been important to achieving its programmatic goals. The Evidence-Based 

Policymaking Commission Act of 2016 (P.L. 114-140) was signed into law on March 30, 2016. The Act 

establishes in the executive branch a 15-member Commission on Evidence-Based Policymaking (the 

Commission) appointed by the President and congressional leaders. The Commission will consider 

whether a clearinghouse for program and survey data should be established and how to create such 

clearinghouse. Per Section 4 of the Act, the Commission must conduct a comprehensive study of the data 

inventory, data infrastructure, database security, and statistical protocols related to Federal policymaking. 

Commission must also conduct studies of the agencies responsible for maintaining that data to, among 

other objectives, determine the optimal arrangement by which administrative data on Federal programs 

may be integrated and made available to facilitate continuous program improvement. While the 

clearinghouse will not specifically focus on data matching across assistance programs, the Commission 

has been instructed to evaluate how data and results of research can be used to inform program 

administrators and policymakers to improve program design. 

As with the Tri-Agency Interoperability initiative, should the Commission deem a clearinghouse is feasible, 

it could be a system where data collected by one agency could eliminate the need for other agencies to 

collect the same information again. Unlike interoperability data exchanges that are functioning and 

growing in several States, it may be years before a functioning clearinghouse is built if the Commission 

recommends pursuing the idea. Nevertheless, it is a development that FNS should, at a minimum, 

monitor as the agency develops its long-term plans for improving its NSLP data matching processes for 

direct certification and direct verification. The importance of this initiative of integrating data at the State 

level is that through its participation in the Commission, FNS may be able to identify new ways to support 

State-level efforts. 

In summary, this White Paper reports on whether computer matching in the NSLP and SBP may be 

expanded to include additional data sources. 2M placed the effort to improve NSLP computer matching in 

the broader context of initiatives such as computer matching by other Federal agencies, the HHS-USDA 

Tri-Agency Interoperability Workgroup, and the recent establishment of the Commission on EvidenceBased Policymaking. 2M concludes that additional data matching in the NSLP is feasible, has the 

potential to increase participation among eligible students and reduce program error, and can reduce 

administrative burden to administering agencies and participating households. 2M has provided several 

recommendations for next steps to facilitate not only expanding matching to additional programs, but 

also to assist States with their current data matching efforts. 

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Appendix A. Domains of Inquiry by Research Method 

Literature Review Interviews with Expert Consultants Interviews with States 

Databases/Program Data 

• Not applicable 

Databases/Program Data 

• Matching elements available 

• Data collection and management (e.g., frequency of 

collection and updating, data sharing, and privacy 

policies) 

Databases/Program Data 

• What databases are they using? 

• If the regulations permitted, would any of the 

additional means-tested data sets reviewed by the 

expert consultants be feasible for data matching for 

direct certification or direct verification in your State 

or district? Why or why not? 

Usage/Purpose 

• How were SAs and LEAs using computer 

matches for the NSLP in the most 

recent published information? 

• For what specific purposes were 

computer matches used in the most 

recent data available? Direct 

certification? Direct verification? 

Verification for cause and other error 

reduction purposes? 

Usage/Purpose 

• Which, if any, additional data sets can be used for direct 

certification, direct verification, matching to earnings 

data, and error reduction? 

Usage/Purpose 

• Are categorical applications verified through data 

matching? 

• How much follow-up is needed for income verification? 

• Could these other means-tested programs be used to 

establish that household income qualifies a student for 

F/RP meals? 

• Is matching with wage or earnings data feasible? How 

complete, accurate, and timely are the data? 

• What data elements are needed to conduct a reliable 

wage match? 

Challenges/Barriers 

• Do any State policies on computer 

matching, privacy protection, or data 

sharing inhibit computer matching in 

the NSLP? 

Challenges/Barriers 

• Which challenges and barriers in the Literature Review 

might candidate data sets help overcome or avoid? 

Where would they present the same challenges and 

barriers? 

Challenges/Barriers 

• What challenges do SAs encounter? 

• [If applicable] Why does your SA or LEA not use other 

databases permitted by the regulations, (e.g., TANF, 

FDPIR, Foster Care, migrant children, homeless, 

runaways, Head Start) in its direct certification processes? 

In direct verification? Why does your State not use 

Medicaid for direct verification? Do the data sets 

discussed with the expert consultants encounter these 

same challenges and barriers? Do you need any help 

address any of these challenges and barriers? 

• What would be the obstacles to successful computer 

matching with these additional data sets? 

Benefits 

• What benefits have computer matches 

brought? 

• Has computer matching provided real, 

meaningful potential to improve 

certification and/or verification 

accuracy in the NSLP without increasing 

household burden or reducing 

participation among eligible children? 

Benefits 

• Is there evidence or grounds for expecting that any of the 

new means-tested data sets could potentially certify 

additional students for F/RP meals who are not captured 

through direct certification using SNAP, TANF, or other 

program files? Is there evidence or grounds for expecting 

they would not? 

Benefits 

• Are there reasons to believe that any of the candidate 

means-tested data sets could potentially certify additional 

students for F/RP meals who are not captured through 

direct certification with SNAP, TANF, or other files 

currently approved for data matching? Are there reasons 

to believe they would not? 

Cost Cost Cost 

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• Is there evidence on how effective or • What costs would be encountered in changing regulations • Have SAs/LEAs developed or tested matching processes 

cost-effective State computer matching to permit use of any of these additional data sets, in that they concluded were ineffective or cost-inefficient and 

efforts are by type of database? reaching data sharing and privacy protection agreements, 

and in obtaining and matching the data? 

• Can meaningful gains in certification and/or verification 

accuracy be achieved at reasonable cost through 

additional data matching? 

no longer use? 

• Are there reasons to expect that any of the new data sets 

would be cost-effective? Too costly or cost-ineffective? 

Feasibility 

• Are there steps that FNS, SAs or LEAs 

could take to make computer matching 

more feasible? 

• Are there other means-tested programs 

(besides SNAP, TANF, FDPIR, Medicaid, 

Foster Care, migrant, runaway, 

homeless, and Head Start) that could 

be used to directly certify children for 

F/RP meals? 

• Are there specific States where the 

feasibility of using these other meanstested programs could best be 

examined? 

• Is matching with wage earnings or 

income data feasible? How complete, 

accurate, and timely are the data? What 

data elements are needed to conduct a 

reliable match to wage and income 

data? 

Feasibility 

• What legal and privacy issues must be addressed to share 

computer match data? 

Feasibility 

• What legal, privacy issues, and other barriers must be 

addressed in order for your SAs or LEAs to share and 

match data from programs that regulations permit States 

to use, but you do not currently use? 

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Appendix B. Programs Reviewed, But Not Selected, in Objective 2 

Appalachian Regional Commission 

Appalachian Regional Development 

Department of Agriculture 

Child and Adult Care Food Program 

Commodity Supplemental Food Program 

Farmers' Market Nutrition Program 

Nutrition Program for the Elderly, Nutrition Service Incentives 

Rural Housing Insurance Fund 

Rural Housing Services 

The Emergency Food Assistance Program 

Department of Commerce 

Economic Development Administration 

Department of Education 

21st Century Learning Centers 

Adult Basic Education Grants 

Aid for Graduate and Professional Study for Disadvantaged and Minorities 

Even Start 

Gear-Up 

Leveraging Educational Assistance Partnership (LEAP) Program (formerly State Student Incentive Grant 

Program [SSIG]) 

Migrant Education 

Pell Grants 

Supplemental Educational Opportunity Grants 

Title One Grants to Local Education Authorities 

Department of Health and Human Services 

Adoption Assistance Title IV-E 

Assets for Independence 

Childcare and Child Development Block Grant 

Childcare Entitlement to the States 

Community Service Block Grant 

Consolidated Health Centers/Community Health Centers 

Healthy Marriage and Responsible Fatherhood Grants 

Healthy Start 

Independent Living (Chafee Foster Care Independence Program) 

Independent Living Training Vouchers 

Indian Health Services 

Maternal and Child Health 

Maternal, Infants, and Children Home Visitation 

Medical Assistance to Refugees 

Refugee Assistance 

Safe and Stable Families 

Social Services for Refugees, Asylees, and Humanitarian Cases 

TANF Block Grant Child Care 

TANF Block Grant Services 

TANF Work Activities and Training 

Title III Aging Americans Act 

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Title X Family Planning 

Department of Homeland Security-Federal Emergency Management Agency 

Emergency Food and Shelter Program 

Department of Housing and Urban Development 

Choice Neighborhoods 

Community Development Block Grant and Related Development Funds 

Empowerment Zones, Enterprise Communities, Renewal Communities 

Home Investment Partnership Program 

Housing for Persons with Disabilities 

Housing for the Elderly 

Low Income Housing Tax Credit for Developers 

Native American Housing Block Grants 

Other Assisted Housing Programs 

Department of the Interior-Bureau of Indian Affairs (BIA), Financial Assistance and Social Services 

General Assistance to Indians 

Department of Labor 

Job Corps 

Migrant Training 

Native American Training 

Senior Community Service Employment 

WIA Adult Employment and Training formerly JTPA IIA Training for Disadvantaged Adults & Youth 

WIA Youth Opportunity Grants Formerly Summer Youth Employment 

YouthBuild 

Federal Communications Commission 

Universal Service Fund - Subsidized Phone Services for Low Income Persons 

Internal Revenue Service 

Make Work Pay Tax Credit (Refundable portion) 

Refundable Child Credit 

Social Security Administration 

Title XX Social Services Block Grant 

Supplemental Security Income/Old-Age Assistance (SSI/OAA) 

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Appendix C. Findings on Candidate Programs 

(1) Special Supplemental Nutrition Program for Women, 

Infants, and Children (WIC) 

Background 

WIC provides certain low-income women, infants, and children who are at nutritional risk with 

supplemental nutritious foods, nutrition education and counseling, and referrals to healthcare 

services.145,146 

WIC is administered at the Federal level by FNS, which provides grant funding to 90 SAs, including 50 

States, the District of Columbia, 34 Indian Tribal Organizations, and 5 U.S. territories. WIC services are 

provided through approximately 1,900 local agencies and 10,000 clinic sites. Service provider sites can 

include county health departments, hospitals, mobile clinics (vans), community centers, schools, public 

housing sites, migrant health centers and camps, and Indian Health Service (IHS) facilities.147 

Collection of Personally Identifiable Information (PII) 

WIC only collects PII on the women, infants, or children applying for benefits under the program. Name 

and DOB are recorded for each adult applicant, and SSN is requested, but not required. For infants and 

children, only the name and address of the mother or authorized representative are collected. One of the 

interviewed WIC experts confirmed, “We do not capture information about family members other than the 

eligible person or authorized representative.”148 

Income and Poverty Data 

There are three ways a WIC applicant can be income-eligible to receive WIC benefits. An applicant may be 

adjunctively income-eligible due to their (or a certain family member’s) eligibility to receive benefits from 

specific means-tested programs (i.e., SNAP, Medicaid, or TANF). An applicant may be automatically 

income-eligible if they participate in a specific means-tested program that has income guidelines at or 

below WIC’s income guidelines. An applicant may also be determined to be eligible through a traditional 

income assessment. 

To be income-eligible for the WIC program, an applicant’s gross income must fall at or below 185 percent 

of FPL.149 The gross income threshold for income eligibility is set by WIC Federal regulations may be lower 

as a State option, but cannot be lower than 100 percent of FPL. To determine income eligibility, SAs have 

the discretion to use either the family’s or economic unit’s gross income for the past 12 months, or the 

current family or economic unit’s gross income (i.e., past 30 days), whichever is the most accurate 

reflection of their economic status.150,151 

145 U.S. Department of Agriculture, Food and Nutrition Service. (n.d.). Women, Infants, and Children (WIC): About WIC-WIC’s 

mission. Retrieved on April 29, 2016 from: http://www.fns.usda.gov/wic/about-wic-wics-mission 146 Applicants eligible for WIC include pregnant women (from pregnancy and up to 6 weeks after birth, or after pregnancy ends); 

breastfeeding women (up to the infant’s 1st birthday); non-breastfeeding postpartum women (up to 6 months after the birth of 

an infant or after pregnancy ends); infants (up to 1st birthday); and children up to their 5th birthday. 147	 U.S. Department of Agriculture, Food and Nutrition Service. (n.d.). Women, Infants, and Children (WIC): About WIC-WIC at a 

glance. Retrieved on April 22, 2016 from: http://www.fns.usda.gov/wic/about-wic-wic-glance 148 Expert Interview, April 14, 2016, FNS Supplemental Foods Program Division. 149 U.S. Department of Agriculture, Food and Nutrition Service. (n.d.). Women, Infants, and Children (WIC): WIC eligibility 

requirements. Retrieved on April 25, 2016 from: http://www.fns.usda.gov/wic/wic-eligibility-requirements 150	 U.S. Department of Agriculture, Food and Nutrition Service. (n.d.). Women, Infants, and Children (WIC): WIC income eligibility 

guidelines. Retrieved on April 30, 2016 from: http://www.fns.usda.gov/wic/wic-income-eligibility-guidelines 151 Certification of participants, C.F.R. § 246.7 (2011). 

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Income sources used for determining eligibility include: 

• monetary compensation for services, including wages, salary, commissions, or fees; 

• net income from farm and non-farm self-employment; 

• Social Security benefits; 

• dividends or interest on savings or bonds, income from estates or trusts, or net rental income; 

• public assistance or welfare payments; 

• unemployment compensation; 

• government civilian employee or military retirement or pensions or veterans’ payments; 

• payments to military personnel for housing on or off military installations;152 

• private pensions or annuities; 

• alimony or child support payments; 

• regular contributions from persons not living in the household; 

• net royalties; and
 

• other cash income.153
 

States are required to request that applicants provide documentation of family/economic unit income at 

the time of certification (e.g., current pay stubs, unemployment benefits stubs, earnings statements, W-2 

forms). State and local agencies have discretion to “require verification of questionable information which 

it determines necessary to confirm income eligibility for program benefits.”154 

Recertification Periods 

Eligibility is determined after each certification period expires. Certification periods vary for each category 

of eligible participants. Table C-1 summarizes the certification periods for pregnant, postpartum, and 

breastfeeding women, and for infants and children. WIC participants may reapply to receive program 

benefits and, if eligible, are certified into the appropriate category. 

Table C-1: WIC Certification Period by Eligibility Category 

Eligibility Category Certification Period 

Pregnant women For the duration of her pregnancy, and up to the last day of the month in which the 

infant becomes 6 weeks old, or the pregnancy ends. 

Postpartum women Up to the last day of the sixth month after the baby is born, or the pregnancy ends 

(postpartum). 

Breastfeeding women 

Approximately every 6 months. The SA may permit its local agencies to certify a 

breastfeeding woman up to the last day of the month in which her infant turns 1 year 

of age, or until the woman ceases breastfeeding, whichever occurs first. 

Infants 

Approximately every 6 months. The SA may permit its local agencies to certify an 

infant under 6 months of age up to the last day of the month in which the infant 

turns 1 year of age. 

Children 

Approximately every 6 months, ending with the last day of the month in which a child 

reaches his/her fifth birthday. The SA may permit its local agencies to certify a child 

for a period of up to 1 year. 

152 SAs are given the option whether or not to include military housing payments as income. 153	 Other cash income includes, but is not limited to, cash amounts received or withdrawn from any source, including savings, 

investments, trust accounts, and other resources which are readily available to the family. 154 Certification of participants, C.F.R. § 246.7 (2011). 

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Source: 7 C.F.R. § 246.7 

Data Sharing and Privacy Protections 

Federal regulations currently grant WIC SAs “the authority to use and disclose confidential applicant and 

participant information for non-WIC purposes, such as conducting outreach for other programs, 

streamlining administrative procedures to minimize burdens on participants and staff, and assessing and 

evaluating a State’s health system’s responsiveness to participants’ healthcare needs and healthcare 

outcomes.”155 Applicants must be notified of this intent, and that the information disclosed will be used 

by public organizations which administer programs that serve those individuals who are eligible for the 

WIC program.156 

Discussion 

Data matching with WIC program records seems unlikely to enhance direct certification and direct 

verification in the NSLP. 

More specifically, only a small number of WIC participants receive school meals—as confirmed by one WIC 

expert interviewed, “We generally don’t serve school age children.”157 

Children are eligible for WIC benefits until the end of the month of their fifth birthday. Special 

circumstances would be required for a 4-year-old to enter into kindergarten, and the child would therefore 

be unlikely to receive school meals. The only WIC participants likely to receive school meals would be 

school-aged pregnant youth. One of the WIC experts interviewed indicated that “some States operate WIC 

clinics in high schools. You could have teens as participants. We have people that have been pregnant 

prior to being a teen.”158 Although some of these WIC participants might be matched to student 

enrollment records, only a small number (3.4 percent) of women (pregnant, breastfeeding, and 

postpartum) who participate in WIC are under the age of 18 years.159 

Limited PII is collected on WIC participants and is not collected on a participant’s school-age children or 

siblings because they are ineligible for WIC benefits, eliminating the ability to data match school-age 

children in a WIC participant’s family/economic unit. 

Furthermore, many WIC participants are enrolled in other assistance programs. For example, in 2014, 

approximately 73 percent of WIC participants received benefits from one or more of the following 

programs: Medicaid (69 percent), SNAP (35 percent), and/or TANF (8 percent).160 These program files are 

already used for data matching in the NSLP (SNAP and TANF), or are under evaluation (Medicaid).161 

Therefore, it is likely that WIC files would certify few, if any, additional school-age children who are not 

already captured in these adjunct and means-tested programs. 

155 Certification of participants, C.F.R. § 246.7 (2011).
 156 Ibid.
 157 Expert Interview, April 14, 2016, FNS Supplemental Foods Program Division.
 158 Thorn, B., Tadler, C., Huret, N., Ayo, E., Trippe, C., Mendelson, M., . . . Tran, V. (2015, November). WIC participant and program
 

characteristics 2014 final report. Prepared by Insight Policy Research under Contract No. AG‐3198‐C‐11‐0010. Alexandria, 

VA: U.S. Department of Agriculture, Food and Nutrition Service. 159 Ibid. 160 Ibid. 161 U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. (2015). Evaluation of demonstrations of 

National School Lunch Program and School Breakfast Program direct certification of children receiving Medicaid benefits: 

Year 1 report. Prepared by Mathematica Policy Research, Contract No. AG-3198-B-12-0006. Alexandria, VA: U.S. Retrieved 

December 11, 2015, from: http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationMedicaidYr1.pdf 

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(2) Affordable Care Act (ACA) Health Insurance Marketplace 

Background 

The ACA was passed by Congress in 2010. Provisions set forth in the ACA affected several aspects of 

healthcare in the United States, including the establishment of Marketplace(s) and the expansion of 

Medicaid.162 

States may operate their own Marketplace (called a State-based Marketplace) or can enter into a StatePartnership Marketplace. In this partnership, a Federally facilitated Marketplace is set up for the State; 

however, the State retains responsibility for certain functions.163 As of June 2016, 17 States operated 

their own Marketplaces, and 7 States participated in State-Partnership Marketplaces.164 In States with 

neither, consumers can apply for health insurance through the Federal Marketplace (Healthcare.gov).165 

In the Marketplace, consumers can purchase a QHP166 and may be able to receive tax credits to help 

cover the cost of their health insurance.167 The Marketplace also identifies people who could qualify for 

other forms of health coverage, including Medicaid and CHIP.168 Eligibility information for Medicaid or 

CHIP is transmitted to the applicant’s State Medicaid agency with an eligibility assessment, and in some 

States, a final eligibility determination.169,170 Consumers can also apply for Medicaid/CHIP directly with 

their State Medicaid agency.171 

Collection of Personally Identifiable Information (PII) 

The HHS Privacy Impact Assessment for the Marketplace states that Healthcare.gov collects and stores 

PII to determine eligibility and enroll applicants in QHPs, Medicaid, and CHIP.172 PII is also used to 

calculate the APTC and CSR used to offset the cost of health insurance for low-income persons who do 

not qualify for Medicaid.173 

162	 Patient Protection and Affordable Care Act, P.L. 111-148, 124 STAT 119 (2010). 163	 U.S. Department of Health and Human Services. (2013, January 3). Affordable insurance exchanges guidance: Guidance on 

the State partnership exchange. Retrieved April 21, 2016 from: https://www.cms.gov/CCIIO/Resources/Fact-Sheets-andFAQs/Downloads/partnership-guidance-01-03-2013.pdf 164	 U.S. Centers for Medicare and Medicaid Services. (n.d.). Medicaid and CHIP marketplace interactions. Retrieved May 4, 2016 

from: https://www.medicaid.gov/medicaid-chip-program-information/program-information/medicaid-and-chip-and-themarketplace/medicaid-chip-marketplace-interactions.html 165 U.S. Centers for Medicare and Medicaid Services. (n.d.). The marketplace in your state. Retrieved May 2, 2016 from: 

https://www.healthcare.gov/marketplace-in-your-state/ 166	 Qualified Health Plans (QHPS) provide essential health benefits, follow established limits on cost-sharing (deductibles, 

copayments, and out-of-pocket maximum amounts), and meet other requirements. QHPs are certified by each Marketplace in 

which they are sold. See https://www.healthcare.gov/glossary/qualified-health-plan/ 167	 U.S. Centers for Medicare and Medicaid Services. (n.d.). State health insurance marketplaces. Retrieved April 21, 2016 from: 

https://www.cms.gov/CCIIO/Resources/Fact-Sheets-and-FAQs/state-marketplaces.html 168 U.S. Centers for Medicare and Medicaid Services. (n.d.). Medicaid and CHIP coverage. Retrieved May 2, 2016 from: 

https://www.healthcare.gov/medicaid-chip/getting-medicaid-chip/ 169 Privacy Act of 1974, Supplementary Information: Health Insurance Exchanges Program; Notice to establish a new system of 

records, 78 Fed. Reg. 25 (February 6, 2013), pp. 8539–8542. 170 States may accept Medicaid/CHIP eligibility determinations made in the Federal Marketplace without further verification. See 

https://www.medicaid.gov/medicaid-chip-program-information/program-information/medicaid-and-chip-and-themarketplace/medicaid-chip-marketplace-interactions.html
 171 Ibid. 172	 U.S. Department of Health and Human Services. (2015, February 10). Privacy impact assessment: Federally facilitated 

marketplaces (P-1710508-331195). Retrieved April 22, 2016 from: http://www.hhs.gov/sites/default/files/CMSFFM_remediated.pdf 173 See Income and Poverty Data section below. 

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The PII collected to establish eligibility and enroll applicants in Medicaid, CHIP, and QHPs include the 

following, inter alia:174 

• SSN, 

• Name 

• Driver’s License Number 

• Email Address 

• Phone Numbers 

• Passport Number 

• Immigration Documents 

• Federal Tax Information 

• Tobacco Use 

• DOB 

• Photographic Identifiers 

• Mother’s Maiden Name 

• Mailing Address 

• Military Status 

• Taxpayer ID 

• Pregnancy Inquiry 

• Wage Data; Certificate Numbers for Eligibility Exemptions 

Income and Poverty Data 

The Marketplace uses income data and FPL standards to determine if anyone in the applicant household 

is eligible for QHPs, Medicaid, or CHIP.175 As of January 1, 2016, the State eligibility thresholds for 

Medicaid among children ages 6 through 18 ranged from 133 percent FPL to 324 percent FPL, with a 

median FPL of 142 percent.176 Given this range, it is not practical to use Medicaid data for categorical 

certification. However, Medicaid, CHIP, and Marketplace data offer the potential for direct certification if 

income data contained within the files are examined. Several demonstration projects are underway to 

further assess the potential for using income information in Medicaid data files to directly certify 

students.177,178 

Applicants with an expected household income between 100 and 400 percent of the FPL who are not 

enrolled in insurance through the Marketplace (either State or Federal) qualify for the APTC. The credit 

provides a subsidy on monthly premiums for QHPs.179 However, insureds must pay back any portion of 

174	 U.S. Department of Health and Human Services. (2015, February 10). Privacy impact assessment: Federally facilitated 

marketplaces (P-1710508-331195). Retrieved April 22, 2016 from: http://www.hhs.gov/sites/default/files/CMSFFM_remediated.pdf 175	 U.S. Centers for Medicare and Medicaid Services. (n.d.). Medicaid and CHIP coverage. Retrieved April 21, 2016 from 

Healthcare.gov website: https://www.healthcare.gov/medicaid-chip/getting-medicaid-chip/ 176	 Henry J. Kaiser Family Foundation. (2016, January). Medicaid and CHIP income eligibility limits for children as a percent of the 

Federal poverty level. Retrieved May 4, 2016 from: http://kff.org/health-reform/state-indicator/medicaid-and-chip-incomeeligibility-limits-for-children-as-a-percent-of-the-federal-poverty-level/ 177	 Endahl, J., & Abelev, M. (2015, Spring). FNS research corner: Summary of research. The Journal of Child Nutrition and 

Management, 39(1). Retrieved May 4, 2016 from: 

https://schoolnutrition.org/uploadedFiles/5_News_and_Publications/4_The_Journal_of_Child_Nutrition_and_Management/S 

pring_2015/FNS%20Research%20Corner%202015%203-16-15%20print%20ready.pdf 178	 U.S. Department of Agriculture, Food and Nutrition Service. (2016, June). Year 2 demonstration impacts of using Medicaid 

data to directly certify students for free school meals. Retrieved June 28, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/DirectCertwithMedicaidYear2.pdf 179 Internal Revenue Service. (n.d.). Eligibility for the premium tax credit. Retrieved April 30, 2016 from: 

https://www.irs.gov/Affordable-Care-Act/Individuals-and-Families/Eligibility-for-the-Premium-Tax-Credit 

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APTC that exceeds the actual APTC calculated on income reported on tax returns at the end of the year. 

Insureds may also receive a refund if they received a lower APTC than their actual income for the year for 

which they would have been entitled.180 

Minimum income eligibility levels calculated in the Marketplace are based on expected household 

income.181 A household includes the tax filer (applicant), and his/her spouse and tax dependents 

(including those who do not need coverage). Applicants estimate the amount of income that each 

household member will earn in the current year, based on adjusted gross income reported on the most 

recent year’s tax return. This estimate is then used to calculate an expected MAGI, and to determine 

eligibility for the programs and benefit levels.182,183 

The income sources used to determine eligibility in the Marketplace differ from the sources used in NSLP 

income determinations. Military pay, worker’s compensation, SSI, and State cash assistance are not 

included in the Marketplace, but can be included in the NSLP at a State’s discretion (see Table C-2 

below).184,185 

180	 Internal Revenue Service. (n.d.). Premium tax credit: Claiming the credit and reconciling advance credit payments. Retrieved 

April 30, 2016 from: https://www.irs.gov/Affordable-Care-Act/Individuals-and-Families/Premium-Tax-Credit:-Claiming-theCredit-and-Reconciling-Advance-Credit-Payments 181	 U.S. Centers for Medicare and Medicaid Services. (n.d.). How to count income and household members: What to include as 

income. Retrieved April 21, 2016 from Healthcare.gov website: https://www.healthcare.gov/income-and-householdinformation/income/ 182	 U.S. Centers for Medicare and Medicaid Services. (n.d.). How to count income and household members: How to estimate your 

expected 2016 income. Retrieved April 26, 2016 from Healthcare.gov website: https://www.healthcare.gov/income-andhousehold-information/how-to-report/ 183	 MAGI includes adjusted gross income (AGI) reported on Federal tax returns, excluded foreign income, nontaxable Social 

Security benefits (including tier 1 railroad retirement benefits), and tax-exempt interest. MAGI does not include Social Security 

Income (SSI). 184	 U.S. Centers for Medicare and Medicaid Services. (n.d.). How to count income & household members: What to include as 

income. Retrieved April 21, 2016 from Healthcare.gov website: https://www.healthcare.gov/income-and-householdinformation/income/ 185	 U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals: Determining and 

verifying eligibility. Retrieved on April 21, 2016 from: 

http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-2015a.pdf 

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Table C-2: Sources of Income Used in Eligibility Determinations in the Health Insurance Marketplace and the 

NSLP 

Sources of Income Used in Eligibility Determinations Health Insurance 

Marketplace NSLP 

Wages, salaries, tips, commissions, and cash bonuses X X 

Net income from self-owned business and farms X X 

Strike benefits, unemployment compensation, and 

worker’s compensation -- X 

Military basic pay and cash bonuses (excluding combat 

pay, Family Substance Supplemental Allowance, and 

privatized housing allowances) 

-- X 

Allowances for off-base housing, food, and clothing -- X 

Adoption assistance payments -- X 

Unemployment benefits X X 

Worker’s compensation -- X 

Supplemental Security Income (SSI)1 -- X 

Regular cash assistance from State or local government -- X 

Alimony payments X X 

Child support payments X X 

Veterans’ benefits X2 X 

Pensions X X 

Retirement Social Security (including railroad retirement 

and black lung benefits) X X 

Private pensions or disability benefits X X 

Income from trusts or estates -- X 

Annuities X X 

Investment Income X X 

Earned interest X X 

Rental income X X 

Regular cash payments from outside household X X 

Any other money that may be available to pay for 

children’s meals X X 

1 Supplemental Security Income (SSI) is not included in income determinations; however Social Security and Social Security Disability 

Income (SSDI) is included for the Marketplace. 2 Veteran’s disability payments only. 

Sources: U.S. Centers for Medicare and Medicaid Services. (n.d.). How to count income and household members. Retrieved on April 21, 

2016 from Healthcare.gov website: https://www.healthcare.gov/income-and-household-information/income/ 

U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals determining and verifying 

eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-2015a.pdf 

Definitions of household also differ between Medicaid, the Marketplace, and the NSLP. The NSLP uses 

economic units as the basis for household determination. An economic unit includes individuals residing 

in the same house.186 

The Marketplace defines a household based on a tax unit, comprised of the taxpayer, and his/her 

spouse, and any tax dependents. Tax dependents, and even spouses, do not necessarily reside in the 

186 U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals determining and 

verifying eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-

2015a.pdf 

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same physical household.187 Household composition varies depending upon the individual applying for 

coverage, and on his or her tax filing status.188,189 

Recertification Periods 

Each year in the fall, individuals with QHPs receive letters from insurance companies and the 

Marketplace which explain their status and options for health insurance coverage during open enrollment 

for the following year.190 

QHPs are renewed each year during this time, and insureds must report any changes in income and 

household size, although they can also update information throughout the year.191 Insureds who did not 

experience changes in household size and income do not need to take any action, and are auto-enrolled 

in the same plan from the previous year.192 Under the ACA, States must assess eligibility for Medicaid and 

CHIP benefit renewals once every 12 months using information available in specified data sources.193,194 

Data Sharing and Privacy Protections 

Section 1413(c)(1) of the ACA requires that each State develop a secure electronic interface and 

implement data exchanges for applicable State health subsidy programs. In accordance with the Privacy 

Act of 1974,195 CMS maintains the PII collected on applications submitted to the Marketplaces in the 

Program system of records,196 and then matches and verifies the PII against data from several Federal, 

State, and local agencies, and from private sector corporations.197 State-Based Exchanges and State 

Medicaid agencies can also request data matching through the Data Services Hub.198 

Applicants are prompted at the beginning of the application to acknowledge a Privacy Policy which details 

the use and disclosure of the PII. CMS maintains Computer Matching Agreements and Information 

187	 U.S. Centers for Medicare and Medicaid Services. (n.d.). How to count income and household members: Who to include in 

your household. Retrieved April 21, 2016 from Healthcare.gov website: https://www.healthcare.gov/income-and-householdinformation/household-size/ 188	 Center on Budget and Policy Priorities. (2013, August 7). Determining households for Medicaid and premium tax credits: 

Beyond the basics. Retrieved April 21, 2016 from: http://www.healthreformbeyondthebasics.org/wpcontent/uploads/2013/08/Household-Definitions-Webinar-7Aug13.pdf 189 Applicants belong to one of three categories: tax filers not claimed as a tax dependent; tax dependents; and non-filers, not 

claimed as a tax dependent. 190 U.S. Centers for Medicare and Medicaid Services. (n.d.). Your 2016 health insurance letters. Retrieved on April 21, 2016 from 

Healthcare.gov website: https://www.healthcare.gov/reenrollment-notices/ 191	 U.S. Centers for Medicare and Medicaid Services. (n.d.). We’re making it easy for you to keep your marketplace coverage next 

year. Retrieved April 21, 2016 from Healthcare.gov website: https://www.healthcare.gov/blog/we-are-making-it-easy-for-youto-keep-your-marketplace-coverage-next-year/ 192 If an insured’s current qualified health plan is no longer available they will be automatically enrolled in a similar health plan, 

unless another is chosen. 193 U.S. Centers for Medicare and Medicaid Services. (2012, March 23). CMS Medicaid and CHIP eligibility changes under the 

Affordable Care Act (CMS-2349-F): Section-by-section summary. Retrieved April 21, 2016 from Medicaid.gov website: 

https://www.medicaid.gov/affordablecareact/provisions/downloads/medicaid-eligibility-and-enrollment-final-rule-section-bysection-summary.pdf 194 Medicaid State agencies must use sources specified in 42 C.F.R. §§ 435.948, 435.949 and 435.956.
 195 Privacy Act of 1974, 5 U.S.C. 552a (1974).
 196 Privacy Act of 1974, Supplementary Information: Health Insurance Exchanges Program; Notice to establish a new system of
 

records, 78 Fed. Reg. 25 (February 6, 2013), pp. 8539–8542. 197 Section 1411 of the ACA requires verification of the information received from applicants/enrollees. 198 Section 1943(b) of the ACA requires that Medicaid and CHIP agencies use the same enrollment system and electronic 

interface established in section 1413 and described in sections 1411(c) and 1411(d) of the ACA as the Marketplace to verify 

information. 

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Exchange Agreements with all Federal, State, and local agencies, and with private sector corporations 

such as insurance providers and credit bureaus (see Table C-3 below).199 

Table C-3: Federal, State, or Local Agencies and Private Sector Entities that Receive PII from ACA Marketplace 

for Various Purposes 

Agency Type of 

Agreement Purpose 

Within HHS 

Email Outreach GovDelivery 

Medicare N/A 

Other Federal Agencies 

Internal Revenue Service CMA, IEA 

Eligibility determination for healthcare 

coverage 

Social Security Administration CMA 

Department of Homeland Security CMA 

Department of Defense CMA 

Veterans Administration CMA 

State or Local Agencies 

State Medicaid IEA Eligibility determination for healthcare 

Children’s Health Insurance Program IEA coverage 

Private Sector 

Service Corporation N/A Eligibility support 

Experian N/A Remote identity proofing data match only 

Symantex N/A Multi-factor authentication only 

Insurance Providers N/A Enrollment information 

Equifax N/A Current income source validation 

Source: U.S. Department of Health and Human Services. (2015, February 10). Privacy impact assessment: Federally facilitated 

marketplaces (P-1710508-331195). Retrieved April 22, 2016 from: http://www.hhs.gov/sites/default/files/CMS-FFM_remediated.pdf 

Note. CMS is currently working on establishing CMAs with the Office of Personnel Management and the Peace Corps. IEAs also exist 

between CMS and State-based Exchanges. 

Discussion 

The application data collected to determine eligibility for Medicaid, CHIP, and QHPs is potentially viable 

for direct certification and direct verification in the NSLP. FNS is currently studying the utility and costeffectiveness of using Medicaid program data for these purposes in more detailed demonstration 

studies.200,201 

The PII that the Marketplace collects and maintains could permit data matches in the NSLP. Since PII is 

collected for all household members, students could be identified using application data. SSNs must be 

provided for any household member whose income could be used to help pay for health coverage, and 

CMS verifies household income and family size against IRS records.202 This process provides the 

199	 U.S. Department of Health and Human Services. (2015, February 10). Privacy impact assessment: Federally facilitated 

marketplaces (P-1710508-331195). Retrieved April 22, 2016 from: http://www.hhs.gov/sites/default/files/CMSFFM_remediated.pdf 200	 U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. (2015, January). Evaluation of 

demonstrations of National School Lunch Program and School Breakfast Program direct certification of children receiving 

Medicaid benefits: Year 1 report. (Nutrition Assistance Program Report). Retrieved May 4, 2016 from: 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationMedicaidYr1.pdf 201	 U.S. Department of Agriculture, Food and Nutrition Service. (2016, January 27). Request for applications to participate in 

demonstration projects to evaluate direct certification with Medicaid. Retrieved May 4, 2016 from: 

http://www.fns.usda.gov/sites/default/files/cn/SP23-2016a.pdf 202	 U.S. Centers for Medicare and Medicaid Services. (n.d.). How we use your data. Retrieved April 21, 2016 from Healthcare.gov 

website: https://www.healthcare.gov/how-we-use-your-data/ 

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information needed to verify the identity of children claimed as tax dependents on applicant tax returns, 

and also provides a quality source of household income data for direct verification. 

Marketplaces provide sufficient information to calculate a household’s income as a percent of FPL, albeit 

with limitations. The Marketplace uses MAGI based on the past year’s tax return for Medicaid and 

Marketplace income determinations. However, income determinations in the NSLP are based on current 

income, defined as the gross household income received (before deductions) for the current month or the 

month prior to application, or as the amount projected for the first month for which the application is 

completed.203 

Despite this difference, the income data collected from Medicaid applications are similar to those needed 

for data matching in the NSLP. Further study would be needed to assess whether income eligibility 

determinations differ significantly when based upon expected income, which is used to determine 

eligibility for APTC and CSR, rather than monthly income as used to determine eligibility in the NSLP. 

Eligibility determinations for APTC and CSR that are based on expected income may also cause limitations 

if the tax credit amounts differ when based on actual income earned for the year. More specifically, 

households that are either entitled to additional refunds, or those that are required to pay back APTC and 

CSR received in the previous year, indicate that income data for the QHPs may not be an accurate basis 

for direct certification or direct verification in the NSLP. Variations in household definitions may also 

cause complications in matching NSLP records to Medicaid and Marketplace records for direct 

certification or verification of income. SAs administering the NSLP would need to accept FPL as 

determined by Medicaid or the Marketplace for use in direct certification. 

The standard open enrollment period for health coverage in the Marketplace begins on November 1, and 

ends January 31 of the following year.204 Direct certification in the NSLP occurs at or near the beginning 

of the school year, beginning as early as July in most States.205 Data from the Marketplace could 

therefore provide timely information for use in direct certification, since it would be approximately 6 to 8 

months old.206 The timeliness of Medicaid renewals can vary by State; however, under the ACA, Medicaid 

State agencies are required to assess recipient eligibility for benefit renewals at least once every 12 

months, using information available in existing sources.207,208 

The Marketplaces created under the ACA provide the infrastructure and PII necessary for computer 

matching to NSLP student enrollment records. The NSLP SAs would need to establish legal grounds for 

accessing the PII in Federal or State-based Marketplaces. 

203 U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals: Determining and 

verifying eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-

2015a.pdf 204 U.S. Centers for Medicare and Medicaid Services. (n.d.). A quick guide to the health insurance marketplace: Dates and 

deadlines for 2016 health insurance. Retrieved May 4, 2016 from the Healthcare.gov website at: 

https://www.healthcare.gov/quick-guide/dates-and-deadlines/ 205 U.S. Department of Agriculture, Food and Nutrition Service. (2014, August). The National School Lunch Program direct 

certification improvement study: Main report. Retrieved April 29, 2016 from 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 206 2016 open enrollment for ACA ended on January 31, 2016. Most States perform the first DC match in July for their districts, 

but some perform the first DC match in August or September. 207 U.S. Centers for Medicare and Medicaid Services. (2012, March 23). CMS Medicaid and CHIP eligibility changes under the 

Affordable Care Act (CMS-2349-F), section-by-section summary – March 23, 2012. Retrieved April 21, 2016 from: 

https://www.medicaid.gov/affordablecareact/provisions/downloads/medicaid-eligibility-and-enrollment-final-rule-section-bysection-summary.pdf 208 Medicaid State agencies must use sources specified in 42 C.F.R. § 435.948, § 435.949 and § 435.956. 

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(3) Public Housing Program and Housing Choice Voucher 

Program 

Background 

The Public Housing Program provides rental housing for eligible, low-income families, the elderly, and 

persons with disabilities. HUD administers the program at the Federal level, and provides funding and 

technical assistance to over 3,300 PHAs. 

Local housing agencies determine eligibility based on gross annual income, having an elderly or disabled 

person in the household, and documentation of U.S. citizenship or eligible immigration status. Eligible 

individuals submit paper applications to their local housing agency, reporting information and income 

about each household member. Upon approval, applicants sign a lease with the local housing agency, 

and the Total Tenant Payment is determined based on income and other eligibility information provided in 

the application.209,210 

The Housing Choice Voucher Program also assists low-income families, the elderly, and persons with 

disabilities in obtaining affordable housing. The participating individual or family pays the balance of the 

rent after the PHA pays the housing subsidy to the landlord or Third Party Processor. However, while the 

Public Housing Program provides housing in developments that are owned, managed, and maintained by 

the local housing agency, the Housing Choice Voucher Program provides a rental subsidy for privately 

owned housing units chosen by tenants.211 

The sources of income, recertification period, and data sharing and privacy protections are identical for 

both the Public Housing and Housing Choice Voucher programs. 

Collection of Personally Identifiable Information (PII) 

Using Form HUD-50058, local housing agencies electronically submit data to HUD on families 

participating in the Public Housing and Housing Choice Voucher programs.212 This form is submitted 

annually, and provides HUD with information such as demographic characteristics, citizenship status, and 

income of participants.213 The following PII is collected on each household member residing in a public 

housing unit through the Public Housing Program or receiving housing subsidies through the Housing 

Choice Voucher Program: 

• Last Name • Relationship to Family Head 

• First Name • Citizenship 

• Middle Initial • Disabled 

• DOB • Ethnicity 

• Age • SSN (of all family members age 6 years or older) 

• Gender • Alien Registration Number 

209 U.S. Department of Housing and Urban Development. (n.d.). HUD’s Public Housing Program. Retrieved April 26, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/topics/rental_assistance/phprog 210 The Total Tenant Payment refers to the amount paid for rent and utilities by tenants. 211 U.S. Department of Housing and Urban Development. (n.d.). Housing choice vouchers fact sheet. Retrieved April 30, 2016 

from: http://portal.hud.gov/hudportal/HUD?src=/topics/housing_choice_voucher_program_section_8 212 U.S. Department of Housing and Urban Development. (n.d.). About IMS/PIC. Retrieved April 26, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/systems/pic/about#f5 213 U.S. Department of Housing and Urban Development. (n.d.). HUD user manual. Retrieved April 26, 2016 from: 

http://portal.hud.gov/hudportal/documents/huddoc?id=DOC_11667.pdf 

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Income and Poverty Data 

The main criterion for enrollment in either the Public Housing Program or the Housing Choice Voucher 

Program is annual gross income. Applicants must provide specific documentation, such as the names 

and addresses of employers and banks, to verify income and deductions. Sources of income used in 

eligibility determination for both the Public Housing Program and the Housing Choice Voucher Program 

are essentially identical to those used in the NSLP (see Table C-4 below).214,215 

Income eligibility limits are established by HUD for both programs. Low income limits are set at 80 

percent and very low income limits at 50 percent of median income for the county or metropolitan area in 

which participants choose to live. The income limits set by the local housing agencies can vary by area of 

residency, however. HUD publishes an annual list of all metropolitan areas and counties in each State, 

and the respective income limits by family size.216,217 

Table C-4: Sources of Income Used in Eligibility Determinations by HUD 

Sources of Income Public Housing and Housing 

Choice Voucher Programs NSLP 

Wages, salaries, tips, commissions, and cash bonuses X X 

Net income from self-owned business and farms X X 

Strike benefits, unemployment compensation, and 

worker’s compensation X X 

Military basic pay and cash bonuses (excluding combat 

pay, Family Substance Supplemental Allowance, and 

privatized housing allowances) 

X X 

Allowances for off-base housing, food, and clothing X X 

Adoption assistance payments X X 

Unemployment benefits X X 

Worker’s compensation X X 

Supplemental Security Income (SSI) X X 

Regular cash assistance from State or local 

government X X 

Alimony payments X X 

Child support payments X X 

Veteran’s benefits X X 

Pensions X X 

Retirement Social Security (including railroad 

retirement and black lung benefits) X X 

Private pensions or disability benefits X X 

Income from trusts or estates X X 

Annuities X X 

Investment income X X 

214 U.S. Department of Housing and Urban Development. (n.d.). HUD’s Public Housing Program. Retrieved April 26, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/topics/rental_assistance/phprog 215	 Income received from Worker’s Compensation is included in income eligibility determinations in the NSLP, but not in the 

Housing Voucher Program. 216	 FY 2016 Housing Choice Voucher income limits can be found at https://www.huduser.gov/portal/datasets/il/il16/Section8-

IncomeLimits-FY16.pdf 217	 U.S. Department of Housing and Urban Development. (n.d.). Public housing occupancy handbook (7465.1), Chapter 3: 

Eligibility for admission. Retrieved May 2, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/program_offices/administration/hudclips/handbooks/pihh/74651 

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Earned interest X X 

Rental income X X 

Regular cash payments from outside household X X 

Any other money that may be available to pay for 

children’s meals X X 

1 Supplemental Security Income (SSI) is not included in income determinations; however Social Security Disability Insurance (SSDI) is 

included. 

Sources: U.S. Department of Housing and Urban Development. (n.d.). Attachment A: Section 8 definition of annual income, 24 CFR, Part 5, 

Subpart F (Section 5.609). Retrieved on April 27, 2016 from: 

http://portal.hud.gov/hudportal/documents/huddoc?id=Calculatingattachment.pdf 

U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals: Determining and verifying 

eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-2015a.pdf 

Recertification Periods 

Family income and composition are reexamined at least once every 12 months.218,219 These may be 

assessed more frequently if there are any changes to participants’ income or family composition that may 

affect their eligibility. During the recertification process, tenants provide requested documentation and 

sign consent and asset declaration forms, permitting HUD (or housing unit owners) to perform third-party 

verification of income.220 

Data Sharing and Privacy Protections 

All applicants and participants (at least 18 years of age) must sign the Authorization for Release of 

Information/Privacy Act Notice (Form HUD-9886). It is valid for 15 months, and must be signed annually 

during the recertification process.221 Form HUD-9886 authorizes (1) HUD and the local housing 

agency/PHA to access information to verify wage information at the time of application or recertification; 

(2) HUD and the local housing agency/PHA to verify income eligibility (or level of assistance) through 

income information from current or former employers; and (3) HUD to verify income eligibility (or level of 

assistance) by obtaining tax return information from the IRS and SSA.222 

HUD maintains data collected on Form HUD-50058 in several HUD systems of records.223 The data 

maintained in these systems of records are compliant with the Privacy Act of 1974, which requires the 

establishment of data matching agreements with external agencies seeking access to participant 

records.224 

Discussion 

Despite the potential limitations, including differences in the income limits used in eligibility 

determinations and in certification periods, as well as the long waiting lists due to the limited availability 

218	 U.S. Department of Housing and Urban Development. (n.d.). HUD’s Public Housing Program. Retrieved April 30, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/topics/rental_assistance/phprog 219 U.S. Department of Housing and Urban Development. (n.d.). Housing choice vouchers fact sheet. Retrieved April 30, 2016 

from: http://portal.hud.gov/hudportal/HUD?src=/topics/housing_choice_voucher_program_section_8 220 U.S. Department of Housing and Urban Development. (2007, July). HUD multifamily occupancy handbook (4350.3, REV-1), 

chapter 7. recertification, unit transfers, and gross rent changes. Retrieved May 4, 2016 from: 

http://portal.hud.gov/hudportal/documents/huddoc?id=DOC_35651.doc 221	 U.S. Department of Housing and Urban Development. (n.d.). Housing choice voucher guidebook (7420.10G), chapter 5: 

Eligibility and denial of assistance. Retrieved May 2, 2016 from: 

http://portal.hud.gov/hudportal/HUD?src=/program_offices/administration/hudclips/guidebooks/7420.10G 222	 U.S. Department of Housing and Urban Development. (2007, July). HUD multifamily occupancy handbook (4350.3, REV-1), 

chapter 7. recertification, unit transfers, and gross rent changes. Retrieved May 4, 2016 from: 

http://portal.hud.gov/hudportal/documents/huddoc?id=DOC_35651.doc 223 Notification of an amendment to an existing System of Records, Enterprise Income Verification System (EIVS), HUD/PIH-574. 

Fed. Reg. 168, p. 45235–425240 [FR Doc E9-21087] (2009, September 1). Retrieved April 29, 2916 from: 

http://portal.hud.gov/hudportal/documents/huddoc?id=DOC_15179.pdf 224 Privacy Act of 1974, Records maintained on individuals, 5 U.S. Code § 552a(o) (1974). 

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of housing,225 data matching the student enrollment records with HUD records is likely to provide quality 

information for use in direct certification and verification in the NSLP. 

Although income limits are based on median income, HUD publishes these income figures by household 

size for each county and metropolitan area.226 This provides sufficient information to calculate income as 

a percent of FPL for comparisons with the NSLP income guidelines. FPL could also be calculated directly 

from the information on household income and composition. 

HUD collects the PII needed to perform data matching with student enrollment records in the NSLP. The 

HUD expert indicated that data linkage itself is not difficult. The challenge is how to link the data in a 

manner that provides the appropriate security standards.227 To access PII collected on the HUD-50058 

for use in data matching, either FNS or each of the NSLP SAs would need to establish a data matching 

agreement with HUD at the Federal level.228 

(4) Low Income Home Energy Assistance Program (LIHEAP) 

Background 

LIHEAP is administered at the Federal level by HHS through block or formula grants to the 50 States, the 

District of Columbia, several Indian Tribal Organizations, and U.S. territories.229,230 LIHEAP assists lowincome households with home heating and cooling costs, energy crises, and weatherization 

assistance.231 Federal statute requires that States make LIHEAP benefits available to households with 

income within 150 percent of FPL or 60 percent of State median income, whichever is higher.232 Exact 

income criteria vary by State, but income limits cannot be set lower than 110 percent of FPL.233 States 

must also extend categorical eligibility to households with at least one member who receives assistance 

from TANF, SSI, SNAP, or “certain needs-tested veterans’ benefits.”234,235 Most LIHEAP funds are block 

grant funds.236 Simply being eligible for LIHEAP does not assure that a household will receive LIHEAP 

benefits. Although participation rates likely vary by State, a 2015 Congressional Research Service report 

225 U.S. Department of Housing and Urban Development. (n.d.). Housing choice vouchers fact sheet. Retrieved April 30, 2016 

from: http://portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/programs/hcv/about/fact_sheet 226 U.S. Department of Housing and Urban Development. (2016). Section 8 income limits FY 2016 [Dataset]. Retrieved April 28, 

2016 from: https://www.huduser.gov/portal/datasets/il/il16/Section8-IncomeLimits-FY16.pdf 227 Expert Interview, April 6, 2016, U.S. Department of Housing and Urban Development, Office of Research, Evaluation and 

Monitoring. 228 Notification of an amendment to an existing System of Records, Enterprise Income Verification System (EIVS), HUD/PIH-574. 

Fed. Reg. 168, p. 45235–425240 [FR Doc E9-21087] (2009, September 1). Retrieved April 29, 2916 from: 

http://portal.hud.gov/hudportal/documents/huddoc?id=DOC_15179.pdf 229	 LIHEAP funding is available in American Samoa, Commonwealth of the Northern Mariana Islands, Guam, Puerto Rico, and the 

Virgin Islands. A list of LIHEAP Indian Tribal Organization contacts can be found at 

http://www.acf.hhs.gov/programs/ocs/liheap-tribal-contact-listing 230	 U.S. Department of Health and Human Services. (n.d.). LIHEAP and WAP funding. Retrieved April 21, 2016 from: 

https://liheapch.acf.hhs.gov/Funding/funding.htm 231 U.S. Department of Health and Human Services, Administration for Children and Families. (2016, February). About LIHEAP. 

Retrieved on April 21, 2016 from: http://www.acf.hhs.gov/programs/ocs/programs/liheap/about 232 See Section 2605 of the Low-Income Home Energy Assistance Act of 1981 (Title XXVI of the Omnibus Budget Reconciliation 

Act of 1981, Public Law 97-35, as amended). 233 LIHEAP Clearinghouse. (n.d.). Eligibility: Household income. Retrieved April 30, 2016 from: 

https://liheapch.acf.hhs.gov/delivery/eligibility-houseincome.htm#Income 234 U.S. Department of Health and Human Services. (n.d.). LIHEAP service eligibility FY 2016. Retrieved April 30, 2016 from: 

http://www.acf.hhs.gov/programs/ocs/resource/liheap-eligibility-criteria 235	 Certain needs-tested veterans’ benefits include payments under Veterans’ Benefits, 38 USC §§ 415, 521, 541, or 542, or 

under P.L. 95-588, Section 306 of the Veterans' and Survivors' Pension Improvement Act of 1978. See LIHEAP Clearinghouse. 

(n.d.). LIHEAP assurances. Retrieved May 4, 2016 from: https://liheapch.acf.hhs.gov/pubs/assurances.htm 236	 Perl, L., Congressional Research Service. (2015, July). LIHEAP: Program and funding. CRS Report for Congress (7-5700 

RL31865). Retrieved from National Energy Assistant Director’s Association website at: http://neada.org/wpcontent/uploads/2013/08/CRSLIHEAPProgramRL318651.pdf 

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indicated that there was only a 21 percent and 22 percent participation rate in fiscal year 2009 and FY 

2010, respectively.236 Furthermore, each State has the flexibility to prioritize the distribution of funds; 

some States may process applications for households with seniors first, other States may weight 

households with seniors and children equally, and some States do not prioritize. In addition, many States 

choose to accept applications at only certain times of year.237 

Collection of Personally Identifiable Information (PII) 

States submit data on assisted and participating households to HHS as part of the annual LIHEAP grant 

application.238 The LIHEAP expert indicated that these data are reported in aggregate, however, and do 

not include PII.239 HHS does not maintain PII at the Federal level for the administration of LIHEAP. 

As noted by the LIHEAP expert, States are usually responsible for maintaining PII, but in some States, 

such as Texas and Florida, local agencies maintain the PII collected on LIHEAP applications.240 The data 

collected from individuals on LIHEAP applications can vary by State and local agency. While most 

household applications include fields for first and last name, DOB, and SSN for all household 

members,241 the LIHEAP expert indicated that States at least collect the names and DOBs for all 

household members.242 

The LIHEAP Clearinghouse provides links to the State Energy Assistance Applications for several States 

and the District of Columbia.243 Table C-5 shows PII commonly collected on LIHEAP applications, but only 

in the States that have their applications available on the LIHEAP Clearinghouse. 

237	 LIHEAP Clearinghouse. (n.d.). State cooling programs. Retrieved June 30, 2016 from: 

https://liheapch.acf.hhs.gov/tables/FY2013/cooling.htm 238	 As required by Section 309 of the Human Services Amendments of 1994 (P.L. 103-252). 239	 Expert Interview, February 17, 2016, HHS Division of Energy Assistance. 240	 Ibid. 241	 HHS encourages States to require LIHEAP applicants to provide SSNs, under authority granted in Section 205(c)(2)(C)(i) of the 

Social Security Act. 242	 Expert Interview, February 17, 2016, HHS Division of Energy Assistance. 243	 The National Center for Appropriate Technology (NCAT) operates the LIHEAP Clearinghouse through a training and technical 

assistance contract from the U.S. Department of Health and Human Services, Administration for Children and Families, Office 

of Community Services, Division of Energy Assistance. The Clearinghouse serves as an information resource and sharing 

center for State, tribal, and local LIHEAP providers, and other stakeholders. See https://liheapch.acf.hhs.gov/ 

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Table C-5: Data Elements Available from State LIHEAP Applications 

State Data Elements 

Name DOB SSN Gender 

Alaska A,H A,H A,H A,H 

Colorado A,H A,H A,H A,H 

District of Columbia A,H A,H A,H A 

Idaho A,H A,H A,H A,H 

Massachusetts A,H A,H A,H A 

Minnesota A,H A,H A,H A,H 

Missouri A,H A,H A,H A,H 

Montana A,H A,H A,H A,H 

New Jersey A,H A,H A,H A,H 

North Dakota A,H A,H A,H A 

Ohio A,H A,H A,H --

South Dakota A,H A,H A,H A,H 

Tennessee A,H A,H A,H A,H 

Utah A,H A,H A,H A,H 

Vermont A,H A,H A,H A,H 

Virginia A,H A,H A,H A 

Wyoming A,H A,H A,H A,H 

Source: U.S. Department of Health and Human Services. (n.d.). State low-Income energy profiles. Retrieved April 21, 2016 from: 

https://liheapch.acf.hhs.gov/profiles/energyhelp.htm 

Note. A-indicates that the data element is collected for the applicants; H-indicates the data element is collected for household members. 

Income and Poverty Data 

States collect income information, but the sources of income and period for which applicants must report 

income is variable. The LIHEAP Clearinghouse indicates that most States define income to include wages, 

salaries, and tips, and means-tested benefits such as SSI, Social Security, and veteran’s benefits for 

every member of the household.244 Table C-6 shows, as an example, the sources of income used in 

eligibility determinations in Massachusetts. 

The LIHEAP expert indicated that most States collect household income information for the last 30 days, 

but some collect income for the past 3 months or past year.245 The LIHEAP expert stated that once 

income eligibility is established, applicants are typically certified for 1 year.246 

244 LIHEAP Clearinghouse. (n.d.). Eligibility: Household income. Retrieved April 30, 2016 from: 

https://liheapch.acf.hhs.gov/delivery/eligibility-houseincome.htm#Income 245 Expert Interview, February 17, 2016, HHS Division of Energy Assistance. 246 Year indicates a 12-month period based on a calendar, Federal, or State fiscal year–varies by State. 

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Table C-6: Sources of Income Used in Eligibility Determinations by LIHEAP (Massachusetts Example) 

Source LIHEAP 

(Massachusetts) NSLP 

Wages, salaries, tips, commissions, and cash bonuses X X 

Net income from self-owned business and farms X X 

Strike benefits, unemployment compensation, and worker’s compensation X X 

Military basic pay and cash bonuses (excluding combat pay, Family Substance 

Supplemental Allowance, and privatized housing allowances) -- X 

Allowances for off-base housing, food, and clothing -- X 

Adoption assistance payments -- X 

Unemployment benefits X X 

Worker’s compensation X X 

Supplemental Security Income (SSI) X X 

Regular cash assistance from State or local government X1 X 

Alimony payments -- X 

Child support payments X X 

Veteran’s benefits X X 

Pensions -- X 

Retirement Social Security (including railroad retirement and black lung 

benefits) -- X 

Private pensions or disability benefits X X 

Income from trusts or estates X X 

Annuities X X 

Investment income X X 

Earned interest X X 

Rental income X X 

Regular cash payments from outside household X X 

Any other money that may be available to pay for children’s meals -- X 1 TANF, Emergency Aid to Elders, Disabled and Children (EAEDC). 

Sources: MA Executive Office of Housing and Economic Development (EOHED) (n.d.). Fiscal Year 2016 Massachusetts Low-Income Home 

Energy Assistance (LIHEAP) standardized application items and authorization. Retrieved April 26, 2016 from: 

http://www.mass.gov/hed/docs/dhcd/cd/liheap/fy2016liheapapplication.pdf 

U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals: Determining and verifying 

eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-2015a.pdf 

For Massachusetts and States that use similar sources of income for LIHEAP eligibility determination, 

LIHEAP income data could provide an accurate basis for direct certification determinations in NSLP. As 

indicated by the LIHEAP Expert, most States conduct verification activities, and over 50 percent of States 

already use data matching to verify LIHEAP applicant information against records of SSA, State TANF or 

SNAP systems, and the State Directory of New Hires (see Table C-7).247,248,249 The LIHEAP Expert 

247 Expert Interview, February 17, 2016, HHS Division of Energy Assistance. 248 Alaska verifies LIHEAP applicant and household member data against their State’s prisoner database (ACOM), by downloading 

reports twice a month, and querying based on name, DOB, and partial SSN. Connecticut performs verification against the 

Refugee Cash Assistance Program and State Supplement to the Aged, Blind and Disabled using its DSS Eligibility Management 

System (EMS). Iowa verifies applicant SSNs against documentation including SS card, military ID, financial statements, and 

Medicare card. Montana checks its Public Assistance System which performs matches with SSA’s Bendex system, and also 

verifies all household members over the age of 16 against State wage and unemployment information (MISTICS system). The 

State is currently considering matches with Death Registry System (MDRS) and other databases. Oregon requires multiple 

forms of documentation and maintains a statewide database for LIHEAP households (OPUS) that includes personal identifiers 

(name, DOB, SSN, and physical address of all household members). Washington State verifies LIHEAP applicant identities 

using State ID, SS card, and birth certificates for children against TANF, food assistance, and other State services. In 2014, 

New Jersey had plans to implement verification against SSA systems. Utah maintains a central database (State Energy 

Assistance Lifeline (SEALWorks) and stores applicant information for cross-check against the Utah Department of Workforce 

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indicated that LIHEAP income data are also verified against State wage and unemployment data in States 

that maintain a State Department of Labor (DOL) database.250 

Table C-7: States that Verify Income Reported in LIHEAP Using Data Matching with Other Agency Records 

State Other Agency Records 

State TANF or SNAP 

System State DOL SSA State Directory of New Hires 

Alaska X X -- --

District of Columbia X -- -- --

Georgia X -- X --

Idaho X X -- --

Kansas X X X --

Maryland X X X --

Massachusetts X -- X --

Missouri X X X --

Montana X X -- --

Nebraska X X X X 

Nevada X X X X 

New Mexico X X X X 

North Carolina X X X X 

North Dakota -- X X X 

New York X X -- --

Ohio -- -- -- --

Oklahoma X X X X 

Pennsylvania X -- X X 

South Dakota X X X --

Tennessee X -- -- --

Utah X X -- --

Virginia X X X --

Washington X X X X 

West Virginia X X X X 

Wisconsin X X X X 

Wyoming X -- -- --

Source: FY 2016 model plan data received from expert at HHS. 

Recertification Periods 

Because LIHEAP funds are administered as a block grant, benefits are tied to the funds available from a 

State’s individual appropriations. As a result, recertification periods vary by State. Each State also 

determines how often eligible applicants receive benefits before they must provide updated income 

information. The LIHEAP expert noted that States typically allow individuals to receive LIHEAP assistance 

three times per year, but recipients are not necessarily required to recertify at those times.251 The LIHEAP 

Services. E-Share Statewide Prisoner and Wage databases, along with the Utah DOH, Vital Records Office will also be accessed 

for LIHEAP verification in the future. 249	 U.S. Department of Health and Human Services. (n.d.). LIHEAP identity verification examples from states. Retrieved April 21, 

2016 from: https://liheapch.acf.hhs.gov/delivery/verification_identityexamples.htm 250 Expert Interview, February 17, 2016, HHS Division of Energy Assistance. 251 Expert Interview, February 17, 2016, HHS Division of Energy Assistance. 

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expert also indicated that most States use eligibility determinations for a 12-month period before 

requiring recertification.252 

Data Sharing and Privacy Protections 

The LIHEAP expert noted that data sharing agreements for LIHEAP are negotiated by the States.253 

LIHEAP SAs that verify income through data matching usually have agreements with the SSA, and with the 

SAs administering SNAP and TANF.254 

The LIHEAP expert also indicated that most States include release of information clauses or disclosures 

on the LIHEAP application, informing applicants that their information may be used in the administration 

of LIHEAP benefits.255 The LIHEAP statute does not govern data sharing, and each State adheres to 

Federal and State privacy laws and regulations. At the State level, the LIHEAP expert noted that there are 

at least 15 States that have data exchanges with the SSA for income verification. The LIHEAP expert also 

stated that some States also have data exchanges with SNAP, TANF, and some other State-level social 

services programs. 

Discussion 

Nearly one-third of SAs administering LIHEAP collect the data elements needed to conduct direct 

certification matching in the NSLP.256,257 However, this was assessed only for States with State LowIncome Energy Profiles on the LIHEAP Clearinghouse, and where applications were available online. It is 

therefore possible that more than one-third collect relevant data elements. Of the 17 applications that 

were available for review on the LIHEAP Clearinghouse website, most collected the first and last names 

and DOB for all household members, allowing school-age children to be matched to student enrollment 

records. 

Data matching with LIHEAP has the potential to aid in the expansion of direct certification and verification 

efforts in the NSLP. Since the income data used to determine LIHEAP eligibility is calculated as FPL, it 

could be used to directly certify students for meals in the NSLP, especially in States where LIHEAP income 

definitions are similar to those used in the NSLP. Establishing agreements is unlikely to be difficult in 

States that already exchange data with several other agencies, such as SSA and State SNAP and TANF 

agencies. 

States are also encouraged to require SSN for LIHEAP eligibility, which would contribute to data quality 

and the identification of school-age children associated with applicants. 

NSLP SAs seeking to use LIHEAP for direct certification could extend categorical eligibility to school-age 

children in households that received LIHEAP assistance within the last 12 months, but this would involve 

certifying students in families between 130 and 150 percent of FPL. It would also be possible to request 

lists of children in households participating in LIHEAP with incomes 130 percent of FPL. In States that 

operate a statewide LIHEAP database, student enrollment records could be matched against data for the 

current year’s LIHEAP recipients. However, LIHEAP recertification periods may present a challenge in 

some States. Income qualifications are not reassessed at each point of service in most States, and can 

252 Ibid.
 253 Ibid.
 254 This arrangement is between the Federal Social Security Administration and the LIHEAP State agency.
 255 Expert Interview, February 17, 2016, HHS Division of Energy Assistance.
 256 LIHEAP Clearinghouse. (n.d.). State low-income energy profiles. Retrieved April 21, 2016 from:
 

https://liheapch.acf.hhs.gov/profiles/energyhelp.htm 257 Out of 50 State agencies, District of Columbia, American Samoa, Common Wealth of the Northern Mariana Islands, Guam, 

Puerto Rico, and Virgin Islands. See http://www.acf.hhs.gov/programs/ocs/liheap-state-and-territory-contact-listing 

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be up to 12 months old in most States. NSLP SAs may also need to assess whether LIHEAP definitions of 

income are similar to those used in the NSLP in their State. 

(5) Weatherization Assistance Program (WAP) 

Background 

Created in 1976 under Title IV of the Energy Conservation and Production Act, WAP is administered 

through States, the District of Columbia, U.S. territories, and Federally recognized Indian Tribes.258,259 The 

DOE provides formula grants to SAs, who contract with local governments and nonprofit agencies. 

Low-income families apply for cost-effective weatherization services by contacting their local 

weatherization agency, and providing information about their household size and income. Families that 

receive SSI or TANF are automatically eligible for weatherization assistance under DOE guidelines. 

Persons over 60 years of age, families with a member with a disability, and families with children are also 

given priority by States.260 

The Act also amended several aspects of the administration of WAP, including changing income eligibility 

limits and benefit levels. This includes: 

1. the increase of the income eligibility threshold from 150 percent to 200 percent of FPL, and 

2. the increase of the maximum assistance level per dwelling unit from $2,500 to $6,500.261 

Collection of Personally Identifiable Information (PII) 

The collection of PII used to determine eligibility can vary by State and the local administering agency. 

However, a generic application provided by the Weatherization Assistance Program Technical Assistance 

Center (WAPTAC)262 suggests collecting the following information: 

• Last Name • First Name 

• Middle Initial • Street Address 

• City	 • County 

• Zip Code • Home/Work/Cell Phone Number 

• Mailing Address • City 

• Zip 

Source: Weatherization Assistance Program Technical Assistance Center (WAPTAC). (n.d.). Weatherization program client 

application. Retrieved April 21, 2016 from: http://www.waptac.org/Best-Practices.aspx#Client-Outreach 

Income and Poverty Data 

Each State can set its own income eligibility requirements for WAP, at or below 200 percent of FPL.263 

Eligibility based on income qualification is established by assessing an applicant’s earned and received 

gross income.264 

258 D.C. Department of Energy and Environment. (n.d.). Weatherization Assistance Program. Retrieved April 29, 2016 from: 

http://doee.dc.gov/service/weatherization-assistance-program 259	 Tonn, B., Carrol, D., Pigg, S., Blasknik, M., Dalhoff, G., Berger, J., . . . Cowan, C. (2014). Weatherization works—Summary of 

findings from the retrospective evaluation of the U.S. Department of Energy's Weatherization Assistance Program. (Report No. 

ORNL/TM-2014/338). USDOE Office of Energy Efficiency and Renewable Energy (EERE): United States. 

doi:10.2172/1223642. Retrieved from U.S. Department of Energy Sci-Tech Connect website: 

http://www.osti.gov/scitech/biblio/1223642 260 U.S. Department of Energy. (n.d.). Where to apply for weatherization assistance. Retrieved April 22, 2016 from: 

http://energy.gov/eere/wipo/where-apply-weatherization-assistance e 261 The Energy Conservation and Production Act of 1976 (42 U.S.C. 6865(c)1 § 415(c). 262 The Weatherization Assistance Program Technical Assistance Center provides a “virtual” library of all rules, regulations, 

policies, and procedures required by the DOE’s weatherization program. See http://www.waptac.org/ 

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WAPTAC suggests that local service providers request documentation supporting any income reported on 

a WAP application, and include fields to record the income of all household members. Documentation 

varies by State, but may include pay stubs for the past 3 months for each employed household member, 

and income from Social Security, retirement, disability, alimony, workers’ compensation, net rental 

income, net gambling or lottery winnings, unemployment compensation, royalties, periodic payments from 

estates or trusts, and self-employment (see Table C-8).265 

The expert on WAP noted that applicants indicating participation in TANF, SSI, LIHEAP, HUD housing 

assistance programs, or other assistance programs that confer categorical eligibility for WAP, must 

usually also provide documentation such as a printout from the local TANF office, a monthly SSI 

statement, or a copy of LIHEAP eligibility.266,267,268 

Table C-8: Sources of Income Used in Eligibility Determinations in the WAP and the NSLP 

Income Source1 WAP NSLP 

Wages, salaries, tips, commissions, and cash bonuses X X 

Net income from self-owned business and farms X X 

Strike benefits, unemployment compensation, and worker’s 

compensation X X 

Military basic pay and cash bonuses (excluding combat pay, Family 

Substance Supplemental Allowance, and privatized housing 

allowances) 

X X 

Allowances for off-base housing, food, and clothing -- X 

Adoption assistance payments -- X 

Unemployment benefits X X 

Worker’s compensation X X 

Supplemental Security Income (SSI) X X2 

Regular cash assistance from State or local government -- X 

Alimony payments X X 

Child support payments -- X 

Veteran’s benefits X3 X4 

Pensions X X 

Retirement Social Security (including railroad retirement and black 

lung benefits) X X 

Private pensions or disability benefits X X 

Income from trusts or estates X X 

Annuities X X 

Investment income X X 

Earned interest X X 

Rental income X X 

263 Weatherization Assistance for Low-Income Persons, 10 C.F.R. §440.22. 264 U.S. Department of Energy. (2016, February 10). 2016 poverty income guidelines and definition of income. Retrieved on April 

21, 2016 from Weatherization Assistance Program Technical Assistance Center website: 

http://www.waptac.org/data/files/website_docs/government/guidance/2016/wpn_16-3-pigs-final.pdf 265 The Weatherization program client application can be found in the Client and Outreach Forms section at 

http://www.waptac.org/Best-Practices.aspx#Client-Outreach 266	 U.S. Department of Energy. (2016, February 10). 2016 poverty income guidelines and definition of income. Retrieved on April 

21, 2016 from Weatherization Assistance Program Technical Assistance Center website: 

http://www.waptac.org/data/files/website_docs/government/guidance/2016/wpn_16-3-pigs-final.pdf 267 The Weatherization program client application can be found in the Client and Outreach Forms section at 

http://www.waptac.org/Best-Practices.aspx#Client-Outreach 268 Expert Interview, March 4, 2016, DOE Weatherization and Intergovernmental Programs Office. 

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Regular cash payments from outside household -- X 

Any other money that may be available to pay for children’s meals -- X 1 WAP also considers net gambling or lottery winnings. 

2 Supplemental Security Income (SSI) is not included in income determinations; however Social Security Disability Income (SSDI) is 

included. 

3 Veteran’s payments, training stipends, and military allotments. 4 Veteran’s disability payments only. 

Sources: U.S. Department of Energy. (2016, February 10). 2016 poverty income guidelines and definition of income. Retrieved on April 21, 

2016 from Weatherization Assistance Program Technical Assistance Center website: 

http://www.waptac.org/data/files/website_docs/government/guidance/2016/wpn_16-3-pigs-final.pdf 

U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals: Determining and verifying 

eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-2015a.pdf 

Recertification Periods 

The WAP expert noted that applicants and recipients must recertify income within 12 months of service, 

but States have the discretion to implement more restrictive recertification periods.269 In some States, 

recertification occurs on a quarterly basis; in others, every 6 months. For States with limited funding 

compared to demand, applicants on waiting lists for more than a 12-month period must have their 

income recertified to remain on the list. 

Data Sharing and Privacy Protections 

At the Federal level, the WAP expert indicated that DOE does not have existing MOUs for data sharing 

agreements with WAP. DOE often partners with HHS for LIHEAP.270 However, the WAP expert indicated 

that this is an informal arrangement, where WAP monitoring reports are shared with LIHEAP staff, and 

quarterly calls and technical assistance are exchanged; it does not include the exchange of PII.271 

This arrangement developed primarily because of the overlap between LIHEAP and WAP services and 

funding at the State and local levels. States are allowed to allocate LIHEAP funds to weatherization 

programs.272 DOE regulations also permit States to extend categorical eligibility to households that 

receive LIHEAP benefits, allowing participants in LIHEAP to automatically qualify to receive weatherization 

services.273 

The WAP expert indicated that in States where LIHEAP and WAP are administered by the same agency, 

the benefits database for both programs is often connected, with LIHEAP recipient information available 

immediately for categorical certification in WAP.274 For other States, where the administering agencies 

differ, or where benefits information is housed separately, LIHEAP recipient information is transferred to 

WAP agencies based on priority of service. The WAP expert also indicated that data matches may also be 

performed to collect utility bill information. Applicants agree to authorize the release of energy utility bills 

and income data requested for participation in WAP.275 

269 Expert Interview, March 4, 2016, Department of Energy Weatherization and Intergovernmental Programs Office.
 270 Ibid.
 271 Ibid.
 272 Tonn, B., Carrol, D., Pigg, S., Blasknik, M., Dalhoff, G., Berger, J., . . . Cowan, C. (2014). Weatherization works—Summary of
 

findings from the retrospective evaluation of the U.S. Department of Energy's Weatherization Assistance Program. (Report No. 

ORNL/TM-2014/338). USDOE Office of Energy Efficiency and Renewable Energy (EERE): United States. 

doi:10.2172/1223642. Retrieved from U.S. Department of Energy Sci-Tech Connect website: 

http://www.osti.gov/scitech/biblio/1223642 273 Weatherization Assistance for Low-Income Persons, 10 C.F.R. 440.22 (2011). 274 Expert Interview, March 4, 2016, Department of Energy Weatherization and Intergovernmental Programs Office. 275 The Weatherization program client application can be found in the Client and Outreach Forms section at 

http://www.waptac.org/Best-Practices.aspx#Client-Outreach 

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Discussion 

WAP does not seem to be a feasible candidate for direct certification and verification efforts in the NSLP. 

Household income and FPL determinations may be inaccurate, and may be based upon incomplete 

information about all members in the household. SSNs are not typically collected, and therefore reported 

income may not include all sources. 

In addition, most States and local districts use first name, last name, and DOB in their direct certification 

matching process in the NSLP.276 States that collect only the PII recommended by WAPTAC would lack the 

DOB identifier, the third most commonly used data element for data matching in the NSLP. Without DOB 

as a primary identifier for data matching efforts, Child Nutrition (CN) SAs may have difficulty confirming 

whether a WAP record is an exact match to a school-age child appearing in enrollment records. 

Income disclosed on WAP applications may have potential use for NSLP direct verification, especially in 

States where WAP certification periods occur within 180 days of service.277 For other States, income 

reported to WAP may be up to 12 months old, depending upon the time of initial service or application. 

For these States, further verification might be needed to ensure the accuracy of reported income, causing 

additional administrative burden. 

The income eligibility threshold for WAP exceeds that of the NSLP income threshold. However, WAP may 

capture additional households with incomes within the percentage of FPL required to receive F/RP meals 

in the NSLP. The income definitions used for WAP also generally align with the definitions used in the 

NSLP, and income eligibility determinations are likely to be similar across the two programs. 

However, many WAP participants are referrals from the LIHEAP program who need further assistance with 

their home energy costs, or who may benefit from weatherization services.278 Clients categorically eligible 

to receive weatherization assistance based on participation in LIHEAP have incomes within 150 percent 

of FPL. LIHEAP data collection and verification processes are more robust than WAP, and would provide a 

higher quality source of information. 

In sum, the WAP expert indicated that data matching with LIHEAP records, rather than WAP records 

directly, would probably more efficiently capture school-age children in WAP households. 

(6) Earned Income Tax Credit (EITC) 

Background 

The EITC was first introduced in 1975 in an effort to guarantee a minimum standard of living as part of an 

anti-poverty program, and currently provides savings for working people within low- to moderate-income 

levels.279 In 2015, a reported 27.5 million tax filers received EITC credits totaling $66.7 billion.280 

276 U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. (2014). The National School Lunch 

Program direct certification improvement study: Main report. Retrieved April 28, 2016 from 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 277 180 days would potentially satisfy the definition of “the most recently available information” clause specified in P.L. 108-265 

and described in the Richard B. Russell National School Lunch Act. See 7 C.F.R. Parts 245.6 (g). 278 Tonn, B., Carrol, D., Pigg, S., Blasknik, M., Dalhoff, G., Berger, J., . . . Cowan, C. (2014). Weatherization works—Summary of 

findings from the retrospective evaluation of the U.S. Department of Energy's Weatherization Assistance Program. (Report No. 

ORNL/TM-2014/338). USDOE Office of Energy Efficiency and Renewable Energy (EERE): United States. 

doi:10.2172/1223642. Retrieved from U.S. Department of Energy Sci-Tech Connect website: 

http://www.osti.gov/scitech/biblio/1223642 279 Hotz, V., & Scholz, J. (2001, January). The earned income tax credit. National Bureau of Economic Research, Working Paper 

No. 8078. Retrieved from: http://www.nber.org/papers/w8078 

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To qualify, individuals must have an earned income, with or without qualifying children, and must provide 

an SSN valid for employment.281 Qualifying children must have an SSN that is valid for employment, and 

pass tests for relationship, age, and residency; they can only be claimed on one individual or joint 

return.282 Eligible filers pay reduced annual taxes, or may receive a tax refund.283 

Collection of Personally Identifiable Information (PII) 

Individuals must complete Schedule EIC and attach it to Form 1040A, 1040, or 1040EZ. Form 1040A or 

1040 must be used to claim a qualifying child. On Schedule EIC, tax filers report the first and last name, 

SSN, year of birth, whether the child was permanently and totally disabled for the tax year, the 

relationship of the qualifying child to the filer, and the number of months the child resided in the same 

residence during the tax year.284 Table C-9 shows the PII and data elements collected for tax filers and 

dependents on forms 1040A, 1040, and 1040EZ. 

280 Internal Revenue Service. (2016, January). Statistics for tax returns with EITC. Retrieved April 22, 2016 from: 

https://www.eitc.irs.gov/EITC-Central/eitcstats 281 Internal Revenue Service. (2016, March 7). Do I qualify for EITC? Retrieved on April 22, 2016 from: 

https://www.irs.gov/credits-deductions/individuals/earned-income-tax-credit/do-i-qualify-for-earned-income-tax-credit-eitc 282 Internal Revenue Service. (2015, November 3). Qualifying child rules. Retrieved on April 22, 2016 from: 

https://www.irs.gov/credits-deductions/individuals/earned-income-tax-credit/qualifying-child-rules 283 Internal Revenue Service. (2016, March 11). Earned Income Tax Credit (EITC). Retrieved April 22, 2016 from: 

https://www.irs.gov/credits-deductions/individuals/earned-income-tax-credit 284 Internal Revenue Service. (2015). Earned Income Tax Credit Qualifying Child Information: Schedule EIC. Retrieved April 22, 

2016 from: https://www.irs.gov/pub/irs-pdf/f1040sei.pdf 

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Table C-9: PII Collected on Tax Forms Required to Claim the EITC 

PII Collected 1040A 1040 1040EZ 

Tax filer first, last name, and middle 

initial X X X 

Spouse’s first, last name, and middle 

initial1 X X X 

Tax filer SSN X X X 

Spouse’s SSN X X X 

Home address X X X 

City, State, Zip X X X 

Foreign country name X X X 

Foreign province/state/country X X X 

Foreign postal code X X X 

Filing status2 X X --

Exemptions3 X X --

Dependent(s) first, last name X X --

Dependent(s) SSN X X --

Dependent(s) relationship X X --

Qualifying child indicator X X -- 1 If filing a joint return. 

2 Single, married filing jointly, married filing separately, head of household, qualifying widow(er) with dependent child 

3 Tax filer and/or spouse. 

Sources: Internal Revenue Service. (2015). U.S. Individual Income Tax Return: Form 1040A. Retrieved April 22, 2016 from 

https://www.irs.gov/pub/irs-pdf/f1040a.pdf 

Internal Revenue Service. (2015). U.S. Individual Income Tax Return: Form 1040. Retrieved April 22, 2016 from: 

https://www.irs.gov/pub/irs-pdf/f1040.pdf 

Internal Revenue Service. (2015). Income Tax Return for Single and Joint Filers with No Dependents: Form 1040EZ. Retrieved April 22, 

2016 from: https://www.irs.gov/pub/irs-pdf/f1040ez.pdf 

Internal Revenue Service. (2015). Earned Income Tax Credit Qualifying Child Information: Schedule EIC. Retrieved April 22, 2016 from: 

https://www.irs.gov/pub/irs-pdf/f1040sei.pdf 

Income and Poverty Data 

Earned and AGI must be lower than the minimum income eligibility limits set for each filing year, and are 

based on filing status and the number of qualifying children claimed.285,286,287 Only four sources of 

income used to determine eligibility are common between EITC and the NSLP (Table C-10). These are:288 

1.	 wages, salaries, tips, commissions, and cash bonuses; 

2.	 net income from self-owned business and farms; 

3.	 strike benefits, unemployment compensation, and worker’s compensation; and 

4.	 military basic pay and cash bonuses (excluding combat pay, Family Substance Supplemental 

Allowance, and privatized housing allowances).289 

Table C-10: Sources of Income Used in Eligibility Determinations for the EITC and Meal Benefits in the NSLP 

Sources of Income Used in Eligibility Determinations EITC NSLP 

285 Includes wages, salaries, tips and other taxable employee pay, union strike benefits, long-term disability benefits received prior 

to minimum retirement age, and net earnings from self-employment. 286 Defined as gross income minus adjustments to income. See https://www.irs.gov/uac/definition-of-adjusted-gross-income 287 For these limits, see Internal Revenue Service. (2016, March 7). Do I qualify for EITC? Retrieved April 22, 2016 from: 

https://www.irs.gov/credits-deductions/individuals/earned-income-tax-credit/do-i-qualify-for-earned-income-tax-credit-eitc 288 Internal Revenue Service. (2015, November 2). What is earned income? Retrieved from: https://www.irs.gov/creditsdeductions/individuals/earned-income-tax-credit/earned-income 289 Tax filers can elect to have their Nontaxable Combat pay included in earned income for EITC. 

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Wages, salaries, tips, commissions, and cash bonuses X X 

Net income from self-owned business and farms X X 

Strike benefits, unemployment compensation, and worker’s compensation X X 

Military basic pay and cash bonuses (excluding combat pay, Family Substance Supplemental 

Allowance, and privatized housing allowances) X1 X 

Allowances for off-base housing, food, and clothing -- X 

Adoption assistance payments -- X 

Unemployment benefits -- X 

Worker’s compensation -- X 

Supplemental Security Income (SSI) -- X2 

Regular cash assistance from State or local government -- X 

Alimony payments -- X 

Child support payments -- X 

Veteran’s benefits -- X3 

Pensions -- X 

Retirement Social Security (including railroad retirement and black lung benefits) -- X 

Private pensions or disability benefits -- X 

Income from trusts or estates -- X 

Annuities -- X 

Investment Income -- X 

Earned interest -- X 

Rental income -- X 

Regular cash payments from outside household -- X 

Any other money that may be available to pay for children’s meals -- X 1 Tax filers can elect to have their Nontaxable Combat pay included in earned income for EITC. 2 Supplemental Security Income (SSI) is not included in income determinations; however Social Security Disability Income (SSDI) is 

included. 

3 Veteran’s disability payments only. 

Sources: Internal Revenue Service. (2015, November) What is earned income? Retrieved April 22, 2016 from: 

https://www.irs.gov/Credits-&-Deductions/Individuals/Earned-Income-Tax-Credit/What-is-Earned-Income 

U.S. Department of Agriculture, Food and Nutrition Service. (2015, July). Eligibility manual for school meals determining and verifying 

eligibility. Retrieved April 21, 2016 from: http://www.fns.usda.gov/sites/default/files/cn/SP40_CACFP18_SFSP20-2015a.pdf 

Recertification Periods 

Tax filers may claim the EITC annually when submitting tax returns. Eligibility determinations are based on 

income earned in the reporting tax year. 

Data Sharing and Privacy Protections 

Disclosure of information collected on tax returns is governed under IRS Code Section 6103. The IRS 

may, upon written request, disclose return information about unearned income to Federal, State, and 

local agencies administering certain assistance programs covered under the Food and Nutrition Act of 

2008.290 The SSA is also allowed to disclose return information for this purpose, but only information on 

net earnings from self-employment, wages, and retirement income payments. 

Discussion 

The EITC has a sufficient set of reliable PII to support matching with student enrollment records. The first 

and last name, birth year, and SSN are collected for children listed by tax claimants on Schedule EIC. Tax 

filers indicate whether dependents are children under the age of 17 years on Forms 1040A and 1040. 

Together, these forms provide important data elements needed to perform effective matches against 

student enrollment records for direct certification in the NSLP. 

290 Internal Revenue Code, Confidentiality and disclosure of returns and return information, 26 U.S. Code §6103(l)(7) 

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Although income information disclosed for EITC could potentially be used for direct certifications based on 

income eligibility determinations, it may be 6 to 9 months old, depending upon when a State/district 

performs initial direct certifications.291 Thus, income information reported for EITC might be too old for 

use in direct verification.292 

The IRS is not currently permitted to share data with the NSLP.293 SAs and districts would need legislative 

authorization to access IRS data. IRS personnel indicated that data matches with EITC would require 

legislative changes to the IRS Code. MOUs could not be written without these legislative changes. IRS 

experts also noted that disclosure provisions in the IRS Code would be barriers to using EITC data for 

direct certification and verification.294 

Another limitation of EITC data is that the income information collected may not include all members 

residing in the household, and can also include tax dependents not living in the household. A spouse 

residing in the same household may not be listed at all, for example, if joint tax returns are not filed. 

Conversely, children can be claimed as tax dependents regardless of whether they reside with the tax 

filer. The estimates of household size and of income as a percent of FPL needed to determine income 

eligibility in the NSLP could therefore differ from those calculated from EITC records, limiting the utility of 

EITC data for direct verification. 

High improper payment rates would also negate the accuracy of any eligibility determinations based on 

EITC, IRS experts stated. The IRS estimates that 22 to 26 percent of EITC payments are made in error. 

Ambiguous relationships of children to tax filers and of residency account for approximately 70 percent of 

these errors. These errors often involve non-custodial parents claiming children from divorced and 

separated households.295 

(7) Income and Eligibility Verification System (IEVS) and 

Unemployment Insurance (UI) 

Background 

The IEVS was established under DEFRA in an effort to reduce errors in eligibility and benefit-level 

determinations.296 States are required to implement an IEVS and verify applicant income information 

collected in certain programs, including Medicaid, unemployment compensation, SNAP, TANF, and child 

welfare services, as well as in such State programs as elderly assistance, SSI, and other assistance 

programs for the blind and disabled.297,298 

291 Initial direct certification matching typically occurs in July, August, or September. 292 Districts must use income information reflecting income during the last 180-day period immediately prior to the date of school 

meals application. 293 Established under the National School Lunch Act in 1946, P.L. 396, 79th Congress, June 4, 1946, 60 Stat. 231. See 

http://www.fns.usda.gov/nslp/national-school-lunch-program-nslp 294	 Expert Interview, April 8, 2016, IRS Government Liaison Headquarters Office, Office or Research, Headquarters Disclosure 

Office, Refundable Credits Administration (RCA). 295	 Center on Budget and Policy Priorities. (2015, December 1). Reducing overpayments in the Earned Income Tax Credit. 

Retrieved May 2, 2016 from: http://www.cbpp.org/research/federal-tax/reducing-overpayments-in-the-earned-income-taxcredit 296	 U.S. Department of Health and Human Services, Office of Inspector General. (1994, October). State income and eligibility 

verification systems (IEVS): State profiles, part I, Alabama through Montana. Retrieved May 4, 2016 from: 

http://oig.hhs.gov/oei/reports/oei-06-92-00081.pdf 297 See Income and eligibility verification system, Applicable programs, 42 U.S.C. 1320b—7(b). 298 Use of the IEVS for SNAP was made optional by the Personal Responsibility and Work Opportunity Act of 1996 (PRWORA) ((PL 

104-193). See http://www.fns.usda.gov/sites/default/files/ComputerMatching_summary.pdf 

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SAs administering the programs mentioned above verify eligibility and benefit issuance determinations 

against wage and income information maintained by SSA and IRS, and quarterly wage information 

reported by employers to the State Wage Information Collection Agency (SWICA).299, 300 Employers report 

wages for each employee within 30 days from the end of each calendar quarter. IEVS also requires that 

programs administered by SAs adhere to standardized formats and procedures that facilitate data 

exchanges among participating programs.301 This information is also required to be available for child 

support programs, Federal Old-Age, Survivors, and Disability Insurance benefits, and SSI for aged, blind, 

and disabled. 

Collection of Personally Identifiable Information (PII) 

The wage information in IEVS contains the SSN, first and last name, and middle initial for all employees 

covered by the State’s unemployment compensation laws.302 SAs administering the participating 

programs must collect the SSN of applicants “as a condition of eligibility for benefits.”303 

Income and Poverty Data 

Information for all household members is available in IEVS, including, but not limited to: 

1.	 wage information from SWICA; 

2.	 net earnings from self-employment, wages, and retirement income, and survivors, disability, SSI 

and related benefits from SSA; 

3.	 unearned income reported to the IRS; and 

4.	 wage and claim information from the agency administering UI benefits. 

Recertification Periods/Frequency of Updates to IEVS 

Employers are required to report wage information to SWICA within 30 days from the end of each 

calendar quarter.304 To request and receive data from SWICA, SAs would have to enter into agreements 

with their SWICA on the allowable frequency of requests, and these would probably vary. However, the 

SWICA and UI benefits agency cannot provide information to other SAs more than twice monthly.305 

Data Sharing and Privacy Protections 

SAs are allowed to exchange information on households in IEVS with other programs having potential for 

establishing or verifying eligibility or benefit amounts, including: 

1.	 TANF; 

2.	 Medicaid; 

3.	 Unemployment Compensation; 

4.	 SNAP; 

5.	 Any State program administered under the adult categories (Title I, X, or XIV) or Title XVI of the 

Social Security Act; and 

299 See Income and eligibility verification system, Requirements of State eligibility systems, 42 U.S.C. 1320b—7(a)(2).
 300 See Income and eligibility verification requirements, 45 C.F.R. 205.51(b).
 301 See Income and eligibility verification system, Requirements of State eligibility systems, 42 U.S.C. 1320b—7(a)(4)(A).
 302 See Income and eligibility verification requirements, 45 C.F.R. 205.51(c).
 303 See Income and eligibility verification system, Requirements of State eligibility systems, 42 U.S.C. 1320b—7(a)(1).
 304 See Income and eligibility verification requirements, 45 C.F.R. 205.51(c)(2).
 305 See Income and eligibility information; specific agreements required between the State agency and the agency supplying the
 

information, 45 C.F.R. 205.58(b)(3). 

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6.	 Agencies administering the Child Support Program; Federal Old-Age, Survivors, and Disability 

Insurance benefits; and SSI for the Aged, Blind, and Disabled.306 

The information exchanged and received for applicant and recipient households through IEVS must be 

adequately protected against unauthorized disclosure, as provided in regulations established by the 

Secretaries of Agriculture, Health and Human Services, and Labor, and by the IRS.307,308 States must also 

notify all applicants and recipients of benefits under any participating program, “at the time of 

application, and periodically thereafter,” that information available in IEVS will be used to determine and 

verify eligibility for the program.309 

No Federal, State, or local agency may use information in IEVS to terminate, deny, suspend, or reduce any 

of an individual’s benefits without independently verifying information on the asset or income involved, 

and the period when the individual actually had access to it. The individual has a right to contest such 

findings. This is to protect applicants for, and recipients of, benefits under these programs.310 

Discussion 

The promise and potential of establishing IEVS systems is clear: the complex data on income and public 

assistance program participation could be centralized into a single system, enabling several State 

agencies to conduct one-stop verifications of the eligibility and benefit-level determinations for the 

means-tested assistance programs they administer. 

The CN Programs expert indicated that the impetus for establishing such programs was rooted, on one 

hand, in reducing the burden needed to certify or verify individuals and families for benefits under several 

programs, and thus increasing participation in these programs by those eligible for benefits. On the other 

hand, the IEVS system may also provide a tool for identifying and reducing improper payments 

attributable to errors in determining eligibility and benefits to which participants were entitled. 

This promise and potential remains strong. While DEFRA mandates that certain agencies participate in an 

IEVS, nonparticipant agencies are adopting the components of IEVS as a model in the administration of 

other public assistance programs. HUD, for example, is in the process of developing an Upfront Income 

Verification system for its PHAs to use for income eligibility verification, and setting rent levels and utility 

assistance for the Public Housing and Housing Choice Voucher programs. HUD has signed agreements 

with 25 States, and is in the process of negotiating agreements with 13 more.311,312 

There is also evidence, however, that some agencies have concluded that IEVS systems fall short in 

meeting some objectives for which they were designed. In 2014, the HHS ACF announced funding for a 

$3.5 million study in at least three pilot States to improve TANF’s program integrity by finding alternative 

processes that could better detect overpayments and underpayments than IEVS. Alternatives included 

enhancing the use of data matches with the National Directory of New Hires to verify employment and 

306 See Income and eligibility verification system, Applicable programs, 42 U.S. Code § 1320b–7.
 307 See Income and eligibility verification requirements, 45 CFR 205.51.
 308 For Department of Agriculture (SNAP) disclosure regulations, see 7 C.F.R. 272.1(c)(2); for Health and Human Services, see 42
 

C.F.R. 431.306(g); for Department of Labor (UC) disclosure regulations, see 20 C.F.R. 603; for Internal Revenue Service 

disclosure regulations see Section 6103(l) of the Internal Revenue Code. 309 See Income and eligibility verification system, Requirements of State eligibility systems, 42 U.S. Code § 1320b–7(a)(6). 310 See Income and eligibility verification system, Protection of applicants from improper use of information, 42 U.S. Code § 

1320b–7(c)(1). 311 District of Columbia, Florida, Iowa, Illinois, Indiana, Kansas, Louisiana, Massachusetts, Michigan, Minnesota, Missouri, North 

Carolina, North Dakota, Nebraska, New Mexico, New York, Ohio, Pennsylvania, Rhode Island, South Carolina, South Dakota, 

Texas, Vermont, and West Virginia. 312	 Alabama, Arkansas, California, Colorado, Hawaii, Kentucky, Maine, New Hampshire, Tennessee, Utah, Virginia, Washington, 

and Wyoming. 

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earnings data, and using SNAP Quality Control data and methods to improve TANF program integrity 

benefits.313 They also sought innovations that might better identify families who are eligible for TANF, but 

are not receiving benefits. 

In addition, the most recent review of direct verification practices for the NSLP found that only 25 States 

had an SFA that conducted direct verification in SY 2011–12, and half of all States (50 percent, N = 26) 

have never conducted direct verification.314 Almost all of the 25 States where direct verification was 

conducted used SNAP (92 percent) and TANF (88 percent), and some used Medicaid (40 percent) or CHIP 

(8 percent). None used State Unemployment Office files, and no use of an IEVS system was reported. 

Important to consider, however, is that currently, only the last four digits of one household member’s SSN 

is required to apply for NSLP benefits.315 Use of available Unemployment Compensation and IEVS 

systems without collecting additional SSN identifiers for household members would therefore seem costeffective only if FNS returned to collecting the full SSN of all family members. 

313	 U.S. Department of Health and Human Services, Administration for Children and Families. (2014, May). Partnership fund pilot 

award summary: Identifying State innovations for improving Temporary Assistance for Needy Families (TANF) program 

administration. Retrieved May 4, 2016 from: https://www.partner4solutions.gov/files/2014/05/TANF-Pilot-AwardSummary.pdf 314	 U.S. Department of Agriculture, Food and Nutrition Services, Office of Policy Support. (2014, March). Special nutrition program 

operations study: State and school food authority policies and practices for school meals programs school year 2011-12 

(Nutrition Assistance Program Report). Retrieved May 4, 2016 from: 

http://www.fns.usda.gov/sites/default/files/SNOPSYear1.pdf 315	 A copy of the Free & Reduced Price School Meals Family Application may be found at: 

https://www.education.ne.gov/NS/forms/nslpforms/Attachments/C.pdf 

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Appendix D. Expert Interview Guide 

Background 

The U.S. Department of Agriculture has contracted 2M Research Services to obtain more information 

about the participant data maintained for programs such as the [Name of Program] program. Our study is 

designed to identify data from means-tested programs that may have potential for use in the direct 

certification of children in the National School Lunch Program (referred to as the NSLP) and School 

Breakfast Program (referred to as the SBP), and for the direct verification of household income. 

First, I want to give you some quick background information on how the NSLP works. 

Children in households under 130 percent of the Federal Poverty Level are eligible for free meals, and 

those with household incomes between 130 and 185 percent of the Federal Poverty Level are eligible for 

reduced price meals. Families can submit applications with income and household size, or provide case 

numbers demonstrating participation in SNAP, TANF, FDPIR, or other means-tested programs. State and 

local agencies can also perform direct certification by computer matching their student enrollment lists 

against SNAP, TANF, FDPIR, or other agencies’ records. 

We think the [Name of Program] program has potential use for direct certification in the NSLP. The 

purpose of our call is to find out more about the data collected to determine applicant eligibility and 

recipient benefit levels. We greatly appreciate your assistance with this important project. The interview 

should take no longer than 1 hour. 

To ensure that we fully and accurately capture your feedback, we would like to digitally record this 

conversation. Please note that the interviews will remain confidential. The recordings will NOT be shared 

with anyone outside the 2M team. We will only use the recordings to ensure accuracy of the transcription. 

After transcriptions are complete, recordings will be destroyed. Any identifying information that you 

mention, such as your name or someone else’s, will be deleted from the final transcript. No names will 

appear in the final report given to FNS. 

Are you okay with me recording this conversation? It will help me with note taking. We can turn it off any 

time you want. 

Interview Questions 

Elements of Primary Data Files 

We are first interested in getting a better understanding of your agency’s data files related to the [Name 

of Program]. 

Q1. Are data from applications collected and maintained at the State or the local level? Is information 

transferred between the State and local agencies? How? 

Q2. IF AVAILABLE: We understand that your data files contain the following identifiers for each household 

member: [READ KEY ELEMENTS FROM DATA FILES]. Does your file contain any other identifiers, or are 

any of these not in your files? 

IF NOT AVAILABLE: What data elements/identifiers does your agency collect on applicants and 

recipients? 

Q3. How often/frequently does your agency update these data? 

Q3.1 How current are the data when they are updated? 

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Work with External Agencies/Data Files 

We understand that [Name of Program] uses data from [Name of Program] and [Name of Program] in its 

verification process. 

Q4. Do you have Memoranda of Understanding (MOUs) with the [Name of Program] and [Name of 

Program]? 

Q5. Do you verify income and household composition by comparing the application against any other data 

(e.g., the Federal Data Services Hub or other sources)? 

Q6. Do you have MOUs with these other agencies? Describe the process of establishing these 

agreements. How long does the process typically take? 

Q7. Does the MOU need to be renewed on an annual basis? 

Q8. Do you have any information on the percentage of families in the [Name of Program] program who 

also participate in SNAP or TANF? 

Q9. IF AVAILABLE: We have reviewed your agency’s privacy protection and data sharing policies from your 

published Privacy Impact Assessment. 

IF NOT AVAILABLE, ASK: Are you familiar with your agency’s ongoing data sharing agreements or 

MOUs with other agencies or organizations? Please list any additional agencies and organizations 

not included in the Privacy Impact Assessment (or provide a link to such a list). 

Data Matching Activities 

We understand that [Name of Program] conducts data matching with tax return data received from the
 

[Name of Program] and [Name of Program]. We have a few questions about this process.
 

Q10. What identifiers does your agency use to match [Name of Program] records with [Name of Program]
 

data? With [Name of Program] data?
 

Q11. How is the match performed? What databases are involved? How is data transferred and received?
 

Q12. Do you perform the match on at least an annual basis?
 

FROM ANSWERS IN PREVIOUS SECTION:
 

You stated that your agency has MOUs with [LIST NAME OF AGENCIES]. 

Q13. Does your agency extend [Name of Program] eligibility (categorical eligibility) to applicants who 

participate in programs administered by these agencies? 

IF YES: Which programs? 

Q14. Does your agency conduct data matching with program data to verify participation? 

IF YES: Please describe this process. What identifiers are used? How is the match performed? 

What databases are involved? How are data transferred and received between your agency and 

[NAME EACH AGENCY]? How often is the match performed? 

Q15. Does your agency conduct matches to the State IEVS? Other data brokers to verify information 

collected on applications. (e.g., clearinghouses of employer wage information, credit bureaus, etc.)? 

IF YES: Which identifiers or data elements are most important for your matching process? 

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Q16. What identifiers would need to be available in CN State agency enrollment data in order to match 

individuals with your program data? 

Q17. How timely would the CN State agency’s data need to be in order to provide a quality match? (e.g., 

updates made every 6 months, 3 months, etc.) 

Q18. Do you encounter any challenges or barriers when matching records with other agencies? 

Policy and Regulatory Matters 

Q19. IF AVAILABLE: We have reviewed your agency’s privacy protection and data sharing policies from 

your published Privacy Impact Assessment. 

IF NOT AVAILABLE, ASK: Are you familiar with your agency’s privacy protection and data sharing 

policies? Can you provide us with a link or copy of the policy if published? 

Q20. What other key regulations/laws/statutes does your agency adhere to in the management of this 

data? 

Q21. Would your agency allow CN State agencies to directly access your program data? If not, how often 

would your agency be able to provide information on participants? 

Q22. Would any regulatory changes be necessary in order to share data with a State CN agency? If so, 

describe the nature of these changes. 

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Appendix E. Study Methodology 

2M conducted three major research activities designed to identify additional datasets from means-tested 

programs that may be feasible for NSLP computer matching. These activities included (1) a review of 

available literature about the most current data matching practices used by States, (2) interviews with 

experts from Federal agencies administering candidate means-tested assistance programs, and (3) 

interviews with NSLP SA officials and staff knowledgeable about the data matching processes used in 

their State. 

Literature Review 

The literature review produced a comprehensive summary of the datasets and matching practices used 

by States and districts as indicated in the most recent FNS-sponsored studies.316 2M documented which 

programs were widely in use by States for data matching, the identifiers that States used to match 

student enrollment records with children in these program data, and the processes used to conduct data 

matching. Using this information as a baseline, 70 means-tested programs, not currently permitted for 

use in NSLP data matching, were reviewed to assess whether they possessed the characteristics needed 

to support NSLP data matching related to NSLP direct certification and/or direct verification. 

The programs were assessed across a number of criteria, including whether beneficiary households 

included school-age children, whether eligibility requirements of the program were similar to the NSLP, 

and whether information was available on income and family size in the program data in order to 

calculate household income as a percent of FPL. The review also examined identifiers available in the 

program datasets, how frequently the data were updated, and privacy protections and data sharing 

policies that would allow NSLP data matching. 

The review identified six programs that had potential for NSLP data matching: 

• Special Supplemental Nutrition Program for Women, Infants, and Children (WIC); 

• Affordable Care Act (ACA) Health Insurance Marketplaces;317 

• Public Housing and Housing Choice Voucher Programs; 

• Low Income Home Energy Assistance Program (LIHEAP); 

• Weatherization Assistance Program (WAP); and 

• Earned Income Tax Credit (EITC). 

Expert Interviews 

2M identified experts knowledgeable about each identified program. These experts were contacted 

beginning in February 2016, and were invited to participate in a 1-hour interview; the invitation included a 

brief description of the research that 2M was contracted to conduct and a letter from FNS outlining the 

project and requesting participation. Interviews were conducted between February 17, 2016 and May 3, 

2016. A copy of the semi-structured interview guide used can be found in Appendix D. 

316	 U.S. Department of Agriculture, Food and Nutrition Service. (2014, August). The National School Lunch Program direct 

certification improvement study: Main report. Retrieved June 3, 2016 from 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 317 Affordable Care Act Health Insurance Marketplace is not program, but a set of organizations established to facilitate the 

purchase of health insurance in accordance with the Patient Protection and Affordable Care Act (P.L. 111-148). Nevertheless, 

to enhance readability, the Marketplace is referred to as a “program” and the data potentially available from the Marketplace 

are referred to as “program data.” 

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The expert interviews identified both the possibilities for, and challenges to, adding data from their 

program to those already used by the NSLP for data matching. Topics included the data elements 

available for matching; the frequency of data updates; legislative and regulatory issues with the 

transmission of student, parent, and/or household data between agencies; and perceived costs and 

benefits of the NSLP matching to these data. The results of the expert interviews and a review of 

published information on the programs suggested that only the HUD housing programs and LIHEAP had 

sufficient potential for NSLP data matching to merit further study. 

State Interviews 

Eight State CN directors and the staff who conduct and manage the matching processes in their State 

were interviewed from May 16, 2016 to May 20, 2016. A semi-structured interview guide was designed 

to gain insights into whether or not the data from LIHEAP and the HUD housing programs might be useful 

for inclusion in the NSLP data matching processes of their State. The interview probed for any specific 

benefits, challenges, or barriers that might be anticipated if these additional programs were added to 

their current matching efforts. State personnel were also asked why their State did not use all of the 

programs currently permitted by NSLP data matching regulations for direct certification and direct 

verification (7 CFR Part 245 and 7 CFR Parts 210). 

State Selection for Interviews 

Using information contained in the State profiles found in the National School Lunch Program Direct 

Certification Improvement Study: Main Report, 2M classified all States and the District of Columbia by 

which programs they used for data matching in SY 2012–13.318 It was assumed that the same factors 

influencing a State’s use of programs that are currently permitted would also influence the State’s 

consideration of using additional program databases that are not currently permitted. 

States were identified that: 

•	 only used SNAP for data matching; 

•	 used SNAP and TANF, but no other data files for data matching; 

•	 used SNAP and TANF, but also used Foster Care; 

•	 used SNAP and TANF, but also used FDPIR; and 

•	 used SNAP and TANF, but are also pilot-testing the use of Medicaid program data for direct 

certification. 

States were further classified within each of these categories as using central matching or local matching 

processes for direct certification, and finally as having a relatively low or high Direct Certification 

Performance Rate in SY 2013–14.319 States for which 2015–16 site visit reports had been completed 

were given a higher priority for selection, as these reports provided information which was unavailable 

(and would otherwise have to be inquired about during State interviews) for States that did not have 

recent site visit reports. This additional information included (1) the type of SSIS, (2) the transmission of 

data between States and LEAs, (3) the agencies involved with data matching for each identified program, 

318 U.S. Department of Agriculture, Food and Nutrition Service. (2014, August). The National School Lunch Program direct 

certification improvement study: Main report. Retrieved April 29, 2016 from 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationImprovement.pdf 319 DC rates equal to or below the national average of 87 percent were classified as relatively low; DC rates above the national 

average were classified as relatively high. 

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and (4) the frequency of data updates and direct certification matches. 320 The selection process is 

illustrated in Figure E-1 below. 

Figure E-1: Selection Process for Study States 

All States and District of Columbia 

SNAP & TANF 

Central Local 

Hi 

(>=87%) 

Lo 

(<87%) 

Hi 

(>=87%) 

Lo 

(<87%) 

SNAP Only SNAP, TANF, & 

Foster Care 

SNAP, TANF, & 

FDPIR 

SNAP, TANF, & 

Medicaid 

DC rates equal to or below the national average of 87 percent were classified as relatively low; DC rates above the national 

average were classified as relatively high. 

Table E-1 summarizes the characteristics of the eight States that were selected for interview based on 

whether they (1) used data from SNAP, TANF, Foster Care, FDPIR, and Medicaid; (2) used central or local 

data matching processes; (3) had relatively high or low DC Performance Rates in SY 2013–14; and (4) 

had 2015–16 site visit reports available. Preference was also given to Nebraska because it used the 

Medicaid program data in direct verification. None of the States from the second category—States that 

use SNAP and TANF only—were recommended as study States for the reasons described below. 

320 Each year, the Food and Nutrition Service (FNS) Training and Technical Assistance (TTA) team visits States to provide 

assistance and support on direct certification efforts. States respond to a questionnaire and results are published in the Direct 

Certification Evaluation and Technical Assistance site visit summary reports. 

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Table E-1: States Selected for Interview 

State Characteristic Central Data Matching Local Data Matching 

SNAP Only 

Arkansas** 

Colorado 

Louisiana 

Mississippi* 

SNAP and TANF - -

SNAP, TANF, and Foster Care 

California 

Illinois 

Nebraska 

Kentucky 

Maryland** 

Tennessee** 

Wyoming 

SNAP, TANF, and FDPIR California -

SNAP, TANF, and Medicaid Florida 

Illinois Kentucky 

Medicaid for Direct Verification Nebraska -

Total Count 6 3 

Note: *Mississippi had not yet been interviewed at the time of this first draft. ** Alternate. 

(1) States that only use SNAP 

Among the States that only use SNAP for direct certification (see Table E-2), Colorado and Mississippi 

were selected as the only central and local matching States, respectively, for which a 2015–16 site visit 

report was available. 

Because Colorado had a relatively low DC rate in SY 2013–14, 2M considered Arkansas or Louisiana for 

selection as central matching States with relatively high SY 2013–14 DC rates. Both States belong to the 

FNS Southwest Region, and have similar matching processes The greatest contrast between them in SY 

2012–13 was that Arkansas was 1 of only 5 States that updated its enrollment files only once per year, 

while Louisiana was 1 of 10 States that updated files at least three times each year. Both States now 

update more frequently.321 Because Louisiana used an updating frequency twice as common as that of 

Arkansas, 2M selected it for the State interviews to acquire more representative insights. 

(2) States that use SNAP and TANF 

TANF was the second most commonly used program for direct certification after SNAP during SY 2012– 

13, and this category had the largest number of States. Because nearly all States that used Foster Care 

or FDPIR also used TANF, it was assumed that any questions concerning the use of TANF could be 

captured in the interviews with States that used all three programs for data matching. Therefore, States 

that only used SNAP and TANF for direct certification were not selected for interview. 

321 Conversation with FNS staff, May 18, 2016. 

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(3) States that used SNAP, TANF, and Foster Care 

A total of 16 States used Foster Care data for direct certification in SY 2012–13. Of the 12 central 

matching States that used TANF, California was selected because it also used FDPIR, and had a 2015– 

16 site visit report available. Illinois and Kentucky were also selected. Both Illinois and Kentucky also had 

relatively high DC rates, and both were among the five States allowed at that time to use Medicaid for 

direct certification. See Table E-2. 

Wyoming was selected for interview as the only State that used Foster Care, SNAP, and TANF for direct 

certification.322 Nebraska, on the other hand, was the only State that used Medicaid program data for 

direct verification of income in SY 2012–13, although all States were permitted to use it, and was 

selected for this reason.323 

(4) States that used SNAP, TANF, and FDPIR 

Only four States used FDPIR for direct certification in SY 2012–13, and all four were central matching at 

that time. California, a State with a 2015–16 site visit report, was selected from among the States using 

SNAP, TANF, and Foster Care, and also reported using FDPIR. Thus, no additional States were selected 

from the four States that use FDPIR data for NSLP matching. See Table C-6. 

(5) States that used SNAP, TANF, and Medicaid 

HHFKA authorized USDA to conduct and evaluate multiyear demonstration projects to test the 

effectiveness of direct certification using Medicaid eligibility and income data in determining eligibility for 

free school meals. Florida, Illinois, Kentucky, New York, and Pennsylvania were selected for these 

demonstrations beginning in SY 2012–13.324 2M selected Illinois and Kentucky for the State interviews 

because they (1) represented a central and a local matching State, respectively; (2) used Medicaid data; 

and (3) had relatively high DC rates in SY 2013–14. Illinois was also the only central matching State that 

participated in the Medicaid demonstration project at that time. Florida, which FNS staff identified as 

especially progressive in its exploration of additional data sources that could be used for direct 

certification, was also selected. 

Table E-2: Programs that States Use for Direct Certification 

States Using Only SNAP Direct Certification Performance 

Rate Recommendations for Study States 

Central Data Matching 

Colorado* Relatively Low Selected 

Arkansas Relatively High 

Louisiana Relatively High Selected 

Montana Relatively Low 

South Carolina Relatively Low 

Local Data Matching 

Mississippi Relatively Low Selected 

States Using SNAP and TANF Direct Certification Performance Recommendations for Study States 

322	 FNS staff suggested considering Maryland, as it would represent the Mid-Atlantic FNS region. However, Maryland was 

participating in another study. 323 Verification of Eligibility for Free and Reduced Price Meals in the National School Lunch and School Breakfast Programs, 7 

C.F.R. 210 and 245 (2008). 324	 U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. (2015). Evaluation of demonstrations of 

National School Lunch Program and School Breakfast Program direct certification of children receiving Medicaid benefits: 

Year 1 report. Retrieved December 11, 2015, from 

http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertificationMedicaidYr1.pdf 

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Rate 

Central Data Matching 

Arizona* Relatively Low 

Georgia* Relatively Low 

Hawaii Relatively High 

Idaho* Relatively High 

Massachusetts Relatively High 

Minnesota Relatively High 

New Jersey Relatively High 

North Dakota Relatively High 

Ohio* Relatively High 

Oklahoma* Relatively High 

Oregon* Relatively High 

Rhode Island Relatively High 

South Dakota* Relatively High 

Texas* Relatively High 

Vermont* Relatively High 

Washington Relatively High 

Wisconsin Relatively High 

Local Data Matching 

Connecticut* Relatively High 

Maine Relatively Low 

Missouri Relatively Low 

Nevada* Relatively Low 

New Mexico Relatively Low 

Virginia Relatively High 

States Using SNAP, TANF, Foster Care Direct Certification Performance 

Rate Recommendations for Study States 

Central Data Matching 

Alabama Relatively Low 

Alaska Relatively Low 

California (also uses FDPIR) Relatively Low Selected 

District of Columbia Relatively Low 

Illinois (also uses Medicaid) Relatively High Selected 

Indiana Relatively High 

Iowa Relatively High 

Kansas Relatively Low 

Michigan* Relatively High 

Nebraska Relatively High Selected 

Utah* Relatively High 

West Virginia Relatively High 

Local Data Matching 

Kentucky (also uses Medicaid) Relatively High Selected 

Maryland Relatively High 

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Tennessee Relatively High 

Wyoming (does not use TANF) Relatively Low Selected 

States Using SNAP, TANF, FDPIR Direct Certification Performance 

Rate Recommendations for Study States 

Central Data Matching 

California (also uses Foster Care) Relatively Low Selected 

Delaware Relatively High 

New Hampshire Relatively Low 

North Carolina Relatively High 

Local Data Matching 

None 

*States for which 2015 site visit reports are available. 

Source: U.S. Department of Agriculture, Food and Nutrition Service, Office of Research and Analysis. (2015). Direct certification in the 

National School Lunch Program: State implementation progress school year 2013–2014 Report to Congress. Retrieved December 11, 

2015, from http://www.fns.usda.gov/sites/default/files/ops/NSLPDirectCertification2014.pdf 

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Appendix F. State Interview Guide 

[Introductions] • Introduce the 2M team participating in the call 

• Ask the interviewee to introduce him/herself 

We really appreciate your time in speaking with us today. 

The U.S. Department of Agriculture (USDA) has contracted 2M Research Services to obtain more 

information about the participant data maintained for means-tested programs (e.g., LIHEAP, 

Weatherization Assistance Program, Public Housing, Unemployment Insurance, etc.) that may have 

potential use in the direct certification of children in the National School Lunch Program (NSLP) and for 

the direct verification of household income. 

The purpose of this call is to find out more about the processes that the YOUR STATE AGENCY uses for 

direct certification and direct verification. We would also like to know about the current data sets used in 

your State. In addition, we would like to discuss your views on using some additional means-tested 

programs. In particular, we are interested in hearing about any barriers or challenges you can anticipate, 

as well as any benefits you foresee. 

The interview should take no longer than 1 hour. Do we have your permission to record this conversation? 

The recordings will not be shared with anyone outside the 2M team and we will only use the recordings to 

assist in note taking. Any identifying information, such as your name or anyone else’s name, will be 

omitted. No names will appear in the final report given to FNS. We can turn the recording off any time you 

want. 

Background of Study 

First, let me give you some quick background information on some of our research conducted prior to 

beginning the interview portion of this phone call. From among 70 means-tested assistance programs, 

2M found 6 programs that merited further investigation. These programs have eligibility requirements 

and information similar to those used to certify children for free or reduced price meals in the NSLP (130 

to 185 percent of the Federal Poverty Guidelines [FPL]). 

We conducted interviews with experts from the Federal agencies administering each potential meanstested program. These included the Departments of Health and Human Services, Energy, and Housing 

and Urban Development. We also interviewed experts from the Internal Revenue Service and the Centers 

for Medicare and Medicaid Services. Questions were designed to gain information about the data 

collection and maintenance occurring at the Federal and State level, and about privacy and data-sharing 

policies. Based on our interviews and research, we believe that two means-tested programs may have the 

potential to be added for use for NSLP direct verification. We will discuss the candidate programs in 

greater detail later during our interview. 

Data File Use 

We’d first like to begin by discussing how your State constructs and maintains its data files currently used 

for direct certification and direct verification. 

Q1. IF AVAILABLE: We understand that your State currently data matches to SNAP records for direct 

certification. Does your State use any additional programs for this purpose? 

IF NOT AVAILABLE: What databases does your State currently use for the purpose of direct 

certification or direct verification? 

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Q2. What student identifiers do you use to perform the direct certification match? Do you use any parent 

identifiers for Direct Certification matching? 

Q3. Do you collect data on other household members? 

Q4. How often are your student enrollment data updated? How often are the program data used for direct 

certification updated? 

Data Matching Activities 

The next set of questions address the processes that your State uses to match enrollment data files with 

program data files for the purpose of direct certification. 

Q5. IF AVAILABLE: We understand that the Department of Human Services, Division of Food and Energy 

Assistance, administers the SNAP program. 

IF NOT AVAILABLE: What agency administers the SNAP program? 

Q6. Please describe the matching process for SNAP; how are student enrollment records and SNAP 

records matched? PROVIDE EXAMPLE: File transfer from the SNAP State agency to the Department of 

Education? Direct upload? 

Q7. How often can direct certification matching occur with SNAP records matched? 

Q8. Describe the Memoranda of Understanding currently in place allowing the Department of Education 

to share data with State agency administering SNAP records matched. 

Q9. Would you be able to forward copies of these arrangements to our team, or provide us with a link for 

this information? 

Q10. What are the biggest challenges that your State encounters (if any) with the program data it 

currently uses for direct certification? 

Q11. IF STATE DOES NOT USE ALL PERMITTED PROGRAMS: Why does your State or LEA not use other 

programs currently permitted by the regulations (e.g., TANF, Foster Care, FDPIR, homeless, migrant, 

runaway) in its direct certification? 

Verification Processes 

I am now going to focus more specifically on your verification processes for the next several questions. 

Q12. How does your State or local agency verify household income for applications in the verification 

sample? 

Q13. Does your State use any of the program files for direct verification (e.g. SNAP, TANF, Medicaid, CHIP, 

FDPIR, other)? 

Q14. Does your State perform direct verification by data matching with wage or program data from other 

State agencies (e.g., Medicaid, etc.)? 

IF YES: Please describe the process for each data source used. How do you identify income for all 

household members? 

PROBE: Do you match using social security numbers for all household members? What 

other identifiers are matched for all household members? 

IF NO: What are the reasons that your State does not perform direct verification with wage or 

program data from other State agencies? 

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Candidate Means-Tested Programs 

The next set of questions address the advantages and challenges of adding more means-tested programs 

to the list of programs currently used in your State. One purpose of this project has been to identify 

candidate means-tested programs that, if statutorily approved, could be used for direct certification or 

direct verification. We have identified two potential programs in your State that could be used for this 

purpose. 

We will briefly discuss the characteristics of HUD’s Public Housing Program and the Housing Choice 

Voucher Programs and the Low Income Home Energy Assistance Program (or LIHEAP) and then ask you a 

few questions. 

Public Housing and Housing Choice Voucher 

The Public Housing and Housing Choice Voucher programs provide rental housing for eligible, low-income 

families, the elderly, and persons with disabilities. Local housing agencies determine eligibility based on 

gross annual income, having an elderly or disabled person in the household, and documentation of U.S. 

citizenship or eligible immigration status. 

Income eligibility limits are set at either 80 percent or 50 percent of the median income for the county or 

metropolitan area in which participants choose to live. Therefore, these limits will likely vary across LEAs. 

The income sources used to determine eligibility for HUD’s Public Housing and Housing Choice Voucher 

programs are essentially identical to those used in the NSLP. Family income and composition are 

reexamined at least once every 12 months, but can be reviewed more frequently, if there are any 

changes to participant income or family composition. The PII needed to conduct direct certification 

matching in the NSLP is collected by HUD each year. The following information is available in HUD records 

for households receiving housing or rental subsidies: 

• Last Name • Relationship to Family Head 

• First Name • Citizenship 

• Middle Initial • Disabled 

• DOB • Ethnicity 

• Age • SSN (of all family members age 6 years or older) 

• Gender • Alien Registration Number 

HUD maintains these records pursuant to the Privacy Act of 1974. Data-matching agreements would 

need to be established to exchange information with NSLP State agencies. 

LIHEAP 

LIHEAP assists low-income households with home heating and cooling costs, energy crises, and 

weatherization assistance. Federal statute requires that States make LIHEAP benefits available to 

households with income within 150 percent of FPL or 60 percent of State median income, whichever is 

higher. Exact income criteria vary by State, but income limits cannot be set lower than 110 percent of 

FPL. Sources of income used to determine eligibility also vary by State. 

Eligibility determinations are generally used for a 12-month period before recertification is required. 

Nearly one-third of State Agencies administering LIHEAP collect the data elements needed to conduct 

direct certification matching in the NSLP, including name, date of birth, Social Security Number, and 

gender for applicants and household members. HHS does not collect or maintain personally identifiable 

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information for LIHEAP, so data sharing agreements to access this information would be negotiated with 

the State agency administering the program. 

Q15. Based on the identifiers that are available in the two HUD programs and LIHEAP, do you think that 

you could certify additional students for free meals that are not captured through direct certification with 

currently permitted programs? 

Q16. What are the biggest challenges and barriers your State/LEA would encounter if you added the two 

HUD programs or LIHEAP for direct verification? 

Q17. What would help address the challenges and barriers that you mentioned if you added the two HUD 

programs or LIHEAP for direct verification? 

Q18. Would allowing the use of the two HUD programs or LIHEAP for direct certification and verification 

efforts cause any additional administrative burden? Please explain. 

Feasibility and Policy 

Our last set of questions addresses the feasibility of using the additional program datasets we have 

identified for direct certification and direct verification. 

Q19. What legal and privacy issues must be addressed in order for your State/LEA to share and match 

data from the two HUD programs or LIHEAP if regulations permitted their use? 

Q20. Do you think that using the two HUD programs or LIHEAP would be cost-effective? Too costly? Why? 

Q21. If the regulations permitted, would the two HUD programs or LIHEAP be feasible for data matching 

for direct certification or direct verification in your State? Why or why not? 

[Wrap-up] 

That concludes the questions that we have for you today. We greatly appreciate you setting aside 

the time to assist us with the study. Did you have any questions at this time? Please feel free to 

contact us if you think of anything. Would you mind if we contact you if we have any additional 

items that need clarification? 

Thank you again for your assistance. 

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