Reduced Administrative Burden for SNAP

SNAP (the Supplemental Nutrition Assistance Program, formerly known as the Food Stamp Program) is an entitlement in-kind benefit transfer program that provides low-income individuals and families with vouchers that can be spent only on food. Benefit levels are determined by the proportion of the cost of the United States Department of Agriculture (USDA) Thrifty Food Plan that a household can afford to pay without assistance; the maximum benefit is given to households with no income, and the benefit size decreases gradually as household income increases.¹⁸ Although benefit levels and general eligibility criteria are set at the federal level, states have flexibility to adjust eligibility requirements and program administration, including the administrative burden associated with program participation. Administrative burden is defined here as barriers that increase the costs (time, money, and psychological distress) of applying for benefits and maintaining eligibility.

Two categories of policies may affect SNAP participation: policies that change program eligibility requirements to make more or fewer individuals eligible, and policies that influence how burdensome it is for eligible participants to apply, enroll, and remain enrolled in SNAP. This summary focuses on the latter, examining the state policies and processes that affect eligible families under current SNAP eligibility rules.

Who Is Affected by SNAP Administrative Burden?

Administrative burden can deter eligible individuals and families from applying for, enrolling in, or recertifying for SNAP, a program that has been shown to support healthy development during the prenatal-to-3 period.²  Cumbersome application or recertification processes for public programs can also affect state employees, who must spend additional time on paperwork, which may reduce efficiency and delay benefits for participants.

SNAP is available to all low-incomeⁱ households and is not targeted toward a particular subpopulation, although the majority of SNAP recipients are in households with children.³ Data from 2018 suggest that 31.5 percent of families with children under the age of 3 receive SNAP, and 24.1 percent of children under age 3 were living in households that reported receiving SNAP in the prior 12 months, totaling 2.7 million children.ⁱⁱ As of January 2020, over 18.9 million households were enrolled in SNAP, covering approximately 37 million people, but the caseload has risen to as high as 42 million in recent years.⁴ Participation in SNAP among those eligible, based on federal eligibility rules, rose in recent years from 53 percent in 2001 to 85 percent in 2016, but this percentage varies considerably by state.⁵

What Are the Funding Options for Reduced Administrative Burden for SNAP?

States can use a variety of funding sources, including state and federal funds, to reduce the administrative burden associated with SNAP participation. Whereas the federal government pays for 100 percent of SNAP in-kind benefits, the federal Food and Nutrition Service only reimburses states for 50 percent of most administrative costs.⁶ Research shows that states may reduce costs by eliminating burdensome policies⁶ (see the section on the return on investment, later in this summary), and administrative costs range from $10 to $34 per case, according to a 2016 federal audit report.⁷

1. Federal requirements set eligibility criteria as (a) gross income at or below 130 percent of the federal poverty level, (b) net income less than or equal to the poverty level, and (c) assets below $2,250 for households without an elderly individual or person with a disability.
2. Calculations were done by the Prenatal-to-3 Policy Impact Center using 2018 American Community Survey data and Current Population Survey data run through TRIM3 (Transfer Income Model, version 3).

Reducing the administrative burden associated with applying for and maintaining enrollment in public benefit programs can help more caregivers and children access the assistance and benefits they need to keep their families healthy.

Research has shown that SNAP receipt is associated with improved birth outcomes,⁸ reduced childhood food insecurity (by up to 36 percent),² increased health care use among children,⁹ and improved long-term child health.¹⁰ State policies aiming to increase SNAP participation among eligible households may have a positive impact on child and family wellbeing during the prenatal-to-3 period.

SNAP caseloads are expected to be cyclical, increasing during economic downturns to assist more families in need and decreasing as the economy recovers and family financial wellbeing strengthens. However, recent research has found that macroeconomic changes do not fully explain variation in SNAP caseloads over time; changes in public policy also play a role in SNAP participation.^H

Policies that make it more burdensome to apply or maintain enrollment in SNAP may decrease program participation. For example, short intervals between eligibility recertifications that must be completed in person may require participants to more frequently take time off of work or find transportation or child care, increasing the time and monetary costs associated with participation. Policies such as simplified income reportingⁱⁱⁱ or longer recertification intervals may reduce the administrative burden and therefore increase participation among those eligible for SNAP.

3. Simplified income reporting requires SNAP participants to report income changes only if the change raises their income above eligibility levels. In contrast, states without simplified reporting require participants to report all changes to income, greatly increasing the cost of maintaining eligibility among those with variable work schedules or employment.

Overall, evidence suggests that more burdensome administrative policies can reduce participation in SNAP. Studies have evaluated the impact of a variety of policies to alleviate the administrative burden of SNAP enrollment, including simplified reporting, changes to recertification intervals, online applications, waiving face-to-face interviews, timing interviews differently, and improving outreach. In this review, longer recertification intervals were found to be the most effective individual policy to improve SNAP participation, but no policy was found to be as effective as a set of low-burden policies implemented together. Of the nine causal studies that examined the length of recertification intervals, eight studies examined the impact of intervals less than or greater than 3 months, and one studied the impact of intervals greater than 12 months.^E However, the research base has been outpaced by state policy progress; as of 2018, 32 states^iv had median recertification intervals of 12 months or greater for households with children under age 18. Given that the evidence shows that longer intervals lead to greater SNAP participation among eligible households, states aiming to increase SNAP participation may find that longer intervals, those of 12 months or more, are most effective for providing access to SNAP among the eligible.

The research discussed here meets our standards of evidence for being methodologically strong and allowing for causal inference, unless otherwise noted. Each strong causal study reviewed has been assigned a letter, and a complete list of causal studies can be found at the end of this review, along with more details about our standards of evidence and review method. The findings from each strong causal study reviewed align with one of our eight policy goals from Table 1. The Evidence of Effectiveness table below displays the findings associated with SNAP administrative policies (beneficial, null,^v or detrimental) for each of the strong studies (A through L) in the causal studies reference list, as well as our conclusions about the overall impact on each studied policy goal. The assessment of the overall impact for each studied policy goal weighs the timing of publication and relative strength of each study, as well as the size and direction of all measured indicators.

^θTrending indicates that the evidence is from fewer than two strong causal studies or multiple studies that include only one location, author, or data set.

Access to Needed Services

Research shows that implementing a combination of policies to reduce administrative burden has a significant positive effect on SNAP participation, increasing families’ access to nutrition assistance. Evidence supporting the impact of a combination of policies, as well as each individual policy examined in the research, is presented below.

Combination of Low-Burden Policies

A large national study concluded that changes in SNAP administrative policies explained 28.5 percent of the increase in SNAP participation between 2007 and 2011 (the caseload rose 68.7 percent over that period).^H Using another study’s effect sizes,^A the authors of a 2018 USDA research brief created a weighted index of policies related to SNAP eligibility and administration (including transaction costs^vi, stigma, and outreach).¹⁶ The report concluded that the index better explained state variation in SNAP participation than the effects of each individual policy, suggesting that policies to reduce administrative burden may be more effective when implemented together. However, the authors’ 2018 analysis was not meant be causal – their second, causal analysis, to be released in 2020, was not yet published at the time of this review.

Another study similarly found that an index of state SNAP policies^vii increased SNAP enrollment by 22 to 34 percent, twice the effect size on participation compared to that of any individual policy.^B A simulation study found that changes toward more accommodative^viii SNAP policies explained 16 percent of the increase in the SNAP caseload between 2000 and 2009 (the caseload rose by 93 percent over this period, which included the beginning of the Great Recession), whereas changes to welfare policies explained an additional 6 percent.^K Finally, a 2019 study estimated that if all states had implemented the most accommodative policies,^ix the total SNAP caseload would have been 5 percent higher in 2016 than it was.^A When the authors examined transaction costs and stigma separately from eligibility policies, they found that changes to policies affecting transaction costs and stigma explained 14.6 percent of the SNAP caseload increase from 2000 to 2016, with the largest effect (24.5 percent) from 2000 to 2007. Below, effects are presented for each of the individual SNAP administrative policies discussed in the research.

Longer Recertification Intervals

Evidence suggests that longer recertification intervals can increase SNAP participation significantly; this is the SNAP policy best supported by the evidence as contributing to higher participation among eligible households. One study estimated that recertification intervals greater than 12 months increased participation in SNAP among households with children by 11 to 12 percentage points (depending on whether the household was headed by a single mother).^E Another study, examining states that implemented recertification intervals longer than 3 months, found that such policies were associated with an 11 percent increase in SNAP enrollment.^B A large national study found that policies lengthening recertification intervals to greater than 3 months were associated with a 5.8 percent increase in SNAP participation from 2000 to 2009.^K

Six studies have examined the impact of short recertification intervals, defined as recertification required every 3 months or more frequently, finding overall that short intervals decrease SNAP participation. One of the studies found that for each 10 percentage point increase in the number of households with recertification intervals of 3 months or shorter, the SNAP caseload decreased by up to 2.1 percent.^A Another study estimated that a 10 percentage point increase in the proportion of households subject to 3-month recertification intervals (compared to 6 months or longer) was associated with a 0.85 percentage point decrease in SNAP participation for two-parent households and a 0.54 percentage point decrease for single-parent families.^I A third study found that a 10 percentage point increase in short recertification intervals was associated with a 0.2 percentage point reduction (2.8 percent) in SNAP participation rates, estimating that 10 percent of the decline in SNAP participation from 1994 to 2000 can be explained by short recertification intervals alone.^J A 2010 analysis found that a 1 percentage point increase in the share of SNAP-enrolled households with short recertification intervals (of 1 to 3 months) led to a decline in participation of 0.3 percent.^G

Another study, with a sample limited to South Carolina, found that shorter recertification intervals (quarterly versus semiannual and annual intervals) were significantly associated with transitions off of food stamps among eligible participants.^F In contrast, after the state lengthened its recertification intervals for households with earnings in 2002, the median length of participation increased by 3 months, and the caseload rose by 8 percent.^F One study, the results of which make it an outlier, found that shorter recertification intervals were associated with a slight increase (0.05 percentage points) in SNAP participation, but the impact was not statistically significant.^H

Removal of In-Person Interview Requirements

Only two studies looked at the impact of waiving in-person interview requirements, but the evidence is promising. A 2013 study found that states that waived the requirement for a face-to-face interview (conducting phone interviews instead) had participation rates that were 0.4 to 0.5 percentage points higher than states that retained the requirement.^D Another study from 2018 found that policies to waive such requirements were associated with a 7 percent increase in SNAP enrollment.^B

Timing of Interviews

A 2019 study of recertification in California’s SNAP program found that the timing of randomly assigned interview dates had a significant effect on the success of participants’ recertification.^L The authors found that SNAP recipients who were assigned a recertification interview toward the end of the month (using the 28^th day as a benchmark) were 9.4 percentage points (or 20 percent) less likely to successfully recertify than those whose interviews were assigned closer to the beginning of the recertification month. In particular, each day delay in interview date was associated with a 0.34 percentage point decrease in the likelihood of completing the recertification process. The authors suggested that later interview dates meant that applicants had fewer options for successfully rescheduling if the first assigned date did not work, and they had less time after the interview to successfully gather needed documents and meet the recertification deadline at the end of the month.

Simplified Income Reporting

Overall, evidence on the effectiveness of simplified income reporting to increase SNAP enrollment is mixed. Three studies examining national data from the Survey of Income and Program Participation and the USDA found no significant effect of simplified or quarterly income reporting on SNAP participation.^B,E,I Another study that examined national data from the USDA, however, found simplified income reporting was associated with a 4.5 percent increase in SNAP participation.^A Two other studies with national samples also found that simplified income reporting was associated with a 0.68 to 0.8 percentage point increase in SNAP participation.^D,H

Online Case Management and Applications

Evidence on the impact of online case management is also mixed. A 2019 study of Michigan’s SNAP program found that the roll-out of an online case management tool reduced program exit at recertification by 12 percent (or 2 percentage points).^C The online tool allows participants to apply, renew, view the status of benefits, view correspondence from the state agency, and find their caseworker’s contact information. Four other national studies, however, found no significant impact of online applications on overall SNAP participation.^A,B,D,H

Call Centers

A national study from 2018 found that the implementation of call centers, which aim to provide quick and direct assistance for SNAP applicants, was associated with a 5 percent increase in overall SNAP enrollment.^B Two other studies, however, found no significant impact of call centers on SNAP participation.^D,H

In addition, a 2019 experimental study that was excluded from the evidence review because it focused on individuals ages 60 and older found that relative to a control group who received no outreach, elderly individuals who were eligible for SNAP were three times more likely to apply if they received a letter and phone call informing them about their SNAP eligibility and offering assistance with the application.¹³ This finding underscores the importance of outreach to reduce informational barriers and administrative burden, especially for vulnerable populations which may include low-income families with young children.

4. State counts include the District of Columbia.
5. An impact is considered statistically significant if p<0.05.
6. Transaction costs included the frequency with which working households are required to recertify for SNAP, whether the state has adopted simplified income reporting, and the availability of online applications.
7. Policies included simplified income reporting, longer recertification intervals, phone interviews, call centers, online applications, Supplemental Security Income interfacing, vehicle exemptions from asset tests, and broad-based categorical eligibility.
8. Accommodative refers to policies that reduce the administrative burden of applying and maintaining enrollment.
9. This study examined policies affecting eligibility, transaction costs, stigma, and outreach.

The evidence to date does not examine differential impacts of administrative burden by race, ethnicity, or socioeconomic status for families already eligible for SNAP benefits.

Evidence from two studies examining effects on participation in other public assistance programs (Medicaid and the Special Supplemental Nutrition Program for Women, Infants, and Children, or WIC) suggests that the administrative burden of public safety net programs falls disproportionately on communities of color and low-income communities, and that reducing the administrative burden can have a positive impact on their enrollment rates in programs that support health and nutrition.^14,15 These findings would likely be applicable to SNAP participation as well, but more research specific to the disparate impact of SNAP administrative burden would be necessary to understand the true effect of such policies.

10. Disparities are defined here as differential outcomes by race, ethnicity, or socioeconomic status (SES).

The studies included in the evidence review of administrative policies that affect SNAP participation did not examine the return on investment generated by the program, but other reliable sources have examined the economic impacts of SNAP and of reduced administrative burden. A 2019 report by the USDA found that states that implemented more streamlined administrative policies decreased their per-case costs.⁶ For example, adoption of policies such as broad-based categorical eligibility^xi and simplified income reporting lowered state administrative costs by up to 14 percent (7 percent per policy). Overall, states that implemented a set of low-burden policies saw lower administrative costs than states that adopted individual policies. A correlational analysis also found that states with higher access (greater SNAP participation rates among those eligible) benefited from lower per-case administrative costs, and states with lower participation rates saw higher per-case costs.⁶

Greater SNAP participation has been found to have beneficial economic effects. For example, an analysis found that every $1 increase in SNAP benefits in 2009 (during the recession) spurred $1.70 in economic activity.¹⁶ SNAP benefits allow low-income families to spend earned income on other necessities besides food, further stimulating the economy and ensuring families have access to needed resources. SNAP benefits are also well-targeted to reach very low income families; research has found that 92 percent of SNAP benefits are provided to households at or below the poverty line, and 55 percent of benefits are provided to those at or below 50 percent of the poverty line.¹⁶ The Center on Budget and Policy Priorities reports that 97 percent of SNAP dollars are spent within a month, allowing the benefits to flow back into the economy quickly.¹⁶

A more comprehensive analysis of the return on investment is forthcoming.

11. Broad-based categorical eligibility is a policy allowing households or individuals to automatically qualify for SNAP if they have already been deemed eligible for other means-tested public programs such as Temporary Assistance for Needy Families (TANF).

The evidence shows that state decisions related to the administration of SNAP have a significant impact on participation rates among eligible individuals, over and above macroeconomic factors. Some state administrative policies that increase administrative burden (short recertification intervals) are associated with significantly lower SNAP participation rates, whereas others (longer recertification intervals, bundles of accommodative policies) are associated with significantly higher participation rates. The effect sizes of most policies individually are relatively small, and the evidence suggests that a bundle of policies is more impactful on participation than any one policy alone.

Future research is needed to assess what combination(s) of policies have the largest impact on enrollment and take-up of benefits among eligible SNAP participants. More recent administrative practices, such as the use of mobile technology, online applications, and customer service call centers, vary widely in their adoption across states and should continue to be assessed as more states implement them. Eligibility criteria for SNAP also differ by state and may explain some of the variation in SNAP caseloads; this issue will be explored in a later review.

In addition, more research is needed to assess the disparate impact of administrative burden on families of color, as research on other programs, including WIC and Medicaid, suggests that the burden may fall disproportionately on families who are already economically and socially marginalized.

The evidence base for SNAP administrative policies demonstrates that state decisions that make it easier or more difficult for eligible households to apply and maintain enrollment in the SNAP program affect program participation rates. The most effective policies to increase participation in SNAP are longer recertification intervals and implementing a combination of low-burden policies together. Whereas most of the research has examined intervals greater than 3 months, showing longer intervals to be an effective policy for increasing SNAP uptake among eligible households, all states now have median recertification intervals of 5 months or greater for households with children, and more than half have median recertification intervals of 12 months or greater for households with children.

Individual states have flexibility to alter the SNAP federal eligibility guidelines and to administer the program in different ways, contributing to variation in SNAP participation rates across states. For example, states can adjust the costs associated with establishing and maintaining eligibility by setting the interval of time required between recertifications of program eligibility and by deciding what types of income changes participants must report and how frequently.¹¹ As of October 1, 2020, in 32 states, households with SNAP-eligible children will have a median recertification interval of at least 12 months. See Table 3 for additional details by state.

A recent report from the USDA assigned a policy index score to each state to reflect how accommodative states are in their SNAP administration.¹⁶ Those states with the lowest overall burden were given the highest scores out of 10 possible points. The average score across states was 9.2, with scores ranging from 8.2 to 9.6, indicating overall that states have adopted many accommodative policies to reduce the administrative burden for potential SNAP participants. However, income reporting and recertification requirements still vary substantially across states. States also offer different tools on their websites to help potential applicants understand the program and their eligibility status; for example, 45 states offer online eligibility screening tools and/or benefit calculators.¹² Benefit calculators can help prospective applicants understand approximately how much assistance they can receive each month, which may make them more likely to apply if the benefits outweigh the time and energy costs of applying. States also vary in whether they allow participants to manage their benefits online and whether their SNAP online application tools allow for joint processing with other programs, such as TANF and Medicaid.¹² 

Policy adoption data and data for percentage of beneficiary families with children under age 3 are as of 2018. United States Department of Agriculture (USDA) Fiscal Year 2018 Supplemental Nutrition Assistance Program Quality Control Database and the QC Minimodel.
Data for length of recertification interval are as of June 30, 2020. State Supplemental Nutrition Assistance Program manuals.
For additional source and calculation information, please go to Methods and Sources.

Method of Review

This evidence review began with a broad search of all literature related to the policy and its impacts on child and family wellbeing during the prenatal-to-3 period. First, we identified and collected relevant peer-reviewed academic studies as well as research briefs, government reports, and working papers, using predefined search parameters, keywords, and trusted search engines. From this large body of work, we then singled out for more careful review those studies that endeavored to identify causal links between the policy and our outcomes of interest, taking into consideration characteristics such as the research designs put in place, the analytic methods used, and the relevance of the populations and outcomes studied. We then subjected this literature to an in-depth critique and chose only the most methodologically rigorous research to inform our conclusions about policy effectiveness. All studies considered to date for this review were released on or before March 31, 2020.

Standards of Strong Causal Evidence

When conducting a policy review, we consider only the strongest studies to be part of the evidence base for accurately assessing policy effectiveness. A strong study has a sufficiently large, representative sample, has been subjected to methodologically rigorous analyses, and has a well-executed research design allowing for causal inference—in other words, it demonstrates that changes in the outcome of interest were likely caused by the policy being studied.

The study design considered most reliable for establishing causality is a randomized control trial (RCT), an approach in which an intervention is applied to a randomly assigned subset of people. This approach is rare in policy evaluation because policies typically affect entire populations; application of a policy only to a subset of people is ethically and logistically prohibitive under most circumstances. However, when available, randomized control trials are an integral part of a policy’s evidence base and an invaluable resource for understanding policy effectiveness.

The strongest designs typically used for studying policy impacts are quasi-experimental designs (QEDs) and longitudinal studies with adequate controls for internal validity (for example, using statistical methods to ensure that the policy, rather than some other variable, is the most likely cause of any changes in the outcomes of interest). Our conclusions are informed largely by these types of studies, which employ sophisticated techniques to identify causal relationships between policies and outcomes. Rigorous meta-analyses with sufficient numbers of studies, when available, also inform our conclusions.

Studies That Meet Standards of Strong Causal Evidence

1. Dickert-Conlin, S., Fitzpatrick, K., Tiehen, L., & Stacy, B. (2019). The downs and ups of the SNAP caseload: What matters? [Unpublished update to published 2016 manuscript.] US Dept. of Agriculture, Michigan State University. Provided to the Prenatal-to-3 Policy Impact Center via email on November 4, 2019.
2. Ganong, P., & Liebman, J. B. (2018). The decline, rebound, and further rise in SNAP enrollment: Disentangling business cycle fluctuations and policy changes. American Economic Journal: Economic Policy, 10(4), 153–176. https://doi.org/10.1257/pol.20140016
3. Gray, C. (2019). Leaving benefits on the table: Evidence from SNAP. Journal of Public Economics, 179, 1–15. https://doi.org/10.1016/j.jpubeco.2019.104054
4. Pomerleau, K. (2013). Just a phone call away: The association between state SNAP caseloads and the waiver of the face-to-face certification interview. Georgetown University Master’s Thesis. https://pdfs.semanticscholar.org/4aae/ff187c3975dfb0553eaf79066d3bb889a4eb.pdf?_ga=2.97474265.715027600.1580333108-254500070.1580333108
5. Ratcliffe, C., McKernan, S., & Finegold, K. (2008). Effects of food stamp and TANF policies on food stamp receipt. Social Service Review, 82(2), 291–334. https://doi.org/10.1086/589707
6. Ribar, D. C., Edelhoch, M., & Liu, Q. (2008). Watching the clocks: The role of food stamp recertification and TANF time limits in caseload dynamics. The Journal of Human Resources, 43(1), 208–239. https://doi.org/10.1353/jhr.2008.0018
7. Mabli, J., & Ferrerosa, C. (2010). Supplemental Nutrition Assistance Program caseload trends and changes in measures of unemployment, labor underutilization, and program policy from 2000 to 2008. Mathematica Policy Research, Inc. https://www.mathematica.org/our-publications-and-findings/publications/supplemental-nutrition-assistance-program-caseload-trends-and-changes-in-measures-of-unemployment-labor-underutilization-and-program-policy-from-2000-to-2008
8. Ziliak, J. P. (2016). Why are so many Americans on food stamps? The role of the economy, policy, and demographics. In Ziliak, J. P., Bartfeld, J., Gundersen, C., Smeeding, T. (Eds.), SNAP matters: How food stamps affect health and well-being (pp. 18–48). Stanford University Press.
9. Hanratty, M. J. (2006). Has the food stamp program become more accessible? Impacts of recent changes in reporting requirements and asset eligibility limits. Journal of Policy Analysis and Management, 25(3), 603–621. https://doi.org/10.1002/pam.20193
10. Kabbani, N. S., & Wilde, P. E. (2003). Short recertification periods in the US food stamp program. The Journal of Human Resources, 38, 1112–1138. https://doi.org/10.2307/3558983
11. Klerman, J. A., & Danielson, C. (2011). The transformation of the Supplemental Nutrition Assistance Program. Journal of Policy Analysis and Management, 30(4), 863–888. https://doi.org/10.1002/pam.20601
12. Homonoff, T., & Somerville, J. (2019). Program recertification costs: Evidence from SNAP. New York University Wagner School of Public Service. https://wagner.nyu.edu/files/faculty/publications/Homonoff%20%26%20Somerville%20-%20April%202019_0_0.pdf

Other References

1. Shonkoff, J., & Phillips, D. (2000). From neurons to neighborhoods: The science of early childhood development. Washington, DC: The National Academies Press. https://doi.org/10.17226/9824
2. Mabli, J., & Worthington, J. (2014). Supplemental Nutrition Assistance Program participation and child food security. Pediatrics, 133(4), 610–619. https://doi.org/10.1542/peds.2013-2823
3. Gray, K. F., Fisher, S., & Lauffer, S. (2016). Characteristics of Supplemental Nutrition Assistance Program households: Fiscal Year 2015. US Department of Agriculture, Food and Nutrition Service, Office of Policy Support. https://fns-prod.azureedge.net/sites/default/files/ops/Characteristics2015.pdf
4. US Dept. of Agriculture, Food and Nutrition Service. (2020). Supplemental Nutrition Assistance Program: SNAP data tables. [Tables: Number of Households Participating, Number of Persons Participating, January 2020]. https://www.fns.usda.gov/pd/supplemental-nutrition-assistance-program-snap
5. Whitmore Schanzenbach, D. (2019). Exploring options to improve the Supplemental Nutrition Assistance Program. The Annals of the American Academy of Political and Social Science, 686(1), 204–228. https://doi.org/10.1177%2F0002716219882677
6. Geller, D., Isaacs, J., Braga, B., & Zic, B. (2019). Exploring the causes of state variation in SNAP administrative costs. Prepared by Manhattan Strategy Group and the Urban Institute for the US Department of Agriculture, Food and Nutrition Service. https://fns-prod.azureedge.net/sites/default/files/media/file/SNAP-State-Variation-Admin-Costs-FullReport.pdf
7. US Office of the Inspector General. (2016). SNAP administrative costs: Audit report. https://www.usda.gov/oig/webdocs/27601-0003-22.pdf
8. Almond, D., Hoynes, H. W., & Schanzenbach, D. W. (2011). Inside the war on poverty: The impact of food stamps on birth outcomes. The Review of Economics and Statistics, 93(2), 387–403. https://doi.org/10.1162/REST_a_00089
9. Bronchetti, E., Christensen, G., & Hoynes, H. (2018). Local food prices, SNAP purchasing power, and child health (No. w24762). National Bureau of Economic Research. https://doi.org/10.3386/w24762
10. Hoynes, H., Schanzenbach, D. W., & Almond, D. (2016). Long-run impacts of childhood access to the safety net. American Economic Review, 106(4), 903–934. https://doi.org/10.1257/aer.20130375
11. USDA-FNS. (2018). State options report: Supplemental Nutrition Assistance Program, 14^th Edition. https://fns-prod.azureedge.net/sites/default/files/snap/14-State-Options.pdf
12. Center on Budget and Policy Priorities. (2020). SNAP online: A review of state government SNAP websites. https://www.cbpp.org/research/food-assistance/snap-online-a-review-of-state-government-snap-websites
13. Finkelstein, A., & Notowidigo, M. (2019). Take-up and targeting: Experimental evidence from SNAP. The Quarterly Journal of Economics, 134(3), 1505–1556. https://doi.org/10.1093/qje/qjz013
14. Stuber, J. P., Maloy, K. A., Rosenbaum, S., & Jones, K.C. (2000). Beyond stigma: What barriers actually affect the decisions of low-income families to enroll in Medicaid? The George Washington University School of Public Health and Health Services. https://hsrc.himmelfarb.gwu.edu/sphhs_policy_briefs/53/
15. Brien, M., & Swann, C. (1999). Prenatal WIC participation and infant health: Selection and maternal fixed effects. Deloitte Financial Advisory Services, LLP, and University of North Carolina, Greensboro. https://www.researchgate.net/profile/Michael_Brien/publication/241815776_Prenatal_WIC_Participation_and_Infant_Health_Selection_and_Maternal_Fixed_Effects/links/555b32b108ae6fd2d829a9cd.pdf
16. Center on Budget and Policy Priorities. (2019). Policy basics: The Supplemental Nutrition Assistance Program. https://www.cbpp.org/research/food-assistance/policy-basics-the-supplemental-nutrition-assistance-program-snap
17. Stacy, B., Tiehen, L., & Marquardt, D. (2018). Using a policy index to capture trends and differences in state administration of USDA’s Supplemental Nutrition Assistance Program. ERR-244, US Department of Agriculture, Economic Research Service. https://www.ers.usda.gov/webdocs/publications/87096/err-244.pdf?v=0
18. USDA Food Plans: Cost of Food (monthly reports). https://www.fns.usda.gov/cnpp/usda-food-plans-cost-food-reports