Strengthening the Public Health Impacts of the Supplemental Nutrition Assistance Program Through Policy

Abstract

The US Department of Agriculture (USDA) Supplemental Nutrition Assistance Program (SNAP) is the cornerstone of the US nutrition safety net. Each month, SNAP provides assistance to 40 million low-income Americans—nearly half of them children. A number of changes could strengthen the public health impacts of SNAP. This review first presents a framework describing the mechanisms through which SNAP policy can influence public health, particularly by affecting the food security, the diet quality, and, subsequently, the health of SNAP participants. We then discusspolicy opportunities with the greatest potential to strengthen the public health impacts of SNAP, organized into three areas: ( a) food production and distribution, ( b) benefit allocation, and ( c) eligibility and enrollment. For each section, we describe current policy and limitations of the status quo, suggest evidence-based opportunities for policy change to improve public health, and identify important areas for future research.

How are SNAP Benefits Spent? Evidence from a Retail Panel

“…Studying the effect of legislated benefit increases between 2007 and 2010 survey data, Beatty and Tuttle (2015) estimate an MPCF out of SNAP benefits of 0.53 to 0.64 (they do not report a confidence interval on these values) and an MPCF out of cash income of 0.15. 9 Closest to our study, Bruich (2014) uses retail scanner data with method-of-payment information to study the effect of a 2013 SNAP benefit reduction, estimating an MPCF out of SNAP benefits of 0.3 with confidence interval radius of 0.15. 10 We estimate an MPCF out of SNAP benefits of 0.5 to 0.6 with confidence interval radius as low as 0.015, and an MPCF out of cash income of no more than 0.1.…”

“…In this sense, our paper contributes to the growing literature on structural behavioral economics (DellaVigna forthcoming). 9 Studying the effect of the benefit increase arising from the 2009 American Recovery and Reinvestment Act (ARRA) using survey data, Tuttle (2016) estimates an MPCF out of SNAP of 0.53 with confidence interval radius of 0.38. Nord and Prell (2011) estimate the effect of the 2009 benefit expansion on food security and food expenditures.…”

Impact of Restricting Sugar-Sweetened Beverages From the Supplemental Nutrition Assistance Program on Children's Health

“…45,46 SNAP participants reported that they are in favor of policies that facilitate purchases of healthful foods and limit purchases of unhealthful foods, specifically SSBs. 47 Currently, in the absence of all state waivers to empirically test SSB restrictions in SNAP, 16 results from this simulation model can provide valuable insights by providing potential benefits of SSB restriction on childhood dental caries and obesity. Although prior simulation studies that evaluated the impact of SSB restriction on adult SNAP participants show that the restriction could lower chronic disease morbidity and mortality, 12,13 the potential impact of SSB restriction on children's consumption and health has not been evaluated.…”

“…Thus, a simulation approach is an ideal way to study this issue because all state waivers requesting permission from the U.S. Department of Agriculture (USDA, the agency that administers SNAP) have been denied. 16 To fill this important gap in the literature, this study uses nationally representative data and a newly developed microsimulation model to estimate the impact of restricting SSB purchases with SNAP benefits on children's consumption and health.…”

How Are SNAP Benefits Spent? Evidence from a Retail Panel

“…We assume that an individual belongs to the household of her most recent spell, does not change households between the end of any given spell and the start of the next spell, and belongs to the household of her first spell as of the start of the sample period. We determine each individual's age in each month, and we exclude from our analysis any household whose membership we cannot uniquely identify in every month, 12 or whose adult (over 18) composition changes during the sample period. The final sample consists of 184,308 unique households.…”