Identifying the Effects of SNAP (Food Stamps) on Child Health Outcomes When Participation Is Endogenous and Misreported

Abstract

The literature assessing the efficacy of the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp Program, has long puzzled over positive associations between SNAP receipt and various undesirable health outcomes such as food insecurity. Assessing the causal impacts of SNAP, however, is hampered by two key identification problems: endogenous selection into participation and extensive systematic underreporting of participation status. Using data from the National Health and Nutrition Examination Survey (NHANES), we extend partial identification bounding methods to account for these two identification problems in a single unifying framework. Specifically, we derive informative bounds on the average treatment effect (ATE) of SNAP on child food insecurity, poor general health, obesity, and anemia across a range of different assumptions used to address the selection and classification error problems. In particular, to address the selection problem, we apply relatively weak nonparametric assumptions on the latent outcomes, selected treatments, and observed covariates. To address the classification error problem, we formalize a new approach that uses auxiliary administrative data on the size of the SNAP caseload to restrict the magnitudes and patterns of SNAP reporting errors. Layering successively stronger assumptions, an objective of our analysis is to make transparent how the strength of the conclusions varies with the strength of the identifying assumptions. Under the weakest restrictions, there is substantial ambiguity; we cannot rule out the possibility that SNAP increases or decreases poor health. Under stronger but plausible assumptions used to address the selection and classification error problems, we find that commonly cited relationships between SNAP and poor health outcomes provide a misleading picture about the true impacts of the program. Our tightest bounds identify favorable impacts of SNAP on child health.

KEY WORDS:

• Food insecurity
• Food Stamp Program
• Health outcomes
• Nonclassical measurement error
• Nonparametric bounds
• Partial identification
• Supplemental Nutrition Assistance Program
• Treatment effect

Acknowledgments

The authors received valuable comments from Arie Beresteanu, Ed Olsen, Mark Prell, David Smallwood, Jeff Smith, and seminar participants at Claremont McKenna, Duke, Iowa State University, University of California-Riverside, University of Virginia, the Economic Research Service (ERS) of the U.S. Department of Agriculture (USDA), the Food and Nutrition Service of the USDA, and meetings of APPAM, the Econometric Society, the Economic Demography Workshop at PAA, and the Conference on Inference in Partially Identified Models with Applications. Laura Tiehen generously provided us with state-level data on Supplemental Nutrition Assistance Program (SNAP) administrative rules. Gundersen and Kreider acknowledge financial support from the Food Assistance and Nutrition Research Program of ERS, USDA, contract number 59-5000-7-0109. Pepper acknowledges financial support from the Bankard Fund for Political Economy. The views expressed in this article are solely those of the authors.