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Re-Evaluating the Effects of SNAP on Reducing Food Insecurity: The Roles of Econometric Methods and Heterogeneity

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  • Kunwar, Samir
  • Smith, Travis

Abstract

This paper examines how different econometric models, each with distinct functional form assumptions, influence estimates of the Supplemental Nutrition Assistance Program (SNAP) effect on food insecurity. To address potential endogeneity in SNAP participation, we employ a range of instrumental variables methods, including two-stage least squares, control function approaches, and (recursive) bivariate probit models, analyzing SNAP’s impact under various linear and nonlinear specifications in both the first-stage reduced form and second-stage structural equations. Using data from the 1996-2008 panels of the Survey of Income and Program Participation (SIPP), our findings reveal that the magnitude and direction of SNAP’s effect on food insecurity vary across model specifications, with estimates ranging from null effects to reductions of approximately 27 percentage points. Furthermore, heterogeneity analysis indicates that SNAP’s effectiveness is more pronounced among certain subpopulations.

Suggested Citation

  • Kunwar, Samir & Smith, Travis, 2026. "Re-Evaluating the Effects of SNAP on Reducing Food Insecurity: The Roles of Econometric Methods and Heterogeneity," 2026 Annual Meeting, July 26 - 28, 2026, Kansas City, Missouri 404555, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea26:404555
    DOI: 10.22004/ag.econ.404555
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    References listed on IDEAS

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    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    2. Elton Mykerezi & Bradford Mills, 2010. "The Impact of Food Stamp Program Participation on Household Food Insecurity," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(5), pages 1379-1391.
    3. Li, Chuhui & Poskitt, D.S. & Zhao, Xueyan, 2019. "The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification," Journal of Econometrics, Elsevier, vol. 209(1), pages 94-113.
    4. Craig Gundersen & Brent Kreider & John V. Pepper, 2017. "Partial Identification Methods for Evaluating Food Assistance Programs: A Case Study of the Causal Impact of SNAP on Food Insecurity," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(4), pages 875-893.
    5. Jeffrey M. Wooldridge, 2015. "Control Function Methods in Applied Econometrics," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 420-445.
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