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The Effect of SNAP and School Food Programs on Food Security, Diet Quality, and Food Spending: Sensitivity to Program Reporting Error

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  • Kyung Min Kang
  • Robert A. Moffitt

Abstract

There is an extensive research literature on the effects of the Supplemental Nutrition Assistance Program (SNAP) on food‐related outcomes which has shown somewhat mixed results but generally favorable effects. However, most of the research has used data sets whose information on SNAP participation is gathered from responses on household surveys, and such responses are subject to reporting error. This study uses the National Household Food Acquisition and Purchase Survey data set to examine the effect of reporting error on food‐related outcomes, for that data set contains information on SNAP participation gathered from government administrative records. Our analysis shows that the degree of reporting error is small and has little effect on the estimated impact of participation in the SNAP program on food security, diet quality, and food spending. A supplemental analysis of the effect of school food programs likewise shows no difference in using survey or administrative data in the analysis.

Suggested Citation

  • Kyung Min Kang & Robert A. Moffitt, 2019. "The Effect of SNAP and School Food Programs on Food Security, Diet Quality, and Food Spending: Sensitivity to Program Reporting Error," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 156-201, July.
    • Handle: RePEc:wly:soecon:v:86:y:2019:i:1:p:156-201
    DOI: 10.1002/soej.12344
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    as
    1. Brent Kreider, 2010. "Regression Coefficient Identification Decay in The Presence of Infrequent Classification Errors," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1017-1023, November.
    2. Aigner, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," Journal of Econometrics, Elsevier, vol. 1(1), pages 49-59, March.
    3. Hoynes, Hilary Williamson & Schanzenbach, Diane Whitmore, 2012. "Work incentives and the Food Stamp Program," Journal of Public Economics, Elsevier, vol. 96(1), pages 151-162.
    4. Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2019. "On the estimation of treatment effects with endogenous misreporting," Journal of Econometrics, Elsevier, vol. 208(2), pages 487-506.
    5. Ariun Ishdorj & Mary Kay Crepinsek & Helen H. Jensen, 2013. "Children's Consumption of Fruits and Vegetables: Do School Environment and Policies Affect Choices at School and Away from School?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 35(2), pages 341-359.
    6. Arthur Lewbel, 2007. "Estimation of Average Treatment Effects with Misclassification," Econometrica, Econometric Society, vol. 75(2), pages 537-551, March.
    7. Daniel L. Millimet & Rusty Tchernis & Muna Husain, 2010. "School Nutrition Programs and the Incidence of Childhood Obesity," Journal of Human Resources, University of Wisconsin Press, vol. 45(3).
    8. Sandra L. Hofferth & Sally Curtin, 2005. "Poverty, food programs, and childhood obesity," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 24(4), pages 703-726.
    9. Thomas J. Kane & Cecilia Elena Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," NBER Working Papers 7235, National Bureau of Economic Research, Inc.
    10. Peter Hinrichs, 2010. "The effects of the National School Lunch Program on education and health," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 479-505.
    11. Clay, Marie & Ver Ploeg, Michele & Coleman-Jensen, Alisha & Elitzak, Howard & Gregory, Christian & Levin, David & Newman, Constance & Rabbitt, Mathew, 2016. "Comparing National Household Food Acquisition and Purchase Survey (FoodAPS) Data With Other National Food Surveys’ Data," Economic Information Bulletin 242451, United States Department of Agriculture, Economic Research Service.
    12. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    13. Bruce Meyer & Robert Goerge, 2011. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Working Papers 11-14, Center for Economic Studies, U.S. Census Bureau.
    14. Frisvold, David E., 2015. "Nutrition and cognitive achievement: An evaluation of the School Breakfast Program," Journal of Public Economics, Elsevier, vol. 124(C), pages 91-104.
    15. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
    16. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    17. Bruce D. Meyer & Derek Wu, 2018. "The Poverty Reduction of Social Security and Means-Tested Transfers," NBER Working Papers 24567, National Bureau of Economic Research, Inc.
    18. repec:fth:prinin:419 is not listed on IDEAS
    19. Baum, Charles, 2007. "The Effects of Food Stamps on Obesity," Contractor and Cooperator Reports 292009, United States Department of Agriculture, Economic Research Service.
    20. Meyer, Bruce D. & Goerge, Robert M., 2011. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Contractor and Cooperator Reports 312394, United States Department of Agriculture, Economic Research Service.
    21. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2008. "Using Selection on Observed Variables to Assess Bias from Unobservables When Evaluating Swan-Ganz Catheterization," American Economic Review, American Economic Association, vol. 98(2), pages 345-350, May.
    22. Todd, Jessica E. & Scharadin, Benjamin, 2016. "Where Households Get Food in a Typical Week: Findings From USDA's FoodAPS," Economic Information Bulletin 242450, United States Department of Agriculture, Economic Research Service.
    23. Ratcliffe, Caroline & McKernan, Signe-Mary, 2010. "How Much Does Snap Reduce Food Insecurity?," Contractor and Cooperator Reports 292080, United States Department of Agriculture, Economic Research Service.
    24. Gregory, Christian & Ver Ploeg, Michele & Andrews, Margaret & Coleman-Jensen, Alisha, 2013. "Supplemental Nutrition Assistance Program (SNAP) Participation Leads to Modest Changes in Diet Quality," Economic Research Report 262225, United States Department of Agriculture, Economic Research Service.
    25. Hilary Hoynes & Diane Whitmore Schanzenbach & Douglas Almond, 2016. "Long-Run Impacts of Childhood Access to the Safety Net," American Economic Review, American Economic Association, vol. 106(4), pages 903-934, April.
    26. AIGNER, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," LIDAM Reprints CORE 130, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    27. Bruce D. Meyer & Derek Wu, 2018. "The Poverty Reduction of Social Security and Means-Tested Transfers," ILR Review, Cornell University, ILR School, vol. 71(5), pages 1106-1153, October.
    28. Thomas J. Kane & Cecilia Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
    29. Tiehen, Laura & Newman, Constance & Kirlin, John A., 2017. "The Food-Spending Patterns of Households Participating in the Supplemental Nutrition Assistance Program: Findings From USDA's FoodAPS," Economic Information Bulletin 262461, United States Department of Agriculture, Economic Research Service.
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    1. Dean Jolliffe & Juan Margitic & Martin Ravallion & Laura Tiehen, 2024. "Food stamps and America's poorest," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(4), pages 1380-1409, August.
    2. Bolbocean, Corneliu & Tylavsky, Frances A., 2021. "The impact of safety net programs on early-life developmental outcomes," Food Policy, Elsevier, vol. 100(C).
    3. Marianne P. Bitler & Christian Gregory, 2019. "Food Access, Program Participation, and Health: Research Using FoodAPS," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 9-17, July.
    4. Suttles, Shellye A. & Babb, Angela & Knudsen, Daniel, 2022. "Submitted and Denied: Understanding Variation in Case Status Across Supplemental Nutrition Assistance Program (SNAP) Applications," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322195, Agricultural and Applied Economics Association.
    5. Meyer, Bruce D. & Mittag, Nikolas, 2021. "An empirical total survey error decomposition using data combination," Journal of Econometrics, Elsevier, vol. 224(2), pages 286-305.
    6. Jun Zhang & Yanghao Wang & Steven T. Yen, 2021. "Does Supplemental Nutrition Assistance Program Reduce Food Insecurity among Households with Children? Evidence from the Current Population Survey," IJERPH, MDPI, vol. 18(6), pages 1-15, March.
    7. Jinglin Feng & Linlin Fan & Edward C. Jaenicke, 2024. "The distributional impact of SNAP on dietary quality," Agricultural Economics, International Association of Agricultural Economists, vol. 55(1), pages 104-139, January.
    8. Seung Jin Cho, 2022. "The effect of aging out of the Women, Infants, and Children (WIC) program on food insecurity," Health Economics, John Wiley & Sons, Ltd., vol. 31(4), pages 664-685, April.

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