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The Design of Sampling Strata for the National Household Food Acquisition and Purchase Survey

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Listed:
  • Jonathan Eggleston
  • Linden McBride
  • Mark Klee

Abstract

The National Household Food Acquisition and Purchase Survey (FoodAPS), sponsored by the United States Department of Agriculture’s (USDA) Economic Research Service (ERS) and Food and Nutrition Service (FNS), examines the food purchasing behavior of various subgroups of the U.S. population. These subgroups include participants in the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), as well as households who are eligible for but don’t participate in these programs. Participants in these social protection programs constitute small proportions of the U.S. population; obtaining an adequate number of such participants in a survey would be challenging absent stratified sampling to target SNAP and WIC participating households. This document describes how the U.S. Census Bureau (which is planning to conduct future versions of the FoodAPS survey on behalf of USDA) created sampling strata to flag the FoodAPS targeted subpopulations using machine learning applications in linked survey and administrative data. We describe the data, modeling techniques, and how well the sampling flags target low-income households and households receiving WIC and SNAP benefits. We additionally situate these efforts in the nascent literature on the use of big data and machine learning for the improvement of survey efficiency.

Suggested Citation

  • Jonathan Eggleston & Linden McBride & Mark Klee, 2025. "The Design of Sampling Strata for the National Household Food Acquisition and Purchase Survey," Working Papers 25-13, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:25-13
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    File URL: https://www2.census.gov/library/working-papers/2025/adrm/ces/CES-WP-25-13.pdf
    File Function: First version, 2025
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    References listed on IDEAS

    as
    1. Brittany Bond & J. David Brown & Adela Luque & Amy O’Hara, 2014. "The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey," CARRA Working Papers 2014-08, Center for Economic Studies, U.S. Census Bureau.
    2. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
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