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Papers written by Author(s): 'Linden McBride'

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  • Working Paper

    Access to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) at the State and Substate Levels: Meaning and Measurement

    July 2026

    Working Paper Number:

    CES-26-44

    This study estimates eligibility and access rates for the U.S. Department of Agriculture's (USDA) Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) administrative data linked with American Community Survey (ACS) data. This study is one result of a long-term research collaboration among USDA's Economic Research Service; the U.S. Department of Commerce, Bureau of the Census; USDA's Food and Nutrition Service (FNS); and participating state WIC agencies.'By analyzing WIC participation at the state and substate levels, the report provides insights into program reach and demographic differences. The findings confirm that the Census Bureau estimates meet high statistical reliability standards, providing valuable data for program officials and managers, and other stakeholders, to enhance program outreach and effectiveness. A key focus of the report is the comparison between Census Bureau and USDA, FNS estimates, which differ in methodology and measurement scope.
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  • Working Paper

    Integrating Administrative and Survey Data to Estimate WIC Eligibility and Access

    July 2026

    Working Paper Number:

    CES-26-43

    The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) provides benefits to low-income, nutritionally at-risk women, infants, and children. To administer WIC, officials and program managers at the federal and state level want to understand who is eligible for the program, who among the eligible population chooses to participate, and who is not accessing the program despite their eligibility. Novel individual-level data linkages between restricted-use WIC Administrative Records and the American Community Survey provide WIC access rates estimated at the state and county levels, as well as estimates disaggregated by the demographic and socioeconomic characteristics of individuals and their households. These estimates are developed by the Census-FNS-ERS Joint Project, a research partnership among the U.S. Census Bureau, the US Department of Agriculture's Food and Nutrition Service and Economic Research Service, and state WIC agencies that provide the requisite WIC administrative data to the Census Bureau. This paper details and evaluates our current data linkage and estimation methods, reports results, and identifies areas for improvement and further research.
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  • Working Paper

    The Impact of Childcare Costs on Mothers' Labor Force Participation

    April 2025

    Working Paper Number:

    CES-25-25

    The rising costs of childcare pose challenges for families, leading to difficult choices including those impacting mothers' labor force participation. This paper investigates the relationship between childcare costs and maternal employment. Using data from the National Database of Childcare Prices, the American Community Survey, and the Longitudinal Employer Household Dynamics, we estimate the impact of childcare costs on mothers' labor force participation through two empirical strategies. A fixed-effects approach controls for geographic and temporal heterogeneity in costs as well as mothers' idiosyncratic preferences for work and childcare, while an instrumental variables approach addresses the endogeneity of mothers' preferences for work and childcare by leveraging exogenous geographic and temporal variation in childcare licensing requirements. Our findings across both research designs indicate that higher childcare costs reduce labor force participation among mothers, with lower-income mothers exhibiting greater responsiveness to changes in childcare costs.
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  • Working Paper

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

    February 2025

    Working Paper Number:

    CES-25-13

    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.
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