Papers written by Author(s): 'Linden McBride'
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Working PaperThe 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.View Full Paper PDF
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Working PaperThe 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.View Full Paper PDF
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