Abstract

In this paper we examine the effect of dollar stores on children’s Body Mass Index (BMI). We use a dataset compiled by the Arkansas Center for Health Improvement that reflects a BMI screening program for public school children in the state of Arkansas. We combine propensity score matching with difference-in-differences methods to deal with time-invariant as well time-varying unobserved factors. We find no evidence that the presence of dollar stores within a reasonably close proximity of the child’s residence increases BMI. In fact, we see an increase in BMI when dollar stores leave a child’s neighborhood. Given the proliferation of dollar stores in rural and low-income urban areas, the question of whether dollar stores are contributing to high rates of childhood obesity is policy relevant. However, our results provide some evidence that exposure to dollar stores is not a causal factor.

Highlights

  • At present, nearly 35 percent of young Americans aged 6 to 19 are overweight and 19 percent are obese [1]

  • In this study we examine the effect of access to dollar stores (DS) on children’s Body Mass Index (BMI)

  • Matching is performed for each age cohort separately, we report balancing tests after we pool together the matched observations from all age cohorts given that the difference in differences (DiD) estimates come from the pooled age cohorts

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Summary

Introduction

Nearly 35 percent of young Americans aged 6 to 19 are overweight and 19 percent are obese [1]. Proposals to address childhood obesity are often aimed at augmenting features of the environment by improving access to healthy foods in or around the home and school, reducing accessibility and exposure to unhealthy food, and/or providing more opportunities for exercise and vigorous play. Story et al [7] acknowledge that the systematic study of interactions between features of the environment, policy interventions, and nutrition outcomes is a relatively new field of study. As such, it lacks well established models and faces numerous challenges in terms of measurement of environmental attributes and empirical design. The dataset we use is an unbalanced panel which contains information for schoolchildren from 2004 to 2010

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