Abstract
In this paper, we study the impact of child care subsidy receipt on low-income children’s weight outcomes in the fall and spring of kindergarten using data from the Early Childhood Longitudinal Study, Kindergarten Cohort. Our results suggest that subsidy receipt is associated with increases in BMI and a greater likelihood of being overweight and obese. Using quantile regression methods, we find substantial variation in subsidy effects across the BMI distribution. Specifically, child care subsidies have no effect on BMI at the lower end of the distribution, inconsistent effects in the middle of the distribution, and large effects at the top of the distribution. Our results point to the use of non-parental child care, particularly center-based services, as the key mechanism through which subsidies influence children’s weight outcomes.
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Notes
See http://www.cdc.gov/nccdphp/dnpa/obesity/childhood/index.htm. See Anderson and Butcher (2006) for a review of trends.
Indeed, childhood obesity has been identified as one of the most pressing health problems facing the US (US Department of Health and Human Services 2001) and there are a large number of policy efforts underway to stop or reverse this trend among school-age children. For example, the Child Nutrition and Women Infants and Children (WIC) Reauthorization Act of 2004 requires that all local education agencies participating in the National School Lunch Program create local wellness policies no later than July 2006. The Kids Walk-to-School program developed by the Centers for Disease Control and Prevention (CDC) aims to increase opportunities for daily physical activity by encouraging children to walk to and from school in groups accompanied by adults. An increasing number of schools are limiting access to foods high in fats and sugars by banning soda machines and snack bars in cafeterias and school stores. The School Breakfast and the National School Lunch Programs are two federal entitlement programs that provide nutritionally balanced, low-cost or free breakfasts and lunches to millions of children each school day.
Participation rates in formal child care settings among low-income children now rival those of high-income children (49% compared to 53%) (Magnuson et al. 2007).
The CCDF was created by the 1996 welfare reform legislation, which consolidated four preexisting subsidy programs into a single block grant, increased funding for child care funding substantially, and gave individual states greater flexibility in program design and administration (Herbst and Tekin 2010). In 2006, states spent approximately $9.3 billion on child care assistance and served more than 1.7 million children in an average month (Child Care Bureau 2006).
Story et al. (2006) provide a comprehensive summary of these studies.
A publication put forth every 5 years by the US Department of Health and Human Services, the The Dietary Guidelines for Americans provide authoritative advice for people ages two and over about the relationship between increased healthy dietary habits and the reduction in the risk of major chronic diseases. This publication also serves as the basis for Federal food and nutrition education programs. Additional information may be found here: http://www.health.gov/DietaryGuidelines/.
Information on the revised pyramid can be found here: http://www.cnpp.usda.gov/FGP4Children.htm.
The 2005 The Dietary Guidelines for Americans recommend that children and adolescents engage in no less than 60 min of physical activity each day. Furthermore, the National Association for Sport and Physical Education (NASPE) recommends that toddlers receive at least 30 min of structured physical and 60 min of unstructured activity each day. Preschoolers should receive 60 min of structured play and another 60 min of unstructured play each day.
The ECLS-K is sponsored by the US Department of Education. For more information, see the ECLS-K website at http://nces.ed.gov/ecls/kindergarten.asp.
The ECLS-K used a multistage probability sample design to select the sample of children attending kindergarten in 1998. The primary sampling units (PSUs) were geographic areas consisting of counties or groups of counties. The second stage consisted of public and private schools within sampled PSUs. The final stage units were students within schools. The school frame was freshened in the spring of 1998 to include newly opened schools that were not included in the original sample. Once the sample children were identified, parent contact information was obtained from the school, which was used to locate parents and seek consent for the child assessments and parent interviews. Completion rates (or response rates that are conditioned on earlier stages of data collection) for the fall of kindergarten interviews were high: 89.9% of child assessments were completed, 85.3% of parent interviews were completed, and over 90% of the teacher interviews were completed.
Single mothers are identified in the ECLS-K by using the variable P1HPARNT, which describes the child’s living arrangements. We define single mother families as those in which the child lived with the “biological mother only.”
Additional minor exclusions from the sample are made due to an inability to match children to the 2000 Census geocoded data (five observations) and mothers with nonsensical ages (three observations).
For additional information on the CDC growth charts, see http://www.cdc.gov/nccdphp/dnpa/growthcharts/resources/growthchart.pdf. Also, see http://www.cdc.gov/growthcharts/. Until recently, this nomenclature differed across children (ages two to 19) and adults. Children with BMIs above the 95th percentile of the gender- and age-specific distribution were considered “overweight,” and those above the 85th percentile were considered “at-risk-for-overweight.” However, an expert committee convened by the American Medical Association (AMA) in collaboration with the Department of Health and Human Services Health Resources and Services Administration (HRSA) and the CDC recently endorsed the use of “overweight” and “obese” for children. See this link for more information on the adjustment: http://www.ama-assn.org/ama1/pub/upload/mm/433/ped_obesity_recs.pdf.
Rates of child care subsidy receipt calculated by researchers using the NSAF match closely our ECLS-K estimate. For example, Tekin (2007) calculates a participation rate of 11.6% for a sample of single mothers, and Herbst (2008) estimates a take-up rate of 13.9%, also from a sample of single mothers.
Our decision rule is constructed so that we drop only those children who receive exclusively Head Start. Therefore, our indicator of subsidy receipt omits those reporting subsidy receipt while participating only in Head Start. A child participating in Head Start along with another service is coded as participating in the non-Head Start service. The remaining tie-breakers are settled as follows: relative and center: center; non-relative and center: center; relative and school: school; non-relative and school: school; non-relative, relative, and center: center; non-relative, relative, and school: school.
This figure is based on data from 2003 to 2006. See http://www.cdc.gov/nccdphp/dnpa/obesity/childhood/index.htm.
The least squares estimates of coefficients in linear probability models are consistent estimates of average probability derivatives, but the standard errors are biased as a result of heteroskedasticity (Angrist and Krueger 1999). As we note in the text, we report standard errors that are robust to any form of heteroskedasticity.
An alternative approach to obtaining an unbiased estimate of α is through the use of instrumental variables (IV). The IV method requires at least one variable that is correlated with subsidy receipt but uncorrelated with children’s weight outcomes. We experiment with models using characteristics of the states’ child care subsidies system (e.g., CCDF expenditures per child, reimbursement rates, and eligibility thresholds) as identifying instruments. One concern is that these variables, while being correlated with subsidy receipt, may also reflect the states’ generosity or attitude toward assisting children and this may be correlated with weight outcomes. The subsidy coefficients from these models are qualitatively similar to those presented in the paper, but they are much larger in magnitude. Given the concerns about the plausibility of the instruments and the fact that these results are qualitatively similar to the current results, we do not present the IV models.
In other words, OLS assumes that covariates only affect the location of the conditional distribution of the outcomes, not its scale or any other attribute of its distribution (Koenker and Hallock 2001).
The term “quantile” refers to the general case.
The p n (.) is called the “check function” because it looks like check-mark when it is plotted (Angrist and Pischke 2008). Specifically,
\( p_{n} (u) = u(n - I(u < 0)) = \left\{ {\begin{array}{*{20}c} {n \, x \, u, \, u \, \ge 0 \, } \\ {(n - 1) \, x \, u, \, u < 0} \\ \end{array} } \right.. \)
To estimate the quantile regression models, we use the ivqte command developed by Frolich and Melly (2008) for STATA 10.1.
Note that quantile regression is not the same as fitting OLS models for subsets of the conditional distribution of BMI. The latter approach is equivalent to estimating a model on samples based on truncated dependent variables, resulting in biased estimates.
Quantile regression methods have been increasingly utilized in economics to study the returns to education (Arias et al. 2001), changes in wage structure and inequality (Buchinsky 1994, 1997; Gonzales and Miles 2001; Garcia et al. 2001), union wage effects (Chamberlain 1994), and birth outcomes (Abrevaya 2001). See Empirical Economics vol. 26, no. 1, 2001 for further examples on some of the applications of quantile regressions. We also located a few papers using quantile regression to estimate child and adult correlates of BMI. Examples of this work include: Terry et al. (2007) and Beyerlein et al. (2008).
To economize on space, we only present the coefficients on our key variables. The coefficient estimates from other variables are mostly consistent with our expectations and in line with those in the relevant literature. They are available from the authors upon request.
We subject this assertion to a more rigorous test. We re-estimate the basic regression of children’s weight outcomes on subsidy receipt, but only among children in different child care arrangements. In the model using the log of BMI as the dependent variable, the coefficient on subsidy receipt implies a statistically significant 3.8% increase in BMI among children in center-based and family child care homes. As for the obesity regression, the coefficient on subsidy receipt implies a statistically significant 6.8% point increase in obesity among such children. Across no other arrangement were any of the subsidy coefficients significant.
We also estimated quantile regression models using ln BMI. The results from these models are very similar to those with the BMI in terms of both pattern and significance.
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Acknowledgments
The authors would like to thank the seminar participants at the Hohenheim University in Germany, and the participants at the 2009 Meetings of the Population Association of America, the European Society for Population Economics, and the Association for Public Policy Analysis and Management for helpful comments.
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Herbst, C.M., Tekin, E. Child care subsidies and childhood obesity. Rev Econ Household 9, 349–378 (2011). https://doi.org/10.1007/s11150-010-9087-0
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DOI: https://doi.org/10.1007/s11150-010-9087-0