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For residents of urban areas, the neighborhood density per square mile of small grocery stores was positively and significantly related to obesity and BMI, but the neighborhood densities of other types of food retail and food service establishments were not significantly related to weight status. For residents of rural areas there were no significant relationships between neighborhood establishment densities and weight status. The results of the empirical analyses are consistent with the hypothesis that an increase in the neighborhood density of small grocery stores in urban leads to increased calorie consumption for neighborhood residents.

Objective: The objective of this study is to understand the effect of obesity on wages. Although other studies have linked obesity to wages, the validity of their estimation results remains questionable due to potential weaknesses in the strategies employed to control for the endogeneity of obesity. I identified the effect of obesity on wages with exogenous state-level variation in multiple variables. The over-identification of obesity with exogenous instruments will provide valid parameter estimates if the identification is supported.

Methodology: This study employed an amplified dataset based on the National Longitudinal Survey of Youth 1979 (NLSY79). NLSY79 provides ongoing panel information with a nationally representative sample of 12,686 young men and women who were 14 to 22 years old when first surveyed in 1979. I have augmented the publicly available data by obtaining confidential geographic information for individuals.

Body-mass index (BMI) was used to measure the extent of obesity. Wages were assessed separately by gender as a function of BMI splines and interactions of BMI splines with two race dummies (non-Hispanic Black and Hispanic).

This study used two-stage estimation techniques to identify the effect of obesity on wages in conjunction with individual fixed effects model. I specified an overidentified first-stage equation using exogenous state-level variation to instrument individual obesity. Instruments for obesity included the following state-level variables: cigarette prices, per capita number of restaurants, per capita number of food stores, fast-food price, cost of alcoholic drinks (inclusive of beer, wine, liquor), and cost of food.

A Heckman selection model was used to control for the selection into the labor force with the following state-level identifying instruments: unemployment rate, number of business establishments, and number of Social Security Program beneficiaries.

Results: Specification tests support the exclusion of the instruments from the main equation and the strength of the instruments in the first-stage equation. Preliminary study results indicate that an increase in BMI after being overweight has a negative effect on wage earnings for both males and females, even after adjusting for selection into the labor force.

Conclusion: The results will support the understanding of the economic cost of obesity to an individual that arise from sources other than adverse health effects. This spillover effect will increase the total cost of obesity to both individuals and society as a whole. The negative effect of obesity on labor market outcomes could raise further attention to the epidemic of obesity.

Objectives: The objective of this study is to evaluate the impacts of poverty, FSP, and maternal employment on the risk of obesity in early childhood.

Methodology: This study investigates the interactions between poverty, FSP participation, and maternal employment, as well as their impacts on the risk of obesity among children who are raised in single mother families. The main data source in this study is from the matched mother/child data from the National Longitudinal Survey of Youth (NLSY79). Utilizing the full information maximum likelihood (FIML) method, the empirical model estimates the joint decisions of FSP enrollment and maternal employment along with child health (weight) production function simultaneously to account for the potential endogeneity of these health inputs. The identification strategy used in this paper exploits the time series interactions of all current and lagged exogenous variables and a set of instruments that capture the variations of welfare regulations across states and over time.

Results: The results suggest that FSP participation mitigates the risk of childhood obesity among the poor while maternal employment is positively linked to incidents of obesity over the whole socio-economic spectrum. Both findings are statistically significant. In addition, the racial gap of the weight health problem is widening over years, even after accounting for family income and maternal employment. Finally, estimation methods without adequate control for endogeneity of FSP participation and maternal employment are likely to yield biased results.

Conclusions: The childhood health disparities among different socio-economic stratums are widening, especially for racial minorities. The food assistance programs modestly mitigate the risk of obesity among impoverished children while the risk could be aggravated because of mother's excessive time spent in the labor market. These findings imply that when single mothers transitioning from welfare to the labor market, policy considerations should be given to their family, especially on relaxed eligibility rules for food assistance.

This paper tests the hypothesis that more schooling at the college level leads to (a) lower levels of obesity and (b) lower levels of obesity among one's children. Obesity is a particularly important health outcome because it is rapidly approaching smoking as a cause of premature morbidity and mortality. This hypothesis cannot be tested by examining a simple association between education and body mass index (BMI) because both of these outcomes may be influenced by unobservable characteristics of the individual. For example, the relative value individuals place on current and future consumption may vary. Those who place a high value on future consumption may make large investments in education and health while they are young that involve sacrifices in the form of foregone wages and leisure time. Those who place a high value on current consumption may not be willing to make these sacrifices when they are young, and as a consequence may enjoy lower earnings and health when they are older. Thus, to infer from the simple association between education and BMI that education reduces obesity risk is invalid.

Ideally, to isolate the effect of education on obesity, one would randomly assign individuals to different education levels and then follow the evolution of their BMI over time. Because this is clearly infeasible, I instead use a quasi-experimental design that attempts to mimic this random assignment. Use of this quasi-experiment requires that the experiment predict education, but have no direct effect on BMI. My quasi-experiment is the number of colleges and universities in an individual's county of residence at age 17. Previous researchers have relied on this quasi-experiment to study the effect of education on wages (Card, 1995) and civic participation (Dee, 2004). To acquire data on schools, I use the Higher Education General Information Survey (HEGIS), which provides data on the number of 2-year and 4-year colleges in each county in the United States. Then, I match this measure of college availability by county with respondents from the NLSY79 and their children from the NLSY79 Child/Young Adult Survey. Preliminary OLS results indicate a strong negative association between education and obesity, but it is too early to report results from the quasi-experimental research design outlined above.