...workplace discrimination against women or lower productivity levels for these workers, while the results presented in Cawley (2004) suggest that obesity may have an important negative economic effect. Our current understanding of the adverse economic impact of obesity may be understated if obesity also negatively affects early human capital accumulation. If increased body weight reduces the academic performance of adolescents or young adults, then the obesity-specific wage gap estimated by Cawley may reflect only part of the economic harm of obesity.

Exploring the effect of adolescent obesity on human capital accumulation is also important in the context of the current public policy environment. There are increased efforts by policy makers to fight childhood obesity by improving the nutritional quality of foods provided in public schools. For example, in September 2005, California governor Arnold Schwarzenegger signed legislation creating the nation's most rigorous nutrition standards in state public schools. Effective in July 2007, the new California law will limit the sale of many "junk foods," by regulating fat content, sugar content, and portion size. While the promotion of such school policies highlights the potential public health benefits of combating obesity, there may also be positive educational spillovers associated with improving adolescent body weight. In the context of the current policy environment for preventing and reversing childhood obesity, and building on the work of Cawley (2004), this study examines whether adolescent obesity adversely affects early human capital accumulation.

There are several reasons to expect a negative relationship between body weight and academic performance. First, it may be that poor academic performance causes higher body weight. This may be the case if, for example, adolescents choose to eat excessively to psychologically compensate for doing poorly in school. Second, obesity could cause a decline in academic performance. This could occur if teachers discriminate against overweight students by giving them poorer grades or if obesity has adverse psychological and physiological effects that impede productive studying. Finally, it may be that there is no causal link between body weight and academic performance, but rather an association that is explained by unobserved individual-level characteristics. For example, it may be that those with the least personal discipline expend the least amount of effort exercising and the least amount of effort studying.

Alternatively, there may be a positive relationship between body weight and academic performance. Poor academic performance may cause psychological stress, which reduces one's appetite and resultant body weight. Or, there may be a positive relationship between body weight and academic performance due to an unobserved heterogeneity. For example, if individuals must allocate their time between efforts to improve (or maintain) their physical well-being and efforts to enhance academic performance, then individuals with the least to gain from physical health investments (or the most to gain from investments in academic pursuits) may choose to devote more time and efforts toward academic endeavors and less toward monitoring and maintaining their weight. (1)

This paper examines the sensitivity of the association between adolescent body weight and academic performance to potential biases caused by unmeasured heterogeneity. Using the National Longitudinal Study of Adolescent Health, I estimate the relationship between several measures of adolescent body weight and grade point average (GPA). Ordinary least squares (OLS), instrumental variables (IV), and individual fixed effects (FE) estimates produce consistent evidence of a negative relationship between body weight and academic performance for white females aged 14-17. Conservative estimates reflect a difference in weight of 50 to 60 pounds (approximately two standard deviations) is associated with an 8 to 10 percentile difference in standing in the GPA distribution. For nonwhite females and white males, I find little evidence of a significant relationship between body weight and academic performance after controlling for unobserved heterogeneity. For nonwhite males, however, there is some evidence of a nonlinear relationship between body mass index (BMI) and GPA. Taken together, these findings indicate that adolescent obesity may have adverse academic consequences for white females. Thus, in principle, targeting obesity-reduction policies to adolescents may not only improve health outcomes, but may also have a positive impact on human capital accumulation.

2. Empirical Literature

Several empirical studies have found a negative association between adolescent obesity and academic achievement. Sargent and Blanchflower (1994) find that females who were obese at age 16 had lower reading and math test scores later in life than those who were not obese at the same age. Crosnoe and Muller (2004) show that adolescents in the 85th or higher percentile of the BMI distribution for their age-gender group have lower mean GPAs than those in the lower 85th percent of the distribution. They find that GPAs were even lower for obese adolescents in schools with higher rates of romantic relationships and lower average body size among students. Their results suggest that self-appraisal of weight, relative to one's peers, may have an independent effect on academic achievement.

Other related work has examined the relationship between obesity and educational attainment. Gortmaker et al. (1993) find that relative to women who were not overweight in 1981, women who were overweight in that same year had fewer years of education accumulated five years later. Sargent and Blanchflower (1994) also find that females who were obese at age 16 accumulated fewer years of schooling later in life than those who were not obese at age 16.

Each of these findings suggests evidence of a negative relationship between obesity and human capital accumulation, but in each of these studies, estimates could be biased by unmeasured characteristics associated with both obesity and educational attainment. With regard to heterogeneity bias, if the least disciplined individuals are most likely to become obese and to achieve less in school, and this level of personal discipline is unobservable, cross-section estimates of the effect of obesity on academic achievement will be biased upward. On the other hand, if unobserved time and effort must be allocated between monitoring or regulating one's physical health and investing in productive study time, and the most academically motivated individuals choose to devote more time to studying and less time to personal health care, then OLS estimates may be biased downward. Moreover, as noted above, it is not difficult to imagine reverse causality, whereby schooling outcomes could affect body weight.

While not specifically examining the relationship between body weight and academic achievement, related work has shown that the relationship between obesity and wages is quite sensitive to assumptions about unobservables (see, for example, Pagan and Davila 1997; Behrman and Rosenzweig 2001; Baum and Ford 2004; Norton and Han 2006). Using a sample of twins to control for unobserved family-level characteristics, Behrman and Rosenzweig (2001) find no significant relationship between obesity and wages. However, this may be due to the limited power of the design implied by small sample sizes. Pagan and Davila (1997) find a negative relationship between obesity and wages using instrumental variables. However, Cawley (2004) notes that their choice of instruments--health limitations and family poverty--may not be credible because they may be directly related to wages. Norton and Han (2006) use genetic information from specific genes linked to obesity as instruments in identifying the causal effects of obesity on female labor supply and wages. They find a small positive effect of obesity on employment probabilities and no effect on wages. While the instruments are credible, the relatively small sample size available for the wage equations (around 500) suggests that obesity effects may be imprecisely estimated.

Cawley (2004) provides convincing evidence of a significant negative relationship between body weight and wages using a large, nationally representative sample of workers from the National Longitudinal Survey of Youth (NLSY79). Cross-section estimates show a consistent negative relationship between obesity and wages for white women, Hispanic women, and black women. However, after including individual FE to control for fixed individual-level unobserved heterogeneity, he finds that the relationship between obesity and wages is only significant for white women, reflecting that selection on unobservables likely explains the strong association for black women and Hispanic women. Cawley finds similar results when he attempts to control for potential endogeneity bias with instrumental variables models, using a biological sibling's BMI as the key exclusion restriction.

Cawley (2004) offers one plausible explanation for race- and gender-specific differences in the effect of obesity on wages. He cites the sociological literature, which suggests that obesity may have a more adverse psychological effect on white women than on black and Hispanic women. In fact, for black and Hispanic females, Stearns (1997) finds that heavier weight is associated with greater self-perceived stability and power. Averett and Korenman (1996) find that obesity is correlated with lower self-esteem for white females (but not for nonwhite females or males), but that controlling for self-esteem does not fully explain race-specific differences in the association between obesity and wages.

This paper builds on the work by Cawley (2004) by examining whether adolescent obesity affects academic achievement. The current study is the first to explore the sensitivity of the association between body weight and academic performance to unmeasured heterogeneity. Using an IV strategy to control for endogeneity bias and individual FE models to control for time-invariant unobserved heterogeneity bias, this study presents evidence on the appropriateness of inferring a causal link between adolescent body weight and academic performance.

3. Methodology

OLS Model

The most common estimator used in the literature to identify the effect of obesity on education attainment is the OLS estimator, given by [[gamma].sub.1] in the following equation: (2)

[A.sub.1] = X'[[beta].sub.1] + [BW.sub.2][[gamma].sub.1] + [[epsilon].sub.1], (1)

where A is a measure of academic achievement, X is a vector of individual-level, family-level, and community-level observables, and BW is a measure of the adolescent's body weight. The estimate of [[gamma].sub.1] will only be an unbiased estimate of the effect of obesity on academic performance if there are no unobservable individual-, school-, or family-level characteristics correlated with both obesity and academic achievement; that is E([epsilon]\BW) = 0. If this identification assumption is violated, then the OLS estimator will be biased. This will be the case in the presence of endogeneity or heterogeneity bias.

IV Models

A common method of addressing endogeneity bias is through the use of instrumental variables. If the identification assumptions underlying the IV model are satisfied, then this estimate will control for any reverse causality whereby academic performance may cause changes in obesity. For example, poor grades may cause adolescents to psychologically compensate for their academic shortcomings by consuming more food. On the other hand, poor academic performance may cause an increase in stress, which may suppress appetite and reduce bodyweight. (3)

IV estimation requires finding observable characteristics that provide exogenous variation in adolescent body weight that are uncorrelated with academic achievement except through body weight. The two-stage least squares (2SLS) model jointly estimates the academic performance in Equation 1 and a body weight equation:

[BW.sub.2] = X'[[beta].sub.2] + [[epsilon].sub.2]. (2)

The classic IV identification assumption requires setting one or more elements in [[beta].sub.1] = 0. This implies that subset of X will serve as exclusion restrictions (Z) to identify the model.

Two exclusion restrictions are chosen for identification of the standard IV model: (i) the parent's report that the adolescent's biological mother suffers from an obesity problem, and (ii) the parent's report that the adolescent's biological father suffers from an obesity problem. (4) The identification assumption of the standard IV model first requires parental self-reports of obesity to be strongly correlated to/with adolescent obesity. This is theoretically expected to be the case because recent studies have found that approximately half of the variation in weight can be explained by genetics (Comuzzie and Allison 1998).

Identification of the model also requires that parental obesity not be correlated with unmeasured determinants of adolescent academic performance. This assumption may be problematic if parental obesity serves as a proxy for unobserved family-level environmental characteristics that are associated with schooling outcomes. For example, if parental obesity is correlated with a lack of motivation by parents and this motivation is both unmeasured and correlated with less monitoring of adolescents' school work, then parental obesity may have an independent effect on academic achievement, resulting in upwardly biased IV estimates.

However, there is some empirical evidence to suggest that a biologically related individual's BMI may serve as a credible instrument. Using the BMI of a sibling as his key exclusion restriction, Cawley (2004) provides a compelling case to suggest little empirical evidence of an effect of common household environment on body weight. In particular, he focuses on adoption studies that show that (i) the association in BMI between children and their biological parents is the same for children who live with their birth parents and those who live with adoptive parents. (Stunkard et al. 1986; Vogler et al. 1995), and (ii) the correlation in weight between biologically unrelated adopted children is statistically equal to zero (Grilo and Pogue-Geile 1991). This scientific evidence suggests that genetics rather than household environment is the most prominent influence on body weight.

Given the concern that parental obesity may be correlated with family-level schooling sentiment that could affect adolescents' academic performance, I control for several measures of family-level schooling sentiment. The Add Health dataset provides a rich set of family-level observable characteristics that capture schooling attitudes. These variables include: whether the parent moved to the neighborhood because of the quality of the local schools, whether the parent is a member of the Parent-Teacher Association, whether the parent prioritizes scholastic brilliance by their children, whether the mother has graduated from college, whether the parent talks with the adolescent about schoolwork, and the degree to which the parent monitors their child's curfew and friends.

While the scientific literature and the included schooling sentiment controls may enhance the credibility of the instrument exogeneity assumption, there remains a concern that parental obesity may capture unobserved genetic characteristics that contribute to both adolescent obesity and to adolescent academic performance. While I do control for a measure of innate academic intelligence using the student's Add Health Picture and Vocabulary Test (AHPVT) score, an unobserved genetic trait correlated with a genetic predisposition to obesity and a genetic predisposition to intelligence could lead to a violation of the identification assumption of the IV model. Because there are two exclusion restrictions for one potentially endogenous variable, overidentification tests are conducted to examine whether it...

NOTE: All illustrations and photos have been removed from this article.



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