Although obese students are more likely to exhibit the symptoms of depression than their slimmer counterparts and often do poorly in school, it is not clear whether these associations are spurious or causal in nature. Drawing on data from the National Longitudinal Study of Adolescent Health, we use an instrumental variables (IV) approach to distinguish between these hypotheses. IV estimates suggest that body weight leads to decreased self-esteem and increased depressive symptomatology among female, but not male, respondents. In addition, we find that body weight is negatively related to female academic achievement. Finally, we explore the degree to which the relationship between body weight and female academic achievement is explained by psychological wellbeing. We find that psychological wellbeing accounts for up to 30 % of this relationship.
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See Mallory et al. (1989), Rodriguez et al. (2002), Must and Anderson (2003), Hannon et al. (2005), Trent et al. (2005), and Tauman and Gozal (2006) for more information on adolescent obesity and asthma, menstrual abnormalities, sleep apnea, and type 2 diabetes. Among adults, obesity is associated with a wider set of pathologies including stroke, heart disease, and some types of cancer (World Health Organization 2000). See Oliver (2006) for an iconoclastic view of the relationship between obesity and health.
See also Renman et al. (2007) and Swallen et al. (2005). Renman et al. (2007) matched 58 obese adolescents with 58 non-obese peers. They found no differences in self-esteem between the two groups. Swallen et al. (2005) used data from the first wave of the Add Health to examine the relationship between body weight and psychological wellbeing. These authors found that overweight and obese 12–14-year-olds were more likely to be depressed and more likely to have low self-esteem than their counterparts with BMIs in the normal range. However, they found no evidence to suggest that being overweight or obese was related to the psychological wellbeing of older adolescents.
Using data from the Early Childhood Longitudinal Study-Kindergarten (ECLS-K), Zavodny (2013) found that BMI is negatively related to teacher assessment of student academic performance, but is essentially unrelated to standardized test scores. Zavodny interprets these findings as evidence that teachers may discriminate against overweight pupils.
The initial Add Health data collection effort yielded information on over 20,000 respondents ages 11 through 21, approximately 17 % of whom had a participating biological sibling. Ninety-five percent of Add Health respondents were between the ages of 13 and 18 at the time of the baseline survey. See Harris et al. (2008) for more information on the Add Health data and how they were collected.
The two missing items from the Adolescent Health questionnaire were “my sleep was restless,” and “I had crying spells.”
Sabia (2007) used a self-reported contemporaneous measure of grade point average in his study.
Neither High School Diploma nor College Completion measure the quality of education received. As noted by Fletcher.
See Kuczmarski et al (2002) for a discussion of these age- and gender-specific weight and BMI distributions.
See Sabia (2007, p. 879) and Cawley (2004, p. 455) for a discussion of this issue. Wave II of the Add Health includes both measured and self-reported height and weight. In estimates not presented here, but available upon request, we find that self-reported and measured height and weight measures in the Add Health produce qualitatively similar findings to those presented below. Random measurement error (unrelated to psychological wellbeing or education) could lead to attenuation bias in OLS models but not the IV models described below.
In addition to these controls, the vector X i included a set of indicators for missing values for each of the control variables. However, the findings presented below are robust to (1) restricting the sample to those respondents with non-missing information on each of the control variables, and (2) using a mean imputation method to fill in missing values of the control variables. For female respondents, we include a control for ever having been pregnant. There is evidence that parental education and family structure are associated with body weight. See, for instance, Kemptner and Marcus (2013 ).
BMI of the respondent’s biological is available for respondents whose biological sibling was interviewed by the Add Health at Wave I. The second instrument is based on an item in the parental survey, which was usually completed by the respondent’s biological mother. The parent was asked, “[d]oes the adolescent’s biological mother now have [the health problem] of obesity?” The respondent’s mother was coded as being obese if this question was answered in the affirmative. A total of 3,467 respondents had non-missing information on their own body weight, their sibling’s body weight, and their biological mother’s obesity status.
We note that the relationship between psychological wellbeing and academic performance is potentially bidirectional. However, our purpose is not to estimate the causal effect of psychological wellbeing on academic performance. Rather, it is to examine whether psychological wellbeing mediates the relationship between body weight and academic performance. The estimates of the relationship between psychological wellbeing and academic performance presented bellow should not be interpreted causally.
Because the IV sample represents approximately 20 percent of all Add Health respondents, we explored the degree to which selection into this sample might produce non-representative estimates. Specifically, we estimated a probit model of the probability of being in the IV sample found that, among female respondents, neither BMI nor psychological wellbeing predicted this probability. Moreover, there were no statistically significant differences in family background or a wide set of individual characteristics. The only differences we found were that female respondents who were part of the IV sample tended to come from larger families (by construction, given that respondents had to have a sibling to be part of the IV sample), were less likely to be Asian, and were more likely to come from rural, non-Eastern parts of the United States. For males, the correlates were similar, although the IV sample was composed of individuals of slightly lower body weight than those in the OLS sample.
In unreported results available upon request, we conduct gender-specific IV analyses by race and ethnicity. We find that the adverse psychological effects of adolescent bodyweight are strongest for white women and, depending on the specification, for Hispanic women. For black women, however, there is little evidence of a causal link.
This third instrument is based on an item in the parental survey, which was usually completed by the respondent’s biological mother. The parent was asked, “[d]oes the adolescent’s biological father now have [the health problem] of obesity?” The respondent’s mother was coded as being obese if this question was answered in the affirmative.
It should be noted that one concern with using the biological mother’s own reported obesity status as an instrument is that it could be correlated with unobserved attitudes related to mental health (or schooling). Therefore, we experimented with using sibling BMI as the sole instrument. TSLS estimates of the effect of body weight on academic achievement using sibling BMI as an instrument are qualitatively and quantitatively similar to the results reported in Table 5A and 7A.
By focusing on the contemporaneous (i.e., cross-sectional) relationship between body weight and psychological wellbeing, our approach avoids modeling the complex long-run interactions between bodyweight, psychological wellbeing, and academic achievement. It should be noted however, that body weight at Wave I is highly correlated with body weight at Wave IV. In fact, less than 2 percent of respondents who were overweight at Wave I were in the healthy weight category at Wave IV. In unreported results available upon request, we estimate the relationship between Wave I body weight (in pounds) and Wave IV academic achievement controlling for Wave IV body weight (in pounds) for female respondents. The OLS and TSLS results still provide evidence of a negative relationship between Wave I body weight and academic achievement. However, these estimates are considerably smaller than those reported in the Table 7A perhaps because Wave I body weight is so closely tied to Wave IV body weight. A large number of studies have examined the long-run impact of academic achievement on body weight. See, for example, Fletcher and Frisvold (2014 ).
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The authors thank participants at the 2010 Southern Economic Association meetings for useful comments and suggestions on an earlier draft of this paper. This research uses data from the National Longitudinal Study of Adolescent Health, designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a Grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (http://www.cpc.unc.edu/addhealth/contract.html).
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Sabia, J.J., Rees, D.I. Body weight, mental health capital, and academic achievement. Rev Econ Household 13, 653–684 (2015). https://doi.org/10.1007/s11150-014-9272-7
- Body weight
- Academic achievement