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This paper examines the role physical activity plays in determining body mass using data from the American Time Use Survey. Our work is the first to address the measurement error that arises when time use during a single day-rather than average daily time use over an extended period-is used as an explanatory variable. We show that failing to account for day-to-day variation in activities results in the effects of time use on a typical day being understated. Furthermore, we account for the possibility that physical activity and body mass are jointly determined by implementing Lewbel's instrumental variables estimator that exploits first-stage heteroskedasticity rather than traditional exclusion restrictions. While averaging 30 min of transportation-related biking or walking per day lowers the BMI of men by 1.5, we find no effect of physically active leisure on the BMI of men in our sample. In contrast, 30 min of per day of either type of physical activity lowers the BMI of women by 1.
Asunto(s)
Ejercicio Físico , Obesidad , Índice de Masa Corporal , Femenino , Humanos , Actividades Recreativas , Masculino , Obesidad/epidemiología , CaminataRESUMEN
This paper considers effects of body mass on wages in the years following labor market entry. The preferred models allow current wages to be affected by both past and current body mass, as well as past wages, while also addressing the endogeneity of body mass. I find that a history of severe obesity has a large negative effect on the wages of white men. White women face a penalty for a history of being overweight, with some evidence of additional penalties that begin above the threshold for severe obesity. Furthermore, the effects of past wages on current wages imply that past body mass has additional, indirect effects on wages, especially for white women.
Asunto(s)
Índice de Masa Corporal , Obesidad/epidemiología , Salarios y Beneficios/estadística & datos numéricos , Adolescente , Adulto , Empleo , Femenino , Humanos , Masculino , Modelos Econométricos , Ocupaciones , Sobrepeso/epidemiología , Índice de Severidad de la Enfermedad , Población Blanca , Adulto JovenRESUMEN
We propose a new method for using validation data to correct self-reported weight and height in surveys that do not measure respondents. The standard correction in prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of measured weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples. This makes our correction more robust to differences in measurement error across surveys as long as both surveys represent the same population. We examine three nationally representative datasets and find that misreporting appears to be sensitive to differences in survey context. When we compare predicted BMI distributions using the two validation approaches, we find that the standard correction is affected by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates.