Joshua C. Pinkston
College of Business
University of Louisville
Louisville, KY 40292
Institutional Affiliation: University of Louisville
Information about this author at RePEc
NBER Working Papers and Publications
|January 2015||Can Changing Economic Factors Explain the Rise in Obesity?|
with Charles J. Courtemanche, Christopher J. Ruhm, George Wehby: w20892
A growing literature examines the effects of economic variables on obesity, typically focusing on only one or a few factors at a time. We build a more comprehensive economic model of body weight, combining the 1990-2010 Behavioral Risk Factor Surveillance System with 27 state-level variables related to general economic conditions, labor supply, and the monetary or time costs of calorie intake, physical activity, and cigarette smoking. Controlling for demographic characteristics and state and year fixed effects, changes in these economic variables collectively explain 37% of the rise in BMI, 43% of the rise in obesity, and 59% of the rise in class II/III obesity. Quantile regressions also point to large effects among the heaviest individuals, with half the rise in the 90th percentile of BMI...
Published: Charles J. Courtemanche & Joshua C. Pinkston & Christopher J. Ruhm & George L. Wehby, 2016. "Can Changing Economic Factors Explain the Rise in Obesity?," Southern Economic Journal, Southern Economic Association, vol. 82(4), pages 1266-1310, April. citation courtesy of
|February 2014||Adjusting Body Mass for Measurement Error with Invalid Validation Data|
with Charles Courtemanche, Jay Stewart: w19928
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 differ...
Published: Courtemanche, Charles & Pinkston, Joshua C. & Stewart, Jay, 2015. "Adjusting body mass for measurement error with invalid validation data," Economics & Human Biology, Elsevier, vol. 19(C), pages 275-293. citation courtesy of