John Cawley


Department of Policy Analysis and Management, and Department of Economics, Cornell University

Current member of the NLS Technical Review Committee

NLS user since 1994

  • Cawley, John. 2004. “The Impact of Obesity on Wages.” Journal of Human Resources, 39(2): 451-474.
  • Cawley, John, Sara Markowitz, and John Tauras. 2004. “Lighting Up and Slimming Down: The Effects of Body Weight and Cigarette Prices on Adolescent Smoking Initiation.” Journal of Health Economics, 23(2): 293-311.
  • Cawley, John. 2000. “An Instrumental Variables Approach to Measuring the Effect of Body Weight on Employment Disability.” Health Services Research, 35(5, Part II): 1159-1179.
  • Cawley, John, James Heckman and Edward Vytlacil. 1999. “On Policies to Reward the Value Added by Educators.” Review of Economics and Statistics, 81(4): 720-728.
  • Cawley, John, James Heckman and Edward Vytlacil. 1999. “Meritocracy in America: An Examination of Wages Within and Across Occupations.” Industrial Relations, 38(3): 250-296
What I learned from NLS data

The intergenerational features of the NLSY were incredibly useful in my research on the labor market consequences of obesity. By matching the female NLSY 1979 Cohort respondents to their children, I was able to exploit the heritable component of weight as a natural experiment and estimate the causal impact of weight on wages. This paper, published in the Journal of Human Resources in 2004, has been cited over 850 times (according to Google Scholar). I could not have done that work without the NLSY; thank you!

Why I chose NLS data

The NLSY is incredibly rich data. Its longitudinal nature allows researchers to study people at many different life stages, or over the course of the life cycle. Its intergenerational features allow researchers to study questions such as intergenerational transmission of inequality. The restricted-use geofiles allow researchers to examine the influence of local factors, such as the impact of local macroeconomic conditions on employment. Finally, the fact that the non-restricted-use NLSY data are so freely and easily available provides egalitarian access to fantastic data to researchers of all ages, incomes, and nationalities. For many graduate students, their first data analysis takes place with public-use NLSY data. The NLSY has served as a vital resource for decades, and I hope it continues to for many decades to come.