This blog is part of the Labor Network Blog series.

The U.S. economy had around 135 million jobs in May 2014, which the Bureau of Labor Statistics classified into hundreds of occupational categories. There were approximately 603,000 lawyers, 174,000 electrical engineers, 118,000 head chefs and 1.1 million restaurant cooks. In total, 840 detailed occupational categories were used in the most recent issue of the Occupational Employment Statistics.

Various psychological theories argue that work life, and occupations in particular, are a central component of human identity (Budd, 2011). In addition, occupations implicitly carry substantial information about a worker's human capital. For example, all practicing lawyers and physicians finished professional school, electrical engineers have at least a college degree, and head chefs have more work experience than regular cooks. There is valuable information embedded in occupational choices.

What are the main characteristics of any occupation? Can occupations be measured and compared to one another, with the goal of extracting such valuable information? The research by industrial and organizational psychologists who have explored these questions is the foundation of the Occupational Information Network, known as O*Net, a publicly-available database financed by the U.S. Department of Labor (Peterson et al., 2001). O*Net describes in detail the skills, abilities, tasks and educational requirements for all the occupations in the U.S. economy.

O*Net is a powerful research tool for labor economists. It is one of the main databases used by Acemoglu and Autor (2011) to study the interaction between skills, tasks and technological change and measure the routine content of occupations. Following Acemoglu and Autor (2011), I combine data from the National Longitudinal Study of Youth, 1979 cohort (NLSY79), with information from the Occupational Information Network in a recent paper (Chaparro, 2016). The goal of the paper is to measure and decompose the wage return to cognitive and non-cognitive skills, accounting for detailed occupational choices (Heckman and Honoré, 1990; Heckman, Stixrud and Urzúa, 2006).

I instrument the importance of a skill for a worker's occupation in her thirties and forties (occupational choices) with the importance of the same skill for the worker's preferred occupation back in her early twenties (occupational aspirations). This empirical strategy is feasible due to the longitudinal structure of the NLSY79 and the detailed information on skills requirements for hundreds of occupations available in the O*Net.

There is growing interest in the role of skills in Latin America’s labor markets: the most recent meeting of LACEA’s Labor Network focused on skills; the Inter-American Development Bank (IADB) and the Development Bank of Latin America (CAF) are currently working on flagship reports on this topic; and there are now several household surveys which include direct measures of human skills, like the Encuesta Nacional de Habilidades (ENHAB) for Perú.

Unfortunately, we do not have a database equivalent to O*Net in Latin America. Efforts to measure and report the skill content of occupations in our region are highly welcome. In the meantime, any research which attempts to combine O*Net with data from Latin America should take into account the measurement error created in the process. Do lawyers in México require a different set of skills than lawyers in the U.S.? How different are the tasks of an electrical engineer in Brazil when compared to the tasks of an American colleague? Are the fundamental abilities of Peruvian head chefs any different than their counterparts in the United States? This sort of questions should guide our immediate analysis of the skill content of occupations of Latin American workers.

References:

Acemoglu, D. and Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. Handbook of Labor Economics, 4b:1043 - 1171.

Budd, J. W. (2011). The Thought of Work. Cornell University Press.

Chaparro, J. (2016). Occupational Choice and Returns to Skills: evidence from the NLSY79 and O*Net. PhD Dissertation, Department of Applied Economics. University of Minnesota.

Heckman, J. and Honoré, B. (1990). The Empirical Content of the Roy Model. Econometrica, 58(5):1121-1149.

Heckman, J., Stixrud, J., and Urzúa, S. (2006). The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior. Journal of Labor Economics, 24(3): 411 - 482.

Peterson, N. G., Mumford, M. D., Borman, W. C., Jeanneret, P. R., Fleishman, E. A., Levin, K. Y., Campion, M. A., Mayfield, M. S., Morgeson, F. P., Pearlman, K., Gowing, M., Lancaster, A., Silver, M., Dye, D. (2001). Understanding Work using the Occupational Information Network (O*Net): Implications for Practice and Research. Personnel Psychology, 54(2):451 - 492.