1 Multiple Skills, Multiple Types of Education, and the Labor Market: A Research Agenda1 Joseph G. Altonji Department of Economics Yale University September, 2010 Summary I propose a major program of research on skill, education, and the labor market. The program will build on four facts. First, ability and skill are multidimensional. Second, secondary and postsecondary education is heterogeneous in quality and in the types of skills and knowledge provided. Third, jobs differ substantially in what they require. Finally, technical change, globalization, and shifts in the composition of demand for goods and services alter the demand for particular skills in the labor market relative to supply, with important implications for the wage distribution. In essence, the research program will place the multidimensionality of ability, skills, and knowledge at the center stage of theoretical and empirical research on child development, educational attainment, and labor market careers. In this document, I discuss why the program is needed and why the prospects for success are high. I provide a brief sketch rather than a full blown proposal and of necessity use a very broad brush. Why is Research on Multiple Types of Skill and Education Needed? 1
This work is licensed under the Creative Commons Attribution‐NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by‐nd/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.
2 Since the pioneering work of Gary Becker and Jacob Mincer, a large community of scholars has studied the demand for education and the economic return to education. There have been important advances in the use of instrumental variables methods and in the use of structural models of education choice and labor market outcomes. As a result of these developments, we know much more than before about the average return to a year in school. However, the overwhelming majority of the studies abstract from type of education. The focus is either on years of school completed or broad education categories such as high school, some college, etc. This is unfortunate because basic descriptive analyses show large differences in the labor market payoff across subject areas. The substantial differences by gender and race in course of study contribute to observed gaps in labor market outcomes. And mismatch between the skills and knowledge the education system produces and the types valued in the labor market is a perennial public concern. Over the past ten years, the role of non‐cognitive traits and cognitive traits in the acquisition of human capital and in the labor market return to human capital has received considerable attention in economics. Much of this work has focused upon child development, educational attainment, and early labor market success. (See, for example, Cuhna, Heckman and Shannach (2010)). There is also current research on how cognitive skills and personality traits arise from genetic influences, early childhood environment, and formal education. Progress in developmental psychology, cognitive psychology, genetics, and neuroscience make this a promising area for research by economists on the production of human capital, broadly defined. So far, this research has not been systematically extended into models of the type of 3 secondary and higher education people acquire or models of the effects of skills and education on career paths. Finally, research on the distribution of earnings has paid increasing attention to the effects of technical change, globalization, and changes in product demand on the demand for particular types of skills. Autor, Levy and Murnane (2003) and Autor and Handel (2009) are good examples. To understand trends in the level and distribution of wages, employment, and unemployment in the US and other countries, we need models that distinguish workers along multiple dimensions of skill and knowledge, and that distinguish jobs in parallel fashion. There has been considerable progress in this area over the past decade for researchers to build on. In particular, researchers in the U.S. and Europe have had success in quantifying the importance of occupation specific skills for wages and job mobility patterns. Potential for Success: The time is ripe, for several reasons. First, research on the child development process child development is progressing rapidly, fueled by advances in the understanding of the role of genes and environment in shaping the talents and personality traits that matter for particular programs of study and subsequent career paths. This is likely to continue. Second, progress has been made in the development of models of course selection in high school and in college that place the role of differences in predetermined abilities, knowledge, and preferences at center stage. Advances in computer power and econometric methods have made estimation of such models feasible. Empirical research on the causal effects of particular courses of study is in an early stage but should follow two paths. The first 4 is to use dynamic choice models to understand educational decisions and use the restrictions of the model to account for selection bias when measuring causal effects. Arcidiacono’s (2004) study of college major is one of a small set of papers that can be built upon. The second path is to use quasi‐experimental methods that exploit variation in institutional features that influence how students are assigned to course sequences in high school or to majors in college. Research in other countries that rely more heavily on test scores to decide type of primary and secondary education and admission to particular college majors will be valuable. Third, advances in computation and in simulation‐based estimation methodologies are making it possible to estimate an integrated model of child development, human capital accumulation, and labor market success that spans birth to adulthood by combining data sets that individually lack the necessary information. Fourth, advances in modeling and especially in computation will make it feasible to incorporate multiple skill and education types into general equilibrium models of the supply and demand for labor that have been used in macroeconomic studies of wage growth and distribution. Fifth, the data are getting better. State level longitudinal data systems that track individual students are revolutionizing research on the “education production function”. Research on the return to student achievement, high school curriculum, vocational programs, and college major require more data sets that track individuals well into the labor market. Data for Florida and Texas have been matched to data on higher education and to earnings records. These longitudinal data systems have enormous potential for the study of how student achievement and field of study affect labor market performance. Other countries (notably 5 Denmark), have administrative data sets that can be used to research student achievement, field of study, and labor market success. Excellent survey based panel data sets beginning in childhood have been collected in other countries. Sixth, the data can and should be improved. Part of the research program should be to invest in new data sets that begin in early childhood and continue until career patterns are well established. The NLSY79: Children and Young Adults data project has the promise of accomplishing this. The Early Childhood Longitudinal Study is having an enormous impact on research on the role of family and schools in child development. The longer it is continued, the more valuable it will be. To the extent its age coverage overlaps with the early period of other data sets that follow children into adulthood, it can play a key role in research designs that use multiple data sets for estimation. Re‐surveying members of existing panel data sets that start in adolescence but stop in the mid 20s would have a huge payoff. Here I have in mind the National Education Longitudinal Survey: 1988, an extraordinarily rich data set that started with a national sample of 8th graders in 1988 but ended in 2000. In a few more years NLSY:97, which started with about 9,000 children between 12 and 16 in 1997, will become an extremely valuable resource. Extending survey data through matches to administrative earnings records is another avenue that should be explored, perhaps using advances in statistical methods for construction of synthetic data sets that mimic the statistical properties of the original data but completely hide data on individuals. A critical need is for better data on the information on job content and skill requirements in panel data sets. Thee NLSY79 and NLSY97 and the PSID and cross‐sectional 6 surveys such as the CPS provide very little information on the tasks people perform at work and the skills that they need to perform them. Most researchers rely upon variables from the Dictionary of Occupational Titles and its successor data set, ONET, that can be merged into household surveys using 3 digit occupation codes. Similar data is available for other countries. However, jobs vary a lot within a broad occupation classification. A major effort is needed to develop survey modules that can be used to provide more information about what people do and the skills that are involved. Autor and Handel (2009) achieved some success in using a short serious of questions to elicit information about what people do on‐the‐job and what skills they need. References Cited Arcidiacono, Peter “Ability Sorting and the Returns to College Major” Journal of Econometrics, Vol. 121, Nos. 1‐2 (August, 2004), 343‐375 Autor, David and Michael Handel, “Putting Tasks to the Test: Human Capital, Job Tasks, and Wages.” NBER Working Paper No. 15116, June 2009. Autor, David, Richard J. Murnane, and Frank Levy, “The Skill Content of Recent Technological Change: An Empirical Exploration.” Quarterly Journal of Economics, 118(4), November 2003, 1279–1334 Cuhna, Flavio, James J. Heckman, and Susanne M. Shannach, “Estimating the Technology of Cognitive and Noncognitive Skill Formation”, Econometrica 78 (May 2010).