Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-17T23:45:09.840Z Has data issue: false hasContentIssue false
  • English
  • Français

AN INTERVIEW WITH JAMES J. HECKMAN

Published online by Cambridge University Press:  09 September 2010

Donna K. Ginther
Donna K. Ginther*
Affiliation:
University of Kansas
*
Address correspondence to: Donna Ginther, Department of Economics, University of Kansas, 1460 Jayhawk Blvd., 333 Snow Hall, Lawrence, KS 66045, USA; e-mail: dginther@ku.edu.
Get access Rights & Permissions [Opens in a new window]

Extract

James Heckman is one of the most important and influential scholars to have graced the economics profession. His work is deeply rooted at the intersection of economic theory and empirical microeconomics, and he has made significant contributions to the study of labor economics, microeconometrics, and the use of micro data in macroeconomic analysis. Heckman's work is motivated by the scientific method, in which theory is held up to the scrutiny of the data and empirical analysis is informed by economic theory. During the course of his work, he has made lasting contributions to the study of sample selection bias, duration analysis, heterogeneity, and treatment effects in microeconometrics. In labor economics, he has applied these econometric methods to the study of labor supply and life-cycle dynamic models of unemployment, wage growth, and skill formation. In addition, he is the leading scholar on the evaluation of active labor market programs. As an applied microeconomist, one cannot do research on labor supply, sample selection, duration models, or life-cycle dynamics without encountering Jim Heckman's work.

Type
MD Interview
Information
Macroeconomic Dynamics , Volume 14 , Issue 4 , September 2010 , pp. 548 - 584
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Angrist, Joshua D., Imbens, Guido W., and Rubin, Donald (1996) Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91 (434), 444–455.CrossRefGoogle Scholar
Ashenfelter, Orley and Heckman, James J. (1974) The estimation of income and substitution effects in a model of family labor supply. Econometrica 42 (1), 73–86.CrossRefGoogle Scholar
Ben-Porath, Yoram (1973) Labor-force participation rates and the supply of labor. Journal of Political Economy 81 (3), 697–704.CrossRefGoogle Scholar
Björklund, Anders and Moffitt, Robert (1987) The estimation of wage gains and welfare gains in self-selection. Review of Economics and Statistics 69 (1), 42–49.CrossRefGoogle Scholar
Borghans, Lex, Duckworth, Angela L., Heckman, James J., and ter Weel, Bas (2008) The economics and psychology of personality traits. Journal of Human Resources 43 (4), 972–1059.CrossRefGoogle Scholar
Browning, Martin, Hansen, Lars Peter, and Heckman, James J. (1999) Micro data and general equilibrium models. In Taylor, J.B. and Woodford, M. (eds.), Handbook of Macroeconomics, Vol. 1A, pp. 543–633. Amsterdam: Elsevier.CrossRefGoogle Scholar
Butler, Richard J., Heckman, James J., and Payner, Brooks (1989) The impact of the economy and the state on the economic status of blacks: A study of South Carolina. In Galenson, D.W. (ed.), Markets in History: Economic Studies of the Past, pp. 321–343. New York: Cambridge University Press.Google Scholar
Cunha, Flavio and Heckman, James J. (2007) The Evolution of Inequality, Heterogeneity, and Uncertainty in Labor Earnings in the U.S. Economy. NBER Working Paper Series No. 13526.CrossRefGoogle Scholar
Cunha, Flavio and Heckman, James J. (2009) The economics and psychology of inequality and human development. Journal of the European Economic Association 7 (2–3), 320–364.CrossRefGoogle ScholarPubMed
Cunha, Flavio, Heckman, James J., and Navarro, Salvador (2005) Separating uncertainty from heterogeneity in life cycle earnings, the 2004 Hicks Lecture. Oxford Economic Papers 57 (2), 191–261.CrossRefGoogle Scholar
Cunha, Flavio, Heckman, James J., and Schennach, Susanne M. (2010) Estimating the technology of cognitive and noncognitive skill formation. Econometrica 78 (3), 883–931.Google ScholarPubMed
Durbin, J. (1954) Errors in variables. Review of the International Statistical Institute 22, 23–32.CrossRefGoogle Scholar
Friedman, Milton (1957) A Theory of the Consumption Function. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Gamow, George (1961) One, Two, Three . . . Infinity: Facts and Speculation of Science. New York: Viking Press.Google Scholar
Goldberger, Arthur S. (1964) Econometric Theory. New York: Wiley.Google Scholar
Gronau, Reuben (1973) The intrafamily allocation of time: The value of the housewives' time. American Economic Review 63 (4), 634–651.Google Scholar
Hansen, Karsten T., Heckman, James J., and Mullen, Kathleen J. (2004) The effect of schooling and ability on achievement test scores. Journal of Econometrics 121 (1–2), 39–98.CrossRefGoogle Scholar
Hansen, Lars Peter and Heckman, James J. (1996) The empirical foundations of calibration. Journal of Economic Perspectives 10 (1), 87–104.CrossRefGoogle Scholar
Hausman, Jerry and Ruud, Paul (1984) Family labor supply with taxes. American Economic Review 74 (2), 242–248.Google Scholar
Heckman, James J. (1974) Shadow prices, market wages, and labor supply. Econometrica 42 (4), 679–694.CrossRefGoogle Scholar
Heckman, James J. (1976) The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5 (4), 475–492.Google Scholar
Heckman, James J. (1978) A partial survey of recent research on the labor supply of women. American Economic Review 68 (2), 200–207.Google Scholar
Heckman, James J. (1990) Varieties of selection bias. American Economic Review 80 (2), 313–318.Google Scholar
Heckman, James J. (1995) Lessons from the bell curve. Journal of Political Economy 103 (5), 1091–1120.CrossRefGoogle Scholar
Heckman, James J. (1997) Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources 32 (3), 441–462.CrossRefGoogle Scholar
Heckman, James J. (2004) James J. Heckman. In Breit, W. and Hirsch, B.T. (eds.), Lives of the Laureates: Eighteen Nobel Economists, pp. 299–333. Cambridge and London: MIT Press.Google Scholar
Heckman, James J. (2010) Building bridges between structural and program evaluation approaches to evaluating policy. Journal of Economic Literature 48 (2), 356–398.CrossRefGoogle ScholarPubMed
Heckman, James J. and Ashenfelter, Orley (1973) Estimating labor supply functions. In Cain, G.W. and Watts, H. (eds.), Income Maintenance and Labor Supply, pp. 265–278. New York: Academic Press.Google Scholar
Heckman, James J. and Honoré, Bo E. (1990) The empirical content of the Roy model. Econometrica 58 (5), 1121–1149.CrossRefGoogle Scholar
Heckman, James J., Humphries, John Eric, and Mader, Nicholas (in press) The GED and the Problem of Non-cognitive Skills in America. Chicago: University of Chicago Press.Google Scholar
Heckman, James J., Lochner, Lance J., and Taber, Christopher (1998) Explaining rising wage inequality: Explorations with a dynamic general equilibrium model of labor earnings with heterogeneous agents. Review of Economic Dynamics 1 (1), 1–58.CrossRefGoogle Scholar
Heckman, James J., Malofeeva, Lena, Pinto, Rodrigo and Savelyev, Peter (2010) Understanding the mechanisms through which an influential early childhood program boosted adult outcomes. Unpublished manuscript, Department of Economics, University of Chicago.Google Scholar
Heckman, James J. and Payner, Brook S. (1989) Determining the impact of federal antidiscrimination policy on the economic status of blacks: A study of South Carolina. American Economic Review 79 (1), 138–177.Google Scholar
Heckman, James J. and Robb, Richard (1985) Alternative methods for evaluating the impact of interventions. In Heckman, J.J. and Singer, B. (eds.), Longitudinal Analysis of Labor Market Data, Vol. 10, pp. 156–245. New York: Cambridge University Press.CrossRefGoogle Scholar
Heckman, James J. and Rubinstein, Yona (2001) The importance of noncognitive skills: Lessons from the GED testing program. American Economic Review 91 (2), 145–149.CrossRefGoogle Scholar
Heckman, James J., Stixrud, Jora, and Urzua, Sergio (2006) The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics 24 (3), 411–482.CrossRefGoogle Scholar
Heckman, James J., Urzua, Sergio, and Vytlacil, Edward J. (2006) Understanding instrumental variables in models with essential heterogeneity. Review of Economics and Statistics 88 (3), 389–432.CrossRefGoogle Scholar
Heckman, James J. and Vytlacil, Edward J. (1998) Instrumental variables methods for the correlated random coefficient model: Estimating the average rate of return to schooling when the return is correlated with schooling. Journal of Human Resources 33 (4), 974–987.CrossRefGoogle Scholar
Heckman, James J. and Vytlacil, Edward J. (1999) Local instrumental variables and latent variable models for identifying and bounding treatment effects. Proceedings of the National Academy of Sciences 96, 4730–4734.CrossRefGoogle ScholarPubMed
Heckman, James J. and Vytlacil, Edward J. (2005) Structural equations, treatment effects and econometric policy evaluation. Econometrica 73 (3), 669–738.Google Scholar
Heckman, James J. and Vytlacil, Edward J. (2007a) Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. In Heckman, J. and Leamer, E. (eds.), Handbook of Econometrics, Vol. 6B, pp. 4779–4874. Amsterdam: Elsevier.CrossRefGoogle Scholar
Heckman, James J. and Vytlacil, Edward J. (2007b) Econometric evaluation of social programs, part II: Using the marginal treatment effect to organize alternative economic estimators to evaluate social programs and to forecast their effects in new environments. In Heckman, J. and Leamer, E. (eds.), Handbook of Econometrics, Vol. 6B, pp. 4875–5144. Amsterdam: Elsevier.CrossRefGoogle Scholar
Herrnstein, Richard J. and Murray, Charles A. (1994) The Bell Curve: Intelligence and Class Structure in American Life. New York: Free Press.Google Scholar
Imbens, Guido W. and Angrist, Joshua D. (1994) Identification and estimation of local average treatment effects. Econometrica 62 (2), 467–475.CrossRefGoogle Scholar
Killingsworth, Mark R. (1983) Labor Supply. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Lewis, H. Gregg (1963) Unionism and Relative Wages in the United States: An Empirical Inquiry. Chicago: University of Chicago Press.Google Scholar
Lewis, W. Arthur (1955) The Theory of Economic Growth. London: Unwin Hyman.Google Scholar
Mincer, Jacob (1962) Labor force participation of married women. In Lewis, H.G. (ed.). Aspects of Labor Economics, pp. 63–106. Princeton, NJ: Princeton University Press.Google Scholar
Moffitt, Robert A. (1999) Models of treatment effects when responses are heterogeneous. Proceedings of the National Academy of Sciences 96, 6575–6576.Google Scholar
Pencavel, John H. (1986) Labor supply of men: A survey. In Ashenfelter, O. and Layard, R. (eds.), Handbook of Labor Economics, Vol. 1, pp. 3–102. Amsterdam: North-Holland.CrossRefGoogle Scholar
Roy, A.D. (1951) Some thoughts on the distribution of earnings. Oxford Economic Papers 3 (2), 135–146.Google Scholar
Schultz, Theodore W. (1964) Transforming Traditional Agriculture. New Haven, CT: Yale University Press.Google Scholar
Theil, Henri (1967) Economics and Information Theory. Studies in Mathematical and Managerial Economics. Amsterdam: North-Holland.Google Scholar
Urzua, Sergio (2008) Racial labor market gaps: The role of abilities and schooling choices. Journal of Human Resources 43 (4), 919–971.CrossRefGoogle Scholar
Willis, Robert J. and Rosen, Sherwin (1979) Education and self-selection. Journal of Political Economy 87(5, Part 2), S7–S36.CrossRefGoogle Scholar