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A NEW FRAMEWORK FOR THE ANALYSIS OF INEQUALITY

Published online by Cambridge University Press:  01 September 2008

Flavio Cunha and
James Heckman
Flavio Cunha
Affiliation:
University of Pennsylvania
James Heckman*
Affiliation:
University of Chicago, American Bar Foundation and University College Dublin
*
Address correspondence to: James Heckman, Department of Economics, University of Chicago, 1126 E. 59th Street, Chicago, IL 60637, USA; e-mail: jjh@uchicago.edu.
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Abstract

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This paper presents a new framework for analyzing inequality that moves beyond the anonymity postulate. We estimate the determinants of sectoral choice and the joint distributions of outcomes across sectors. We determine which components of realized earnings variability are due to uncertainty and which components are due to components of human diversity that are forcastable by agents. Using our tools, we can determine how policies shift persons across sectors and outcome distributions across sectors.

Keywords

InequalityAnonymity PostulateUncertaintyPolicy Evaluation

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 12 , Supplement S2: Inequality , September 2008, pp. 315 - 354
Copyright
Copyright © Cambridge University Press 2008

References

REFERENCES

Aakvik, Arild, Heckman, James J., and Vytlacil, Edward J. (1999) Training Effects on Employment When the Training Effects are Heterogeneous: An Application to Norwegian Vocational Rehabilitation Programs, University of Bergen Working Paper 0599 and University of Chicago.Google Scholar
Aakvik, Arild, Heckman, James J., and Vytlacil, Edward J. (2005) Estimating treatment effects for discrete outcomes when responses to treatment vary: An application to Norwegian vocational rehabilitation programs. Journal of Econometrics 125, 15–51.CrossRefGoogle Scholar
Abbring, Jaap H. and Heckman, James J. (2007) Econometric evaluation of social programs, Part III: Distributional treatment effects, dynamic treatment effects, dynamic discrete choice, and general equilibrium policy evaluation. In Heckman, J. and Leamer, E. (eds.), Handbook of Econometrics, Volume 6B, pp. 5145–5303. Amsterdam: Elsevier.CrossRefGoogle Scholar
Bourguignon, François and Pereira da Silva, Luiz A. (2003) The Impact of Economic Policies on Poverty and Income Distribution. Washington, DC: The World Bank.Google Scholar
Browning, Martin, Hansen, Lars Peter, and Heckman, James J. (1999) Micro data and general equilibrium models. In Taylor, John B. and Woodford, Michael (eds.), Handbook of Macroeconomics, Vol. 1A, Chap. 8, pp. 543–633. Amsterdam: Elsevier.CrossRefGoogle Scholar
Cameron, Stephen V. and Taber, Christopher (2004) Estimation of educational borrowing constraints using returns to schooling. Journal of Political Economy 112, 132–182.CrossRefGoogle Scholar
Card, David (1999) The causal effect of education on earnings. In Ashenfelter, O. and Card, D. (eds.), Handbook of Labor Economics, Vol. 5, pp. 1801–1863. New York: North-Holland.Google Scholar
Carneiro, Pedro, Hansen, Karsten, and Heckman, James J. (2003) Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice, 2001 Lawrence R. Klein Lecture. International Economic Review 44, 361–422.CrossRefGoogle Scholar
Carneiro, Pedro and Heckman, James J. (2002) The evidence on credit constraints in post-secondary schooling. Economic Journal 112, 705–734.CrossRefGoogle Scholar
Chernozhukov, Victor and Hansen, Christian (2005) An IV model of quantile treatment effects. Econometrica 73, 245–261.CrossRefGoogle Scholar
Cunha, Flavio and Heckman, James J. (2006) The evolution of earnings risk in the US economy, Presented at the 9th World Congress of the Econometric Society, London.Google Scholar
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, 191–261.CrossRefGoogle Scholar
Cunha, Flavio, Heckman, James J., and Schennach, Susanne M. (2007) Estimating the technology of cognitive and noncognitive skill formation. Presented at the Yale Conference on Macro and Labor Economics, May 5–7, 2006.Google Scholar
Ferguson, Thomas S. (1983) Bayesian density estimation by mixtures of normal distributions. In Chernoff, H., Rizvi, M.H., Rustagi, J., and Siegmund, D. (eds.), Recent Advances in Statistics: Papers in Honor of Herman Chernoff on his Sixtieth Birthday, pp. 287–302. New York: Academic Press.CrossRefGoogle Scholar
Fields, Gary S. (2003) Economic and social mobility really are multifaceted. Presented at the Conference on Frontiers in Social and Economic Mobility, Cornell University, Ithaca, NY.Google 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, 475–492.Google Scholar
Heckman, James J. (1990) Varieties of selection bias. American Economic Review 80, 313–318.Google Scholar
Heckman, James J. (1992) Randomization and social policy evaluation. In Manski, C. and Garfinkel, I. (eds.), Evaluating Welfare and Training Programs, pp. 201–230. Cambridge, MA: Harvard University Press.Google Scholar
Heckman, James J. (2001) Micro data, heterogeneity, and the evaluation of public policy: Nobel lecture. Journal of Political Economy 109, 673–748.CrossRefGoogle Scholar
Heckman, James J. and Honoré, Bo E. (1990) The empirical content of the Roy model. Econometrica 58, 1121–1149.CrossRefGoogle Scholar
Heckman James, J., Lochner, Lance J., and Taber, Christopher (1998a) Explaining rising wage inequality: Explorations with a dynamic general equilibrium model of labor earnings with heterogeneous agents. Review of Economic Dynamics 1, 1–58.CrossRefGoogle Scholar
Heckman James, J., Lochner, Lance J., and Taber, Christopher (1998b) General-equilibrium treatment effects: A study of tuition policy. American Economic Review 88, 381–386.Google Scholar
Heckman James, J., Lochner, Lance J., and Taber, Christopher (1998c) Tax policy and human-capital formation. American Economic Review 88, 293–297.Google Scholar
Heckman James, J., Lochner, Lance J., and Taber, Christopher (1999) General-equilibrium cost-benefit analysis of education and tax policies. In Ranis, G. and Raut, L.K. (eds.), Trade, Growth and Development: Essays in Honor of T.N. Srinivasan, Chap. 14, pp. 291–349. Amsterdam: Elsevier Science B.V.Google Scholar
Heckman James, J., Lochner, Lance J., and Todd, Petra E. (2006) Earnings equations and rates of return: The Mincer equation and beyond. In Hanushek, Eric A. and Welch, Frank (eds.), Handbook of the Economics of Education, Chap. 7, pp. 307–458. Amsterdam: North-Holland.Google Scholar
Heckman, James J. and Navarro, Salvador (2007) Dynamic discrete choice and dynamic treatment effects. Journal of Econometrics 136, 341–396.CrossRefGoogle Scholar
Heckman James, J., Smith, Jeffrey A., and Clements, Nancy (1997) Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts. Review of Economic Studies 64, 487–536.CrossRefGoogle Scholar
Heckman, James J. and Vytlacil, Edward J. (2005) Structural equations, treatment effects and econometric policy evaluation. Econometrica 73, 669–738.CrossRefGoogle 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 Learner, E. (eds.), Handbook of Econometrics, Volume 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 Learner, E. (eds.), Handbook of Econometrics, Volume 6B, pp. 4875–5144. Amsterdam: Elsevier.CrossRefGoogle Scholar
Jencks, Christopher, Smith, Marshall, Acland, Henry, Bane, Mary Jo, Cohen, David K., Gintis, Herbert, Heyns, Barbara, and Michelson, Stephen (1972) Inequality: A Reassessment of the Effect of Family and Schooling in America. New York: Basic Books.Google Scholar
Jöreskog, Karl G. (1977) Structural equations models in the social sciences: Specification, estimation and testing. In Krishnaiah, P. (ed.), Applications of Statistics, pp. 265–287. New York: North-Holland.Google Scholar
Jöreskog, Karl G. and Goldberger, Arthur S. (1975) Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association 70, 631–639.Google Scholar
Juhn, Chinhui, Murphy, Kevin M., and Pierce, Brooks (1993) Wage inequality and the rise in returns to skill. Journal of Political Economy 101, 410–442.CrossRefGoogle Scholar
Kane, Thomas J. (1994) College entry by blacks since 1970: The role of college costs, family background, and the returns to education. Journal of Political Economy 102, 878–911.CrossRefGoogle Scholar
Lillard, Lee A. and Willis, Robert J. (1978) Dynamic aspects of earning mobility. Econometrica 46, 985–1012.CrossRefGoogle Scholar
Ravallion, Martin (2003) Assessing the poverty impact of an assigned program. In Bourguignon, F. and Silva, L.A.P. (eds.), The Impact of Economic Policies on Poverty and Income Distribution, pp. 103–122. Washington, DC: The World Bank.Google Scholar
Roy, A.D. (1951) Some thoughts on the distribution of earnings. Oxford Economic Papers 3, 135–146.CrossRefGoogle Scholar
Vytlacil, Edward J. (2002) Independence, monotonicity, and latent index models: An equivalence result. Econometrica 70, 331–341.CrossRefGoogle Scholar
Willis, Robert J. and Rosen, Sherwin (1979) Education and self-selection. Journal of Political Economy 87, S7–S36.CrossRefGoogle Scholar