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Comparing IV with structural models: what simple IV can and cannot identify

  • James Heckman

    (Institute for Fiscal Studies and University of Chicago)

  • Sergio Urzua

This paper compares the economic questions addressed by instrumental variables estimators with those addressed by structural approaches. We discuss Marschak's Maxim: estimators should be selected on the basis of their ability to answer well-posed economic problems with minimal assumptions. A key identifying assumption that allows structural methods to be more informative than IV can be tested with data and does not have to be imposed.

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File URL: http://cemmap.ifs.org.uk/wps/cwp0810.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP08/10.

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Date of creation: Apr 2010
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Handle: RePEc:ifs:cemmap:08/10
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  1. Heckman, James J. & Urzua, Sergio & Vytlacil, Edward, 2008. "Instrumental Variables in Models with Multiple Outcomes: The General Unordered Case," IZA Discussion Papers 3565, Institute for the Study of Labor (IZA).
  2. Flavio Cunha & James Heckman, 2007. "The Evolution of Inequality, Heterogeneity and Uncertainty in Labor Earnings in the U.S. Economy," PIER Working Paper Archive 07-032, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  3. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2009. "Understanding Instrumental Variables in Models with Essential Heterogeneity," Working Papers 200941, Geary Institute, University College Dublin.
  4. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
  5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  6. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
  7. Flavio Cunha & James Heckman & Salvador Navarro, 2005. "Separating uncertainty from heterogeneity in life cycle earnings," Oxford Economic Papers, Oxford University Press, vol. 57(2), pages 191-261, April.
  8. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, December.
  9. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70 Elsevier.
  10. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
  11. Flavio Cunha & James J. Heckman & Salvador Navarro, 2007. "The Identification And Economic Content Of Ordered Choice Models With Stochastic Thresholds," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1273-1309, November.
  12. Carneiro, Pedro & Hansen, Karsten T. & 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," IZA Discussion Papers 767, Institute for the Study of Labor (IZA).
  13. James J. Heckman, 2008. "Econometric Causality," NBER Working Papers 13934, National Bureau of Economic Research, Inc.
  14. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters, in: Schooling, Experience, and Earnings, pages 1-4 National Bureau of Economic Research, Inc.
  15. Matzkin, Rosa L., 1986. "Restrictions of economic theory in nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 42, pages 2523-2558 Elsevier.
  16. James Heckman & Daniel Schmierer & Sergio Urzua, 2010. "Testing the correlated random coefficient model," CeMMAP working papers CWP10/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  17. James J. Heckman & Jora Stixrud & Sergio Urzua, 2006. "The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 411-482, July.
  18. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-60, September.
  19. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males," Journal of Political Economy, University of Chicago Press, vol. 106(2), pages 262-333, April.
  20. John M. Barron & Mark C. Berger & Dan A. Black, 2006. "Selective Counteroffers," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 385-410, July.
  21. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, 05.
  22. James J. Heckman & John Eric Humphries & Paul A. LaFontaine & Pedro L. Rodr�guez, 2012. "Taking the Easy Way Out: How the GED Testing Program Induces Students to Drop Out," Journal of Labor Economics, University of Chicago Press, vol. 30(3), pages 495 - 520.
  23. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71 Elsevier.
  24. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-70, March.
  25. Sergio Urzúa, 2008. "Racial Labor Market Gaps: The Role of Abilities and Schooling Choices," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
  26. Matzkin, Rosa L., 2007. "Nonparametric identification," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 73 Elsevier.
  27. Heckman, James & Layne-Farrar, Anne & Todd, Petra, 1996. "Human Capital Pricing Equations with an Application to Estimating the Effect of Schooling Quality on Earnings," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 562-610, November.
  28. Matzkin, Rosa L., 1993. "Nonparametric identification and estimation of polychotomous choice models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 137-168, July.
  29. Abbring, Jaap H. & 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," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 72 Elsevier.
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