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Instrumental Variables: A Cautionary Tale

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  • James J. Heckman

Abstract

This paper considers the use of instrumental variables to estimate the mean effect of treatment on the treated. It reviews previous work on this topic by Heckman and Robb (1985, 1986) and demonstrates that (a) unless the effect of treatment is the same for everyone (conditional on observables), or (b) treatment effects are variable across persons but the person-specific component of the variability not forecastable by observables does not determine participation in the program, widely-used instrumental variable methods produce inconsistent estimators of the parameter of interest. Neither assumption is very palatable. The first assumes a homogeneity that is implausible. The second assumes either very rich data available to the econometrician or that the persons being studied either do not have better information than the econometrician or that they do not use it. Instrumental variable methods do not provide a general solution to the evaluation problem.

Suggested Citation

  • James J. Heckman, 1995. "Instrumental Variables: A Cautionary Tale," NBER Technical Working Papers 0185, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0185
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    References listed on IDEAS

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    1. Nancy Clements & James Heckman & Jeffrey Smith, 1994. "Making the Most Out Of Social Experiments: Reducing the Intrinsic Uncertainty in Evidence from Randomized Trials with an Application to the JTPA Exp," NBER Technical Working Papers 0149, National Bureau of Economic Research, Inc.
    2. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    3. Willis, Robert J & Rosen, Sherwin, 1979. "Education and Self-Selection," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 7-36, October.
    4. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," American Economic Review, American Economic Association, vol. 80(3), pages 313-336, June.
    5. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    6. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, vol. 58(5), pages 1121-1149, September.
    7. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
    8. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc.
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    Citations

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    Cited by:

    1. Cecilia Elena Rouse, 1997. "Private School Vouchers and Student Achievement: An Evaluation of the Milwaukee Parental Choice Program," NBER Working Papers 5964, National Bureau of Economic Research, Inc.
    2. Lanot, Gauthier & Walker, Ian, 1998. "The union/non-union wage differential: An application of semi-parametric methods," Journal of Econometrics, Elsevier, vol. 84(2), pages 327-349, June.
    3. Miguel Angel Malo & Fernando Muñoz-Bullón, 2006. "Employment promotion measures and the quality of the job match for persons with disabilities," Hacienda Pública Española, IEF, vol. 179(4), pages 79-111, September.
    4. Aki Kangasharju, 2007. "Do Wage Subsidies Increase Employment in Subsidized Firms?," Economica, London School of Economics and Political Science, vol. 74(293), pages 51-67, February.
    5. Enrico Santarelli & Hien Thu Tran, 2016. "Diversification strategies and firm performance in Vietnam," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 24(1), pages 31-68, January.
    6. Julie Hotchkiss, 2002. "Endogeneity of tenure in the determination of quit behaviour of young workers," Applied Economics Letters, Taylor & Francis Journals, vol. 9(4), pages 231-233.
    7. Shlomo Yitzhaki & Edna Schechtman, 2004. "The Gini Instrumental Variable, or the “double instrumental variable” estimator," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 287-313.
    8. Muñoz-Bullón, Fernando & Malo, Miguel A., 2005. "Job matching quality effects of employment promotion measures for people with disabilities," DEE - Working Papers. Business Economics. WB wb055315, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    9. Francisco Gallego, 2002. "Competencia y Resultados Educativos: Teoría y Evidencia para Chile," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 39(118), pages 309-352.
    10. Lach, Saul, 2002. "Do R&D Subsidies Stimulate or Displace Private R&D? Evidence from Israel," Journal of Industrial Economics, Wiley Blackwell, vol. 50(4), pages 369-390, December.
    11. Lorraine Dearden, 1999. "Qualifications and earnings in Britain: how reliable are conventional OLS estimates of the returns to education?," IFS Working Papers W99/07, Institute for Fiscal Studies.
    12. Lorraine Dearden, 1998. "Ability, families, education and earnings in Britain," IFS Working Papers W98/14, Institute for Fiscal Studies.
    13. Freund, D.A. & Kniesner, T.J. & LoSasso, A.T., 1996. "How Managed Care Affects Medicaid Utilization : A Synthetic Difference-in-Difference Zero-Inflated Count Model," Discussion Paper 1996-40, Tilburg University, Center for Economic Research.
    14. Yanovitzky, Itzhak & Zanutto, Elaine & Hornik, Robert, 2005. "Estimating causal effects of public health education campaigns using propensity score methodology," Evaluation and Program Planning, Elsevier, vol. 28(2), pages 209-220, May.
    15. Preety Srivastava, 2010. "Does Bingeing Affect Earnings?," The Economic Record, The Economic Society of Australia, vol. 86(275), pages 578-595, December.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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