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Monotone Instrumental Variables: With an Application to the Returns to Schooling

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  • Charles F. Manski
  • John V. Pepper

Abstract

Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement. There is therefore good reason to consider weaker but more credible assumptions. To this end, we introduce monotone instrumental variable (MIV) assumptions and the important special case of monotone treatment selection (MTS). We study the identifying power of MIV assumptions alone and combined with the assumption of monotone treatment response (MTR). We present an empirical application using the MTS and MTR assumptions to place upper bounds on the returns to schooling.

Suggested Citation

  • Charles F. Manski & John V. Pepper, 1998. "Monotone Instrumental Variables: With an Application to the Returns to Schooling," Virginia Economics Online Papers 308, University of Virginia, Department of Economics.
    • Handle: RePEc:vir:virpap:308
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    References listed on IDEAS

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    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    2. 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.
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    7. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
    8. Kenny, Lawrence W, et al, 1979. "Returns to College Education: An Investigation of Self-Selection Bias Based on the Project Talent Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(3), pages 775-789, October.
    9. David Card, 1994. "Earnings, Schooling, and Ability Revisited," Working Papers 710, Princeton University, Department of Economics, Industrial Relations Section..
    10. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
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    14. John V. Pepper, 2000. "The Intergenerational Transmission Of Welfare Receipt: A Nonparametric Bounds Analysis," The Review of Economics and Statistics, MIT Press, vol. 82(3), pages 472-488, August.
    15. Blackburn, McKinley L & Neumark, David, 1995. "Are OLS Estimates of the Return to Schooling Biased Downward? Another Look," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 217-230, May.
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    20. V. Joseph Hotz & Charles H. Mullin & Seth G. Sanders, 1997. "Bounding Causal Effects Using Data from a Contaminated Natural Experiment: Analysing the Effects of Teenage Childbearing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 575-603.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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