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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

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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

    as
    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.
    3. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    4. Lang, Kevin & Ruud, Paul A, 1986. "Returns to Schooling, Implicit Discount Rates and Black-White Wage Differentials," The Review of Economics and Statistics, MIT Press, vol. 68(1), pages 41-47, February.
    5. David Card, 1994. "Earnings, Schooling, and Ability Revisited," Working Papers 710, Princeton University, Department of Economics, Industrial Relations Section..
    6. 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.
    7. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
    8. 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.
    9. Blackburn, McKinley L & Neumark, David, 1993. "Omitted-Ability Bias and the Increase in the Return to Schooling," Journal of Labor Economics, University of Chicago Press, vol. 11(3), pages 521-544, July.
    10. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
    11. 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.
    12. 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.
    13. Charles F. Manski, 1996. "Learning about Treatment Effects from Experiments with Random Assignment of Treatments," Journal of Human Resources, University of Wisconsin Press, vol. 31(4), pages 709-733.
    14. Garen, John, 1984. "The Returns to Schooling: A Selectivity Bias Approach with a Continuous Choice Variable," Econometrica, Econometric Society, vol. 52(5), pages 1199-1218, September.
    15. Joshua D. Angrist & Alan B. Keueger, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, Oxford University Press, vol. 106(4), pages 979-1014.
    16. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    17. Ashenfelter, Orley & Krueger, Alan B, 1994. "Estimates of the Economic Returns to Schooling from a New Sample of Twins," American Economic Review, American Economic Association, vol. 84(5), pages 1157-1173, December.
    18. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
    19. Oosterbeek, Hessel, 1990. "Education and earnings in the Netherlands: an empirical analysis," European Economic Review, Elsevier, vol. 34(7), pages 1353-1375, November.
    20. Blakemore, Arthur E. & Low, Stuart A., 1984. "The high-school dropout decision and its wage consequences," Economics of Education Review, Elsevier, vol. 3(2), pages 111-119, April.
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    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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