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Interpreting Instrumental Variables Estimates of the Returns to Schooling

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  • Jeffrey R. Kling

Abstract

This paper synthesizes economic insights from theoretical models of schooling choice based on individual benefits and econometric work interpreting instrumental variables estimates as weighted averages of individual-specific causal effects. Linkages are illustrated using college proximity to instrument for schooling. After characterizing groups differentially affected by the instrument according to family background, I directly compute weights underlying estimation of the overall return. In analyzing the level of schooling at which individuals change their behavior in response to the instrument, I demonstrate that this instrument has its greatest impact on the transition from high school to college. Specification robustness is also examined.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 7989.

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Date of creation: Oct 2000
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Publication status: published as Kling, Jeffrey R. "Interpreting Instrumental Variables Estimates Of The Returns To Schooling," Journal of Business and Economic Statistics, 2001, v19(3,Jul), 358-364.
Handle: RePEc:nbr:nberwo:7989

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  1. Caroline M. Hoxby, 1997. "How the Changing Market Structure of U.S. Higher Education Explains College Tuition," NBER Working Papers 6323, National Bureau of Economic Research, Inc.
  2. David Card, 1994. "Earnings, Schooling, and Ability Revisited," NBER Working Papers 4832, National Bureau of Economic Research, Inc.
  3. Joshua Angrist & Alan Krueger, 1990. "Does Compulsory School Attendance Affect Schooling and Earnings?," Working Papers, Princeton University, Department of Economics, Industrial Relations Section. 653, Princeton University, Department of Economics, Industrial Relations Section..
  4. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, Econometric Society, vol. 66(2), pages 249-288, March.
  5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, Econometric Society, vol. 62(2), pages 467-75, March.
  6. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
  7. Stephen Cameron & Christopher Taber, 2000. "Borrowing Constraints and the Returns to Schooling," NBER Working Papers 7761, National Bureau of Economic Research, Inc.
  8. Meyer, Bruce D, 1995. "Natural and Quasi-experiments in Economics," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 13(2), pages 151-61, April.
  9. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, Econometric Society, vol. 45(1), pages 1-22, January.
  10. Wooldridge, Jeffrey M., 1997. "On two stage least squares estimation of the average treatment effect in a random coefficient model," Economics Letters, Elsevier, Elsevier, vol. 56(2), pages 129-133, October.
  11. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0136, National Bureau of Economic Research, Inc.
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