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Instrument selection: The case of teenage childbearing and women's educational attainment

  • D. Klepinger
  • S. Lundberg
  • R. Plotnick

Recent research has identified situations in which instrumental variables (IV) estimators are severely biased and has suggested diagnostic tests to identify such situations. We suggest a number of alternative techniques for choosing a set of instruments that satisfy these tests from a universe of a priori plausible candidates, and we apply them to a study of the effects of adolescent childbearing on the educational attainment of young women. We find that substantive results are sensitive to instrument choice, and make two recommendations to the practical researcher: First, it is prudent to begin with a large set of potential instruments, when possible, and pare it down through formal testing rather than to rely on a minimal instrument set justified on a priori grounds. Second, the application of more restrictive tests of instrument validity and relevance can yield results very different from those based on less restrictive tests that produce a more inclusive set of instruments, and is the preferred, conservative approach when improper instrument choice can lead to biased estimates.

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Paper provided by University of Wisconsin Institute for Research on Poverty in its series Institute for Research on Poverty Discussion Papers with number 1077-95.

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Handle: RePEc:wop:wispod:1077-95
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  1. Wu, De-Min, 1973. "Alternative Tests of Independence Between Stochastic Regressors and Disturbances," Econometrica, Econometric Society, vol. 41(4), pages 733-50, July.
  2. Nelson, Charles R & Startz, Richard, 1990. "The Distribution of the Instrumental Variables Estimator and Its t-Ratio When the Instrument Is a Poor One," The Journal of Business, University of Chicago Press, vol. 63(1), pages S125-40, January.
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  8. Mroz, Thomas A, 1987. "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions," Econometrica, Econometric Society, vol. 55(4), pages 765-99, July.
  9. Klepinger, D. & Lundberg, S. & Plotnick, R., 1994. "Adolescent Fertility and the Education Attainment of Young Women," Discussion Papers in Economics at the University of Washington 94-5, Department of Economics at the University of Washington.
  10. Arline T. Geronimus & Sanders Korenman, 1992. "The Socioeconomic Consequences of Teen Childbearing Reconsidered," The Quarterly Journal of Economics, Oxford University Press, vol. 107(4), pages 1187-1214.
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  12. Ribar, D.C., 1991. "A Multinomial Logit Analysis of Teenage Fertility and High School Completion," Papers 5-91-1, Pennsylvania State - Department of Economics.
  13. Randall J. Olsen & George Farkas, 1989. "Endogenous Covariates in Duration Models and the Effect of Adolescent Childbirth on Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 24(1), pages 39-53.
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  15. Alice Nakamura & James R. Walker, 1994. "Model Evaluation and Choice," Journal of Human Resources, University of Wisconsin Press, vol. 29(2), pages 223-247.
  16. Hall, Alastair R & Rudebusch, Glenn D & Wilcox, David W, 1996. "Judging Instrument Relevance in Instrumental Variables Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 37(2), pages 283-98, May.
  17. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-46, March.
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  19. Ruud, Paul A., 1984. "Tests of Specification in Econometrics," Department of Economics, Working Paper Series qt4kq8m0hf, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  20. John Bound & David A. Jaeger & Regina Baker, 1993. "The Cure Can Be Worse than the Disease: A Cautionary Tale Regarding Instrumental Variables," NBER Technical Working Papers 0137, National Bureau of Economic Research, Inc.
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