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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. Klepinger, D. & Lundberg, S. & Plotnick, R., 1994. "Adolescent Fertility and the Education Attainment of Young Women," Working Papers 94-5, University of Washington, Department of Economics.
  2. Thomas Mroz, . "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions," University of Chicago - Population Research Center 84-8, Chicago - Population Research Center.
  3. J. A. Hausman, 1976. "Specification Tests in Econometrics," Working papers 185, Massachusetts Institute of Technology (MIT), Department of Economics.
  4. Wu, De-Min, 1973. "Alternative Tests of Independence Between Stochastic Regressors and Disturbances," Econometrica, Econometric Society, vol. 41(4), pages 733-50, July.
  5. Ribar, David C, 1994. "Teenage Fertility and High School Completion," The Review of Economics and Statistics, MIT Press, vol. 76(3), pages 413-24, August.
  6. 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.
  7. J. A. Hausman & W. E. Taylor, 1980. "Panel Data and Unobservable Individual Effects," Working papers 255, Massachusetts Institute of Technology (MIT), Department of Economics.
  8. Alastair R. Hall & Glenn D. Rudebusch & David W. Wilcox, 1994. "Judging instrument relevance in instrumental variables estimation," Finance and Economics Discussion Series 94-3, Board of Governors of the Federal Reserve System (U.S.).
  9. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-46, March.
  10. Charles R. Nelson & Richard Startz, 1988. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," NBER Technical Working Papers 0068, National Bureau of Economic Research, Inc.
  11. Ribar, David C., 1993. "A multinomial logit analysis of teenage fertility and high school completion," Economics of Education Review, Elsevier, vol. 12(2), pages 153-164, June.
  12. 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.
  13. Vella, F., 1989. "A Simple Estimator For Simultaneous Models With Censored Endogenous Regressors," RCER Working Papers 199, University of Rochester - Center for Economic Research (RCER).
  14. 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.
  15. 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.
  16. 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.
  17. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, May.
  18. Alice Nakamura & James R. Walker, 1994. "Model Evaluation and Choice," Journal of Human Resources, University of Wisconsin Press, vol. 29(2), pages 223-247.
  19. Shea, J., 1993. "Instrument Relevance in Linear Models: A Simple Measure," Working papers 9312, Wisconsin Madison - Social Systems.
  20. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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