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Is there a Causal Effect of High School Math on Labor Market Outcomes?

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  • Juanna Schrøter Joensen
  • Helena Skyt Nielsen

    (School of Economics and Management, University of Aarhus, Denmark)

Abstract

Outsourcing of jobs to low-wage countries has increased the focus on the accumulation of skills - such as Math skills - in high-wage countries. In this paper, we exploit a high school pilot scheme to identify the causal effect of advanced high school Math on labor market outcomes. The pilot scheme reduced the costs of choosing advanced Math because it allowed for at more flexible combination of Math with other courses. We find clear evidence of a causal relationship between Math and earnings for the students who are induced to choose Math after being exposed to the pilot scheme. The effect partly stems from the fact that these students end up with higher education.

Suggested Citation

  • Juanna Schrøter Joensen & Helena Skyt Nielsen, 2006. "Is there a Causal Effect of High School Math on Labor Market Outcomes?," Economics Working Papers 2006-11, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:aarhec:2006-11
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    1. Hansen, Karsten T. & Heckman, James J. & Mullen, K.J.Kathleen J., 2004. "The effect of schooling and ability on achievement test scores," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 39-98.
    2. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    3. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, John Wiley & Sons, vol. 68(2), pages 210-223, October.
    4. 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.
    5. Cameron, Stephen V & Heckman, James J, 1993. "The Nonequivalence of High School Equivalents," Journal of Labor Economics, University of Chicago Press, vol. 11(1), pages 1-47, January.
    6. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    7. Jong–Wha Lee & Robert J. Barro, 2001. "Schooling Quality in a Cross–Section of Countries," Economica, London School of Economics and Political Science, vol. 68(272), pages 465-488, November.
    8. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    9. Albæk, Karsten, 2003. "Optimal adgangsregulering til de videregående uddannelser og elevers valg af fag i gymnasiet," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2003(1), pages 206-224.
    10. Levine, Phillip B & Zimmerman, David J, 1995. "The Benefit of Additional High-School Math and Science Classes for Young Men and Women," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 137-149, April.
    11. Philip Oreopoulos, 2006. "Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter," American Economic Review, American Economic Association, vol. 96(1), pages 152-175, March.
    12. Arcidiacono, Peter, 2004. "Ability sorting and the returns to college major," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 343-375.
    13. 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.
    14. Holler, Manfred J. & Høst, Viggo & Kristensen, Kai, 1992. "Decisions on strategic markets -- An experimental study," Scandinavian Journal of Management, Elsevier, vol. 8(2), pages 133-146, June.
    15. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    16. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    17. Lee, Jong-Wha & Barro, Robert J, 2001. "Schooling Quality in a Cross-Section of Countries," Economica, London School of Economics and Political Science, vol. 68(272), pages 465-488, November.
    18. Murnane, Richard J & Willett, John B & Levy, Frank, 1995. "The Growing Importance of Cognitive Skills in Wage Determination," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 251-266, May.
    19. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 40(4), pages 791-821.
    20. 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.
    21. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
    22. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    23. Melissa Osborne & Herbert Gintis & Samuel Bowles, 2001. "The Determinants of Earnings: A Behavioral Approach," Journal of Economic Literature, American Economic Association, vol. 39(4), pages 1137-1176, December.
    24. Vella, Francis & Verbeek, Marno, 1999. "Estimating and Interpreting Models with Endogenous Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 473-478, October.
    25. Jerik Hanushek & Dennis Kimko, 2006. "Schooling, Labor-force Quality, and the Growth of Nations," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 154-193.
    26. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, John Wiley & Sons, vol. 68(2), pages 210-223, October.
    27. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    28. Janet Currie & Enrico Moretti, 2003. "Mother's Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1495-1532.
    29. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2002. "An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schools," NBER Working Papers 9358, National Bureau of Economic Research, Inc.
    30. Joseph G. Altonji, 1995. "The Effects of High School Curriculum on Education and Labor Market Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 30(3), pages 409-438.
    31. Heather Rose & Julian R. Betts, 2004. "The Effect of High School Courses on Earnings," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 497-513, May.
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    More about this item

    Keywords

    Math; High School Curriculum; Instrumental Variable; Local Average Treatment Effect.;
    All these keywords.

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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    This paper has been announced in the following NEP Reports:

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