IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/9881.html
   My bibliography  Save this paper

The Effect of Schooling and Ability on Achievement Test Scores

Author

Listed:
  • Karsten Hansen
  • James J. Heckman
  • Kathleen J. Mullen

Abstract

This paper develops two methods for estimating the effect of schooling on achievement test scores that control for the endogeneity of schooling by postulating that both schooling and test scores are generated by a common unobserved latent ability. These methods are applied to data on schooling and test scores. Estimates from the two methods are in close agreement. We find that the effects of schooling on test scores are roughly linear across schooling levels. The effects of schooling on measured test scores are slightly larger for lower latent ability levels. We find that schooling increases the AFQT score on average between 2 and 4 percentage points, roughly twice as large as the effect claimed by Herrnstein and Murray (1994) but in agreement with estimates produced by Neal and Johnson (1996) andWinship and Korenman (1997). We extend the previous literature by estimating the impact of schooling on measured test scores at various quantiles of the latent ability distribution.

Suggested Citation

  • Karsten Hansen & James J. Heckman & Kathleen J. Mullen, 2003. "The Effect of Schooling and Ability on Achievement Test Scores," NBER Working Papers 9881, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:9881
    Note: ED PE CH
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w9881.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Joseph G. Altonji & Charles R. Pierret, 2001. "Employer Learning and Statistical Discrimination," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 313-350.
    2. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    3. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
    4. Pakes, Ariel & Olley, Steven, 1995. "A limit theorem for a smooth class of semiparametric estimators," Journal of Econometrics, Elsevier, vol. 65(1), pages 295-332, January.
    5. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, Institute of Labor Economics (IZA).
    6. Aakvik, A. & Heckman, J.J. & Vytlacil, E.J., 1999. "Training Effects on Employment when the Training Effects are Heterogenous : an Application to Norwegian Vocational Rehabilitation Programs," Norway; Department of Economics, University of Bergen 0599, Department of Economics, University of Bergen.
    7. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, vol. 58(5), pages 1121-1149, September.
    8. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "2001 Lawrence R. Klein Lecture Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 361-422, May.
    9. 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.
    10. Matzkin, Rosa L., 1993. "Nonparametric identification and estimation of polychotomous choice models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 137-168, July.
    11. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    12. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    13. Heckman, James J, 1974. "Effects of Child-Care Programs on Women's Work Effort," Journal of Political Economy, University of Chicago Press, vol. 82(2), pages 136-163, Part II, .
    14. 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.
    15. Stephen V. Cameron & James J. Heckman, 2001. "The Dynamics of Educational Attainment for Black, Hispanic, and White Males," Journal of Political Economy, University of Chicago Press, vol. 109(3), pages 455-499, June.
    16. James J. Heckrnan, 1974. "Effects of Child-Care Programs on Women's Work Effort," NBER Chapters, in: Economics of the Family: Marriage, Children, and Human Capital, pages 491-524, National Bureau of Economic Research, Inc.
    17. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males," Journal of Political Economy, University of Chicago Press, vol. 106(2), pages 262-333, April.
    18. Hanushek, Eric A., 2002. "Publicly provided education," Handbook of Public Economics, in: A. J. Auerbach & M. Feldstein (ed.), Handbook of Public Economics, edition 1, volume 4, chapter 30, pages 2045-2141, Elsevier.
    19. Neal, Derek A & Johnson, William R, 1996. "The Role of Premarket Factors in Black-White Wage Differences," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 869-895, October.
    20. James Heckman & Edward Vytlacil, 2001. "Identifying The Role Of Cognitive Ability In Explaining The Level Of And Change In The Return To Schooling," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 1-12, February.
    21. Thompson, T.S., 1989. "Identification Of Semiparametric Discrete Choice Models," Papers 249, Minnesota - Center for Economic Research.
    22. Sylvia Richardson & Laurent Leblond & Isabelle Jaussent & Peter J. Green, 2002. "Mixture models in measurement error problems, with reference to epidemiological studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 549-566, October.
    23. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts," NBER Working Papers 6385, National Bureau of Economic Research, Inc.
    24. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
    25. 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.
    26. Yona Rubinstein & James J. Heckman, 2001. "The Importance of Noncognitive Skills: Lessons from the GED Testing Program," American Economic Review, American Economic Association, vol. 91(2), pages 145-149, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    2. James J. Heckman & Rodrigo Pinto, 2022. "Causality and Econometrics," NBER Working Papers 29787, National Bureau of Economic Research, Inc.
    3. James J. Heckman, 2008. "The Principles Underlying Evaluation Estimators with an Application to Matching," Annals of Economics and Statistics, GENES, issue 91-92, pages 9-73.
    4. Heckman, James & Pinto, Rodrigo, 2024. "Econometric causality: The central role of thought experiments," Journal of Econometrics, Elsevier, vol. 243(1).
    5. James J. Heckman & Jora Stixrud & Sergio Urzua, 2006. "The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 411-482, July.
    6. Heckman, James J., 2010. "The Assumptions Underlying Evaluation Estimators," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(2), December.
    7. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    8. Salvador Navarro, 2011. "Using Observed Choices to Infer Agent's Information: Reconsidering the Importance of Borrowing Constraints, Uncertainty and Preferences in College Attendance," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 20118, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
    9. Chuan, A. & Zhang, W., 2021. "Non-College Occupations, Workplace Routinization, and the Gender Gap in College Enrollment," Cambridge Working Papers in Economics 2177, Faculty of Economics, University of Cambridge.
    10. Martin Nordin & Dan-Olof Rooth, 2014. "Increasing Returns to Schooling by Ability? A Comparison between the USA and Sweden," Manchester School, University of Manchester, vol. 82, pages 1-20, December.
    11. Neal, Derek, 2006. "Why Has Black-White Skill Convergence Stopped?," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 1, chapter 9, pages 511-576, Elsevier.
    12. Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Sarzosa, Miguel, 2023. "Sexual Orientation and Labor Market Disparities," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 723-755.
    14. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    15. Aakvik, Arild & Salvanes, Kjell G. & Vaage, Kjell, 2010. "Measuring heterogeneity in the returns to education using an education reform," European Economic Review, Elsevier, vol. 54(4), pages 483-500, May.
    16. Carneiro, Pedro & Hansen, Karsten & Heckman, James, 2003. "Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice," Working Paper Series 2003:9, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    17. Lewbel, Arthur, 2007. "Endogenous selection or treatment model estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 777-806, December.
    18. Richard J. Murnane, 2013. "U.S. High School Graduation Rates: Patterns and Explanations," Journal of Economic Literature, American Economic Association, vol. 51(2), pages 370-422, June.
    19. Neyt, Brecht & Verhaest, Dieter & Baert, Stijn, 2020. "The impact of dual apprenticeship programmes on early labour market outcomes: A dynamic approach," Economics of Education Review, Elsevier, vol. 78(C).
    20. John K. Dagsvik & TorbjØrn HÆgeland & Arvid Raknerud, 2011. "Estimating the returns to schooling: a likelihood approach based on normal mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 613-640, June.

    More about this item

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:9881. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.