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Education, Health and Wages


  • James Heckman

    () (University of Chicago)

  • John Eric Humphries

    (University of Chicago)

  • Gregory Veramendi

    (Arizona State University)

  • Sergio Urzua

    (University of Maryland)


This paper develops and estimates a model with multiple schooling choices that identifies the causal effect of different levels of schooling on health, health-related behaviors, and labor market outcomes. We develop an approach that is a halfway house between a reduced form treatment effect model and a fully formulated dynamic discrete choice model. It is computationally tractable and identifies the causal effects of educational choices at different margins. We estimate distributions of responses to education and find evidence for substantial heterogeneity in unobserved variables on which agents make choices. The estimated treatment effects of education are decomposed into the direct benefits of attaining a given level of schooling and indirect benefits from the option to continue on to further schooling. Continuation values are an important component of our estimated treatment effects. While the estimated causal effects of education are substantial for most outcomes, we also estimate a quantitatively important effect of unobservables on outcomes. Both cognitive and socioemotional factors contribute to shaping educational choices and labor market and health outcomes. We improve on LATE by identifying the groups affected by variations in the instruments. We find benefits of cognition on most outcomes apart from its effect on schooling attainment. The benefits of socioemotional skills on outcomes beyond their effects on schooling attainment are less precisely estimated.

Suggested Citation

  • James Heckman & John Eric Humphries & Gregory Veramendi & Sergio Urzua, 2014. "Education, Health and Wages," Working Papers 2014-007, Human Capital and Economic Opportunity Working Group.
  • Handle: RePEc:hka:wpaper:2014-007
    Note: ECI

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    References listed on IDEAS

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    Cited by:

    1. María F. Prada & Sergio S. Urzúa, 2014. "One Size does not Fit All: Multiple Dimensions of Ability, College Attendance and Wages," NBER Working Papers 20752, National Bureau of Economic Research, Inc.
    2. Humphries, John Eric & Kosse, Fabian, 2017. "On the interpretation of non-cognitive skills – What is being measured and why it matters," Journal of Economic Behavior & Organization, Elsevier, vol. 136(C), pages 174-185.
    3. Peter Hoeschler & Uschi Backes-Gellner, 2014. "Shooting for the Stars and Failing: College Dropout and Self-Esteem," Economics of Education Working Paper Series 0100, University of Zurich, Department of Business Administration (IBW), revised Apr 2017.
    4. Peter A. Savelyev, 2014. "Psychological Skills, Education, and Longevity of High-Ability Individuals," Vanderbilt University Department of Economics Working Papers 14-00007, Vanderbilt University Department of Economics.
    5. Sergi Jiménez-Martín & Judit Vall & Elena del Rey, 2015. "The effect of changes in the statutory minimum working age on educational, labor and health outcomes," Working Papers 2015-07, FEDEA.
    6. Belskaya, Olga & Peter, Klara Sabirianova & Posso, Christian, 2014. "College Expansion and the Marginal Returns to Education: Evidence from Russia," IZA Discussion Papers 8735, Institute for the Study of Labor (IZA).
    7. Johannes S. Kunz & Kevin E. Staub, 2016. "Subjective completion beliefs and the demand for post-secondary education," Economics of Education Working Paper Series 0120, University of Zurich, Department of Business Administration (IBW).
    8. Bijwaard, Govert E. & van Kippersluis, Hans & Veenman, Justus, 2015. "Education and health: The role of cognitive ability," Journal of Health Economics, Elsevier, vol. 42(C), pages 29-43.
    9. Eckhardt Bode & Lucia Perez Villar, 2017. "Creativity, education or what? On the measurement of regional human capital," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 51-67, March.
    10. Rémi Piatek & Pia Pinger, 2016. "Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 734-755, June.
    11. Anirban Basu & Andrew M. Jones & Pedro Rosa Dias, 2014. "The Roles of Cognitive and Non-Cognitive Skills in Moderating the Effects of Mixed-Ability Schools on Long-Term Health," NBER Working Papers 20811, National Bureau of Economic Research, Inc.

    More about this item


    education; early endowments; factor models; health; treatment effects;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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