IDEAS home Printed from https://ideas.repec.org/p/cir/cirpro/2024rp-06.html

Les déterminants cognitifs et non-cognitifs du choix de filière et leur impact sur la phase initiale du cycle professionnel

Author

Listed:
  • Christian Belzil
  • Jörgen Hansen
  • Julie Pernaudet

Abstract

Using unique data allowing us to link individual cognitive and non-cognitive skill measurements (at age 18) to subsequent education trajectories, we estimate the relative importance of individual skills on the field of study and pay particular attention to the impact of choosing a scientific subject (STEM) on early career outcomes. Our results indicate that individual performance in the International Adult Literacy survey (similar to the PISA test) has no predictive power on the likelihood of enrolling in STEM but is however an important determinant of wages around age 30. STEM enrollment depends primarily on individual academic performance in mathematics and on school motivation measured at age 18. Finally, we find that after controlling for individual skill differences, Ontario students have a higher probability of becoming STEM graduates than Québec students. Grâce à la collecte de données nous permettant de relier les trajectoires éducatives des individus à différentes mesures de compétences, nous étudions les déterminants des choix de filières au Québec et dans le reste du Canada et en particulier, le rôle des compétences cognitives et non-cognitives. Nous évaluons l’impact des études en Sciences, Technologie, Ingénierie, et Mathématiques (STIM) ainsi que l’effet des facteurs cognitifs et non-cognitifs sur un grand nombre de mesures de performance sur le marché du travail. Nos résultats indiquent que les performances individuelles dans le test EIACA (semblable au test PISA) n’ont pratiquement aucun pouvoir prédictif sur la probabilité de compléter un programme scientifique, mais jouent un rôle déterminant sur les salaires à 30 ans. La fréquentation d’un programme STIM est principalement expliquée par la compétence académique en mathématiques mesurée par les notes obtenues à l'âge de 18 ans. Le second déterminant le plus important est de loin le facteur non-cognitif mesurant le degré de motivation pendant les études. Toutes choses égales par ailleurs (à compétences égales), les Ontariens ont une probabilité d’obtenir un diplôme STIM plus élevée que les Québécois.

Suggested Citation

  • Christian Belzil & Jörgen Hansen & Julie Pernaudet, 2024. "Les déterminants cognitifs et non-cognitifs du choix de filière et leur impact sur la phase initiale du cycle professionnel," CIRANO Project Reports 2024rp-06, CIRANO.
    • Handle: RePEc:cir:cirpro:2024rp-06
    as

    Download full text from publisher

    File URL: https://cirano.qc.ca/files/publications/2024RP-06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joseph G. Altonji & Erica Blom & Costas Meghir, 2012. "Heterogeneity in Human Capital Investments: High School Curriculum, College Major, and Careers," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 185-223, July.
    2. 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.
    3. 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, IZA Network @ LISER.
    4. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
    5. Patrick J. Bayer & Peter Arcidiacono & Aurel Hizmo, 2010. "Web Appendix: Beyond Signaling and Human Capital: Education and the Revelation of Ability," Working Papers 10-52, Duke University, Department of Economics.
    6. Susan Dynarski & Judith Scott-Clayton, 2013. "Financial Aid Policy: Lessons from Research," NBER Working Papers 18710, National Bureau of Economic Research, Inc.
    7. 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.
    8. Belzil, Christian & Pernaudet, Julie & Poinas, François, 2021. "Estimating Coherency between Survey Data and Incentivized Experimental Data," IZA Discussion Papers 14594, Institute of Labor Economics (IZA).
    9. Herbert Gintis & Samuel Bowles & Melissa Osborne, 2001. "Incentive-Enhancing Preferences: Personality, Behavior, and Earnings," American Economic Review, American Economic Association, vol. 91(2), pages 155-158, May.
    10. 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.
    11. Belzil, Christian & Hansen, Jörgen & Liu, Xingfei, 2022. "The Evolution of Inequality in Education Trajectories and Graduation Outcomes in the US," IZA Discussion Papers 15338, Institute of Labor Economics (IZA).
    12. Joshua Angrist & David Autor & Amanda Pallais, 2022. "Marginal Effects of Merit Aid for Low-Income Students [“Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(2), pages 1039-1090.
    13. Matthew Wiswall & Basit Zafar, 2015. "Determinants of College Major Choice: Identification using an Information Experiment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(2), pages 791-824.
    14. Esteban Aucejo & Jonathan James, 2021. "The Path to College Education: The Role of Math and Verbal Skills," Journal of Political Economy, University of Chicago Press, vol. 129(10), pages 2905-2946.
    15. 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.
    16. Cathleen Johnson & Claude Montmarquette, 2015. "The lack of loan aversion among Canadian high school students," Canadian Journal of Economics, Canadian Economics Association, vol. 48(2), pages 585-611, May.
    17. Peter Arcidiacono & Patrick Bayer & Aurel Hizmo, 2010. "Beyond Signaling and Human Capital: Education and the Revelation of Ability," American Economic Journal: Applied Economics, American Economic Association, vol. 2(4), pages 76-104, October.
    18. Christian Belzil & Arnaud Maurel & Modibo Sidibé, 2021. "Estimating the Value of Higher Education Financial Aid: Evidence from a Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 39(2), pages 361-395.
    19. Sandra E. Black & Jeffrey T. Denning & Lisa J. Dettling & Sarena Goodman & Lesley J. Turner, 2023. "Taking It to the Limit: Effects of Increased Student Loan Availability on Attainment, Earnings, and Financial Well-Being," American Economic Review, American Economic Association, vol. 113(12), pages 3357-3400, December.
    20. María F. Prada & Sergio Urzúa, 2017. "One Size Does Not Fit All: Multiple Dimensions of Ability, College Attendance, and Earnings," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 953-991.
    21. 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.
    22. 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.
    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. Christian Belzil & Jörgen Hansen & Julie Pernaudet, 2024. "Separating the Structural and Composition Impacts of Financial Aid on the Choice of Major," Working Papers 2024-14, Center for Research in Economics and Statistics.
    2. Jiang, Xuan, 2021. "Women in STEM: Ability, preference, and value," Labour Economics, Elsevier, vol. 70(C).
    3. Chuan, Amanda & Zhang, Weilong, 2023. "Non-college Occupations, Workplace Routinization, and the Gender Gap in College Enrollment," IZA Discussion Papers 16089, IZA Network @ LISER.
    4. Fernando Saltiel, 2019. "What's Math Got to Do With It? Multidimensional Ability and the Gender Gap in STEM," 2019 Meeting Papers 1201, Society for Economic Dynamics.
    5. 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.
    6. 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.
    7. 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).
    8. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    9. Gabi Xuan Jiang, 2018. "Planting the Seeds for Success: Why Women in STEM Don't Stick in The Field," Purdue University Economics Working Papers 1307, Purdue University, Department of Economics.
    10. Christian Belzil & Arnaud Maurel & Modibo Sidibé, 2021. "Estimating the Value of Higher Education Financial Aid: Evidence from a Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 39(2), pages 361-395.
    11. Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Tomás Rau & Eugenio Rojas & Sergio Urzúa, 2013. "Loans for Higher Education: Does the Dream Come True?," NBER Working Papers 19138, National Bureau of Economic Research, Inc.
    13. 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.
    14. James J. Heckman & Rodrigo Pinto, 2022. "Causality and Econometrics," NBER Working Papers 29787, National Bureau of Economic Research, Inc.
    15. Sarzosa, Miguel, 2023. "Sexual Orientation and Labor Market Disparities," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 723-755.
    16. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    17. Tomáš Jagelka, 2024. "Are Economists’ Preferences Psychologists’ Personality Traits? A Structural Approach," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 910-970.
    18. Heckman, James & Pinto, Rodrigo, 2024. "Econometric causality: The central role of thought experiments," Journal of Econometrics, Elsevier, vol. 243(1).
    19. Burgess, Simon, 2016. "Human Capital and Education: The State of the Art in the Economics of Education," IZA Discussion Papers 9885, IZA Network @ LISER.
    20. Heckman, James J., 2010. "The Assumptions Underlying Evaluation Estimators," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(2), December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:cir:cirpro:2024rp-06. 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: Webmaster (email available below). General contact details of provider: https://edirc.repec.org/data/ciranca.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.