IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v36y2021i7p898-916.html
   My bibliography  Save this article

Tracking and specialization of high schools: Heterogeneous effects of school choice

Author

Listed:
  • Olivier De Groote
  • Koen Declercq

Abstract

We analyze the impact of choosing an elite school on high school graduation in an early tracking system in Flanders (Belgium). Whereas elite schools offer only an academic track, most other schools offer multiple tracks. On average, students experience a 3.3 percentage point increase in the likelihood of obtaining a degree. We find that the effects are heterogeneous. On average, students who self‐select into elite schools do not experience an effect. However, students who do not choose an elite school would experience positive effects. Our results can be explained by different tracking decisions in both types of schools.

Suggested Citation

  • Olivier De Groote & Koen Declercq, 2021. "Tracking and specialization of high schools: Heterogeneous effects of school choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 898-916, November.
    • Handle: RePEc:wly:japmet:v:36:y:2021:i:7:p:898-916
    DOI: 10.1002/jae.2856
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/jae.2856
    Download Restriction: no
    File URL: https://libkey.io/10.1002/jae.2856?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cummins, Joseph R., 2017. "Heterogeneous treatment effects in the low track: Revisiting the Kenyan primary school experiment," Economics of Education Review, Elsevier, vol. 56(C), pages 40-51.
    2. Pedro Carneiro & Michael Lokshin & Nithin Umapathi, 2017. "Average and Marginal Returns to Upper Secondary Schooling in Indonesia," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 16-36, January.
    3. Thomas Cornelissen & Christian Dustmann & Anna Raute & Uta Schönberg, 2018. "Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2356-2409.
    4. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    5. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    6. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    7. Esther Duflo & Pascaline Dupas & Michael Kremer, 2011. "Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya," American Economic Review, American Economic Association, vol. 101(5), pages 1739-1774, August.
    8. Galasso, Alberto & Schankerman, Mark, 2015. "Patents and cumulative innovation: causal evidence from the courts," LSE Research Online Documents on Economics 61614, London School of Economics and Political Science, LSE Library.
    9. Atila Abdulkadiroğlu & Joshua Angrist & Parag Pathak, 2014. "The Elite Illusion: Achievement Effects at Boston and New York Exam Schools," Econometrica, Econometric Society, vol. 82(1), pages 137-196, January.
    10. 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.
    11. Andrea M. Mühlenweg & Patrick A. Puhani, 2010. "The Evolution of the School-Entry Age Effect in a School Tracking System," Journal of Human Resources, University of Wisconsin Press, vol. 45(2).
    12. Martin Nybom, 2017. "The Distribution of Lifetime Earnings Returns to College," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 903-952.
    13. Nina Guyon & Eric Maurin & Sandra McNally, 2012. "The Effect of Tracking Students by Ability into Different Schools: A Natural Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 47(3), pages 684-721.
    14. Pekkarinen, Tuomas & Uusitalo, Roope & Kerr, Sari, 2009. "School tracking and intergenerational income mobility: Evidence from the Finnish comprehensive school reform," Journal of Public Economics, Elsevier, vol. 93(7-8), pages 965-973, August.
    15. Eric A. Hanushek & Ludger Wössmann, 2006. "Does Educational Tracking Affect Performance and Inequality? Differences- in-Differences Evidence Across Countries," Economic Journal, Royal Economic Society, vol. 116(510), pages 63-76, March.
    16. Koop, Gary & Poirier, Dale J., 1997. "Learning about the across-regime correlation in switching regression models," Journal of Econometrics, Elsevier, vol. 78(2), pages 217-227, June.
    17. repec:hal:pseose:halshs-00754588 is not listed on IDEAS
    18. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients," Health, Econometrics and Data Group (HEDG) Working Papers 07/07, HEDG, c/o Department of Economics, University of York.
    19. Roller, Marcus & Steinberg, Daniel, 2020. "The distributional effects of early school stratification - non-parametric evidence from Germany," European Economic Review, Elsevier, vol. 125(C).
    20. Barrow, Lisa & Schanzenbach, Diane Whitmore & Claessens, Amy, 2015. "The impact of Chicago’s small high school initiative," Journal of Urban Economics, Elsevier, vol. 87(C), pages 100-113.
    21. Joseph J. Doyle Jr., 2007. "Child Protection and Child Outcomes: Measuring the Effects of Foster Care," American Economic Review, American Economic Association, vol. 97(5), pages 1583-1610, December.
    22. Poirier, Dale J & Tobias, Justin L, 2003. "On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 258-268, April.
    23. Martin Eckhoff Andresen, 2018. "Exploring marginal treatment effects: Flexible estimation using Stata," Stata Journal, StataCorp LP, vol. 18(1), pages 118-158, March.
    24. Anirban Basu & James J. Heckman & Salvador Navarro‐Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self‐selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157, November.
    25. Joseph J. Doyle Jr., 2008. "Child Protection and Adult Crime: Using Investigator Assignment to Estimate Causal Effects of Foster Care," Journal of Political Economy, University of Chicago Press, vol. 116(4), pages 746-770, August.
    26. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    27. De Groote, Olivier, 2019. "Dynamic Effort Choice in High School: Costs and Benefits of an Academic Track," TSE Working Papers 19-1002, Toulouse School of Economics (TSE), revised Jun 2023.
    28. 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.
    29. Chao Fu & Nirav Mehta, 2018. "Ability Tracking, School and Parental Effort, and Student Achievement: A Structural Model and Estimation," Journal of Labor Economics, University of Chicago Press, vol. 36(4), pages 923-979.
    30. Cristian Pop-Eleches & Miguel Urquiola, 2013. "Going to a Better School: Effects and Behavioral Responses," American Economic Review, American Economic Association, vol. 103(4), pages 1289-1324, June.
    31. Damon Clark & Emilia Del Bono, 2016. "The Long-Run Effects of Attending an Elite School: Evidence from the United Kingdom," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 150-176, January.
    32. Aakvik, Arild & Heckman, James J. & Vytlacil, Edward J., 2005. "Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 15-51.
    33. Michael Lokshin & Zurab Sajaia, 2011. "Impact of interventions on discrete outcomes: Maximum likelihood estimation of the binary choice models with binary endogenous regressors," Stata Journal, StataCorp LP, vol. 11(3), pages 368-385, September.
    34. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    35. Alberto Galasso & Mark Schankerman, 2015. "Patents and Cumulative Innovation: Causal Evidence from the Courts," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(1), pages 317-369.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Landaud, Fanny & Maurin, Eric, 2022. "Tracking When Ranking Matters," IZA Discussion Papers 15157, Institute of Labor Economics (IZA).
    2. De Groote, Olivier, 2019. "Dynamic Effort Choice in High School: Costs and Benefits of an Academic Track," TSE Working Papers 19-1002, Toulouse School of Economics (TSE), revised Jun 2023.

    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. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    2. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    3. Gerten, Elisa & Beckmann, Michael & Kräkel, Matthias, 2022. "Information and Communication Technology, Hierarchy, and Job Design," IZA Discussion Papers 15491, Institute of Labor Economics (IZA).
    4. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    5. Laura Schmitz, 2022. "Heterogeneous Effects of After-School Care on Child Development," Discussion Papers of DIW Berlin 2006, DIW Berlin, German Institute for Economic Research.
    6. Hoshino, Tadao & Yanagi, Takahide, 2023. "Treatment effect models with strategic interaction in treatment decisions," Journal of Econometrics, Elsevier, vol. 236(2).
    7. Tafti, Elena Ashtari, 2023. "Technology, Skills, and Performance: The Case of Robots in Surgery," CINCH Working Paper Series (since 2020) 78746, Duisburg-Essen University Library, DuEPublico.
    8. Lina Zhang & David T. Frazier & D. S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Papers 2009.02642, arXiv.org, revised Sep 2022.
    9. Gathmann, Christina & Vonnahme, Christina & Kim, Jongoh & Busse, Anna, 2021. "Marginal Returns to Citizenship and Educational Performance," CEPR Discussion Papers 16636, C.E.P.R. Discussion Papers.
    10. Elena Ashtari Tafti, 2022. "Technology, skills, and performance: the case of robots in surgery," IFS Working Papers W22/46, Institute for Fiscal Studies.
    11. Michelle Yin & Garima Siwach & Dajun Lin, 2023. "Vocational Rehabilitation Services and Labor Market Outcomes for Transition‐Age Youth with Disabilities in Maine," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 166-197, January.
    12. Ivan A Canay & Magne Mogstad & Jack Mount, 2024. "On the Use of Outcome Tests for Detecting Bias in Decision Making," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(4), pages 2135-2167.
    13. Robert A. Moffitt & Matthew V. Zahn, 2019. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," NBER Working Papers 26028, National Bureau of Economic Research, Inc.
    14. Hoshino Tadao & Yanagi Takahide, 2022. "Estimating marginal treatment effects under unobserved group heterogeneity," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 197-216, January.
    15. Matthias Westphal & Daniel A Kamhöfer & Hendrik Schmitz, 2022. "Marginal College Wage Premiums Under Selection Into Employment," The Economic Journal, Royal Economic Society, vol. 132(646), pages 2231-2272.
    16. Akabayashi, Hideo & Ruberg, Tim & Shikishima, Chizuru & Yamashita, Jun, 2023. "Education-oriented and care-oriented preschools: Implications on child development," Labour Economics, Elsevier, vol. 84(C).
    17. Mario Fiorini & Katrien Stevens, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1475-1526, December.
    18. Daniel A Kamhöfer & Hendrik Schmitz & Matthias Westphal, 2019. "Heterogeneity in Marginal Non-Monetary Returns to Higher Education," Journal of the European Economic Association, European Economic Association, vol. 17(1), pages 205-244.
    19. Thomas Cornelissen & Christian Dustmann & Anna Raute & Uta Schönberg, 2018. "Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2356-2409.
    20. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.

    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

    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:wly:japmet:v:36:y:2021:i:7:p:898-916. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.