IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_8879.html
   My bibliography  Save this paper

The Effects of Free Secondary School Track Choice: A Disaggregated Synthetic Control Approach

Author

Listed:
  • Aderonke Osikominu
  • Gregor Pfeifer
  • Kristina Strohmaier
  • Gregor-Gabriel Pfeifer

Abstract

We exploit a recent state-level reform in Germany that granted parents the right to decide on the highest secondary school track suitable for their child, changing the purpose of the primary teacher's recommendation from mandatory to informational. Applying a disaggregated synthetic control approach to administrative district-level data, we find that transition rates to the higher school tracks increased substantially, with stronger responses among children from richer districts. Simultaneously, grade repetition in the first grades of secondary school increased dramatically, suggesting that parents choose school tracks also to align with their own aspirations – resulting in greater misallocation of students.

Suggested Citation

  • Aderonke Osikominu & Gregor Pfeifer & Kristina Strohmaier & Gregor-Gabriel Pfeifer, 2021. "The Effects of Free Secondary School Track Choice: A Disaggregated Synthetic Control Approach," CESifo Working Paper Series 8879, CESifo.
    • Handle: RePEc:ces:ceswps:_8879
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp8879.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Acemoglu, Daron & Johnson, Simon & Kermani, Amir & Kwak, James & Mitton, Todd, 2016. "The value of connections in turbulent times: Evidence from the United States," Journal of Financial Economics, Elsevier, vol. 121(2), pages 368-391.
    2. Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), 2011. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 4, number 4, June.
    3. 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.
    4. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    5. David Card & Laura Giuliano, 2016. "Can Tracking Raise the Test Scores of High-Ability Minority Students?," American Economic Review, American Economic Association, vol. 106(10), pages 2783-2816, October.
    6. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2011. "Synth: An R Package for Synthetic Control Methods in Comparative Case Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i13).
    7. Malamud, Ofer & Pop-Eleches, Cristian, 2011. "School tracking and access to higher education among disadvantaged groups," Journal of Public Economics, Elsevier, vol. 95(11), pages 1538-1549.
    8. Canaan, Serena, 2020. "The long-run effects of reducing early school tracking," Journal of Public Economics, Elsevier, vol. 187(C).
    9. Stacy Dickert-Conlin & Todd Elder & Keith Teltser, 2019. "Allocating Scarce Organs: How a Change in Supply Affects Transplant Waiting Lists and Transplant Recipients," American Economic Journal: Applied Economics, American Economic Association, vol. 11(4), pages 210-239, October.
    10. Christian Dustmann, 2004. "Parental background, secondary school track choice, and wages," Oxford Economic Papers, Oxford University Press, vol. 56(2), pages 209-230, April.
    11. Christian Dustmann & Patrick A. Puhani & Uta Schönberg, 2017. "The Long‐term Effects of Early Track Choice," Economic Journal, Royal Economic Society, vol. 127(603), pages 1348-1380, August.
    12. Sari Pekkala Kerr & Tuomas Pekkarinen & Roope Uusitalo, 2013. "School Tracking and Development of Cognitive Skills," Journal of Labor Economics, University of Chicago Press, vol. 31(3), pages 577-602.
    13. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    14. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    15. Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), 2011. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 3, number 3, June.
    16. Sönke Hendrik Matthewes, 2020. "Better together? Heterogeneous effects of tracking on student achievement," CEP Discussion Papers dp1706.pdf, Centre for Economic Performance, LSE.
    17. Caroline Hall, 2012. "The Effects of Reducing Tracking in Upper Secondary School: Evidence from a Large-Scale Pilot Scheme," Journal of Human Resources, University of Wisconsin Press, vol. 47(1), pages 237-269.
    18. Tamm, Marcus, 2008. "Does money buy higher schooling?: Evidence from secondary school track choice in Germany," Economics of Education Review, Elsevier, vol. 27(5), pages 536-545, October.
    19. Betts, Julian R., 2011. "The Economics of Tracking in Education," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 7, pages 341-381, Elsevier.
    20. Costas Meghir & Mårten Palme, 2005. "Educational Reform, Ability, and Family Background," American Economic Review, American Economic Association, vol. 95(1), pages 414-424, March.
    21. Dube, Arindrajit & Zipperer, Ben, 2015. "Pooling Multiple Case Studies Using Synthetic Controls: An Application to Minimum Wage Policies," IZA Discussion Papers 8944, Institute of Labor Economics (IZA).
    22. 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.
    23. Bach, Maximilian & Fischer, Mira, 2020. "Understanding the response to high-stakes incentives in primary education," Discussion Papers, Research Unit: Market Behavior SP II 2020-202, WZB Berlin Social Science Center.
    24. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    25. Klößner, Stefan & Pfeifer, Gregor, 2015. "Synthesizing Cash for Clunkers: Stabilizing the Car Market, Hurting the Environment," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113207, Verein für Socialpolitik / German Economic Association.
    26. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    27. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    28. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    29. Kaul, Ashok & Klößner, Stefan & Pfeifer, Gregor & Schieler, Manuel, 2015. "Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together With Covariates," MPRA Paper 83790, University Library of Munich, Germany.
    30. Matthewes, Sonke, 2020. "Better together? Heterogeneous effects of tracking on student achievement," LSE Research Online Documents on Economics 108478, London School of Economics and Political Science, LSE Library.
    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. Elisabeth Grewenig, 2021. "School Track Decisions and Teacher Recommendations: Evidence from German State Reforms," ifo Working Paper Series 353, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    2. Cattaneo, Maria A. & Wolter, Stefan C., 2022. "“Against all odds” Does awareness of the risk of failure matter for educational choices?," Economics of Education Review, Elsevier, vol. 87(C).
    3. Ketevani Kapanadze, 2021. "Checkmate! Losing with Borders, Winning with Centers. The Case of European Integration," CERGE-EI Working Papers wp716, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    4. Bach, Maximilian, 2021. "Heterogeneous responses to school track choice: Evidence from the repeal of binding track recommendations," ZEW Discussion Papers 21-104, ZEW - Leibniz Centre for European Economic Research.

    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. Canaan, Serena, 2020. "The long-run effects of reducing early school tracking," Journal of Public Economics, Elsevier, vol. 187(C).
    2. Piopiunik, Marc, 2014. "The effects of early tracking on student performance: Evidence from a school reform in Bavaria," Economics of Education Review, Elsevier, vol. 42(C), pages 12-33.
    3. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    4. Elisabeth Grewenig, 2021. "School Track Decisions and Teacher Recommendations: Evidence from German State Reforms," ifo Working Paper Series 353, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. Pekka Malo & Juha Eskelinen & Xun Zhou & Timo Kuosmanen, 2024. "Computing Synthetic Controls Using Bilevel Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1113-1136, August.
    6. Armin Falk & Fabian Kosse & Pia Pinger, 2020. "Mentoring and Schooling Decisions: Causal Evidence," CESifo Working Paper Series 8382, CESifo.
    7. Bach, Maximilian & Fischer, Mira, 2020. "Understanding the response to high-stakes incentives in primary education," Discussion Papers, Research Unit: Market Behavior SP II 2020-202, WZB Berlin Social Science Center.
    8. Gregor Pfeifer & Fabian Wahl & Martyna Marczak, 2018. "Illuminating the World Cup effect: Night lights evidence from South Africa," Journal of Regional Science, Wiley Blackwell, vol. 58(5), pages 887-920, November.
    9. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2021. "The Augmented Synthetic Control Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1789-1803, October.
    10. Angerer, Silvia & Bolvashenkova, Jana & Glätzle-Rützler, Daniela & Lergetporer, Philipp & Sutter, Matthias, 2023. "Children’s patience and school-track choices several years later: Linking experimental and field data," Journal of Public Economics, Elsevier, vol. 220(C).
    11. Tuomas Pekkarinen, 2018. "School tracking and intergenerational social mobility," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-56, December.
    12. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    13. Dominique Sulzmaier, 2020. "The causal effect of early tracking in German schools on the intergenerational transmission of education," Working Papers 187, Bavarian Graduate Program in Economics (BGPE).
    14. Daniel Albalate & Germà Bel & Ferran A. Mazaira-Font, 2021. "Decoupling synthetic control methods to ensure stability, accuracy and meaningfulness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 549-584, December.
    15. de Roux, Nicolás & Riehl, Evan, 2022. "Do college students benefit from placement into higher-achieving classes?," Journal of Public Economics, Elsevier, vol. 210(C).
    16. Daniel Albalate & Germà Bel & Ferran A. Mazaira-Font, 2020. "Ensuring Stability, Accuracy and Meaningfulness in Synthetic Control Methods: The Regularized SHAP-Distance Method," IREA Working Papers 202005, University of Barcelona, Research Institute of Applied Economics, revised Apr 2020.
    17. Michał Marcin Kobierecki & Michał Pierzgalski, 2022. "Sports Mega-Events and Economic Growth: A Synthetic Control Approach," Journal of Sports Economics, , vol. 23(5), pages 567-597, June.
    18. Tomasz Serwach, 2022. "The European Union and within-country income inequalities. The case of the New Member States," Working Papers hal-03548416, HAL.
    19. Stefan Klößner & Ashok Kaul & Gregor Pfeifer & Manuel Schieler, 2018. "Comparative politics and the synthetic control method revisited: a note on Abadie et al. (2015)," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-11, December.
    20. Isaksen, Elisabeth Thuestad, 2020. "Have international pollution protocols made a difference?," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).

    More about this item

    Keywords

    school tracking; student performance; synthetic control method; treatment effect distributions; treatment effect heterogeneity;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ces:ceswps:_8879. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.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.