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Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis

Author

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  • Andrea Ichino
  • Fabrizia Mealli
  • Javier Viviens

Abstract

We study whether access to standardized test scores improves the quality of teachers' secondary school track recommendations, using Dutch data and a metric based on Principal Stratification in a quasi-randomized setting. Allowing teachers to revise their recommendations when test results exceed expectations increases the share of students successfully placed in more demanding tracks by at least 6%, but misplaces 7% of weaker students. However, only implausibly high weights on the short-term losses of students who must change track because of misplacement would justify prohibiting test-score-based upgrades. Access to test scores also induces fairer recommendations for immigrant and low-SES students.

Suggested Citation

  • Andrea Ichino & Fabrizia Mealli & Javier Viviens, 2025. "Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis," Papers 2511.05128, arXiv.org, revised Jan 2026.
  • Handle: RePEc:arx:papers:2511.05128
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    References listed on IDEAS

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    1. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    2. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    3. Alberto Alesina & Michela Carlana & Eliana La Ferrara & Paolo Pinotti, 2024. "Revealing Stereotypes: Evidence from Immigrants in Schools," American Economic Review, American Economic Association, vol. 114(7), pages 1916-1948, July.
    4. Guildo W. Imbens, 2003. "Sensitivity to Exogeneity Assumptions in Program Evaluation," American Economic Review, American Economic Association, vol. 93(2), pages 126-132, May.
    5. 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.
    6. Holt, Stephen B. & Papageorge, Nicholas W., 2016. "Who believes in me? The effect of student–teacher demographic match on teacher expectationsAuthor-Name: Gershenson, Seth," Economics of Education Review, Elsevier, vol. 52(C), pages 209-224.
    7. Ferman, Bruno & Fontes, Luiz Felipe, 2022. "Assessing knowledge or classroom behavior? Evidence of teachers’ grading bias," Journal of Public Economics, Elsevier, vol. 216(C).
    8. Joppe de Ree & Matthijs Oosterveen & Dinand Webbink, 2023. "The quality of school track assignment decisions by teachers," Papers 2304.10636, arXiv.org, revised Jun 2025.
    9. Fabrizia Mealli & Barbara Pacini, 2013. "Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1120-1131, September.
    10. Osikominu, Aderonke & Pfeifer, Gregor & Strohmaier, Kristina, 2021. "The Effects of Free Secondary School Track Choice: A Disaggregated Synthetic Control Approach," IZA Discussion Papers 14033, Institute of Labor Economics (IZA).
    11. Cattaneo Matias D. & Frandsen Brigham R. & Titiunik Rocío, 2015. "Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate," Journal of Causal Inference, De Gruyter, vol. 3(1), pages 1-24.
    12. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    13. Dean Eckles & Nikolaos Ignatiadis & Stefan Wager & Han Wu, 2020. "Noise-Induced Randomization in Regression Discontinuity Designs," Papers 2004.09458, arXiv.org, revised Mar 2025.
    14. Eli Ben-Michael & Kosuke Imai & Zhichao Jiang, 2024. "Policy Learning with Asymmetric Counterfactual Utilities," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(548), pages 3045-3058, October.
    15. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    16. Simon Burgess & Ellen Greaves, 2013. "Test Scores, Subjective Assessment, and Stereotyping of Ethnic Minorities," Journal of Labor Economics, University of Chicago Press, vol. 31(3), pages 535-576.
    17. Ashesh Rambachan, 2024. "Identifying Prediction Mistakes in Observational Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(3), pages 1665-1711.
    18. Bach, Maximilian, 2023. "Heterogeneous responses to school track choice: Evidence from the repeal of binding track recommendations," Economics of Education Review, Elsevier, vol. 95(C).
    19. Joshua D. Angrist & Miikka Rokkanen, 2015. "Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1331-1344, December.
    20. Avi Feller & Fabrizia Mealli & Luke Miratrix, 2017. "Principal Score Methods: Assumptions, Extensions, and Practical Considerations," Journal of Educational and Behavioral Statistics, , vol. 42(6), pages 726-758, December.
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