Andrea Ichino, Fabrizia Mealli, Javier Viviens
arXiv 7 Nov 2025 · Econometrics
arXiv:2511.05128 · PDF · DOI · OpenAlex · Extracted main text
Every year, over one million EU students choose a secondary school track based on teacher recommendations, yet little evidence shows this yields optimal assignments. Using Dutch data, we examine whether access to standardized test scores improves recommendation quality. We develop a Principal-Stratification metric in a quasi-randomized setting, conduct a welfare analysis that flexibly weights short- and long-term losses, and assess principal fairness by examining whether test-score access affects equity across protected attributes. Results are robust to replacing the Exclusion Restriction assumption underlying our main identification strategy with alternative assumptions. Allowing recommendation upgrades when test scores exceed expectations increases successful placement in more demanding tracks by at least 6%, while misplacing 7% of weaker students. Only unrealistically high weights on short-term losses would justify banning such upgrades. Test-score access also yields fairer recommendations for immigrant and low-SES students. Our methodology and findings contribute to the literature on algorithm-assisted human decisions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | de Ree, J., M. Oosterveen, and D. Webbink (2025) The quality of school track assignment decisions by teachers | 1.000 | 5 | 3 | 100% |
| 2 | Imai, K., Z. Jiang, J. Greiner, R. Halen, and S. Shin (2023) Experimental evaluation of algorithm-assisted human decision-making: Application to pretrial public safety assessment | 0.928 | 15 | 6 | 80% |
| 3 | Imai, K. and Z. Jiang (2022) Principal fairness for human and algorithmic decision-making | 0.928 | 4 | 3 | 100% |
| 4 | Frangakis, C. E. and D. B. Rubin (2002) Principal stratification in causal inference | 0.737 | 3 | 2 | 100% |
| 5 | Angrist, J. D., G. Imbens, and D. Rubin (1996) Identification of causal effects using instrumental variables | 0.644 | 2 | 2 | 100% |
| 6 | Ben-Michael, E., D. J. Greiner, M. Huang, K. Imai, Z. Jiang, and S.… (2024) Does ai help humans make better decisions? a methodological framework for experimental evaluation | 0.644 | 2 | 2 | 100% |
| 7 | Mattei, A., L. Forastiere, and F. Mealli (2023) Assessing principal causal effects using principal score methods | 0.511 | 2 | 1 | 100% |
| 8 | Alesina, A., M. Carlana, E. La Ferrara, and P. Pinotti (2024) Revealing stereotypes: Evidence from immigrants in schools | 0.405 | 1 | 1 | 100% |
| 9 | Imbens, G. W. and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 0.405 | 1 | 1 | 100% |
| 10 | Bach, M (2023) Heterogeneous responses to school track choice: Evidence from the repeal of binding track recommendations | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 44 scored citations.