Marinho Bertanha, Margaux Luflade, Ismael Mourifié
arXiv 26 Jul 2023 · Econometrics
arXiv:2307.14282 · PDF · DOI · OpenAlex · Extracted main text
A growing number of central authorities use assignment mechanisms to allocate students to schools in a way that reflects student preferences and school priorities. However, most real-world mechanisms incentivize students to strategically misreport their preferences. Misreporting complicates the identification of causal parameters that depend on true preferences, which are necessary inputs for a broad class of counterfactual analyses. In this paper, we provide an identification approach that is robust to strategic misreporting and derive sharp bounds on causal effects of school assignment on future outcomes. Our approach applies to any mechanism as long as there exist placement scores and cutoffs that characterize that mechanism's allocation rule. We use data from a deferred acceptance mechanism that assigns students to more than 1,000 university--major combinations in Chile. Matching theory predicts and empirical evidence suggests that students behave strategically in Chile because they face constraints on their submission of preferences and have good a priori information on the schools they will have access to. Our bounds are informative enough to reveal significant heterogeneity in graduation success with respect to preferences and school assignment.
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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 | Haeringer, Guillaume and Klijn, Flip (2009) Constrained School Choice | 1.000 | 10 | 3 | 100% |
| 2 | Azevedo, Eduardo M and Leshno, Jacob D (2016) A Supply and Demand Framework for Two-sided Matching Markets | 1.000 | 6 | 3 | 100% |
| 3 | Fack, Gabrielle and Grenet, Julien and He, Yinghua (2019) Beyond Truth-telling: Preference Estimation with Centralized School Choice and College Admissions | 0.976 | 14 | 5 | 93% |
| 4 | Agarwal, Nikhil and Somaini, Paulo (2018) Demand analysis using strategic reports: An application to a school choice mechanism | 0.969 | 11 | 5 | 91% |
| 5 | Kirkeboen, Lars J and Leuven, Edwin and Mogstad, Magne (2016) Field of Study, Earnings, and Self-selection | 0.961 | 9 | 4 | 89% |
| 6 | Horowitz, Joel L and Manski, Charles F (1995) Identification and Robustness with Contaminated and Corrupted Data | 0.843 | 5 | 3 | 60% |
| 7 | Larroucau, Tomas and Rios, Ignacio (2021) Dynamic College Admissions | 0.811 | 4 | 2 | 100% |
| 8 | Larroucau, Tomas and Rios, Ignacio (2020) Do “Short-List” Students Report Truthfully? Strategic Behavior in the Chilean College Admissions Problem | 0.737 | 3 | 2 | 100% |
| 9 | Georgy Artemov and Yeon-Koo Che and YingHua He (2023) Stable Matching with Mistaken Agents | 0.669 | 10 | 3 | 30% |
| 10 | Abdulkadiroglu, Atila and Angrist, Joshua D and Narita, Yusuke and P… (2022) Breaking Ties: Regression Discontinuity Design Meets Market Design | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Set-Valued Control Functions | 1.000 | 9 | 3 |
| 2 | Nonparametric Treatment Effect Identification in School Choice | 0.899 | 11 | 4 |
| 3 | Robust Counterfactuals in Centralized Schools Choice Systems: Addressing Gender Inequality in STEM Education | 0.405 | 1 | 1 |