Match Effects and the Gains from Alternative Job Assignments: Evidence from a Teacher Labor Market
Bibliographic record
Abstract
Although the literature on assignment mechanisms emphasizes the importance of efficiency based on agents’ preferences, policymakers may want to achieve different goals. For instance, school districts may want to affect student learning outcomes but must take teacher welfare into account when assigning teachers to students in classrooms and schools. This paper studies both the potential efficiency and equity test-score gains from within-district reassignment of teachers to classrooms using novel data that allows us to observe decisions of both teachers and principals in the teacher internal transfer process, and test-scores of students from the observed assignments. We jointly model student achievement and teacher and school principal decisions to account for potential selection on test score gains and to predict teacher effectiveness in unobserved matches. Teachers, but not principals, are averse to assignment based on the teachers’ comparative advantage. Estimates from counterfactual assignments of teachers to classrooms imply that, under a constraint not to reduce any retained teacher’s welfare, average student test scores could rise by 7% of a standard deviation. Although both high and low achievers would experience average gains under this counterfactual, gains would be larger for high-achieving students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".