The Misallocation of Pay and Productivity in the Public Sector: Evidence from the Labor Market for Teachers
Bibliographic record
Abstract
This paper uses a unique dataset of both public and private sector primary school teachers and their students to present among the first estimates in a low income country of (a) teacher effectiveness; (b) teacher value added (TVA) and its correlates; and (c) the link between TVA and teacher wages. Teachers are highly effective in our setting: moving a student from the 5th to the 95th percentile in the public school TVA distribution would increase mean student test scores by 0.54 standard deviations. Although the first two years of experience, as well as content knowledge, are associated with TVA, all observed teacher characteristics explain no more than 5% of the variation in TVA. Finally, there is no correlation between TVA and wages in the public sector (although there is in the private sector), and a policy change that shifted public hiring from permanent to temporary contracts, reducing wages by 35%, had no adverse impact on TVA, either immediately or after 4 years. The study confirms the importance of teachers in low income countries, extends previous experimental results on teacher contracts to a largescale policy change, and provides striking evidence of significant misallocation between pay and productivity in the public sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".