Bibliographic record
Abstract
I use a detailed panel of data and a unique modeling specification to explore how public schoolteachers respond to the incentives embedded in North Carolina’s retirement system. Like most public-sector retirement plans, North Carolina’s teacher pension implicitly encourages teachers to continue working until they are eligible for their pension benefits, and then leave soon afterward. I find that teachers with higher levels of quality, as measured by a teacher’s value-added to her students’ achievement test scores, are more responsive to the “pull” of teacher pensions. Younger teachers, those with higher salaries, and nonwhite teachers are also more likely to stay during the pension “pull.” All teachers show a strong response to the pension “push,” with about a quarter of teachers leaving every year once they become eligible for their pension. I depart from other models of teacher retirement by using a Cox proportional hazard model. Given that salaries are generally fixed by the state, I find that the number of years a teacher must work before she is eligible for her full pension benefit is the major driver of variation in pension wealth. This specification has the benefit of a flexible baseline hazard that can easily capture the sharp incentives driving a teacher’s retirement decision that are dependent on her proximity to retirement eligibility, and can flexibly account for differences driven by local labor market conditions. These analyses highlight important unintended effects that inform education policies going forward to ensure the retention of high-quality teachers in all types of schools.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".