Bibliographic record
Abstract
One issue that has pervaded policy discussions for decades is the difficulty that school districts experience in retaining teachers. Almost a quarter of entering public school teachers leave teaching within the first three years and empirical evidence has related high attrition rates of beginner teachers to family circumstances, such as maternity or marriage. I examine female teachers' career choices and inquire about the effects that wage increases and child care subsidies have on their employment decisions. I set up a dynamic model of job search where individuals simultaneously make employment and fertility decisions, fit it to data from a national longitudinal survey and estimate it by Simulated Method of Moments. Estimates indicate that gains of exiting the teaching workforce to start a family vary between 75% and 88% of the average teaching wage if the exit occurs during the first five years. At late periods and provided a positive stock of children, nonpecuniary penalties to return to teach lie between one and two times the average teaching wage. A 20 percent raise in teaching wages increases retention by 14% and decreases the proportion of teachers giving birth by 50%. Results suggest that fertility changes occur not only at earlier periods but also after a career interruption when teachers are considering a returning decision. The effectiveness of the wage policy in attracting back to the field individuals who left teaching to enroll in nonteaching jobs is positively associated with the greatest impact that the policy has on fertility in nonteaching. Child care subsidies increase retention by 11% and 29% with the lowest and highest subsidy, respectively. New births are concentrated at earlier periods of teachers' careers and thus, generate longer first teaching spells. However, large nonpecuniary rewards at late periods of the non labor market alternative relative to being in teaching as well as exits out of the workforce concentrated at later periods lead the decrease of returning rates of teachers who dropped the workforce altogether.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".