Estimating a Dynamic Adverse-Selection Model: Labor-Force Experience and the Changing Gender Earnings Gap 1968- 97.
Bibliographic record
Abstract
This paper formulates and estimates a dynamic model of labor supply, occupational sorting, human capital accumulation and discrimination to explain the narrowing gender earnings gap from 1968 to 1993. The paper proves the model is identified and develops a three-step estimation technique. Imperfect information significantly amplifies exogenous shocks: statistical discrimination accounts for 36 percent of the observed gender earnings gap in the mid-to-late 1970s, declining to 22 percent in the mid-to-late 1980s. Gender differences in preferences are comparatively less important: the gap would have been at least 56 percent smaller in the mid-to-late 1970s and would have nearly closed by the mid-to-late 1980s if it was driven only by preference. Increases in overall productivity and demographic changes account for a large percentage of the decline in the gender earnings gap and the increase in female labor market experience, while a relative increase in productivity raises women's representation in professional occupations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".