Gender Differences in Wages and Human Capital: Case Study of Female and Male Urban Workers in Mexico from 1984 to 1992
Bibliographic record
Abstract
Este trabajo investiga el diferencial de salarios entre trabajadores urbanos masculinos y femeninos en México de 1984 a 1992. Nuestro objetivo es determinar de qué manera el diferencial de salarios entre trabajadores femeninos y masculinos ha cambiado durante un periodo de ajuste estructural en México, y cómo este cambio se ha relacionado con las diferencias en la dotación de capital humano en cada grupo. Usando información de las encuestas de ingreso-gasto de 1984, 1989 y 1992, concluimos que las mujeres trabajadoras han sido poco beneficiadas por el ajuste estructural. En general, la desigualdad de salarios ha aumentado sustancialmente durante el periodo de ajuste y ha empeorado el salario relativo de las mujeres trabajadoras.ABSTRACTThis paper investigates the wage differential between female and male urban workers in Mexico from 1984 to 1992. Our objective is to determine how the wage differential between male and female workers has changed during the period of structural adjustment in Mexico and how this change has been related to differences in each groups' endowment of human capital. Using data from the income-expenditure surveys for 1994, 1989, and 1992, we conclude that women workers have benefited little from structural adjustment. In general, wage inequality has increased substantially during the period of adjustment and it has worsened the relative wages of female workers.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".