Relative Employment and Earnings of Female Household Heads in Mexico, 1987-1995
Bibliographic record
Abstract
This study analyzes the determinants of employment and earnings for female-headed households from 1987 to 1995 in Mexico. During those years, the percentage of female-headed households and their employment rate substantially increased. The paper explores the possible causes for the relative changes in labor market outcomes for both female and male-headed households. We use individual-level household survey data from Mexico's Encuesta nacional de empleo urbano for the third quarter of 1987 and 1995. Our findings show that the gender educational gap substantially closed, the percentage of divorced female household heads increased, and the percentage of widows fell. From 1987 to 1995, the average number of children decreased by about one child (from 4.79 to 3.72). In addition, the wages of female heads grew significantly faster than that of their male counterparts, and this contributed to a reduction in the gender wage gap ratio from 24.9 to 19.3 percent during the 1987-1995 period. Also, we find that from 1987 to 1995, the rate of return to education increases for both male and female household heads, but the rate is higher for males than for females. As we would expect, wages increase with experience but at a decreasing rate. Furthermore, our finding shows that wage premium for being employed in the public sector is much higher for females than for males, and it actually increased from 22.4 percent in 1987 to 30.6 percent by 1995.
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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 | 0.000 | 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) | 0.002 | 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".