The Gender Wage Gap in Early Modern Toledo, 1550–1650
Bibliographic record
Abstract
We exploit the records of a large Toledan hospital to study the compensation of female labor and the gender wage gap in early modern Castile in the context of nursing—a non-gendered, low-skill occupation in which men and women performed the same clearly defined tasks. We employ a robust methodology to estimate the value of in-kind compensation, and show it to constitute a central part of the labor contract, far exceeding subsistence requirements. Patient admissions records are used to measure nurse productivity, which did not differ across genders. Female compensation varied between 70 percent and 100 percent of male levels, with fluctuations clearly linked to relative labor scarcity. Contrary to common assumptions in the literature, we show that markets played an important role in setting female compensation in early modern Castile. The sources of the gender disparity are, therefore, likely to be found in the broader social and cultural context.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".