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Record W2944906941 · doi:10.1017/s0022050720000145

The Gender Wage Gap in Early Modern Toledo, 1550–1650

2020· article· en· W2944906941 on OpenAlexaff
Mauricio Drelichman, David Agudo

Bibliographic record

VenueThe Journal of Economic History · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
Fundersnot available
KeywordsCompensation (psychology)Context (archaeology)Subsistence agricultureWageScarcityDemographic economicsProductivityExploitValue (mathematics)EconomicsEconomic shortageLabour economicsPsychologyGeographyEconomic growthAgricultureSocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.093
GPT teacher head0.215
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractyes

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