Male labor scarcity, technology adoption and female labor market integration: A quantitative and qualitative study of Portugal
Bibliographic record
Abstract
This paper presents new evidence that male labor scarcity led to an increased female presence in physically demanding occupations. We employ a two-part analytical approach. First, using a quasi-experimental empirical strategy, we show that male scarcity led to an increased female presence in male-dominated and physically demanding occupations. We then present a case study of early female labor force integration, facilitated by technological innovation, that challenged conservative social norms on gender roles. At the beginning of the 1960s, the labor force in the salt ponds in Alcochete, Portugal, was exclusively male. Labor shortages over the decade led employers to recruit females. This trend favored an early technological improvement under a potentially virtuous circle. A wheelbarrow to carry the salt spread from the mid-1960s onward to the rest of the region. Our findings contribute to the literature on labor market dynamics and endogenous technology adoption. • Male labor shortages boosted female employment in physically demanding occupations. • Alcochete salt ponds illustrate how labor shortages led employers to hire women. • The adoption of wheelbarrows facilitated female employment in salt ponds. • Endogenous technology adoption can drive gender integration in labor markets. • Male shortage combined with technology adoption can override social conservatism.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".