Trends in Women’s Employment and Poverty Rates in OECD Countries: A Kitagawa–Blinder–Oaxaca Decomposition
Bibliographic record
Abstract
Abstract Although employment growth is propagated as being crucial to reduce poverty across EU and OECD countries, the actual impact of employment growth on poverty rates is still unclear. This study presents novel estimates of the association between macro-level trends in women’s employment and trends in poverty, across 15 OECD countries from 1971 to 2013. It does so based on over 2 million household-level observations from the LIS Database, using Kitagawa–Blinder–Oaxaca (KBO) decompositions. The results indicate that an increase of 10% points in women’s employment rate was associated with a reduction of about 1% point of poverty across these countries. In part, this reduction compensated for developments in men’s employment that were associated with higher poverty. However, in the Nordic countries no such poverty association was found, as in these countries women’s employment rates were very high and stable throughout the observation period. In countries that initially showed marked increases in women’s employment, such as the Netherlands, Germany, Spain, Canada, and the United States, the initial increases in women’s employment rates were typically followed by a period in which these trends levelled off. Hence, our findings first and foremost suggest that improving gender equality in employment is associated with lower poverty risks. Yet, the results also suggest that the potential of following an employment strategy to (further) reduce poverty in OECD countries has, to a large extent, been depleted.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".