Why Women Leave Earlier: What Is Behind the Earlier Labour Market Exit of Women in the Czech Republic
Bibliographic record
Abstract
The article examines the factors that intervene in decisions to leave the labour market in the Czech Republic from a gender perspective. It uses binary logistic regression to identify the variables that predict the economic inactivity of men and women at the age of 60 plus and the interactions of variables to examine whether the factors that determine when people exit the labour market are the same for men and women. The analysis uses data from the Labour Force Study (LFS) collected in the fourth quarter of 2017 and focuses on people between the ages of 60 and 69 and five independent variables: gender, education, pension eligibility, marital status, and type of job. It studies how gender intersects with other characteristics in the decision to retire from the labour market. Although pension eligibility is the central predictor of economic inactivity after the age of 60, when eligibility is controlled for here, it is evident that gender, education, job type, and marital status all influence the timing of labour market exits. Women leave work earlier than men, and this is found to be true even when we control for their education or pension eligibility. They are also more likely than men to leave work even if they are not yet eligible to collect a pension. The effect of education is not as straightforward for women as for men: women with the lowest and with the highest levels of education are more likely to continue to work than men with the same educational attainment. Policies to prolong people's working lives may thus have a different impact on each gender.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".