Women’s employment trajectories in a low-income setting: Stratification and change in Nepal
Bibliographic record
Abstract
BACKGROUNDAcross the globe, employment for pay outside the home plays a key role in the lives of women, and increasing the proportion of women involved in high-quality jobs is a critical component of reaching several sustainable development goals.While existing research from high-income societies demonstrates that women's employment is not constant over the life course, relatively less is known about women's employment trajectories in lowincome countries. OBJECTIVEWe examine employment trajectories among women in rural Nepal, accounting for job type, employment intensity, and earnings. METHODSUsing eight years of quarterly employment data from the 2016 Female Labor Force Participation and Child Outcomes Study component of the Chitwan Valley Family Study, we identify typologies of employment trajectories by conducting sequence and cluster analyses. RESULTSFirst, half of the women in our sample were never employed in the study period.Second, among women who were ever employed, there were considerable transitions into and out of the workforce.Third, women's employment trajectories are largely determined by job type (wage labor, salaried jobs, and self-employment), with little movement across job types.Additionally, self-employed women and those with salaried jobs had higher earnings and higher employment intensity than women with wage labor jobs. CONCLUSIONSWe see intense stratification into job types, including no employment at all, and substantial transitions into and out of the workforce among workers.Women experience many employment disruptions over the life course, with little sign of upward employment mobility. CONTRIBUTIONThis study provides new empirical portraits of women's employment in low-income settings by investigating the multiple dimensions of women's employment from a life course perspective.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".