Intersectional analysis of the labour market impacts of COVID on women with young children and in low-skilled jobs
Bibliographic record
Abstract
Purpose This paper analyzes the differential experiences of women in the Canadian labour market who hold lower-skilled jobs and have school-age children during two waves of Covid compared with more typical conditions pre-pandemic. The article seeks to test the hypothesis that workers at the intersection of womanhood, motherhood and precarious employment would endure even more disadvantageous labour market outcomes during the Covid pandemic than they did prior to it. Design/methodology/approach We employ a Gender-Based Plus (GBA+) and intersectionality lens to examine the differential effect of Covid on the effect of the trifecta of being a woman in a lower-skilled job and facing a motherhood penalty from school-age children. We use a Difference-in-Difference framework with Canadian Labour Force Survey (LFS) data to examine the differential effect of two waves of Covid on three labour market outcomes: employment, hours worked and hourly wages. Findings We find that being a woman in a lower-skilled job with school-age children is associated with lower employment, hours worked and wages in normal times compared to males in those same situations. Such women also face the most severe adjustment consequence from the Covid shock, with that adjustment concentrated on the margin of employment and restricted to the First Wave and not the subsequent Omicron Wave. Originality/value The paper studies a specific intersectional group, assesses pre-pandemic, peak-pandemic and late-pandemic differences in labour market outcomes and runs separate estimations for different job skill levels. We also study a more comprehensive list of labour market outcomes than most studies of a similar nature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".