<scp>COVID</scp>‐19 job loss and re‐employment among partnered parents: Gender and educational variations
Bibliographic record
Abstract
Abstract Objective This study examines the re‐employment prospects and short‐term career consequences for mothers and fathers who lost their jobs during the COVID‐19 pandemic. Background The pandemic recession has been dubbed a “shecession,” but few studies have explored whether mothers paid a higher or lower price upon labor market re‐entry than fathers. Method This study draws on March 2020–December 2022 Current Population Survey data and focuses on partnered parents with children under age 13 in the household. Exploiting four‐month panels, we use multi‐level discrete‐time event history models to predict re‐employment and linear regression models to predict job‐level wage upon re‐employment, while controlling for a wide array of factors. Results Partnered fathers were more likely than partnered mothers to find re‐employment during the pandemic. The gender gap in re‐employment was concentrated only among parents without a bachelor's degree and persisted when all controls were held constant. Moreover, upon re‐employment, fathers had higher job‐level wages than mothers, which was consistent across educational levels. Even with the same job‐level wage before labor market exit, mothers were penalized on re‐entry relative to fathers and this penalty was rooted in gendered job segregation. Conclusion This study extends previous research by analyzing re‐employment and a critical material outcome for parents (i.e., job‐level wage upon re‐employment) during the entire pandemic, including the “new normal” (late 2022). The results reveal the intersectional inequalities in family and work: Compared to fathers, mothers, particularly less‐educated mothers, paid a higher price for their time out of work during the pandemic.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".