The impact of the Covid-19 pandemic on gender labor market asymmetries in Germany
Bibliographic record
Abstract
The Corona pandemic affected life and working conditions around the world. Some could work from home, some had to risk their lives at the workplace, and some got laid off. The selection of employees to one of these groups, however, was asymmetric about gender. More than 63 percent of employees providing services in Germany are female; females in health professions account for more than 75 percent, and in social professions, including daycare, the share of female employees is at 84 percent. These occupations were in high demand during the pandemic and cannot be practiced at home. Since women do more than 62 percent of housework and childcare, the high demand for female work creates a dilemma. While family obligations increased as childcare facilities and schools closed, women had to decide whether to remain or drop out of the labor market. In this paper’s estimated DSGE model, these choices are addressed by allowing for asymmetries in participation decisions and disutility of effort for male and female workers. While at the beginning of the pandemic, female employment increased relative to male, an increase in disutility drove females out of the labor market during the second lockdown. Instead, predominantly males entered, and females reacted to this increase by staying absent. This pattern resembles previous findings on historical pandemics and, in the literature, is called “the added worker effect.”
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".