Gender Differences in Informal Labor-Market Resilience
Bibliographic record
Abstract
Abstract This paper reports on the universe of garment-making-firm owners in a Ghanaian district capital during the COVID-19 crisis. By July 2020, 80 percent of both male- and female-owned firms were operational. However, pre-pandemic data show that selection into persistent closure differs by gender. Consistent with a “cleansing effect” of recessions and highlighting the presence of marginal female entrepreneurs, female-owned firms that remain closed past the spring lockdown are negatively selected on pre-pandemic sales. The pre-pandemic sales distributions of female survivors and non-survivors are significantly different from each other. Female owners of non-operational firms exit to non-employment and experience large decreases in overall earnings. In contrast, persistently closed male-owned firms are not selected on pre-pandemic firm characteristics. Instead, male non-survivors are 36 percentage points more likely than male survivors to have another income-generating activity prior to the crisis. Male owners of persistently closed firms fully compensate for revenue losses in their core businesses with earnings from these alternative income-generating activities. Taken together, the evidence is most consistent with differential underlying occupational choice fundamentals for self-employed men and women in this context.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.012 | 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".