Impact of Covid-19 on the Economic Situation of Women in the Non-Formal Sector in Southern Benin
Bibliographic record
Abstract
The Covid-19 pandemic has posed one of the most significant challenges globally over the past five years, straining public policies and hindering access to essential services, particularly for women. This article aims to evaluate the pandemic’s impact on the economic situation of women in the informal sector in southern Benin. Data were sourced from a household survey conducted among households with members aged 15-64 as part of the "Rise Bénin" project, funded by the Women Rise program (Canada). The Seemingly Unrelated Bivariate Probit Regression (SURE probit) model was employed to simultaneously analyze the impact of Covid-19 on income and employment loss. The findings reveal that response measures, such as the cordon sanitaire (COSAN) and the closure of entertainment venues, significantly increased the probability of income and employment loss among women. Sustainable post-Covid-19 recovery strategies must incorporate public policies with support measures specifically tailored to the needs of women in the sectors most affected by 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".