Impact of the COVID-19 Pandemic on Income Inequalities in Cameroon: The Influence of Employment Status
Bibliographic record
Abstract
Abstract Due to interruptions and closures of activities resulting from social distancing measures implemented to limit the spread of the virus, individuals have seen their incomes reduced, increasing poverty and pre-crisis inequalities. These inequalities have been exacerbated by measures such as the increase in family allowances, which only benefit civil servants. The objective of this study is to analyse the contribution of the activity situation due to COVID-19 to household income inequalities in Cameroon. The data used are those collected from 604 households by CEREG as part of an IDRC-funded study on the impact of public policies related to the COVID-19 pandemic in Burkina Faso, Cameroon, Côte d'Ivoire and Senegal. The Gini and Theil inequality indices show increased income inequality in households where the head is not employed. The conditional quantile regression shows that employment status has a significant and higher effect during severe restrictions on the incomes of typical households in the 25th, 50th, 75th and 90th percentiles. On the other hand, this increased the distribution of income inequalities within households in the first three quartiles, more than 70% of which can be explained by the change in behaviour resulting from the loss of employment by the heads of household. This result is confirmed by the fact that the share of employment in the formation of income inequalities fell during severe restrictions, according to the Shapley decomposition.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".