COVID-19 and Socio-Economic Inequalities among Workers in Ghana
Bibliographic record
Abstract
In this paper, the causative evidence of COVID-19 and its socio-economic effect on Ghanaian workers are presented. The analysis takes into account the exact policy environment, in which stringent measures were announced and executed in two geographically delimited zones, bringing the major metropolitan centers to a halt, while less stringent controls were in place throughout the country. The effect of the pandemic on the economy was explored by employing discourse analysis and data from secondary sources to determine the effect of the virus from a Ghanaian perspective. The general finding of the study was that the pandemic has caused fiscal imbalances and worsened the level of inequality among workers. The findings revealed that the pandemic has had a negative effect on the socio-economic condition of Ghanaian workers particularly those in the informal sector. The loss of employment and reduced labour wages during the pandemic increased income inequality and eroded the gains made to reduce poverty. The study cites an instance where the country’s tourism sector lost $171 million in the last quarter of 2020 as a result of the measures taken to contain the coronavirus. This accounted partially for an estimated 42,000 individuals losing their jobs during the first two months of the pandemic. Again, 46 percent of businesses claimed to have cut salaries for 25.7 percent of their overall workforce, resulting in wage cuts for an estimated 770,124 people. The analysis from the study indicates that Ghana can turn the obstacles provided by the pandemic into prospects and opportunities by investing heavily in the health sector and providing strategic support to SMEs, which provides a large number of jobs for Ghanaians. Essentially, the lockdown effect highlighted the need to adopt effective strategies to mitigate vulnerabilities and labor market inequalities among women and individuals in the informal space. The research is exploratory and relies on secondary data. Therefore, conducting a study using primary data sources from certain towns or regions across the country is likely to yield different findings and conclusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".