Drivers of Disparity: How Policy Responses to COVID-19 Can Increase Inequalities
Bibliographic record
Abstract
Countries across the world have deployed macroeconomic policies to address the negative economic implications of the COVID-19 pandemic and subsequent lockdown measures. However, these policies can have different outcomes for various segments of the population. This policy briefing assesses the channels through which macroeconomic policy responses in Nigeria and Uganda negatively affect or exclude specific groups, with the aim of resetting policies to achieve more inclusive outcomes that will support economic growth and development in the post COVID future. It finds that the urban poor and the informal sector are being excluded as a result of the poor coverage of cash transfer programmes and the implementation of policies mostly applicable to the formal sector. Loans to low-income borrowers are not likely to increase despite downward revisions to the monetary policy rate, while importers and poorer households will be the worst hit by exchange rate adjustments in Nigeria. While the middle class and rich are affected by the removal of subsidies in Nigeria, those living in poverty do not benefit from the budget restructuring in Uganda.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".