Building Back Better after the COVID-19 Pandemic
Bibliographic record
Abstract
In this policy-oriented paper, we provide a pre- and post-pandemic socioeconomic analysis of Peru, along with a financially sustainable five-year Building Back Better recovery plan, which emphasizes the urgency of addressing some of the country’s structural weaknesses. We underscore the importance of public investment for this effort, but widen the focus to include current public expenditure, in order to take steps towards building a more universal social protection system. We show that this also contributes to reducing the gender imbalances in the labor market that the pandemic exposed and exacerbated. We provide a financial programming exercise that demonstrates that the plan is financially responsible under a reasonable fiscal rule. Four core ideas stand out from our analysis. Firstly, while public investment can be key to reigniting economic growth, it does not go very far in tackling structural weaknesses. Secondly, public spending in health can actually achieve this from two fronts: by beginning to build a universal access social protection system and by addressing gender imbalances in the labor market. Thirdly, focusing public discussion on social protection enables a broader approach to policy reform by including formal employment and productivity enhancing reforms, which are essential for the sustainability of a broad social protection system. Finally, we also show that the sector mix in public investment has an impact on employment results, both in terms of the volume of jobs generated and their gender composition.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.007 |
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".