The Impact of Covid-19 and Social Protection Programs on Poverty in Indonesia
Bibliographic record
Abstract
Covid-19 remained the largest challenge for the Indonesian economy in 2021. In particular, Covid-19 case numbers hit a new high of about 50,000 cases per day in mid-July. In response, the government increased the contact tracing and testing of suspected positive cases, expanded its Covid-19 vaccination drive and introduced stricter mobility and activity restrictions (PPKM Darurat/PPKM Levels 1–4). By the end of October, more than 57% of the target population had received at least one dose of a Covid-19 vaccine. The economy also improved in the first half of 2021. Building on the trough in GDP in the second quarter of 2020, economic growth returned in the second and third quarters of 2021 after contractions in the previous four quarters. Macroeconomic circumstances were also generally favourable, though significant longer-term risks remain. In terms of real wages, however, the recovery tended to benefit the formal sector and well-educated workers, while real wages in the informal sector and for low-educated workers continued to decline. At the same time, the rise in Covid-19 cases and the implementation of stricter mobility and activity restrictions have lowered expectations for economic growth in the second half of 2021. To mitigate the social and economic impact of the pandemic, the government has re-expanded its social protection programs. We find that these programs have mitigated the impact of Covid-19 on the poverty rate by four percentage points, or by about three-quarters—poverty increased to nearly 10%, rather than the 14% that would have been likely without the increased social assistance. The possibility of a K-shaped recovery implies that special social protection programs must continue as the economy recovers from the pandemic, to ensure that the poor and vulnerable are not left behind.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".