Indonesia Economic Prospects, June 2021 : Boosting the Recovery
Bibliographic record
Abstract
COVID-19 (coronavirus) has taken a heavy economic and human toll globally and in Indonesia. According to official statistics, over 3.8 million people have died from COVID as of May 2021. The global economy experienced one of the most severe recessions, shrinking by 3.5 percent in 2020 compared to 1.7 percent in 2009 during the global financial crisis. The recession in Indonesia (-2.1 percent) was milder than among Emerging Markets and Developing Economies, EMDEs (-4.3 percent excluding China). Small and medium-sized firms and businesses in contact-intensive services sectors were severely affected. About 1.8 million Indonesians became unemployed between February 2020 and 2021 and another 3.2 million people exited the labour force. Three hundred thousand fewer youth entered the labour market. About 2.8 million people have fallen into poverty as of September 2020 with the government’s social assistance program mitigating a potentially worse outcome. Indonesia's recovery has been relatively gradual until the first quarter of 2021 but has accelerated more recently. Indonesia’s recovery gap – the difference between real GDP and its pre-crisis trend – narrowed from -7.5 to -7.1 percent between Q2 and Q4 2020. By comparison, the average 'recovery gap' among regional and G20 peers shrank from -13.6 to -5.1 percent. The recovery gap remained elevated at -7.9 percent during the first quarter this year. Consumption and investment growth have been subdued due to the still weak labor market and high uncertainty while trade has recovered more strongly. The recovery gap in contact-intensive services sectors, such as transport and accommodation, has also been elevated compared to manufacturing industries due to social distancing and stronger external demand in manufacturing. But retail sales increased by 11 percent between March and April while the manufacturing continued to expand suggesting a stronger rebound during the second quarter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.021 |
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".