Japanese Economic Performance after the Pandemic: A Sectoral Analysis
Bibliographic record
Abstract
The COVID-19 crisis battered the Japanese economy. The purpose of this paper is to investigate whether the pandemic has left scars. To this end, it employs out-of-sample forecasting models and detailed stock market data for 30 sectors and disaggregated current account data for the 3 years after the first case occurred. The findings indicate that stock prices in sectors such as tourism, education, and cosmetics remain far below forecasted values after three years. Office equipment and semiconductor stock prices initially fell more than predicted but have since recovered. Other sectors such as bicycle parts and home appliances gained at first but are now performing as expected. Sectors such as home delivery and electronic entertainment continue to outperform. The results also indicate that income flows from Japanese investments abroad are much larger than forecasted, keeping the Japanese current account in surplus even as imports of oil and commodities have created persistent trade deficits. Since the travails of hard-hit sectors such as tourism reflect their exposure to the COVID-19 pandemic rather than bad choices made by firms, policymakers should consider employing cost-effective ways to stimulate economic activity in these sectors.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".