Is It Abenomics or Post-Disaster Recovery? A Counterfactual Analysis
Bibliographic record
Abstract
This study is an attempt to assess the impact of policy initiatives launched by Japan’s new Prime Minister Shinzo Abe on Japan’s real GDP in his first quarter in office. We use as a benchmark for measurement a counterfactual estimate of GDP. Since the Japanese economy is also in the midst of reconstruction from the 2011 Tohoku disaster in the first quarter of 2013, we first estimate the counterfactual GDP which would have materialized in the absence of that disaster. We will use a dummy variable method and the statistical method proposed by Cheng Hsia and others. We check the validity of these methods with regard to the Kobe earthquake of 1995, and then estimate the post-disaster counterfactual GDP for the Tohoku disaster. We measure the impact of government policies as the difference between the actual and counterfactual GDP. By doing so, we conclude that government policies have failed to lift Japan’s GDP to the expected level. Even with the help of Abenomics, the gap remains in the rage of 3 to 13 trillion yen per year.
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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 | 0.105 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".