Bibliographic record
Abstract
The Japanese yen depreciated 30 percent from its peak in the fourth quarter of 2011 against its trading partners. This was expected to boost its exports as the lower yen makes Japanese goods more competitive on global markets. Instead, the volume of Japanese exports of goods actually fell by 0.6 percent over this same period, as can be seen in the chart below. Weaker external demand surely contributed to this poor export performance. Yet over the same period, U.S. goods exports grew by more than 6 percent, which suggests that other factors are also at play. In this post, we draw on our recent paper ?Importers, Exporters, and Exchange Rate Disconnect? that highlights another channel to help explain these puzzling developments. In that study, we show that a key to understanding why there is low pass-through from exchange rates into export prices is that large exporters are also large importers, so they face offsetting exchange rate effects on their marginal costs. In the case of Japan, the connection between the yen and production costs has been made stronger since the country replaced nuclear power with imported fuels in the aftermath of the 2011 earthquake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".