Bibliographic record
Abstract
Abstract I propose asymmetric trade liberalizations as a new potential determinant of current account dynamics. I focus on South Korea, which experienced a rise and fall of its current account in the period from 2010 to 2018, when it signed preferential trade agreements (PTAs) with its main trading partners. First, I develop a model where the current account depends on the timing of present and future relative changes of trade costs. Second, I provide empirical evidence supporting the key predictions of the model using the Canada–Korea PTA. PTAs provide predictable, potentially asymmetric, future tariff paths on many products. I use information for over 2,500 HS‐6 products to build relative trade liberalization measures, and show that current (future) high relative trade liberalizations tend to decrease (increase) the trade balance, consistent with the model. Finally, I propose a quantitative investigation of the Korean surplus based on the asymmetric dynamics of trade costs between South Korea and its main trading partners. I develop a two‐country international real business cycle model augmented with trade costs. When fed with the actual asymmetric trends found in the data, the model generates a current account surplus of about 2.15% of GDP, roughly 66% of what was observed in the Korean data.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".