Bibliographic record
Abstract
The large current account deficit of the U.S. is the result of a large deficit in the goods balance and a modest surplus in the service balance. The opposite is true for Japan, Germany and China. Moreover, I document the emergence from the mid-nineties of a strong negative relation between specialization in export of services and current account balances in a large sample of OECD and developing countries.\nStarting from these new stylized facts, I propose in this paper a “service hypothesis” for global imbalances, a new explanation based on the interplay between the U.S. comparative advantage in services and the asymmetric trade liberalization process in goods trade versus service trade that took place in the last 15 years.\nFirst, I use a structural gravity model to show that service trade liberalization lagged behind goods trade liberalization, and I quantify the extent of this asymmetry.\nSecond, I show that a simple two-period model can rationalize the emergence of current account deficits in the presence of such asymmetric liberalization. The key inter-temporal mechanism is the asymmetric timing of trade policies, which affects savings decisions.\nFinally, I explore the quantitative relevance of this explanation for global imbalances. A multi-period version of the model, fed with the asymmetric trade liberalization path found in the data, generates a current account deficit of about 1% of GDP (roughly 20% of what was observed in the U.S. in 2006).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".