Bibliographic record
Abstract
The number of preferential trade agreements (PTAs) has skyrocketed over the past 20 years. In addition to reducing barriers at the border, modern PTAs remove many behind-the-border barriers by regulating foreign direct investment (FDI), liberalizing services, and protecting intellectual property rights. This article surveys the literature explaining the formation of PTAs and their consequences. Regarding the formation of PTAs, studies have gradually moved from exploring the macro-foundation of preferential liberalization to focusing on the micro-foundation of PTAs, relying on industry- and firm-level data. Regarding the effect of PTAs, there is robust evidence that PTAs substantively increase trade flows and FDI and are associated with economic reforms in developing countries, though the general welfare effect of preferential liberalization remains largely unexplored. I make some concrete suggestions on avenues toward which to push the research on PTAs. In particular, I argue that scholars interested in PTAs would benefit from engaging in debate about the distributional consequences of trade liberalization, which not only informs much of the current academic and policy research but also features in political debates taking place in democratic polities.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".