The Effect of Binding Commitments on Services Trade
Bibliographic record
Abstract
Abstract It has long been established in theory that uncertainty impacts on firm behaviour. However, the empirical basis for quantifying the uncertainty-reducing effects of trade agreements has not been firmly established. In this paper, we develop estimates of the effect of reducing uncertainty regarding regulation of foreign services markets by making commitments that are bound under a trade agreement. Specifically, we identify the effect on services trade of services trade restrictions, as measured by the OECD's Services Trade Restrictiveness Index (STRI), and the separate effect of ‘water’ in binding commitments, as assessed by the difference between countries’ commitments under the General Agreement on Trade in Services (GATS) or free trade agreements (FTAs) and applied levels of market access, as captured by STRI scores. Using a gravity model, we find that services trade responds positively but inelastically to reductions in services trade barriers, as measured by the STRI and, in our preferred regression, the response to actual restrictions is more than twice – specifically 2.4 times – as strong as the response to comparable reductions in uncertainty, as measured by water. Moving from GATS commitments to FTA commitments leads to a 4.7% increase in services trade because of the reduction in uncertainty.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".