How Can Uncertainty Affect the Choice of Trade Agreements?
Bibliographic record
Abstract
This paper analyses how uncertainty influences the formation and design of regional trade agreements (TAs). Two sources of uncertainty – in demand and costs – are considered. Using a multi‐stage game, we show that, as long as some decisions are made after uncertainty is resolved, all TAs have option values. But, because TAs differ in their flexibility and degrees of coordination, these option values vary across TAs. Thus, under uncertainty, the usual cost–benefit analysis that underlies the formation and design of TAs is altered to reflect these option values. We also show that, due to the flexibility and coordination differences among TAs, their option values are affected differently by uncertainty. Consequently, the formation and design of TAs are also affected by the nature and degree of uncertainty. We demonstrate that the effects of an increase in uncertainty on the choice of TAs depend on the relative responsiveness of the TAs' option values with respect to the change in uncertainty, which in turn depend on the convexity properties of the countries' welfare functions under the different TAs. In particular, a TA whose option value is more responsive to a change in uncertainty becomes relatively more attractive when uncertainty increases. This enables us to predict which TAs are likely to emerge in an uncertain world. Using a specific example, we then show the effects of a change in both demand and cost uncertainty on the choice of TAs. We also examine the timing of the resolution of uncertainty and its effect on the choice of TAs and show that it can significantly impact the type of TA that countries wish to form.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".