Bibliographic record
Abstract
• Risk averse countries may have an insurance motive for joining a preferential trade agreement. • The desire for insurance by members determines whether a trade agreement forms and its depth. • Differences in member country risk attitudes act as a source of comparative advantage. • Deeper integration allows risk partiality to be exported from less to more risk averse members. • The amount, and correlation, of risk members face influences trade agreement formation and design. We investigate preferential trade agreement (PTA) formation when risk averse countries face demand uncertainty and, hence, have an insurance motive for pursuing trade integration. In this environment, when deciding which type of PTA − if any − they wish to form, countries seek to maximise their net welfare; that is, their expected utility less a risk premium. The desire for insurance influences, not just whether a particular PTA forms, but also the preferred depth of integration. We analyze the insurance implications of free trade agreements (FTAs), customs unions (CUs), and countries choosing to stand alone. We further distinguish between shallow CUs and deep CUs; in the former, members maximise the sum of their individual net welfares, while in the latter they maximise the net value of the sum of their individual expected welfares. We show that differences in country risk attitudes and the levels of risk they face, as well as the degree to which these risks are correlated with each other, each, and together, influence the formation and design of TAs. When countries’ demands are uncorrelated, they form a deep CU if their levels of risk aversion are sufficiently different. If, however, their risk attitudes are similar, countries opt for shallower trade integration − either a shallow CU or a FTA − if they face low levels of uncertainty, and choose to stand alone if one country faces a sufficiently high level of uncertainty. When countries’ demands are correlated, they tend to form a deep CU if their demands are strongly negatively correlated, a FTA if their demands are strongly positively correlated and a shallow CU when their demands are weakly correlated. Intuitively, differences in country risk attitudes (i.e., their degree of risk aversion) act as an additional source of comparative advantage. Deeper integration − particularly via a CU − permits less risk averse members to essentially export their relative partiality for risk to more risk averse partners, thereby effectively providing the latter with insurance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".