Essays on the interaction between multilateral tariff bindings and the formation of preferential trade agreements
Bibliographic record
Abstract
This dissertation studies the effect of continual reduction in the tariff bindings and its implications on the static and dynamic formation of preferential trade agreements (PTAs). Underlying trade model is a three country \competing exporters" model. First, utilizing a static game of endogenous trade agreement formation between three countries, we examine the effects of continual reduction in tariff bindings on the role of PTA formation in attaining global free trade. We show that, in the free trade agreement (FTA) formation game, when countries are completely symmetric, free trade always obtains as the coalition-proof Nash equilibrium (CPNE) of the FTA game. Under the customs union (CU) game, CU members exercise an exclusion incentive and free trade fails to be a CPNE. When countries are asymmetric with respect to their comparative advantage, the country with a weaker comparative advantage has an incentive to free ride on trade liberalization of the two others and continual reduction in tariff bindings facilitates FTA formation in attaining global free trade. Next, we employ a three country dynamic model of PTA formation where countries form PTAs over time and investigate the impact of multilateral tariff binding liberalization on the equilibrium extent of FTA and CU formation in isolation. When forming FTAs under relatively high tariff bindings, a myopic free riding incentive of FTA non-members constrains FTA formation. Thus, tariff binding liberalization can facilitate FTA expansion to global free trade. However, when forward looking countries do not value this myopic free riding incentive, tariff binding liberalization can impede FTA expansion to global free trade. In our CU game, CU formation proceeds to global free trade only for relatively high tariff bindings. Finally, we examine the PTA game where countries endogenously choose between CU and FTA formation. Under such a game, we show that the equilibrium emergence of CUs can prevent global free trade that would otherwise occur through FTAs. In contrast, the equilibrium emergence of FTAs can facilitate global free trade that would otherwise not occur through CUs.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".