Essays on the Incentives to Form International Agreements between Asymmetric Countries
Bibliographic record
Abstract
Abstract: International Environmental Agreements suffer from a strong free-riding incentive that generally leads to failure of coalition formation in the economics literature and have been largely unsuccessful in practice. At the same time, preferential trade agreements (PTAs) have increasingly included elements not specifically related to trade, such as domestic policy over the environment, labour, intellectual property, health and investment. One area that has received a great deal of attention is deep agreements is the environment. In this dissertation, we develop three models of asymmetric countries, that include both trade and environmental externalities and study the incentives to form international trade agreements between those asymmetric countries. First, we develop a two-country, two-good model with cross-border negative spillovers and perfect competition in product markets. We compare shallow (trade only) and deep (trade and environment) trade agreements between those two large asymmetric countries, and we show that deep and shallow trade agreements have different outcomes in terms of world and individual countries welfares, trade specialization and environmental damages. Next, we develop two three-country, three-goods models, a “competing importers model” and a “competing exporters model”, and examine equilibrium agreements and environmental outcomes assuming (i) environmental agreements are negotiated separately, (ii) trade agreements are shallow, and (iii) trade agreements are deep. To examine the stability of endogenous deep and shallow trade agreements we set up various three stage games and use coalition-proof Nash equilibria refinement to circumvent the multiplicity of Nash equilibria. Furthermore, in order to examine the differences between shallow and deep trade agreements, we develop extended games where countries can choose between different deep and shallow PTAs and investigate how equilibrium agreements differ based on being deep or shallow. Therefore, in two different models of multiple asymmetric countries, we ask and answer the following questions: (i) how do equilibrium agreements differ when countries choose deep rather than shallow agreements?; (ii) given the choice between deep and shallow trade agreements, would countries prefer to incorporate an environmental clause into their equilibrium trade deal?; and (iii) what are the implications of including non-trade elements in PTAs for the pursuit of global free trade?
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".