Why settle?: Partisan-based explanation of investor-state dispute outcomes
Bibliographic record
Abstract
Abstract This paper seeks to explain why some investor-state dispute cases are settled before reaching the ruling stage in democracies, focusing on disputes triggered by regulatory changes made by host government. Our argument is grounded in the domestic politics of the respondent country, specifically the partisan orientation of the incumbent government. When faced with regulatory investor claims, respondent governments must balance protecting domestic social welfare with promoting investment. Our theory is that right-leaning governments are more likely to settle because they are more willing to make regulatory concessions to appease foreign investors and attract investment. In contrast, left-leaning governments prefer arbitral rulings over settlements, as they view settlements as a capitulation to foreign investors’ demands at the expense of public welfare. Using original data from 335 investor-state disputes involving democratic host countries between 1994 and 2020, we find support for this claim. Moreover, we provide qualitative evidence from the investor-state dispute between TC Energy Corporation, a Canadian energy company, and the United States, as well as the investor-state disputes triggered by Argentina’s 2002 emergency measures, to confirm our hypothesized causal pathway linking government partisanship to the likelihood of settlement.
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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 | 0.001 |
| 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) | 0.002 | 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".