Bibliographic record
Abstract
Abstract Many theories attempt to explain the determinants of preferential trade agreements (PTAs) and their design. Existing accounts, however, focus almost exclusively on structural or domestic factors and ignore individual leaders. In this paper, I develop and test novel theoretical claims regarding executive leaders’ prior career in business and their trade cooperation policy once in office. I construct a new dataset on the heads of the executive’s business managerial experience and test my main claims in a time-series-cross-sectional setting covering 185 countries from 1948 to 2009. To establish causality, I rely on an instrumental variable strategy and leverage exogenous transitions due to sudden deaths or terminal illness in office. The results show that businesspersons-turned-politicians are more likely to enter PTAs and are more likely to sign deeper PTAs. The relationship is further investigated in an illustrative case study of the 1988—Canada trade deal. The substantive effect of business experience is comparable to that of established factors in the literature, such as regime type, and is robust to numerous tests, specifications, subsamples, and measurements of business experience.
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 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 | 0.000 |
| 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".