Labor Versus Capital in Trade-Policy Determination: The Role of General-Interest and Special-Interest Politics
Bibliographic record
Abstract
Trade policy depends on the extent to which the government wants to redistribute income as well as on a country's overall factor endowments and their distribution.While the government's desire to redistribute income itself is dependent on asset distribution, it is to a large extent also driven by the partisan nature of the government, i.e., whether it is pro-labor or pro-capital.Using cross-country data on factor endowments, inequality and government orientation, we find that, conditional on inequality, left-wing (pro-labor) governments will adopt more protectionist trade policies in capitalrich countries, but adopt more pro-trade policies in labor-rich economies than right-wing (procapital) ones.Also, holding government orientation constant, higher inequality is associated with higher protection in capital-abundant countries while it is associated with lower protection in laborabundant countries.These results are consistent with the simultaneous presence of both general-as well as special-interest politics as determinants of protection within a two-factor, two-sector Heckscher-Ohlin framework.Overall, various statistical tests support an umbrella model (that combines both the general-interest as well as special-interest models) over each of the individual models.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".