Putting the Lid on Lobbying: Tariff Structure and Long-Term Growth when Protection is for Sale
Bibliographic record
Abstract
It has long been recognized that a country's tariffs are the endogenous outcome of a rent-seeking game whose equilibrium reflects national institutions.Thus, the structure of tariffs across industries provides insights into how institutions, as reflected in tariff policies, affect long-term growth.We start with the commonplace perception among politicians that protection of skill-intensive industries generates a growth-enhancing externality.Modifying the Grossman-Helpman protection for sale model to allow for this, we make two predictions.First, a country with good institutions will tolerate high average tariffs provided tariffs are biased towards skill-intensive industries.Second, there need not be any relationship between average tariffs and good institutions.Using data for 17 manufacturing industries in 59 countries over approximately 25 years, we find that average tariffs are uncorrelated with output growth and that the skill-bias of tariff structure is positively correlated with output growth.We interpret this to mean that countries grow faster if they are able and willing to put a lid on the rent-seeking behaviour of special interest lobby groups.We show that our results are not compatible with explanations that appeal to (1) externalities per se, (2) initial industrial structure that is skewed towards skill-intensive industries, or (3) the effects of broader institutions such as rule of law and control of corruption.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".