Comparative Advantage, Capital Destruction, and Hurricanes
Bibliographic record
Abstract
The comparative advantage of countries evolves over time, yet firms do not continuously adapt their production structure to this evolution. This slow adaptation may be due to high adjustment costs, such as those associated with the disposal of existing physical capital. In practice, these costs may explain why we observe that countries export goods at both ends of the comparative advantage spectrum. This article investigates what happens if the cost of adjusting to the dynamics of comparative advantage is unexpectedly reduced. We use hurricanes to evaluate whether a negative exogenous shock to firms' physical capital leads to a reorganization of exports towards comparative advantage industries. Using a panel of 46 countries and 4-digit industries over the period 1980–2000, we show that the effect of hurricanes on exports is monotonically increasing in comparative advantage. Specifically, export levels drop for industries with a low comparative advantage and grow for industries with a high comparative advantage. Our results also indicate that the process of shifting resources towards higher comparative advantage industries intensifies within the three years following the shock. These findings suggest that if the opportunity cost of adjustment decreases, firms tend to build back better and move up the spectrum of comparative advantage.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".