Infrastructure, specialization, and economic growth
Bibliographic record
Abstract
We introduce infrastructure as a cost‐reducing technology in Romer's (1987) model of endogenous growth. We show that infrastructure can promote specialization and long‐run growth, even though its effect on the latter is non‐monotonic, reflecting its resource costs. We provide evidence using data from the U.S. Census of Manufactures that suggests that the degree of specialization is positively correlated with core infrastructure, as predicted by the model. We also provide evidence from cross‐country regressions, using physical measures of infrastructure provision, that shows a robust non‐monotonic relationship between infrastructure and growth. JEL Classification: 041,050 Les auteurs introduisent l'infrastructure en tant que technologie réduisant les coûts dans un modèle de croissance endogène à la Romer (1987). On montre que l'infrastructure peut promouvoir la spécialisation et la croissance en longue péride, même si ses effets sur la croissance ne sont pas monotones et reflètent ses coûts en ressource. On montre, en utilisant les données du recensement des manufactures des Etats Unis, que le degré de spécialisation est relié positivement à l'infrastructure de base, comme le suggère le modèle. On montre aussi à l'aide de régressions transversales, utilisant des mesures physiques de l'infrastructure, qu'il existe une relation non monotone mais robuste entre infrastructure et croissance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".