Can Highway Networks Promote Productivity? Evidence from China
Bibliographic record
Abstract
The total mileage of highways in China ranks first in the world and constitutes an important symbol of China’s modernization. Economists, however, continue to debate whether highways always promote economic growth in every region and how to assess the impact. In this paper, we first use the OD-MATRIX method to calculate the shortest highway traveling time among 332 prefecture-level cities in China from 2000 to 2013. It is shown that the reduction of traveling time brought by highway construction significantly improves enterprise productivity. Second, to further explore the mechanism at work, we apply market potential approach to examine its effect on productivity. It is found that on average, the enhanced market potential induced by highway construction in China positively affect enterprise productivity. Finally, we calculate urban centrality via the space gravity model and conduct a sample regression according to the rank of urban centrality. Interestingly, we find that the impact of highways on productivity varies depending on cities’ degree of urban centrality. Highways have a positive impact in high-centrality cities but a negative impact in low-centrality cities. This correlation can be explained, in turn, by factors that include labor and capital flow from low-centrality cities to high-centrality cities.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".