The Impacts of High-Speed Railway on Urban GDP and Its Agglomeration: Evidence from China
Bibliographic record
Abstract
This study develops difference-in-differences (DID) models to examine the direct and indirect effects of high-speed railway (HSR) operation on the urban gross domestic product (GDP) and its agglomeration. The period from 2010 to 2019 is selected as the study period, and 30 HSR-operating cities across the Chinese mainland are chosen as the study sites to be investigated. Individual fixed effects and time fixed effects are introduced to panel data models to account for the heterogeneities between cities and the endogeneities of explanatory variables. Estimation results suggest that the operation of HSR can improve the development of the urban GDP by accelerating the migration of population to HSR-operating cities, promoting the upgrading of the urban industrial structure, and improving the level of urban scientific research. Moreover, the level of urban economic agglomeration can also be improved as a result of HSR. However, the ability of HSR-promoting economic development is more significant in cities with more developed economies. Therefore, when formulating a sensible plan for the development of HSR, policymakers should prioritize the construction of HSR in more developed cities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".