Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learning

The system of regional ethnic autonomy has endowed ethnic minority areas with greater autonomy in economic and social development, and exploring their regional economic development has a unique significance. This study used panel data from 120 ethnic autonomous counties in China from 2005 to 2021 to...

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Main Authors: Chang Gao, Lianjun Yao, Xueyuan Wang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Cogent Economics & Finance
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/23322039.2025.2522332
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author Chang Gao
Lianjun Yao
Xueyuan Wang
author_facet Chang Gao
Lianjun Yao
Xueyuan Wang
author_sort Chang Gao
collection DOAJ
description The system of regional ethnic autonomy has endowed ethnic minority areas with greater autonomy in economic and social development, and exploring their regional economic development has a unique significance. This study used panel data from 120 ethnic autonomous counties in China from 2005 to 2021 to construct a double machine learning model to empirically test the impact of cross-border railway trade on urban development in ethnic autonomous counties and its mechanism of action. The results show: (1) Cross-border railway trade can significantly promote urban development in ethnically autonomous counties. Each unit increase can promote the urban development of ethnically autonomous counties by 35%. (2) The cross-border railway trade promotes urban development through three pathways: financial development, knowledge spillover, and industrial upgrading. (3) Cross-border railway trade has a better urban development effect on the ‘intermixed living on a large scale’ autonomous counties than the ‘concentrated settlement on a smaller scale’ autonomous counties; and it has a more significant urban development effect on the autonomous counties located in the western transportation corridor and benefited by the policy. The conclusions provide useful policy implications for economic development in regions with concentrated ethnic minorities.
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institution OA Journals
issn 2332-2039
language English
publishDate 2025-12-01
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spelling doaj-art-e67e801fbc5a4675b911ebb375e2a9cb2025-08-20T02:22:02ZengTaylor & Francis GroupCogent Economics & Finance2332-20392025-12-0113110.1080/23322039.2025.2522332Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learningChang Gao0Lianjun Yao1Xueyuan Wang2The School of Economics, Zhejiang Gongshang University, Hangzhou, ChinaZhejiang University of Water Resources and Electric Power College, Hangzhou, ChinaThe School of Economics, Zhejiang Gongshang University, Hangzhou, ChinaThe system of regional ethnic autonomy has endowed ethnic minority areas with greater autonomy in economic and social development, and exploring their regional economic development has a unique significance. This study used panel data from 120 ethnic autonomous counties in China from 2005 to 2021 to construct a double machine learning model to empirically test the impact of cross-border railway trade on urban development in ethnic autonomous counties and its mechanism of action. The results show: (1) Cross-border railway trade can significantly promote urban development in ethnically autonomous counties. Each unit increase can promote the urban development of ethnically autonomous counties by 35%. (2) The cross-border railway trade promotes urban development through three pathways: financial development, knowledge spillover, and industrial upgrading. (3) Cross-border railway trade has a better urban development effect on the ‘intermixed living on a large scale’ autonomous counties than the ‘concentrated settlement on a smaller scale’ autonomous counties; and it has a more significant urban development effect on the autonomous counties located in the western transportation corridor and benefited by the policy. The conclusions provide useful policy implications for economic development in regions with concentrated ethnic minorities.https://www.tandfonline.com/doi/10.1080/23322039.2025.2522332Cross-border railway tradeChina-Europe railway expressethnic autonomous countiesdouble machine learningurban developmentEconomics
spellingShingle Chang Gao
Lianjun Yao
Xueyuan Wang
Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learning
Cogent Economics & Finance
Cross-border railway trade
China-Europe railway express
ethnic autonomous counties
double machine learning
urban development
Economics
title Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learning
title_full Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learning
title_fullStr Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learning
title_full_unstemmed Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learning
title_short Cross border railway trade and urban development in minority ethnic autonomous regions: causal analysis with double machine learning
title_sort cross border railway trade and urban development in minority ethnic autonomous regions causal analysis with double machine learning
topic Cross-border railway trade
China-Europe railway express
ethnic autonomous counties
double machine learning
urban development
Economics
url https://www.tandfonline.com/doi/10.1080/23322039.2025.2522332
work_keys_str_mv AT changgao crossborderrailwaytradeandurbandevelopmentinminorityethnicautonomousregionscausalanalysiswithdoublemachinelearning
AT lianjunyao crossborderrailwaytradeandurbandevelopmentinminorityethnicautonomousregionscausalanalysiswithdoublemachinelearning
AT xueyuanwang crossborderrailwaytradeandurbandevelopmentinminorityethnicautonomousregionscausalanalysiswithdoublemachinelearning