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Transport infrastructure connectivity and conflict resolution: a machine learning analysis

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Abstract

Transport infrastructure connectivity (TIC) has strong endogeneity issues, making it difficult to directly assess its impact on local conflict resolution. This study presents new evidence of the effects of TIC on conflict resolution by conducting a natural experiment and applying machine learning methods to overcome the endogeneity issue. Based on global conflict data from 2010 to 2017, the empirical results show the following: (1) TIC can significantly improve countries’ global ranking for conflict resolution; in particular, the marginal benefit of developed countries is greater than that of developing countries. (2) The mechanism behind this effect is the promotion of trade facilitation, a more balanced employment ratio across genders, and improved income levels through TIC, which further enhances the conflict governance capacity of countries. In light of the findings, policy-makers should consider the opportunity to combine TIC with greater security for the realization of economic and social benefits, taking into account the significant opportunities for developing countries and the importance of balance across genders and income levels.

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Source: HKTDC Research, http://china-trade-research.hktdc.com/business-news/article/One-Belt-One-Road/The-Belt-and-Road-Initiative/obor/en/1/1X000000/1X0A36B7.htm

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Authors and Affiliations

  1. The School of Public Policy and Management, Tsinghua University, Beijing, China

    Ji Luo

  2. The School of Economics, Peking University, Beijing, China

    Guijun Wang

  3. School of International Trade and Economics, Anhui University of Finance and Economics, Bengbu, China

    Guangqin Li

  4. The School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China

    Greta Pesce

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Correspondence to Guangqin Li.

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Luo, J., Wang, G., Li, G. et al. Transport infrastructure connectivity and conflict resolution: a machine learning analysis. Neural Comput & Applic 34, 6585–6601 (2022). https://doi.org/10.1007/s00521-021-06015-5

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