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AIIB Investment and Economic Development of India: The Case of the Gujarat Road Project

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  • Jinxi Chen

    (Nanyang Centre for Public Administration, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore)

  • Bowen Cai

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

The purpose of this study is to verify whether the transportation infrastructure investment carried out by the Asian Infrastructure Investment Bank (AIIB) has promoted the economic development of its recipient countries. Since the establishment of the AIIB, its investments in infrastructure development, aimed at promoting economic growth in Asian developing countries, have garnered considerable attention. This study selects India, the largest recipient country of the AIIB, as the research object and chooses the Gujarat Road Project as the research case, since it is a completed infrastructure construction investment project in the transportation field. This paper provides an overview of the project’s operation and summarizes key factors in the project’s implementation. In the data analysis section, the per capita GDP is selected as the explained variable to measure economic development, and the LASSO regression method is used to select several variables that affect economic development. Moreover, the random forest model is used to obtain the causal relationship between road construction and the per capita GDP from 2001 to 2022. The results indicate that road construction in India has a significant positive effect on per capita GDP growth, the Gujarat Road Project supported by the AIIB also has a positive effect on per capita GDP growth, and this effect is stronger than that at the national level. The main contribution of this work is the validation of the investment strategy of the AIIB and the quantification of the economic contribution of AIIB investment projects to the local area.

Suggested Citation

  • Jinxi Chen & Bowen Cai, 2024. "AIIB Investment and Economic Development of India: The Case of the Gujarat Road Project," JRFM, MDPI, vol. 17(2), pages 1-25, February.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:2:p:64-:d:1335255
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    References listed on IDEAS

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    1. Aggarwal, Shilpa, 2018. "Do rural roads create pathways out of poverty? Evidence from India," Journal of Development Economics, Elsevier, vol. 133(C), pages 375-395.
    2. Jameel Khadaroo & Boopen Seetanah, 2009. "The Role of Transport Infrastructure in FDI: Evidence from Africa using GMM Estimates," Journal of Transport Economics and Policy, University of Bath, vol. 43(3), pages 365-384, September.
    3. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    4. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
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