IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i2p64-d1335255.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/2/64/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/2/64/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    2. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    3. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
    4. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    5. Esiyok, Bulent, 2011. "Determinants of foreign direct investment in Turkey: a panel study approach," MPRA Paper 36568, University Library of Munich, Germany.
    6. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
    7. Plaxedes Gochero & Seetanah Boopen, 2020. "The effect of mining foreign direct investment inflow on the economic growth of Zimbabwe," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 9(1), pages 1-17, December.
    8. Buch, Claudia M. & Vogel, Edgar & Weigert, Benjamin, 2018. "Evaluating macroprudential policies," ESRB Working Paper Series 76, European Systemic Risk Board.
    9. Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
    10. Kashif Munir & Mehwish Iftikhar, 2021. "Impact of Transport and Technological Infrastructure in Attracting FDI in Pakistan," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 93-106.
    11. Kwon, Yujin & Park, Sung Y., 2023. "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, vol. 84(C).
    12. Victor, Kidake, 2018. "Infrastructure and Foreign Direct Investment in Kenya: A Time Series Analysis 1980-2015," MPRA Paper 98014, University Library of Munich, Germany.
    13. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    14. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    15. Yu Xia & Ta Xu & Ming-Xia Wei & Zhen-Ke Wei & Lian-Jie Tang, 2023. "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    16. Wenting Zhang & Shigeyuki Hamori, 2020. "Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?," Energies, MDPI, vol. 13(9), pages 1-22, May.
    17. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
    18. Morgan, Stephen & Farris, Jarrad & Johnson, Michael E., 2022. "Foreign Direct Investment in Africa: Recent Trends Leading up to the African Continental Free Trade Area (AfCFTA)," USDA Miscellaneous 329077, United States Department of Agriculture.
    19. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).
    20. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:17:y:2024:i:2:p:64-:d:1335255. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.