Dynamic Relationship between Chinese FDI, Agricultural and Economic Growth in West African: An Application of the Pool Mean Group Model

Kankou Hadia Fofana*, Enjun Xia, Mamadou Bado Traore

*Corresponding author for this work

    Research output: Contribution to journalConference articlepeer-review

    8 Citations (Scopus)

    Abstract

    This study is an attempt to test the long run relationship between Chinese foreign direct investment (FDI), agricultural and economic growth in host countries is known to have an important role in economic literature suffering from unemployment problems, food security and lack of technological progress. This paper examines this issue for West Africa by applying Pool Mean Group (PMG) and panel-Granger causality Models over the period of 2003 to 2015. The Pool Mean Group tests suggest cointegration between China FDI, economic growth, domestic investment and land agricultural used The article indicates that Chinese FDI, domestic investment and land agricultural spur economic growth contrary to some studies, which found that China FDI does not cause economic growth. The results also show that there is no significant Panel-VECM Granger causality from China FDI to economic growth, from economic growth to Chinese FDI, from agricultural to economic growth and from economic growth to agricultural. This implies that the increment in Chinese FDI inflows would definitely lead to increasing the economic growth, domestic investment and agricultural land in West Africa.

    Original languageEnglish
    Article number012066
    JournalJournal of Physics: Conference Series
    Volume1060
    Issue number1
    DOIs
    Publication statusPublished - 23 Jul 2018
    Event2018 2nd International Conference on Data Mining, Communications and Information Technology, DMCIT 2018 - Shanghai, China
    Duration: 25 May 201827 May 2018

    Fingerprint

    Dive into the research topics of 'Dynamic Relationship between Chinese FDI, Agricultural and Economic Growth in West African: An Application of the Pool Mean Group Model'. Together they form a unique fingerprint.

    Cite this