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An empirical analysis of the relationship between FDI and economic growth in Tanzania

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  • Benedict Huruma Peter Mwakabungu
  • Jignesh Kauangal

Abstract

This study examines causal relationship between foreign direct investment (FDI) inflows and economic growth in Tanzania during 1990–2020. As financial development and trade were not incorporated in extant studies, we included them as intermediate variables because of their intermediation role in this study. FDI inflow is considered an important economic growth catalyst in developing economies. Neoclassical growth theories claim that it enhances economic growth by augmenting capital stock and technology. According to the neoclassical theories, FDI does not enhance the long-run growth rate but instead is related to the level of output. However, empirical evidence is rather mixed, with some supporting the neoclassical theoretical views on economic growth, while others opposing them. We employ the autoregressive distributed lag model and Granger causality tests to analyze the relationship. The results indicate that there exists a long-run relationship among the variables under considerations in Tanzania. Furthermore, the finding reveals positive and statistically significant unidirectional causality running from FDI inflow to economic growth in Tanzania in the long and short run. Hence, we conclude that Tanzania should emphasize FDI-led growth policies to enhance economic growth to realize the desired economic objectives.

Suggested Citation

  • Benedict Huruma Peter Mwakabungu & Jignesh Kauangal, 2023. "An empirical analysis of the relationship between FDI and economic growth in Tanzania," Cogent Economics & Finance, Taylor & Francis Journals, vol. 11(1), pages 2204606-220, December.
    • Handle: RePEc:taf:oaefxx:v:11:y:2023:i:1:p:2204606
    DOI: 10.1080/23322039.2023.2204606
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    Cited by:

    1. Md. Monirul Islam & Arifa Jannat & Kentaka Aruga & Md Mamunur Rashid, 2024. "Forecasting Foreign Direct Investment Inflow to Bangladesh: Using an Autoregressive Integrated Moving Average and a Machine Learning-Based Random Forest Approach," JRFM, MDPI, vol. 17(10), pages 1-20, October.

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