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Exploring the impact of oil revenue on Nigeria's economic growth: A non‐linear autoregressive distributed lag model.

  • Published In: OPEC Energy Review, 2024, v. 48, n. 4. P. 293 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Popoola, Olufunke Mercy; Mutai, Noah Cheruiyot; Dervishaj, Valdrin; Nguyen, Cuong Manh; Kumari, Sushma; Bhatia, Gunjan 3 of 3

Abstract

The influence of oil proceeds on an economy remains a subject of debate among scholars. Most scholars agree that income from oil has direct effect on economic expansion. We explore the impact of revenue from oil on economic growth in Nigeria from 1986 to 2016. Data were collected from the Nigerian Central Bank and the World Data Indicators. We employed a non‐linear autoregressive distributive lag approach to analyse the data. The outcome exposes a direct and significant association between revenue from oil and GDP both in the short and long term. Trade openness exhibits a significant negative effect on GDP from both short‐ and long‐term perspectives. Gross capital formation directly influences GDP in the short and long run, with significant impacts primarily in the long term. The study suggests that revenue from oil has a direct and significant impact on the Nigerian economy during the period from 1981 to 2016. We recommend that the Nigerian government create a conducive environment for private investment to thrive, thereby sustaining the nation's growth potential. Additionally, the government should wisely utilise revenue from oil to revitalise non‐booming productive sectors in the economy, thus diversifying revenue sources. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:OPEC Energy Review. 2024/12, Vol. 48, Issue 4, p293
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:1753-0229
  • DOI:10.1111/opec.12314
  • Accession Number:183913004
  • Copyright Statement:Copyright of OPEC Energy Review is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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