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Financial development and oil resource abundance–growth relations: evidence from panel data

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Abstract

This study investigates whether financial development dampens the negative impact of oil resource abundance on economic growth. Because of substantial cross-sectional dependence in our data, which contain a core sample of 63 oil-producing countries from 1980 through 2010, we use the common correlated effect mean group (CCEMG) estimator to account for the high degree of heterogeneity and drop the outlier countries. The empirical results reveal that oil resource abundance affects the growth rate in output contingent on the degree of development in financial markets. More developed financial markets can channel the revenues from oil into more productive activities and thus offset the negative effects of oil resource abundance on economic growth. Thus, better financial development can reverse resource curse or enhance resource blessing in oil-rich economies.

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Notes

  1. By focusing on the Niger Delta region of Nigeria, a country that is rich in oil, Elum et al. (2016) demonstrate that oil exploitation has increased the rate of environmental degradation and has perpetuated food insecurity as a result of death of fish and crops as well as loss of farm lands and viable rivers for fishing activities leading to loss of livelihood.

  2. With respect to the oil consumption and economic growth, Saboori et al. (2017) point out that economic growth in China and South Korea shows a positive response to oil consumption but responses negatively to the same shock in Japan.

  3. The list of countries is presented in Table 8 (Appendix).

  4. Bank-based and market-based financial systems are two general channels through which the efficient capital allocation in a financial system occurs.

  5. The Cook’s distance outlier test is used to detect the outlier countries. A regression outlier is an observation that has an unusual value of the dependent variable Y, conditional on its value of the independent variable X. The Cook’s D measures the “distance” between Bj and Bj(−i) by calculating an F test for the hypothesis that Bj = Bj(−i), for j = 0,1,…,k. An F statistic is calculated for each observation as follows:

    $$ {D}_i=\frac{E_i^{\prime 2}}{k+1}x\frac{h_i}{1-{h}_i} $$

    where \( {E}_i^{\prime 2} \) is the standardized residual and hi is the hat-value for each observation. The first fraction measures discrepancy, and the second fraction measures leverage. There is no significance test for D i (i.e., the F value here measures only distance) but a cutoff rule of thumb is:

    $$ {D}_i>\frac{4}{n-k-1} $$

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Authors and Affiliations

  1. Department of Economics, Faculty of Economics and Management, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia

    Siong Hook Law

  2. Taylor’s Business School, Taylor’s University, 47500, Subang Jaya, Selangor, Malaysia

    Maryam Moradbeigi

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  1. Siong Hook Law
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  2. Maryam Moradbeigi
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Correspondence to Siong Hook Law.

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Responsible editor: Philippe Garrigues

Appendix

Appendix

Table 8 List of a core sample of 63 countries
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Law, S.H., Moradbeigi, M. Financial development and oil resource abundance–growth relations: evidence from panel data. Environ Sci Pollut Res 24, 22458–22475 (2017). https://doi.org/10.1007/s11356-017-9871-y

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