Natural Resource Rents, Inequality, and Terrorism in Africa

ABSTRACT

This study examines the impact of natural resource rents on terrorism via inequality channel in 34 African economies, straddling the period 1980–2012. This study employs a negative binomial regression, in which the following findings are established: first, the unconditional impact of natural resource rents on terrorism is found to be positive across the model specifications, particularly when Gini and Theil indices are controlled for. Second, inequality has no discernable first-order impact on terrorism across the board. Third, the marginal impacts of interactions between inequality measures, specifically Gini and Theil coefficients and total natural resource rents on terrorism are significantly negative. Four, the corresponding net effects of interactions between natural resource rents and inequality (Gini and Theil coefficients) on terrorism are positive, thus lending support to earlier submission of involving all constitutive variables in the specifications for the parameters to make economic sense. The results are robust to accounting for fixed and country effects using the Poisson Pseudo maximum likelihood high-dimension fixed effects estimator. On the policy front, maintaining fairness and equity in the distribution of rents from the ‘free gifts of nature’ remains a veritable policy menu, especially for the resource-rich economies, to counteracting terrorist activities.

KEYWORDS:

• Natural resource rents
• inequality
• terrorism
• negative binomial regression
• Africa

JEL CLASSIFICATION:

• C25
• D63
• O55
• Q34

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

1. This concept was originally developed with regard to Middle Eastern oil-producing economies such as Iran and the Gulf Monarchies (Smith 2004).

2. With the exception of Ajide, Adenuga, and Raheem (2020), who examined the causal linkages among natural resource rents, political regimes and terrorism for Africa.

3. See http://tinyurl.com/qclzcn6.

4. For want of space, we do not devote much time digging deeper into a large dose of studies that had been conducted in this area.

5. The countries are Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Cote d’Ivoire, Djibouti, Egypt, Arab Rep., Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, The, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Niger, Nigeria, Rwanda, Senegal, Seychelles, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe.

6. For comprehensive discussion, see Gaibulloev and Sandler (2019, 291).

7. We decide to use Atkinson and Palma inequality measures also because both Gini and Theil do not capture tails or extreme points of the inequality distribution.

8. Except for the terrorism indicators with high correlation values, all other values are within acceptable range.

9. See online, appendix 3a-3 c

10. $Terroris{m}_{it}={\alpha }_0+{\alpha }_{1}natresren{t}_{it}+{\alpha }_{2}incomequal+{\alpha }_3(natresrent\ast incomequal{)}_{ie}+{\epsilon }_{it}$To compute marginal effect, we take the partial derivatives of the equation above as:$\frac{\mathrm{\partial }(terrorism)}{\mathrm{\partial }(natresrent)}={\alpha }_1+{\alpha }_3(incomequal{)}_{ie}+{\epsilon }_{it}$

11. The mean, minimum and maximum of Gini coefficients are 48.46, 33.23 and 59.95 obtained from the descriptive statistics table.

12. For more detailed exposition on this estimator, see Correia, Guimarães, and Zylkin (2019); Silva and Tenreyro (2011).