Is there a relationship between inequality and terrorism? Evidence from a semi-parametric approach

ABSTRACT

Understanding links between income inequality and terrorism is crucially important for social scientists. The current empirical literature suggests that this relationship is positive but inconclusive. In this paper we use a semi-parametric approach to model the relationship between inequality and terrorism, in order to identify nonlinearities in the relationship. We use top income percentile shares as our measure of inequality in response to the inequality measurement literature identifying problems with the Gini. We find that after controlling for explanations of terrorism other than inequality, the positive relationship between inequality and terrorism discussed in the existing empirical literature is very weakly evident. We also find that the semi-parametric model crucially exposes the nonlinear nature of the relationship between inequality and terrorism. The results are different for developed and developing countries. We find that the relationship between inequality and terrorism is nonlinear and at most weakly positive for developed countries, with a slightly stronger positive relationship for developing and middle-income countries.

KEYWORDS:

• Terrorism
• inequality
• top income percentile shares
• semi-parametric methods

JEL CLASSIFICATION:

• C14
• D31
• D63
• D74
• O15

Acknowledgments

We are grateful to Daniel Meierrieks and Todd Sandler for providing us with data and Elliott Green for useful discussions. All errors are our own.

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and code that support the findings of this study are available from the authors upon request.

Notes

¹ We are grateful to Todd Sandler for providing us with domestic and transnational terrorism variables (1970 to 2016)

² We group the developing and middle-income countries together because their results were found to be greatly similar.

³ We use the Stata command ‘xtsemipar’ for our estimations, which includes fixed effects. We also cluster the standard errors at the country level.

⁴ A ‘rule-of-thumb’ bandwidth estimator is calculated by Stata for specifying the bandwidth for the non-parametric component. All non-parametric plots have been estimated by trimming the inequality measure, with the plot of the estimates within the 15th to 85th percentile of the inequality measure. This is because non-parametric functions can be outlier-driven near the edges of the sample domain. We thank a referee for this suggestion. Results using other terrorism indicators are available with authors.