The twin impacts of income inequality and unemployment on murder crime in African emerging economies: A mixed models approach - Lindokuhle Talent Zungu; Thamsanqa Reginald Mtshengu (Economies, 2023)
This study analyses the dynamic impact of income inequality and unemployment on crime in a panel of 15 African countries during the period 1994-2019 using four models: the panel vector autoregression model, the generalized method of moments model, the fixed-effect model, and machine learning. These models were chosen due to their ability to address the dynamics of several entities. The variables employed for empirical investigation include income inequality, unemployment, and crime. Machine learning was adopted to find which socioeconomic issues contribute to crime between the two issues at hand. The results show that income inequality accounts for 64% of crime, making it the biggest contributor to crime. The findings further show that an unexpected shock in inequality and unemployment has a significant positive impact on crime in these countries. Even when pre-tax income held by the top 10% and male unemployment is adopted, the study yields similar results. Educational entertainment through secondary enrolment was found to increase crime, while it was found to decrease crime through tertiary enrolment at the tertiary level. Finally, economic development was found to decrease crime. From a policy perspective, the current study suggests to the government that some policies are more appropriate for addressing concerns about income inequality and unemployment (income policy or fiscal policy). Therefore, more policies targeting the distribution of income are crucial, as that might decrease income inequality while at the same time decreasing crime. In addition, policymakers should focus on addressing structural challenges through the implementation of sound structural reform policies that aim to attract investment consistent with job creation, human development, and growth in African economies.
Keywords: 330; income inequality; unemployment; African countries; crime; GMM; machine learning; PVAR