Determinants of Income Inequality in Ethiopia

Assefa Belay
International Journal of Economics and Business Administration, Volume X, Issue 2, 178-200, 2022
DOI: 10.35808/ijeba/772


Purpose: The main objective of this study is to analyze determinants of income inequality in Ethiopia from (1988 to 2018). Design/methodology/approach: This study used quantitative research approach and an explanatory research design in order to achieve its objectives. The method of analysis was econometrics analysis. This paper used Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM) in order to investigate the long-run and short run relationship between the dependent variable (income inequality) and its determinants. To test stationary Augmented Dickey –Fuller (ADF) test and Phillpes Perron (PP) test were used. The error correction coefficient, estimated at -0.84277 is highly significant, has the correct negative sign, and imply a very high speed of adjustment to equilibrium. Findings: According to the econometrics analysis, real GDP per capita and unemployment rate are the main determinants of income inequality for Ethiopia based on ARDL model estimation result, R-squared is 0.7568. This implies that 75.68 % of the income inequality function is explained by the selected explanatory variables. If the value of R-Squared is higher, that model is the greatest the goodness of fit. Therefore, is the R-Squared in the regression model reveals that there is good fitness of value for a given result. The overall model is statistically significant because of P (F- Statistics) is 0.0009, which is less than 5% percent. Practical implications: According to the research, the paper gives some policy recommendations. The government or other responsible bodies should focus on the country’s growth and development, decreasing unemployment rate, inflation rate, the expansion of education access and the support of the social state. Originality value: This is an original research for the state of Ethiopia. The use of specific econometric techniques, ARDL, ECM, VIF, make this research unique.

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