The Impact of Multi-Factor Productivity on Income Inequality

Numerous empirical studies suggest that a technology change is associated with an increase in income inequality. The Gini coefficient (or the Gini index) is commonly calculated to quantify income inequality and analyze the relationship between inequality and other economic variables. However, the availability of Gini index data in a time series (e.g., five-year data) is sparse. Thus, it is difficult to study dynamic effects in panel data. This study utilizes the relative share of income as an inequality measure to analyze the interactions between cross-country income inequality and multi-factor productivity. Additional economic variables are also considered to inform the analysis further. Using the relative share of income enables observation of the long-term relationship dynamics between the two variables of interest because the necessary data are available for individual countries. Panel data are also available for cross-country factors. This study is the first to show that multi-factor productivity has a relationship with income inequality, based on understanding the static and dynamic effects. This study defines a model with some lags of the variable to capture the “dynamic effects.” The estimation method is the panel vector autoregression (Sigmund & Ferstl (2019)[35]) with generalized method of moments (Blundell & Bond (1998)[4]). This method determines the multi-period structure of multi-factor productivity and income inequality. Overall, this approach identifies the dynamic effects of multi-factor productivity on income distribution, which is a novel finding that requires further analysis.