Effects Of Income Inequality
A NOTE ON THE DIFFERENTIAL REGIONAL EFFECTS OF INCOME INEQUALITY: EMPIRICAL EVIDENCE USING U.S. COUNTY-LEVEL DATA is an academic article written by Bebonchu Atems. This article or Journal comes from the Journal of Regional Science Vol. 53, and was published November fourth, 2013. Bebonchu Atems (assistant professor of Economics and Financial Studies) is part of the Department of Economics School of Business at Clarks University. When studies are done on the U.S level on relationship between variables such as income inequality and economic growth, it is normally done on an aggregate level (meaning all regions, areas, economic groups, characteristics and more are clumped together to produce one output). However, this can sometimes be misleading
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One advantage of using county level data instead of aggregate or cross country data is that the result can give more comprehensive analysis to the link between inequality and growth. (Focuses more short term). Mr. Atems first attempt at illustrating this advantage was by the use of images (Figures) that show inequality, growth and their relationship in the different regions across the U.S. The first (Figure 1) shows the different variations in the U.S County-Level per capita income growth rate. Panel A in Figure one (the 1970s) show that there has always been spatial differences in real per capita income growth, however, 30 years later (Panel B) it is more prevailing. Figure 2 shows the spatial variation in inequality across the US counties. Inequality was generally low with patches of high inequality in the 1970s (Figure 2, Panel A), however, in the 2000s (Panel B, Figure 2), inequality rose across many counties. Mr Atems split the sample into metropolitan and non-metropolitan counties and saw that there was a significant positive relationship for urban counties and a significant negative relationship for rural counties, which I related to (because the economy of now urban areas did grow faster than rural areas over the last 30-40 years, and with its positive relationship with inequality explains for inequality increase as well). This variation with urban and rural areas adds more weight to the main argument by Mr. Atems. Figure 3 shows scatter plots of average annual real per capita growth rate against inequality for metro and non-metropolitan areas and the eight BEA regions (which were mentioned earlier). Panel A simply show these scatter plots while Panel B shows the relationship in the changes of the two
One advantage of using county level data instead of aggregate or cross country data is that the result can give more comprehensive analysis to the link between inequality and growth. (Focuses more short term). Mr. Atems first attempt at illustrating this advantage was by the use of images (Figures) that show inequality, growth and their relationship in the different regions across the U.S. The first (Figure 1) shows the different variations in the U.S County-Level per capita income growth rate. Panel A in Figure one (the 1970s) show that there has always been spatial differences in real per capita income growth, however, 30 years later (Panel B) it is more prevailing. Figure 2 shows the spatial variation in inequality across the US counties. Inequality was generally low with patches of high inequality in the 1970s (Figure 2, Panel A), however, in the 2000s (Panel B, Figure 2), inequality rose across many counties. Mr Atems split the sample into metropolitan and non-metropolitan counties and saw that there was a significant positive relationship for urban counties and a significant negative relationship for rural counties, which I related to (because the economy of now urban areas did grow faster than rural areas over the last 30-40 years, and with its positive relationship with inequality explains for inequality increase as well). This variation with urban and rural areas adds more weight to the main argument by Mr. Atems. Figure 3 shows scatter plots of average annual real per capita growth rate against inequality for metro and non-metropolitan areas and the eight BEA regions (which were mentioned earlier). Panel A simply show these scatter plots while Panel B shows the relationship in the changes of the two