Using regression analysis and calculus, economists measure the income concentration of a population by way of a metric known as a Gini index. A Gini index is a number ranging from 0 to 1, where 0 indicates perfect income equality in the measured population and 1 indicates perfect inequality in the measured population. In order to compute a Gini index, we draw a graph that plots the bottom x percent of income earners in a population against the percent of the income (from all available income) that the group has earned. Unlike other Gini index studies, we compute a Gini index in which we omit the top 5% of income earners in each population. As a result, our study considers income distribution in the bulk of a population without the skewed effect of high-income outliers. In addition, our study further differs from other Gini index studies because we compute Gini indexes for subsets of the United States populations by race including: Whites, non-Hispanic Whites, Blacks, Asians, and Hispanics. All of our data hails from the United States Census Bureau. Some questions that this study answers are: how successful are individual racial groups in the United States in achieving income equality? Are some racial groups faring better than others in achieving income equality? By understanding how race affects income distribution, we may inform public policy experts who are in a position to affect positive change in regard to income equality. (Author abstract)Sherwood, R. (2015). Gini index by racial groups in the United States. Retrieved from http://academicarchive.snhu.ed
