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Poverty, Income Inequality and Economic Growth in U.S. Counties: - page 11 / 33





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3 Measuring Inequality

One of the important contributions of this study, I believe, is the construction of

Gini index for all the U.S. counties for 1979 and 1999 to measure income inequality.

Income or wealth inequality measures the distribution of income or wealth in an

economy. Specifically, it measures whether only a few people hold a given amount of

resources. While there are different measures of inequality, one of the most popular and

oldest measures is the Gini Coefficient, which is constructed using the Lorenz Curve. A

Lorenz curve sorts the observation in increasing order and plots the cumulative

percentage of resources against the cumulative percentage of the population. Thus, a

Lorenz curve measures the distribution of income in an economy, with the diagonal

representing equal distribution (called the line of equality).

The Gini coefficient is two times the area enclosed between the line of equality

and the Lorenz curve. It measures the fraction of differences in all possible income

groups to total income, and the value lies between zero and one. With perfect equality,

the Gini coefficient is zero and with perfect inequality (one person holds all the

resources), the value is one. Gini index satisfies basic principles of inequality measure

such as anonymity, scale independence, population independence, transfer properties, and

possesses many desirable properties of an inequality measure such as boundedness. Thus,

I calculate the Gini coefficient for all the U.S. counties to measure inequality and only a

few studies have constructed Gini index at the U.S. county-level, although for answering

different questions (Nielson and Alderson 1997, Ngarambe et al. 1998).

Rupasingha and Goetz (2007), and Rupasingha et al. (2002) use the ratio of mean

over median as a measure of inequality, which suffers from some limitations. First,


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