Chapter 1: Introduction
For the quantile function, the resulting growth curve is called the growth incidence curve. The height of the curve at p = 50 percent gives the growth rate of the median income. Varying p allows us to examine whether this growth rate is robust to the choice of income standard or whether the lower income standards grew at a different rate than the rest. The generalized Lorenz growth curve indicates how the lower partial means are changing over time, so that the height of this curve at p = 20 percent is the rate at which the mean income of the lowest 20 percent of the population changed over time. Finally, the general means growth curve plots the rate of growth of each general mean against the parameter a. When a = 1, the height of the curve is the usual growth rate of the mean income; a = 0 yields the rate of growth for the geometric mean, and so forth. As we will see below, each of these growth curves can be of help in understanding the link between growth and the evolution of inequality over time.
Inequality Measures and Spread The second aspect of the distribution—spread—is evaluated using a numerical inequality measure, which assigns each distribution a number that indicates its level of inequality. The Gini coefficient is the most commonly used measure of inequality. It measures the average or expected difference between pairs of incomes in the distribution, relative to the distribution size, and also is linked to the well-known Lorenz curve (discussed below). The Kuznets ratio measures inequality as the share of the income going to the top fifth divided by the income share of the bottom two-fifths of the population. Finally, the 90/10 ratio is the income at the 90th percentile divided by the 10th percentile income. It is often used by labor economists as a measure of earnings inequality. These are just a few of the many inequality measures used to evaluate income distribution. What Is an Inequality Measure? There are two main ways to understand what an income inequality measure actually gauges. The first way is through the properties it satisfies. The second makes use of a fundamental link between inequality measures and income standards. We begin with the first approach.
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