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@MaxGhenis Add to these the Lorenz curve, Gini coefficient, wealth shares, and variance of log wealth. These are common measures of inequality. And I like the way that OG-USA puts these in an Inequality class object.
Lorenz curve. The Lorenz curve shows what percent of the wealth is held by what percent of the population. A 45-degree line signifies uniformly distributed wealth across the whole distribution of individuals (i.e., x% of the wealth is held by the first x% of the individuals for all x). A right angle from the lower left origin to the lower right origin to the upper right corner signifies ultimately concentrated wealth (i.e., 0% of the wealth is held by the first x% of individuals for all x except for the wealthiest individual who holds 100% of the wealth). The Gini coefficient above is defined as (area A) / (area A + area B). Zhiya Zuo has a blog post on how to plot the Lorenz curve using Python.
Wealth shares of percentiles. The percent of wealth held by individuals in the yth percentile bin, where y is defined as the x_jth percentile minus the x_ith percentile. The deciles, quintiles, and quartiles are a good examples. OG-USA uses the following quantiles [0.25, 0.25, 0.20, 0.10, 0.10, 0.09, 0.01].
Variance of log wealth. The variance of log wealth is another common measure of inequality. However, it has a problem if wealth can be zero or negative.
Wealth percentile ratios. Another measure of inequality is the ratio of wealth levels from different percentiles. For example, the 90/10 ratio is the wealth level at the 90th percentile divided by the wealth level at the 10th percentile.
@MaxGhenis I have been compiling a list of figures that I would like to be able to quickly make. Here are all the ones I have collected so far.
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