The GINI coefficient and segregation on a continuous variable

From Financialization to Sociospatial Polarization of the City? Evidence fromCanada

“…With gini coefficients over 0.193, the metropolitan areas of Toronto, Hamilton, St. John, and Calgary are shown by this measure to exhibit the highest levels of neighborhood income polarization among Canadian metropolitan areas at the end of 2009. Such levels of income segregation are comparable to (in fact, they are slightly higher than) those calculated the same way for Los Angeles (0.185) and Chicago (0.173) using the 2000 U.S. census data (Kim and Jargowsky 2005). At the other end of the 10 Population change is another variable that is highly correlated with household debt as a percentage of income.…”

“…8 The gini concentration ratio, when calculated using neighborhoods as the unit of analysis, in effect combines information on the general level of inequality among households and the degree to which such inequality is segregated among neighborhoods. Kim and Jargowsky (2005) thus argued that it is better understood as a measure of sociospatial inequality, rather than of sorting or active segregation processes, for which they offered their own index. Because each neighborhood contains a mix of incomes, the gini concentration ratio, when calculated using neighborhoods as the unit of analysis, always produces a number that is lower than the gini concentration ratio that results from analyses of all households across a city.…”

“…Because each neighborhood contains a mix of incomes, the gini concentration ratio, when calculated using neighborhoods as the unit of analysis, always produces a number that is lower than the gini concentration ratio that results from analyses of all households across a city. Typically, neighborhood-based gini concentration ratios produce levels roughly half those of aspatial gini indexes calculated using households, although this depends also on the degree of sorting (see Kim and Jargowsky 2005; MacLachlan and Sawada 1997; Walks 2013a). 9 Statistically, housing tenure accounts for 28.5 percent of the variability in credit card debt among census tracts, and 30.6 percent of the variability in mortgage debt, but only 7.5 percent of the variability in other consumer debt (r squares).…”

Space and the Measurement of Income Segregation

“…Jargowsky's (1996) neighborhood sorting index (NSI) is equal to the square root of the ratio of between‐neighborhood income variance to total income variance. Jahn, Schmid, and Schrag (1947) proposes a similar measure based on the Gini index (Gini, 1912) that has been utilized extensively to measure racial segregation and more recently by Kim and Jargowsky (2005) to measure economic segregation. Other inequality metrics such as Theil's (1967) entropy index which are decomposable into between‐ and within‐group components can also be used to measure the magnitude of I B relative to I 0 .…”

“…While the use of the Gini index in segregation measurement dates at least to Jahn, Schmid, and Schrag (1947), it has more commonly been applied toward the measurement of racial segregation. Kim and Jargowsky (2005) propose a measure of income segregation in the spirit of Jahn, Schmid, and Schrag (1947) that is constructed as the ratio of a between‐neighborhood Gini index to a Gini index of overall household income inequality. Their examination is limited to the case of between‐neighborhood income segregation, however, and does not consider the role of spatial proximity among neighborhoods.…”