Income segregation is often considered a result of the rising marks of income inequality shown racially and economically within and between social classes. In “?” (National Bureau of Economic Research, Working Paper 27045, April 2020), authors John R. Logan, Andrew Foster, Hongwei Xu, and Wenquan Zhang report that “rising income segregation has been brought into question by the observation that post-2000 estimates are upwardly biased due to a reduction in the sample sizes on which they are based.”
Fueled by job loss, foreclosure, heightened mortgage requirements, and declining asset values, income segregation, or the separating of people into different communities and neighborhoods based on income level, is on the rise in the United States. The segregation can be seen in the composition of neighborhoods, social groups, and class. Although attempts have been made to measure the effects of income inequality in residential communities across the United States, they lacked consistency. Methods of measuring income inequality and segregation are topics gaining more traction and attention in the statistical community.
As incomes and opportunities of people and families increase, particularly those of minorities, they are expected to “seek more advantaged neighborhoods.” This expectation does not apply clearly to Black families but more readily applies to Hispanic families. More factors affect the residential and social mobility of families than only increases in income. Higher income can influence neighborhood composition, both racially and economically; however, it is not the sole factor of composition.
Logan et al. point out that studies have shown that most of the “socioeconomic residential sorting seen in the last forty years occurred in the 1980s and 2000s.” The authors, while recognizing that income segregation of some families rose in the 1980s 1990s, conclude that the segregation of Black and Hispanic families was not generally higher than that of White families. They further conclude that income segregation is mostly proven by the separation seen in Hispanic families between the bottom 90 percent and top 10 percent.
Sources of data and income segregation indicators, modifications in the collection methods of public data, bias inherent to smaller sample sizes, and changes in income distribution across racial and familial lines have all contributed to inflated estimates of income segregation. Logan, Foster, Xu, and Zhang pose that “rather than focusing on why income segregation seems to be rising in parallel with growing income inequality, scholars need to give more attention to why it may not.”
Census data have long been used to measure changes among and between demography, geography, and economics. Although the base premises are partly true, further techniques for researching the data, collecting the data, and using the collected data are necessary to quantify social factors into measurable units for calculation. The quantifying of social, typically considered immeasurable, factors is needed to develop more accurate and effective measures of income inequality and subsequent segregation. Scholars are now tasked to effectively use available data sources to reflect the nature of reality, remove the bias included in smaller samples, and more accurately calculate multivariate studies to explore the nuances between race, geography, class, and income.