Neighborhood Sorting, Metros, and Tomorrow’s Labor Force

Introduction

Neighborhoods matter for children's economic outcomes. After all, many of the factors that develop personal attributes such as depth of knowledge, skills, and education-components of what economists call "human capital"-are concentrated at the local level. Quality neighborhood schools, strong peer groups, and public safety are a few examples. Greater human capital translates into more labor productivity and higher individual earnings in the future. For instance, attending high-quality schools increases the probability that a child will enroll in and complete college, and attaining a college degree, on average, leads to higher lifetime labor earnings. While quantifying exactly how much these neighborhood effects contribute to future labor productivity and earnings is a tricky exercise, studies such as Ananat (2011), Altonji and Mansfield (2018), and Aliprantis and Richter (2020) suggest that their contributions could be quite substantial. Homebuyers also appear to appreciate this phenomenon, as evidenced by higher prices for housing in communities with high-quality school systems (Seo and Simons, 2009). Given the significance of neighborhood effects, the way households sort into neighborhoods could have implications for aggregate labor productivity by altering opportunities children have to enhance their human capital.

In this Economic Commentary, we estimate an index of neighborhood quality and examine the distribution of households by income, race, and ethnicity into neighborhoods across US metro areas, building on prior work by Aliprantis et al. (2022). Using data from the 2017-2021 American Community Survey (ACS),¹we find that Black households in many metro areas face a significant tradeoff between a neighborhood's Black population share and the quality of the neighborhood, with many high-income Black households residing in lower-quality neighborhoods than is the case for white households of similar income. These patterns are similar to those documented by Aliprantis et al. (2022) using earlier data, suggesting that these neighborhood patterns are deeply entrenched and were unaffected by migration across metro areas associated with the pandemic.²Looking at Hispanic households reveals similar patterns: in most metro areas, Hispanic households appear to trade neighborhood quality for neighborhood representation.³Overall, there are large discrepancies in our neighborhood quality measure between where white and Black households live and between where white and Hispanic households live.⁴

Proxying for Neighborhood Quality with an SES Index

We follow Aliprantis et al. (2022) to look across the 54 largest metro areas in the United States and assign to each neighborhood (as determined by census tract) a measure of socioeconomic status (SES). This SES measure is an index based on neighborhood characteristics that are strongly correlated with the future labor market outcomes of children (Aliprantis and Richter, 2020). These characteristics include measures of the educational attainment (for example, four-year degree completion) and labor income (for example, the poverty rate and the employment-to-population ratio) of residents in a neighborhood and are strongly correlated with measures of school quality (Bayer, Ferriera, and McMillan 2007; Bayer, Fang, and McMillan, 2014), with low SES being associated with reduced school quality. SES also strongly correlates with neighborhood measures of upward intergenerational mobility (Chetty et al., 2020).

When it comes to SES, generally it is the case that higher-income households reside in higher-SES neighborhoods. This is shown in Figure 1, which plots the relationship between household income and neighborhood SES for white, Black, and Hispanic households. All households are first sorted according to their income and then assigned to decile bins, with the first the poorest and the tenth decile the richest.