Article

Has Income Segregation Really Increased? Bias and Bias Correction in Sample-Based Segregation Estimates

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Abstract

Several recent studies have concluded that residential segregation by income in the United States has increased in the decades since 1970, including a significant increase after 2000. Income segregation measures, however, are biased upward when based on sample data. This is a potential concern because the sampling rate of the American Community Survey (ACS)—from which post-2000 income segregation estimates are constructed—was lower than that of the earlier decennial censuses. Thus, the apparent increase in income segregation post-2000 may simply reflect larger upward bias in the estimates from the ACS, and the estimated trend may therefore be inaccurate. In this study, we first derive formulas describing the approximate sampling bias in two measures of segregation. Next, using Monte Carlo simulations, we show that the bias-corrected estimators eliminate virtually all of the bias in segregation estimates in most cases of practical interest, although the correction fails to eliminate bias in some cases when the population is unevenly distributed among geographic units and the average within-unit samples are very small. We then use the bias-corrected estimators to produce unbiased estimates of the trends in income segregation over the last four decades in large U.S. metropolitan areas. Using these corrected estimates, we replicate the central analyses in four prior studies on income segregation. We find that the primary conclusions from these studies remain unchanged, although the true increase in income segregation among families after 2000 was only half as large as that reported in earlier work. Despite this revision, our replications confirm that income segregation has increased sharply in recent decades among families with children and that income inequality is a strong and consistent predictor of income segregation.

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... Most of the literature focuses on the impact of inequality on spatial segregation due to residential sorting (Tammaru et al. 2020;Scarpa 2015;Watson 2009;Mutgan and Mijs 2023;Watson 2006;Jargowsky and Wheeler 2017;Quillian 2012). Studies have found that residential segregation has risen steadily alongside income inequality (Reardon et al. 2018;Watson 2009;Taylor and Fry 2012), but more recent literature accounting for bias due to sampling variation within census long form and American Community Survey (ACS) data found that it has actually remained fairly stable or risen only slowly since 1990 (Logan et al. 2020;Reardon et al. 2018). In particular, evidence suggests that residential segregation is driven mostly by high-income households (Reardon and Bischoff 2011) and households with children given their distinct preferences in schools and other neighborhood resources and features (Reardon et al. 2018;Owens 2016;Bernelius and Vaattovaara 2016). ...
... Most of the literature focuses on the impact of inequality on spatial segregation due to residential sorting (Tammaru et al. 2020;Scarpa 2015;Watson 2009;Mutgan and Mijs 2023;Watson 2006;Jargowsky and Wheeler 2017;Quillian 2012). Studies have found that residential segregation has risen steadily alongside income inequality (Reardon et al. 2018;Watson 2009;Taylor and Fry 2012), but more recent literature accounting for bias due to sampling variation within census long form and American Community Survey (ACS) data found that it has actually remained fairly stable or risen only slowly since 1990 (Logan et al. 2020;Reardon et al. 2018). In particular, evidence suggests that residential segregation is driven mostly by high-income households (Reardon and Bischoff 2011) and households with children given their distinct preferences in schools and other neighborhood resources and features (Reardon et al. 2018;Owens 2016;Bernelius and Vaattovaara 2016). ...
... Studies have found that residential segregation has risen steadily alongside income inequality (Reardon et al. 2018;Watson 2009;Taylor and Fry 2012), but more recent literature accounting for bias due to sampling variation within census long form and American Community Survey (ACS) data found that it has actually remained fairly stable or risen only slowly since 1990 (Logan et al. 2020;Reardon et al. 2018). In particular, evidence suggests that residential segregation is driven mostly by high-income households (Reardon and Bischoff 2011) and households with children given their distinct preferences in schools and other neighborhood resources and features (Reardon et al. 2018;Owens 2016;Bernelius and Vaattovaara 2016). Many studies have also found that racial and ethnic differences play a role in the size of the relationship between economic inequality and residential segregation through discrimination, housing policy, or differential residential preferences (Logan et al. 2020;Reardon et al. 2018Reardon et al. , 2008Huffman and Cohen 2004;Watson 2009;Reardon and Bischoff 2011;Taylor and Fry 2012;Florida and Mellander 2018;Mulder 2013). ...
... Residential segregation by socioeconomic status is an important topic studied by urban scholars because cities around the world continue to experience significant spatially-based divisions [1][2][3][4]. Although globalization is a salient force shaping inequalities in cities [5], the role it plays in influencing socioeconomic residential segregation is subject to debate [6,7]. ...
... Although a substantial body of scholarship examines socioeconomic segregation, it is limited in at least three ways. First, the focus of this research has largely been on income-based residential segregation [3,9,10]. Only a handful of studies have investigated residential segregation by educational status [2,[11][12][13]. ...
... As noted previously, we estimate educational dissimilarity at the census-tract level within metropolitan CBSAs. Consistent with methodological recommendations and prior scholarship, we limit our analyses to metropolitan areas with populations of 500,000 or more [3] and at least 1000 people within each educational attainment category to ensure that we get accurate estimates of segregation [35]. D-scores, which are one of Residential Segregation by Education in the U.S., 2016-2020 DOI: http://dx.doi.org/10.5772/intechopen.1001900 the most commonly used measures of segregation, typically range from a minimum of 0 (indicating no segregation) to a maximum of 1 (indicating complete segregation), but we multiply their values by 100 for ease of interpretation. ...
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While there is much research on income segregation, we know less about the factors that contribute to the uneven distribution of households across neighborhoods by educational attainment. Although globalization is thought to influence segregation, its association with socioeconomic segregation is debated. Using data from the 2016-2020 American Community Survey, the Globalization and World Cities Research Network, and the MIT Election Data + Science Lab, we investigate the correlates of educational segregation within large core-based statistical areas in the United States, focusing on globalization, income inequality, and political preferences in the 2016 presidential election. Multivariate results reveal that globalization and income inequality are the most significant correlates of educational segregation. Political preferences are only significantly associated with residential dissimilarity between those with a master’s degree or higher and those with some college. We discuss the implications of these results for understanding residential inequality on the basis of education in metropolitan America.
... R ISING levels of income inequality in the West have led to concerns that the social divide between rich and poor is growing (Duncan and Murnane 2011;Mijs and Roe 2021;Reardon and Bischoff 2011). Although the statistical reality of growing inequality is virtually undisputed (Atkinson, Piketty, and Saez 2011;McCall and Percheski 2010;Neckerman and Torche 2007;Piketty and Saez 2003), it has not yet been determined whether this trend has been universally accompanied by increasing income segregation (Logan et al. 2020;Reardon et al. 2018). In other words, are rich and poor simply becoming richer and poorer, or has increasing economic inequality also cemented a social and spatial economic divide? ...
... Missing data are virtually non-existent. As such, our analyses of income segregation provide an important complement to census and survey data, because they allow for an investigation of neighborhood income segregation at a more granular level without the need to make assumptions about economic and ethnic variation within different neighborhoods (Reardon et al. 2018). ...
... The first is the rank-order information theory index, H R (Reardon et al. 2006;Reardon and Bischoff 2011), which relies on rank-order income groups based on percentiles and which measures how populations below and above each percentile threshold are segregated between neighborhoods. In recent work, Reardon et al. (2018) and Logan et al. (2020) have noted that the use of grouped income data and sampling weights tends to bias segregation measures. Having access to full-population individuallevel income data, we obtain unbiased estimates of income segregation in Sweden without the need for further sample adjustments. ...
... In order to make these errors more transparent to ACS data users, the USCB publishes a margin of error (MOE) at the 90% confidence level for each ACS estimate. Many studies have shown that the data uncertainty of ACS can lead to inaccurate analysis and/or biased decision-making, and it is essential to assess the impacts of such uncertainty (Bazuin & Fraser, 2013;Jung et al., 2019;Logan et al., 2018;Napierala & Denton, 2017;Reardon et al., 2018;Spielman et al., 2014;Wei et al., 2021). ...
... Among the oldest pursuits in quantitative social science, residential segregation continues to be a foundational driver of spatial inequality in the USA, exerting influence on outcomes ranging from education to health, to earnings. It is increasingly common to use ACS data to measure residential segregation in the past decade due to its data currency (Anacker et al., 2017;Landis, 2019;Lichter et al., 2012;Logan et al., 2018;Reardon et al., 2018). However, only a few efforts explicitly account for uncertainty associated with ACS data. ...
... Napierala and Denton (2017) presented a simulation-based method to derive the confidence interval for the dissimilarity index of two racial/ethnic groups given the published ACS estimates and associated MOE. Logan et al. (2018) and Reardon et al. (2018) proposed approaches to correct the bias toward segregation measures when using ACS income data. Despite the utility of these approaches, ultimately, they rely upon several assumptions regarding the population distribution, each of which remains potentially problematic. ...
Article
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American Community Survey (ACS) data have become the workhorse for the empirical analysis of segregation in the U.S.A. during the past decade. The increased frequency the ACS offers over the 10-year Census, which is the main reason for its popularity, comes with an increased level of uncertainty in the published estimates due to the reduced sampling ratio of ACS (1:40 households) relative to the Census (1:6 households). This paper introduces a new approach to integrate ACS data uncertainty into the analysis of segregation. Our method relies on variance replicate estimates for the 5-year ACS and advances over existing approaches by explicitly taking into account the covariance between ACS estimates when developing sampling distributions for segregation indices. We illustrate our approach with a study of comparative segregation dynamics for 29 metropolitan statistical areas in California, using the 2010–2014 and 2015–2019. Our methods yield different results than the simulation technique described by Napierala and Denton (Demography 54(1):285–309, 2017). Taking the ACS estimate covariance into account yields larger error margins than those generated with the simulated approach when the number of census tracts is large and minority percentage is low, and the converse is true when the number of census tracts is small and minority percentage is high.
... On the one hand, policing strategies have moved away from reactive interventions that emphasize rapid responses to crime towards proactive strategies that utilize constant surveillance to predict outbreaks of crime and displace visible disorder (Fagan and Ash 2017;Herbert, Beckett, and Stuart 2017). On the other, economic segregation between inner cities and surrounding suburbs has steadily increased, while racial segregation within city centers has declined (Lacy 2016;Massey and Tannen 2017;Reardon et al. 2018). Consequently, whereas 20 th century segregation was characterized by pronounced within-municipality racial segregation, 21 st century segregation is characterized by "moderate racialethnic segregation and rising class segregation" (Massey, Rothwell, and Domina 2009:74), especially class segregation between urban centers and surrounding suburban municipalities (Frey 2015;Lacy 2016). ...
... Second, economic segregation has increased while racial segregation has declined (Massey et al. 2009;Reardon and Bischoff 2011). Between 1970 and 2010, extreme racial segregation decreased by 40 percent (Massey and Tannen 2015), while income segregation rose by 25 percent (Reardon et al. 2018). While historically suburbanization was associated with white flight from inner cities (Logan, Stults, and Reynolds 2004), the simultaneous decrease in racial segregation and increase in income segregation since 2000 has been driven by minority households moving into mostly white, suburban areas (Frey 2015;Lacy 2016;Reardon et al. 2018). ...
... Between 1970 and 2010, extreme racial segregation decreased by 40 percent (Massey and Tannen 2015), while income segregation rose by 25 percent (Reardon et al. 2018). While historically suburbanization was associated with white flight from inner cities (Logan, Stults, and Reynolds 2004), the simultaneous decrease in racial segregation and increase in income segregation since 2000 has been driven by minority households moving into mostly white, suburban areas (Frey 2015;Lacy 2016;Reardon et al. 2018). Whereas only 18 percent of metropolitan black households lived in suburban areas in 1970, 40 percent lived in the suburbs by 2010 (Massey and Tannen 2017). ...
Article
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Despite decades of crime decline, police surveillance has continued to expand through a range of tactics oriented towards policing social disadvantage. Yet, despite attention to the linkages between residential inequality and policing, few studies have accounted for two intertwined structural developments since the turn of the 21st century: (1) the shift away from spatially concentrated patterns of racial segregation within urban centers towards sprawling patterns of economic segregation and (2) the turn from reactive policing towards proactive surveillance. Using the case of big data policing, we create a new measure of big data surveillance in metropolitan areas to examine how changes in segregation have affected the expansion of proactive police surveillance. In contrast to theoretical accounts emphasizing the role of police surveillance in governing economic inequality and perpetuating racial segregation, we do not find evidence that racial segregation or income inequality increase big data surveillance. Instead, much of the recent rise in big data policing is explained by increases in sprawling patterns of income segregation. These results provide new insight into the linkages between policing and residential inequality and reveal how changes in metropolitan segregation influence criminal justice surveillance in the era of big data.
... This seems relevant given the extensive research linking income and inequality with life expectancy outcomes [22][23][24][25][26]. However, recent studies have questioned the apparent rise in residential income segregation during the 2000s, attributing it partly to biases from the Census Bureau's shift from the decennial Census to the American Community Survey, underscoring the complexity of measuring income segregation [27][28][29]. Specifically, the evidence shows that income residential segregation has not grown as rapidly in recent decades among Black/African American and Hispanic/Latino American families as previously thought [27,[30][31][32], yet disparities with white families persist [28]. Additionally, a recent study highlights that socioeconomic status, race-ethnic composition, and geographic location each independently influence mortality rates [33]. ...
... However, recent studies have questioned the apparent rise in residential income segregation during the 2000s, attributing it partly to biases from the Census Bureau's shift from the decennial Census to the American Community Survey, underscoring the complexity of measuring income segregation [27][28][29]. Specifically, the evidence shows that income residential segregation has not grown as rapidly in recent decades among Black/African American and Hispanic/Latino American families as previously thought [27,[30][31][32], yet disparities with white families persist [28]. Additionally, a recent study highlights that socioeconomic status, race-ethnic composition, and geographic location each independently influence mortality rates [33]. ...
Article
Recent research shows a significant link between race-ethnicity and income concentration and premature death rates in the U.S. However, most studies focus on Black-White residential concentration, overlooking racial-ethnic diversity. Our study examines the impact of racial-ethnic majority composition on mortality and how this relationship varies across different levels of economic concentration in neighborhoods, as defined by census tracts. Premature death rates (under 65 years of age) were retrieved from abridged period life tables from 67,140 U.S. census tracts derived from the U.S. Small-area Life Expectancy Project. Covariate factors were retrieved from the 2011–2015 American Community Survey (ACS) 5-year estimates. We measured racial-ethnic concentration by grouping neighborhoods using each tract’s majority racial-ethnic group, and approximated income concentration using the Index of Concentration of the Extremes. We used three-level random intercept models to examine the interaction of racial-ethnic and income concentration and its association with neighborhood mortality risk, accounting for covariates. Our study yielded three salient findings. First, mortality risk varied greatly in poor neighborhoods with different racial-ethnic compositions compared to affluent neighborhoods, with notable higher risk in Black-majority areas. Second, in diverse neighborhoods where no single ethnic group forms a majority—referred to as Minority-majority neighborhoods—the mortality risk is comparable to that in White-majority neighborhoods. Third, Hispanic/Latino- and Asian-majority neighborhoods had lower mortality risk than White-majority neighborhoods in areas with a high concentration of poverty, but similar mortality risk in affluent areas. The study suggests that racial-ethnic and socioeconomic area-based measures are important to consider together to address mortality inequities accurately.
... In the United States, economic segregation is very high, with income affecting where one lives 9 , who one marries 10 , and who one meets and befriends 11 . This extreme segregation is costly. ...
... We validated it by recalculating the correlation with a measure of density rather than population size (Spearman correlation = 0.45, P < 10 −4 ; Supplementary Table 7), by controlling for potential confounding factors (Extended Data Table 1 and Supplementary Table 7), by varying the granularity of the analysis ( Fig. 1e and Extended Data Fig. 4) and by testing a variety of specifications of exposure segregation (Supplementary Table 6 and Supplementary Figs. [2][3][4][5][6][7][8][9][10]. The consistent result that larger, denser cities are more segregated runs counter to the hypothesis that such cities promote socioeconomic mixing by attracting diverse individuals and constraining space in ways that oblige them to encounter one other [1][2][3][4][5][6] . ...
Article
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A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1–6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city’s hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.
... Consequently, segregation is on the political agenda of many countries and an abundant literature has focused on interpreting the rise and fall of residential segregation, and income segregation in particular. For the most part, this literature has studied the evolution of segregation in metropolitan areas only (Logan et al., 2018;Reardon et al., 2018). However, some studies have begun to decompose metropolitan areas geographically and distinguish between the macro-and micro-components of segregation Reardon et al., 2008). ...
... Overall, by comparing the results in Table 4 with the previous ones, we show that segregation in France is relatively low, with only the levels of segregation in Paris being comparable with those evidenced in the United States (Reardon et al., 2018). In small cities, levels of segregation are almost negligible (see measures of H R for small UAs in Table 4). ...
... Logan et al. (2018) highlights examples of wealthy African-American households being undercounted in predominantly wealthy White neighborhoods and a dampening of the range of income distributions in general. This has the effect of making ACS-based income segregation appears artificially high relative to the 2000 decennial census since there is less observed within tract variation (Logan et al., 2018;Reardon et al., 2018). A second result of a smaller sample size is a larger standard error, and by extension a larger MOE. ...
... For example, consider a census tract with 1200 households; we would expect the variation in the mean household income to be larger if we repeatedly contacted five households than if we repeatedly contacted 200 households. Logan et al. (2018) and Reardon et al. (2018) present approaches to correct for bias caused by the first issue in combined estimates, specifically in the case of income segregation; we present general approaches for correcting for bias in the MOEs on combined estimates. ...
Article
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The American Community Survey (ACS) is an indispensable tool for studying the United States (US) population. Each year the US Census Bureau (BOC) publishes approximately 11 billion ACS estimates, each of which is accompanied by a margin of error (MOE) specific to that estimate. Researchers, policy makers, and government agencies combine these estimates in myriad ways, which requires an accurate measurement of the MOE on that combined estimate. We compare three options for computing this MOE: the analytic approach uses standard statistically derived formulas, the simulation approach builds an empirical distribution of the combined estimate based on simulated values of the inputs, and the replicate approach uses simulated values published by the BOC based on their internal model that statistically replicates the entire ACS 80 times. We find that since the replicate approach is the only one of the three to incorporate covariance between the input variables, it performs the best. We further find that the simulation and analytic approaches generally match one another and can both overestimate and underestimate the MOE; they have their places when the replicate approach is not feasible.
... Lastly, these measures have not accounted for the larger margins of error in the ACS 5-Year surveys compared to the decennial Census, which has been shown to overestimate aggregate segregation measures such as the racial dissimilarity index 62 , the information theory index, and the variance ratio index for income 63 . Because the decennial Census samples around 1 in 6 households in the United States, or about a 17% sample rate, and the ACS 5-Year's sampling rate has ranged between 8% to 10% 63 , the ACS will generally underestimate the variation in the overall population compared to the decennial Census. ...
Article
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Despite the importance of measuring racial-ethnic segregation and diversity in the United States, current measurements are largely based on the Census and, thus, only reflect segregation and diversity as understood through residential location. This leaves out the social contexts experienced throughout the course of the day during work, leisure, errands, and other activities. The National Experienced Racial-ethnic Diversity (NERD) dataset provides estimates of diversity for the entire United States at the census tract level based on the range of place and times when people have the opportunity to come into contact with one another. Using anonymized and opted-in mobile phone location data to determine co-locations of people and their demographic backgrounds, these measurements of diversity in potential social interactions are estimated at 38.2 m × 19.1 m scale and 15-minute timeframe for a representative year and aggregated to the Census tract level for purposes of data privacy. As well, we detail some of the characteristics and limitations of the data for potential use in national, comparative studies.
... Indeed, Logan et al. (2018) showed that Reardon and Bischoff's (2011) measurement of the evolution of income segregation in the United States was upwardly biased, as the authors were using a sample of declining size and did not correct for the sampling bias. Thus, after correction, Reardon et al. (2018) did find that the increase was substantially diminished, at least divided by three. Recent literature related to the Abowd et al.'s (1999) decomposition also uncovered that earnings segregation based on fixed effects variance is also biased, even when measured on the full population (Bonhomme et al. 2022). ...
... Social polarization and socio-economic segregation became an important dimension of segregation around the world as the idea of the global city began (Reardon et al., 2018;van Ham et al., 2020). Becoming a global phenomenon, urban segregation attracted the attention of a wide range of researchers. ...
Article
Despite more than a century of research on urban segregation, this phenomenon is still one of the main concerns of urban planners and policymakers and a characteristic of cities that require intervention. Any policy-making in this field as an urban reality requires a clear understanding and definition of this phenomenon. But which definition? In response to this question and obtaining a basis for the definition of segregation, many efforts have been made in the past decades. However, these efforts have failed to present a framework that theoretically and practically responds to all dimensions and approaches in defining segregation. To fill this gap, the present study provides a comprehensive and integrated definition of urban segregation based on a meta-synthesis and qualitative content analysis by reviewing, categorizing, and combining the various and complex definitions in the existing literature. It presents two main categories of definitions of urban segregation: definitions based on the structural dimension and definitions based on the spatial dimension. Considering the complexity and multiple dimensions of urban segregation, a comprehensive definition of this phenomenon to present all aspects of segregation must answer six main questions: What is the nature of segregation? What people, on what basis, why, in what places and times, and how are segregated? The answers to the questions show that the presented categorization of the definitions of urban segregation covers different dimensions and aspects of this phenomenon. Therefore, combining them contributes to a comprehensive definition of it. Being more comprehensive than the previous definitions, the presented definition in this study specifies the main dimensions of urban segregation. It can provide an efficient basis for future theories, policies, and planning in urban segregation. Although the presented categories are inseparable parts of a whole, they can be examined in detail separately in future studies. Also, by connecting the definitions in the literature to intellectual paradigms and planning theories, a deeper understanding of the shaping factors and components of urban segregation can be achieved.
... A second body of research describes the level of income-based segregation across cities and its trend. Studies generally find that even after adjusting for sampling bias, income segregation has been on the rise in recent decades in the US, especially among families with children (Reardon et al. 2018). A cross-national study between France and the US reports that socioeconomic segregation in large metro areas is higher in the US than in France and that the high-income are the most segregated income group in both countries (Quillian and Lagrange 2016). ...
Article
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This study examines the temporal changes in income segregation within the ambient population around the clock using mobile phone big data. It employs ordinal entropy, a metric suited for measuring segregation among ordered groups, to quantify the level of segregation among eight income groups within micro-geographic units throughout the 24-h period on a weekday and a weekend day in the urban core of Guangzhou, China. The study further decomposes daily segregation by location and time profile. We identify urban functions and neighborhood contexts relevant for income segregation and explore their temporal variation. Using group-based trajectory analysis, we classify daily segregation trends among 400 m urban grids into seven distinct trajectories for both weekday and weekend. Our findings confirm that segregation fluctuates constantly. The role of local urban functions, particularly retail, accommodation, and offices, and neighborhood context, such as the number of residents and the share of non-local migrants, exhibits a significant temporal rhythm. The seemingly convoluted 24-h segregation time series among urban grids follow just a few distinct trajectories with clear geographical patterns. There is limited variability at individual grids both over the course of a day and across days. Shifts across different trajectory types between weekday and weekend are rare. The dynamic daily segregation in the ambient population per se may be an enduring characteristic of neighborhoods and a real-time channel for neighborhood contextual influences, potentially fueling long-term residential segregation and neighborhood change.
... Thus, in theory, vouchers have the potential to reduce neighborhood inequality by providing lowincome renters more geographic choice and greater access to higher opportunity neighborhoods. 1 However, in practice, voucher holdersespecially minority households-rarely move out of high-poverty neighborhoods and are little more likely to enter low-poverty communities than poor unassisted renters (Devine et al. 2003;McClure 2008;Pendall 2000;Owens 2012;Collinson and Ganong 2018). The HCV program represents a lost opportunity to counter longstanding racial segregation and rising residential income segregation (Reardon et al. 2018) and to increase upward mobility given the growing evidence that neighborhoods shape children's long-term prospects (Chetty, Hendren, and Katz 2016;Chyn and Katz 2021;Sharkey and Elwert 2011). Recent proposals to expand housing vouchers reinforce the need to better understand how to improve the HCV program. ...
... The bulk of this literature comes to the theoretical conclusion that charitable giving should rise in inequality, though empirical results are mixed. In an environment characterized by increasing income segregation (Reardon et al. 2018), high-income and low-income households may both become isolated from the mainstream of American society (Krivo et al. 2013). One natural consequence of this growing social isolation is that potential donors may become less aware of, or less concerned with, recipients' needs. ...
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This paper investigates how donors to food assistance charities respond to exogenous changes in recipients’ unmet needs. When food insecurity rises by one percentage point, the average food assistance charity increases fundraising by 0.9 %. Without this response, private contributions would have fallen by at least 0.2 %. These results are consistent with a model in which economic inequality simultaneously raises the donor’s marginal benefit of giving and reduces their awareness of the recipient’s circumstances. Charitable fundraising plays a key role in maintaining the charity’s revenues at a time when they are most needed.
... As pointed out in Rhode and Strumpf (2003), heterogeneity in policies accros jurisdictions and preferences is decreasing, even though mobility costs have being falling since the end of the second World War. However, recent studies (see, for instance, Reardon et al. 2018;Watson 2009;Reardon 2014 or Reardon andBischoff 2016) show that income segregation has increased. A correlation between residential segregation and income inequality has been highlighted in Tammaru et al. (2020). ...
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This paper analyses how progressive taxation schemes, implemented by local welfarist government, affect the segregative properties of endogenous jurisdiction formation, using a model where local jurisdictions produce a local public good, financed by a progressive tax on households’ wealth. A jurisdiction is composed of all the households that live in the same place. Local taxation parameters are determined so as to maximize a social welfare function. Households can “vote with their feet”, which means that they can choose to move to the jurisdiction that offers the package ”tax - amount of public good” that provides the highest utility level. The main result of this article is the proof that the maximin criterion is more segregative than the utilitarian one. Consequently, it suggests that local governments can not simultaneously struggle against inequalities and segregation.
... These choices then typically lead to a sorting of families closely linked to their earnings; those with high earnings end up in high-earnings neighborhoods whereas those with low earnings end up in low-earnings neighborhoods. Recent empirical evidence shows that this tendency of residential segregation has risen over the past decades both in the USA and Europe, such that neighborhoods have become more homogenous in terms of family income and/or socioeconomic status (Jagowsky, 1996;Bischoff and Reardon, 2013;Marci nczak et al., 2016;Musterd et al., 2017;Reardon et al., 2018). At the same time, existing evidence also indicates that the quality of the childhood neighborhood has a large and potentially long-lasting influence on the educational and economic outcomes of offspring (Crowder and South, 2011;Wodtke et al., 2011;Chetty et al., 2016;Chyn, 2018;Hendren, 2018a, 2018b;Chetty et al., 2020). ...
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Based on Norwegian administrative registers, we provide new empirical evidence on the effects of the childhood neighborhood’s socioeconomic status on early educational performance. A neighborhood’s status is measured annually by its adult inhabitants’ earnings ranks within larger commuting zones, and the childhood neighborhood status is the average status of the neighborhoods inhabited from the year after birth to age 15. Identification of causal effects relies on within-family comparisons only. Our results reveal a distinct hump-shaped relationship between the socioeconomic status of the childhood neighborhood and school results at age 15–16, such that the optimal neighborhood is of medium rank.
... In the U.S., economic segregation is very high, with income affecting where one lives 11 , who one marries 12 , and who one meets and befriends 13 . This extreme segregation is costly: it reduces economic mobility [14][15][16][17] , fosters a wide range of health problems 18,19 , and increases political polarization 20 . ...
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A long-standing expectation is that large, dense, and cosmopolitan areas will support diverse interactions and socioeconomic mixing. It has been difficult to assess this hypothesis because past approaches to measuring socioeconomic mixing have relied on static residential housing data rather than real-life interactions among people meeting at work, in places of leisure, and in home neighborhoods. Here we develop a new measure of interaction segegation (IS) that captures the economic diversity of the set of people that a given person meets in their everyday life. Leveraging cell phone mobility data to represent 1.6 billion interactions among 9.6 million people in the United States, we measure interaction segregation across 382 Metropolitan Statistical Areas (MSAs) and 2829 counties. When averaged across all MSAs, interaction segregation is 38% lower than a conventional static estimate, which means that people meet diverse others mostly when outside their home neighborhoods. But, we also find that interaction segregation is 67% higher in the 10 largest Metropolitan Statistical Areas (MSAs) than in small MSAs with fewer than 100,000 residents. We find evidence that because large cities can offer a greater choice of differentiated spaces targeted to specific socioeconomic groups, they end up promoting -- rather than reducing -- everyday economic segregation. We also discover that this segregation-increasing effect is countered when hubs of interaction (e.g. shopping malls) are positioned to bridge diverse neighborhoods and thus attract people of all socioeconomic statuses. Overall, our findings challenge a long-standing conjecture in human geography and urban design, and highlight how built environment can both prevent and facilitate diverse human interactions.
... Notes 1. In a reanalysis with corrected estimates, Reardon et al. (2018) showed that the central results of previous studies on income segregation (Bischoff and Reardon, 2014;Owens, 2016;Reardon and Bischoff, 2011) ...
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Although residential sorting along socio-economic lines has increased in many cities across Europe, few studies have examined what drives changes in segregation over time. This study looks at the role of school choice expansion in shaping patterns of spatial inequality. We adopt a longitudinal perspective and investigate how the increasing availability of private primary schools is related to the dynamics of socio-economic segregation in German cities. Drawing on a uniquely compiled data set for the years 2005 to 2014 that includes 74 large and medium-sized cities with over 3500 districts, we estimate linear panel regression models with city fixed effects. The analyses show that an increase in the share of private primary schools is associated with a decrease in the segregation of poverty in West German cities but not in East German ones. The association in West Germany is particularly pronounced in local contexts characterised by growing rates of poor residents and growing proportions of young children. Results imply that school choice availability may promote residential integration and at the same time reinforce school segregation.
... They claim that racial discrimination has resulted in a high concentration of ethnic and racial minority groups in neighbourhoods with high crime levels, social disorder, unemployment rates, poor public health and services, and high environmental injustice (Wilson, 2012). With the global city thesis of social polarisation, socioeconomic segregation, which means the residential sorting of socioeconomic groups by income (Haandrikman et al, 2021), working status (Ng et al., 2021), and occupation (Smith et al., 2020) have become a significant dimension of residential segregation (Reardon et al., 2018;Van Ham et al., 2021). Disadvantaged minorities face unequal access to valued resources (e.g., education and job markets) critical to their life chances and social mobility. ...
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Recent theoretical and methodological advances in activity space and big data provide new opportunities to study socio-spatial segregation. This review first provides an overview of the literature in terms of measurements, spatial patterns, underlying causes, and social consequences of spatial segregation. These studies are mainly place-centred and static, ignoring the segregation experience across various activity spaces due to the dynamism of movements. In response to this challenge, we highlight the work in progress toward a new paradigm for segregation studies. Specifically, this review presents how and the extent to which activity space methods can advance segregation research from a people-based perspective. It explains the requirements of mobility-based methods for quantifying the dynamics of segregation due to high movement within the urban context. It then discusses and illustrates a dynamic and multi-dimensional framework to show how big data can enhance understanding segregation by capturing individuals’ spatio-temporal behaviours. The review closes with new directions and challenges for segregation research using big data.
... The majority of people lack exposure to diverse social environments, particularly within the United States. Demography studies reveal high rates of segregation between social groups: For example, high-income American families tend to have low exposure to families from other socioeconomic backgrounds (Bischoff & Reardon, 2014;Reardon et al., 2018), and White American families often live in neighborhoods with other White individuals (Grigoryeva & Ruef, 2015). This issue may also come into play for gender: Although most people have exposure to individuals of the opposite gender at home or at school, they may have few close relationships with different-gender peers that would allow for insight into the other group's experiences (Maccoby & Jacklin, 1987;Mehta & Wilson, 2020). ...
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... We are not aware of any prior studies examining secular trends in the association between SES and self-reported discrimination or unfair treatment. This is surprising given the documented increases in economic inequality and social division since the 1980s (Fuller-Rowell et al., 2021;Reardon et al., 2018). Income and wealth inequalities have increased in the United States, and across most of the developed world, such that the incomes of more affluent groups have grown substantially while the incomes of the bottom 60% of the income distribution have remained relatively stagnant (Alvaredo, 2018;Piketty & Saez, 2014). ...
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Thesis
This publication-based dissertation deals with a ubiquitous phenomenon of modern societies: social ex-clusion or the risk of social exclusion. As a specific manifestation of this phenomenon, it focuses on the stigmatisation of the unemployed and takes a look at two perspectives that have been investigated com-paratively little so far: Prejudice against the unemployed and stigma-consciousness of the unemployed. In a first article, the perspective of the employed population is considered. The central research interests are the extent and determinants of prejudices against the unemployed. In the second and third article, the perspective of the unemployed is explored through their stigma-consciousness, each with different theoretical and methodological approaches. Finally, the fourth article brings both perspectives together. Prejudices in the working population represent an important theoretical mechanism for the development of stigmatisation and stigma-consciousness within the framework of the Labling Approach. The first article finds higher levels of prejudice for some of the studied groups, who are disadvan-taged on the labour market, in line with Social Identity Theory. Although higher prejudice is found for people with lower social status, the assumed mediation of the effects of disadvantaged groups on social status cannot be found. A moderation of this effect by self-efficacy can only be found for the group of persons with a migration background. Respondents with a first-generation migration background have higher prejudices, especially with lower self-efficacy. The second article focuses on the three sub-dimensions of stigma-consciousness – awareness, denial and disaffiliation – and uses a mixed method design to analyse determinants of these dimensions. The hypotheses are derived based on qualitative interview material and Goffman's stigma concept as a sensi-tising concept. The central results are that people receiving unemployment benefit II are more affected by negative attributions than those not receiving benefits or receiving unemployment benefit I, and that the scores on all three dimensions are higher when respondents attribute an additional value to being employed or experience a higher degree of material deprivation. The third article examines determinants of stigma-consciousness as general construct using the la-belling approach and derives three key mechanisms: scope of the norm and extent of deviation, visibility of deviation and formal and informal societal control. Overall, higher stigma-consciousness is found with a second unemployment episode (for men), higher current unemployment duration, material depri-vation, and job search obligation (for men). The results largely support the assumptions of the labelling approach and in particular the mechanisms of extent and visibility of deviation as well as formal societal control. The fourth article is devoted to the surprising result from article three, in which no effect of informal societal control could be found. Instead, an alternative operationalisation of informal societal control is derived via regional variation in the extent of prejudice against the unemployed in the working popula-tion. The assumed positive effect of the regional strength of prejudice on stigma-consciousness can be partially confirmed. However, contrary to expectations, there is a moderating effect of the variability of prejudice within a region, so that the effect of regional strength of prejudice is more positive the more the strength of prejudice varies within the same region.
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Recent trends in income segregation in metropolitan regions show that, after a decline in the 1990s, there was an increase in 2000–2010 that reinforced concerns about the overall growth in U.S. income inequality since the 1970s. Yet the evidence may be systematically biased to exacerbate the upward trend because the effective sample for the American Community Survey (ACS) is much smaller than it was for the 2000 census to which it is being compared. Apparent changes in disparities across census tracts may result partly from a higher level of sampling variation and bias due to the smaller sample. This study uses 100% microdata from the 1940 census to simulate the impact of different sampling rates and applies those approaches to publicly available data for 2000 and 2007–11. The reduction in sample sizes associated with the ACS appears to exaggerate the evidence for increasing income segregation for all measures tested here.
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We show that the neighborhoods in which children grow up shape their earnings, college attendance rates, and fertility and marriage patterns by studying more than 7 million families who move across commuting zones and counties in the United States. Exploiting variation in the age of children when families move, we find that neighborhoods have significant childhood exposure effects: the outcomes of children whose families move to a better neighborhood-as measured by the outcomes of children already living there-improve linearly in proportion to the amount of time they spend growing up in that area, at a rate of approximately 4% per year of exposure. We distinguish the causal effects of neighborhoods from confounding factors by comparing the outcomes of siblings within families, studying moves triggered by displacement shocks, and exploiting sharp variation in predicted place effects across birth cohorts, genders, and quantiles to implement overidentification tests. The findings show that neighborhoods affect intergenerational mobility primarily through childhood exposure, helping reconcile conflicting results in the prior literature. © The Author(s) 2018. Published by Oxford University Press on behalf of the President and Fellows of Harvard College. All rights reserved.
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The American Community Survey (ACS) provides valuable, timely population estimates but with increased levels of sampling error. Although the margin of error is included with aggregate estimates, it has not been incorporated into segregation indexes. With the increasing levels of diversity in small and large places throughout the United States comes a need to track accurately and study changes in racial and ethnic segregation between censuses. The 2005–2009 ACS is used to calculate three dissimilarity indexes (D) for all core-based statistical areas (CBSAs) in the United States. We introduce a simulation method for computing segregation indexes and examine them with particular regard to the size of the CBSAs. Additionally, a subset of CBSAs is used to explore how ACS indexes differ from those computed using the 2000 and 2010 censuses. Findings suggest that the precision and accuracy of D from the ACS is influenced by a number of factors, including the number of tracts and minority population size. For smaller areas, point estimates systematically overstate actual levels of segregation, and large confidence intervals lead to limited statistical power.
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Past research shows that income segregation between neighborhoods increased over the past several decades. In this article, I reexamine income segregation from 1990 to 2010 in the 100 largest metropolitan areas, and I find that income segregation increased only among families with children. Among childless households—two-thirds of the population—income segregation changed little and is half as large as among households with children. I examine two factors that may account for these differences by household composition. First, I find that increasing income inequality, identified by past research as a driver of income segregation, was a much more powerful predictor of income segregation among families with children, among whom income inequality has risen more. Second, I find that local school options, delineated by school district boundaries, contribute to higher segregation among households with children compared to households without. Rising income inequality provided high-income households more resources, and parents used these resources to purchase housing in particular neighborhoods, with residential decisions structured, in part, by school district boundaries. Overall, results indicate that children face greater and increasing stratification in neighborhood contexts than do all residents, and this has implications for growing inequalities in their future outcomes.
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The Moving to Opportunity (MTO) experiment offered randomly selected families housing vouchers to move from high-poverty housing projects to lower-poverty neighborhoods. We analyze MTO's impacts on children's long-term outcomes using tax data. We find that moving to a lower-poverty neighborhood when young (before age 13) increases college attendance and earnings and reduces single parenthood rates. Moving as an adolescent has slightly negative impacts, perhaps because of disruption effects. The decline in the gains from moving with the age when children move suggests that the duration of exposure to better environments during childhood is an important determinant of children's long-term outcomes. (JEL I31, I38, J13, R23, R38).
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New criteria for indexes of residential segregation are developed. It is argued that a pattern of random segregation rather than complete desegregation should be used as a baseline for measuring segregation. It is shown that any index whose relationship to one baseline is independent of the proportion black in a city, necessarily has a dependent relationship with respect to the other baseline. The index of dissimilarity is adjusted to serve as a measure of deviation from random segregation. Eta-square, which was shown by Duncan and Duncan to depend on the proportion black, is shown to be independent of the proportion black when random segregation is used as a baseline. It is argued that segregation should be measured from a situation of complete desegregation when its effects are of concern, but that it should be measured from random segregation when its causes are being analyzed.
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In this paper we derive and evaluate measures of multigroup segregation. After describing four ways to conceptualize the measurement of multigroup segregation—as the disproportionality in group (e.g., race) proportions across organizational units (e.g., schools or census tracts), as the strength of association between nominal variables indexing group and organizational unit membership, as the ratio of between–unit diversity to total diversity, and as the weighted average of two–group segregation indices—we derive six multigroup segregation indices: a dissimilarity index (D), a Gini index (G), an information theory index (H), a squared coefficient of variation index (C), a relative diversity index (R), and a normalized exposure index (P). We evaluate these six indices against a set of seven desirable properties of segregation indices. We conclude that the information theory index H is the most conceptually and mathematically satisfactory index, since it alone obeys the principle of transfers in the multigroup case. Moreover, H is the only multigroup index that can be decomposed into a sum of between– and within–group components.
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American metropolitan areas have experienced rising residential segregation by income since 1970. One potential explanation for this change is growing income inequality. However, measures of residential sorting are typically mechanically related to the income distribution, making it difficult to identify the impact of inequality on residential choice. This paper presents a measure of residential segregation by income, the Centile Gap Index (CGI), which is based on income percentiles. Using the CGI, I find that a one standard deviation increase in income inequality raises residential income segregation by 0.4-0.9 standard deviations. Inequality at the top of the distribution is associated with more segregation of the rich, while inequality at the bottom and declines in labor demand for less-skilled men are associated with residential isolation of the poor. Inequality can fully explain the rise in income segregation between 1970 and 2000. Copyright 2009 The Author. Journal compilation International Association for Research in Income and Wealth 2009.
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This article investigates how the growth in income inequality from 1970 to 2000 affected patterns of income segregation along three dimensions: the spatial segregation of poverty and affluence, race-specific patterns of income segregation, and the geographic scale of income segregation. The evidence reveals a robust relationship between income inequality and income segregation, an effect that is larger for black families than for white families. In addition, income inequality affects income segregation primarily through its effect on the large-scale spatial segregation of affluence rather than by affecting the spatial segregation of poverty or by altering small-scale patterns of income segregation.
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Compared to racial segregation, economic segregation has received little attention in recent empirical literature. Yet a heated debate has arisen concerning Wilson's hypothesis (1987) that increasing economic segregation plays a role in the formation of urban ghettos. This paper presents a methodological critique of the measure of economic segregation used by Massey and Eggers (1990) and finds that it confounds changes in the income distribution with spatial changes. I develop a "pure" measure of economic segregation and present findings on all U.S. metropolitan areas from 1970 to 1990. There have been steady increases in economic segregation for whites, blacks, and Hispanics in both the 1970s and 1980s, but the increases have been particularly large and widespread for blacks and Hispanics in the 1980s. The causes of these changes are explored in a reduced form, fixed-effects model. Social distance theory and structural economic transformations do affect economic segregation, but the large increases in economic segregation among minorities in the 1980s cannot be fully explained within the model. These rapid increases in economic segregation, especially in the context of recent, albeit small, declines in racial segregation, have important implications for urban policy, poverty policy, and the stability of urban communities.
Measures of income segregation (CEPA working paper)
  • S F Reardon
The continuing increase in income segregation
  • S F Reardon
  • K Bischoff
Diversity and disparities: America enters a new century
  • K Bischoff
  • S F Reardon
Measures of Income Segregation
  • Sean F Reardon
Reardon, Sean F. 2011. "Measures of Income Segregation." in CEPA Working Papers. Stanford, CA: Stanford Center for Education Policy Analysis.