Choosing Schools in Changing Places: Examining School Enrollment in Gentrifying Neighborhoods

Abstract

School choice expansion in recent decades has weakened the strong link between neighborhoods and schools created under a strict residence-based school assignment system, decoupling residential and school enrollment decisions for some families. Recent work suggests that the neighborhood-school link is weakening the most in neighborhoods experiencing gentrification. Using a novel combination of individual, school, and neighborhood data that link children to both assigned and enrolled schools, this study examines family, school, and neighborhood factors that shape whether parents enroll in the assigned local school. I find that parents are more likely to opt out of neighborhood schools in gentrifying neighborhoods compared with non-gentrifying neighborhoods when nearby choice options are available. Recent movers to gentrifying neighborhoods bypass local schools more compared with parents who have lived in the neighborhood longer. Results have implications for thinking about neighborhood-school linkages in an era of school choice and urban change.

Keywords

neighborhood change gentrification school choice school preferences inequality

Historically, nearly all children in the United States attended their neighborhood school via a residence-based school assignment system. Because of this school assignment system, residential sorting by race and income not only upheld segregation in neighborhoods but also produced segregated schools. Since the 1990s, however, the proliferation of school choice options—that is, any arrangement allowing children to receive schooling outside of their assigned neighborhood school—has loosened the tight relationship between residential location and school assignment. Nearly 30 percent of families now enroll their children in a school other than their assigned neighborhood school (Snyder, de Brey, and Dillow 2019), and the share of students opting out for non-neighborhood schools is much greater in urban areas (Grady and Bielick 2010).

School choice expansion occurred during a period when white and upper-socioeconomic-status (SES) households began migrating back to diverse neighborhoods in urban cores (Wells 2015). Under a residence-based school assignment system, white and higher-SES households’ mobility into historically lower-SES and minority urban neighborhoods would contribute to changes in both neighborhood and school composition. A few recent aggregate-level studies connecting these trends, however, found that neighborhood composition and school composition become the most dissimilar in demographically and socioeconomically changing areas, particularly these gentrifying neighborhoods (Bischoff and Tach 2018; Candipan 2019; Mader, Hemphill, and Abbas 2018). Such demographic divergences in gentrifying neighborhoods represent the aggregate of many household-level decisions about where to live and where to enroll children in school—that is, whether to activate school choice or enroll in a local neighborhood school.

This study takes these insights as starting points to investigate factors that contribute to opting out of the neighborhood school, focusing particularly on whether opt-out is more likely in gentrifying areas. Observing whether and under what conditions parents in gentrifying neighborhoods choose schools may provide key insights into mechanisms that uphold school segregation even while their surrounding neighborhoods diversify. Using a novel combination of national-level data from the Panel Study of Income Dynamics (PSID), School Attendance Boundary Survey (SABS), Census/American Community Survey (ACS), and National Center for Education Statistics (NCES), I identified whether children attended their assigned (neighborhood) school and examined family, school, and neighborhood factors that shaped school enrollment patterns.

I focused on gentrifying neighborhoods to observe whether commonly understood narratives of race- and income-based school preferences are upheld even when families choose diverse neighborhoods. Gentrification complicates the usual narrative regarding residential sorting and neighborhood segregation in the United States, instead reflecting a process in which white and higher-SES households move into historically lower-SES and minority urban neighborhoods, thus contributing to demographic and SES change. Some have proposed integrating neighborhoods as a means of tackling urban school segregation (Cucchiara and Horvat 2009), and neighborhood revitalization has at times been presented as a way of resolving urban school segregation (Lipman 2008; Smrekar 2009). However, in logistic regression analyses, I found that parents in gentrifying neighborhoods were more likely than parents in socioeconomically stable or declining neighborhoods to opt out of neighborhood schools when there were nearby school choice options. Recent movers were more likely to opt out than longtime residents in gentrifying neighborhoods, and when they did, they tended to enroll their children in non-neighborhood schools that served higher proportions of white students and lower proportions of black students than their assigned neighborhood schools. Therefore, the changing racial and SES profile of gentrifying neighborhoods was not reflected in key neighborhood institutions, such as schools.

The current study explicitly links residential and school processes, which relatively few studies have (Lareau and Goyette 2014). By bringing gentrification under its analytic lens, this study connects processes of neighborhood change with the literature on school and residential sorting—literatures often examined separately in past work. Moreover, little past quantitative research has investigated microlevel processes underlying residential and school enrollment outcomes, and those that have focused on a single city or geographic region. This study broadens the geographic scope using national-level data. Finally, while previous work has modeled individual household-level school enrollment outcomes using nationally representative data (Butler et al. 2013), this is the first national-level quantitative study, to the best of my knowledge, employing both actual enrolled and assigned school data to model individual schooling enrollment outcomes.

Neighborhood and School Choices in the United States

Residence-based school assignment systems, the primary method of assigning students to schools, created a strong link between where a child lived and where a child attended school. Access to high-quality schools under a residence-based school assignment system is rationed through housing markets. White and higher-SES families use neighborhood choice as a means of accessing the most advantaged and highest-performing schools (Goyette 2008; Lavery and Carlson 2015), thus resulting in stratification across communities and schools by race and socioeconomic status.

How individuals select neighborhoods aggregates to shape spatial patterns of inequality, and because school assignment has been so tightly linked to residence in the United States for decades, decisions regarding neighborhood selection also explain why schools have been durably segregated along racial and economic lines. School choice breaks the link between neighborhoods and schools. As the number of public choice options such as magnet and (especially) charter schools in the United States expanded in recent decades, providing alternatives to attending one’s zoned school, the historically tight relationship between where a child lived and where a child attended school loosened (Mickelson, Bottia, and Southworth 2008). Private schooling, a long-standing form of choice, continued to provide non-neighborhood options to families with the economic means to attend, although the share of students served by private schools has declined slightly over the past two decades (Murnane and Reardon 2018). Moreover, rising participation in “soft” forms of school choice—for example, inter- and intradistrict transfer, ability-based (e.g., Gifted and Talented) and school voucher programs, and so on—also contributed to this decoupling between housing and educational markets (Brunner, Cho, and Reback 2012; Loeb, Valant, and Kasman 2011).

In theory, the expansion of non-neighborhood school choice options has the potential to liberate students from segregated schools by severing the neighborhood-school link under a strict residence-based assignment system (Davis and Bauman 2013). Whether parents opt out of neighborhood schools, however, depends on preferences and constraints (often mutually reinforced), both of which vary by race and SES. Middle-class families are often in better positions to act on their school and neighborhood preferences (Bifulco, Ladd, and Ross 2009; Goyette 2008; Kimelberg and Billingham 2013; Makris 2018). In particular, white middle-class parents’ schooling decisions tend to be influenced by the presence of nonwhites, with white parents more likely to flee or avoid schools with higher shares of nonwhite students for magnet, charter, and private schools (Billingham and Hunt 2016; Fairlie and Resch 2002; Hamnett, Butler, and Ramsden 2013; Oberti 2007; Renzulli and Evans 2005; Saporito 2009; Saporito and Sohoni 2006). Whether due to racial prejudice or other factors for which race proxies (Krysan 2002), past work has found that middle-class white parents’ perceptions of school quality drop as the minority (particularly black) racial composition increases (Goyette, Farrie, and Freely 2012; Holme 2002). Moreover, their perceptions of school quality are also shaped by word-of-mouth input from parental networks that are also segregated by race and SES, resulting in socially constructed understandings of “good schools” that are highly correlated with race and SES above and beyond test scores or other evaluative metrics (Holme 2002; Roda and Wells 2013).

The ability to opt out also presupposes existing choice options. How choice is geographically distributed varies widely by district, and whether families opt out of their neighborhood school depends on how they perceive their options (Blagg and Chingos 2017; Denice and Gross 2016). For example, low-income and predominantly nonwhite neighborhoods typically have lower-resourced schools (Owens and Candipan 2019b), so some parents will seek public choice options for better schooling options. Public school choice options serve a larger share of low-SES and minority students in part because they locate more often in low-income and minority neighborhoods (Burdick-Will, Keels, and Schuble 2013; Lubienski and Gulosino 2007). That share could be even higher if low-income parents residing in these neighborhoods had fewer constraints, relative to their higher-earning peers, that shape their preferences and limit their perceptions of plausible non-neighborhood choice options, such as insufficient transportation, complex enrollment procedures, and a lack of information from less connected social networks, among other factors (Blagg and Chingos 2017; Fong and Faude 2018; Neild 2005; Rhodes and DeLuca 2014; Sattin-Bajaj et al. 2018; Schneider et al. 1997). While private schools are distributed throughout most urban areas—most students in urban areas live near at least one private option (70 percent within two miles; 92 percent within five miles) (Blagg and Chingos 2017)—the ability to enroll in private school is economically prohibitive for many low-income families.

Examining Dynamics in Gentrifying Neighborhoods

Families, especially white middle-class families, often take schools into account when choosing where to live (Lareau 2014). Past research shows that white families prefer to live in white neighborhoods in part because they perceive nonwhite neighborhoods to have lower-quality institutions such as schools (Harris 1999). Gentrification, however, represents a different residential sorting process. The original definition describes gentrification as the phenomenon of large numbers of higher-SES residents moving into low-SES neighborhoods (Glass 1964), but subsequent work also documents the racialized process of neighborhood change (Brown-Saracino 2017), with racial transition often occurring alongside class transitions during gentrification in the United States (Owens and Candipan 2019a).

Despite a large body of gentrification scholarship, almost no quantitative work has examined how schools, a key neighborhood institution, change alongside gentrifying neighborhoods. Qualitative studies examining the social mix of demographically and socioeconomically transitioning neighborhoods identify differences between higher-SES newcomers and longtime residents in terms of neighborhood norms, use of public space, and connections to long-standing local organizations, all of which lead to segregated social spaces within mixed-income neighborhoods (Freeman 2006; Hyra 2015; Tach 2009). As gentrifying neighborhoods change along racial and economic lines, will in-moving parents participate in or avoid their neighborhood schools? Given both whites’ preferences for white schools and low-SES families’ constraints, we might expect non-neighborhood choice enrollment patterns to differ both between gentrifying neighborhoods and other types of neighborhoods and within gentrifying neighborhoods between newcomers and existing residents given that incoming families are typically higher SES and white (Owens and Candipan 2019b). Examining how children in gentrifying neighborhoods sort into schools may provide insights into the degree to which school segregation persists even while the neighborhoods they serve diversify. Gentrification thus provides the conditions to observe whether the influx of white and higher-SES residents that produces neighborhood diversity (at least temporarily) translates into a corresponding increase of white students in schools.

Families that move into gentrifying neighborhoods may be served by schools that have been historically disadvantaged in some way or are still in the process of changing and thus composed of relatively greater shares of poor or nonwhite students. Some parents may feel more comfortable residing in gentrifying neighborhoods precisely because there are non-neighborhood school options available. Indeed, one recent study found that gentrification is more likely to occur in minority neighborhoods with non-neighborhood school options (Pearman and Swain 2017). Public choice options such as magnet schools, originally created to attract higher-SES families, offer special programs and curriculums (e.g., language immersion and arts programs) that may appeal to middle-class parents seeking non-neighborhood alternatives (Jordan and Gallagher 2015). Charter schools, with curricular and programmatic flexibility that may appeal particularly to middle-class parents, have expanded substantially in the past decade, more than doubling as a share of all public schools (from 3.1 in 2004 to 6.6 in 2014; Kena et al. 2016). They now sit near the center of public discourse as one of the most prominent public choice alternatives to non-neighborhood schools (Berends 2015). Recent ethnographic work found that middle-class families view enrollment in charter schools, especially prestige charters (Brown and Makris 2018), as an attractive option for bypassing undesirable neighborhood schools. This is particularly true in neighborhoods experiencing gentrification (Kimelberg and Billingham 2013; Makris 2015), where, since the early 2000s, an increasing share of market-oriented charter schools have opened (Brown and Makris 2018; Burdick-Will et al. 2013; Davis and Oakley 2013; Lubienski and Gulosino 2007). Using survey data, Weiher and Tedin (2002) found that white and middle-class families perceived charter schools as higher-quality schools even when performing lower relative to neighborhood schools. Public school choice may therefore grant gentrifier parents who crave a certain threshold of neighborhood diversity the opportunity to remain in diverse neighborhoods without choosing diverse neighborhood schools. Middle-class and white parents’ broad perceptions of local school quality coupled with race- and income-based school preferences may translate into their selective school sorting out of assigned neighborhood schools, particularly when those schools are located in gentrifying low-income or minority neighborhoods (Mader et al. 2018).

On the other hand, gentrifier families, particularly early-wave gentrifiers (Brown-Saracino 2009), may be drawn to cities because they value diversity and other urban qualities in neighborhoods (Billingham and Kimelberg 2013; Kimelberg 2014), suggesting that parents may choose diverse local neighborhood schools that are racially and socioeconomically diverse. Some qualitative work suggests that while gentrifier parents may be willing to choose diverse urban neighborhood schools, their schooling decisions are largely influenced by the enrollment choices of other gentrifier parents (Billingham and Kimelberg 2013; Stillman 2012). When they do choose diverse urban schools, they frequently conceive contingency plans that allow them to opt out if they observe undesirable qualities in their assigned school (Stillman 2012). Qualitative scholarship has found that even when neighborhood schools in gentrifying neighborhoods become racially and socioeconomically diverse due to incoming white and higher-SES students, there are problems of the displacement and disempowerment of existing families or frictions that cause gentrifier parents to eventually exit the local school (Cucchiara 2013; Stillman 2012). Together, these qualitative studies speak to underlying tensions between gentrifier parents’ desire to expose their children to diversity and their actual behaviors when confronting integrated social settings at school (Boterman 2013; Posey-Maddox, Kimelberg, and Cucchiara 2014; Roda and Wells 2013; Stillman 2012; Wells et al. 1999).

The Present Study

Collectively, these insights raise questions about how, in an era of expanding school choice, parents make enrollment decisions in neighborhoods experiencing demographic and socioeconomic change. This study examines whether parents opt out of the neighborhood school by addressing the following empirical questions:

Research Question 1: Does a neighborhood’s school choice context predict whether parents opt out of neighborhood schools?

Research Question 2: Are parents in gentrifying neighborhoods more likely to opt out of neighborhood schools than parents in non-gentrifying neighborhoods?

Research Question 3: Are recent movers more likely to opt out of neighborhood schools, particularly in gentrifying neighborhoods where newcomers and existing residents may differ both demographically and in terms of their knowledge of local schools?

Research Question 4: Do parents who opt out enroll their children in schools with a different racial/ethnic composition than the neighborhood school?

Together, these questions explore whether parents in gentrifying neighborhoods embrace or avoid neighborhood schools that may still serve a relatively larger share of minority and low-income students. Importantly, this study combines multiple data sources in novel ways, linking students to their neighborhood and school contexts, assigned and enrolled schools, and residential histories to gain traction on unexplored aspects of neighborhood and school processes using national quantitative data.

Data and Sample

My analyses require information on where children live and when they moved to that neighborhood as well as where they attend school and whether that school is their local assigned neighborhood school. Restricted-use data from the PSID main interview and 2014 PSID–Child Development Supplement (CDS-14) include identifiers of children’s residential location and enrolled school, making these data uniquely suited to answer questions that connect parents’ decisions about where to live and where to send their children to school. PSID is a nationally representative longitudinal survey of households that began in 1968, with annual follow-up interviews until 1996 and biennial interviews starting in 1997. The CDS-14 was administered to sample children under age 18. PSID includes census tract identifiers and NCES school identifiers, which I use to identify characteristics of children’s neighborhoods from the decennial census and the ACS and characteristics of students’ schools from NCES. I take advantage of PSID’s longitudinal design to track when and where families moved, which allows me to examine differences in school enrollment patterns between newcomers and longtime residents in gentrifying neighborhoods.

Identifying Children’s Neighborhoods and Schools

My analyses require that I know both the school to which children were assigned based on home residence and the school that each child actually attended in 2014. PSID restricted-use Geospatial Match (Geomatch) files provide census block identifiers for the home residence of each PSID household in each survey wave. To determine a child’s assigned school, I created a census block-school crosswalk for nearly all blocks in the United States by spatially joining georeferenced block-level data from the 2010 census and attendance zone shapefiles from the 2014 SABS.¹ SABS level-specific shapefiles identify the school assigned to each block. In my sample, all blocks nested fully inside school attendance boundaries.² I merged the crosswalk with the Geomatch file to identify the school assigned to each child’s block. Next, to determine a child’s enrolled school, I relied on the CDS-14 restricted-use file, which includes school identifiers for each school-age child’s enrolled school. My dependent variable is a measure of whether a student attended his or her assigned school in 2014—that is, whether the assigned and enrolled schools match.

I also used Geomatch files to identify children’s neighborhoods at each survey wave. Following most neighborhood-focused quantitative research, I use census tract as my proxy for neighborhood. Because each census block nests fully and uniquely within one census tract, I was able to assign families to their respective census tracts based on their PSID-provided block identifiers.

The resulting data set is restricted to elementary- and secondary-level children residing in a census-defined metropolitan statistical area (MSA, using 2003 Office of Management and Budget definitions)³ that had complete data on enrolled (via PSID) and assigned school (via SABS) (N = 1,094).⁴ I limited the study to children living in metropolitan areas because this is where gentrification occurs and where school choice has expanded the most. After matching children to both schools and neighborhoods, I then linked to neighborhood and school characteristics that may shape children’s enrollment outcomes. Combining data sources in such a way allows me to explore family, school, and residential factors that predict whether parents bypass or enroll their children in their residentially assigned school.

Dependent Variable: Opt-Out from Neighborhood School

My dependent variable is a dichotomous measure indicating enrollment in a nonassigned neighborhood school option. In addition to well-known choice options such as magnet, charter, and private schools, my measure of opt-out also captures parents who bypassed local schools in favor of homeschooling, participated in district programs such as intradistrict transfer or Gifted and Talented programs, or used any other non-neighborhood school arrangement (including parents who moved into a new school attendance boundary but continued to send their children to the school served by their former neighborhood). All represent options that decouple gentrifying neighborhoods from neighborhood schools.

Key Covariates

School choice context

For parents to opt out of the assigned neighborhood school, there first must be non-neighborhood school options available. My key schooling-related predictor is a proximity measure of school choice, which captures the number of nearby charter, magnet, and private schools in 2014. To construct this measure, I relied on georeferenced data on school location and sector from the Common Core of Data (for charter and magnet schools) and the Private School Universe Survey (for private schools) and used spatial techniques to calculate the number of each type of school that fell inside a two-mile radius of the centroid of each child’s census tract. I use a two-mile radius because this represents a reasonable distance for parents when considering potential school options, following past work (Denice and Gross 2016).⁵ The resulting school proximity measures are the number of grade-specific non-neighborhood public school choices (i.e., magnets and charters) and private options (private schools) nearby each family’s residence. These measures capture the choice context of a given neighborhood, although not necessarily the actual school choices that parents made. Parents’ perceptions of choice options are often disconnected from the reality of being able to activate those options (Makris 2018), so while the perception of choice opportunities may draw particular families into neighborhoods, their actual school enrollment decisions may lead them elsewhere.

Note that many districts have programs, such as intradistrict transfer, that allow parents to choose non-neighborhood schools (Grady and Bielick 2010). Children who bypass local schools via transfer programs still appear in my sample as opt-out students. Comprehensive, detailed national data on these programs are not available, and thus they were not included when constructing my school choice measure, although having this information would improve my measure by capturing a richer set of nonlocal options that parents may perceive as available.

Residential tenure

Next, I focused on how long families had lived in the neighborhood. We might observe different behaviors regarding schooling decisions made by recent in-movers to a neighborhood compared with families that have resided in the neighborhood longer, particularly in gentrifying neighborhoods where there is greater divergence in terms of the SES profiles of newcomers and longtime residents. It could also be the case that existing residents are more acclimated to the neighborhood school and less influenced by reputation (Wells et al. 2018). For my analyses, I constructed a binary measure of residential tenure, newcomer, designating whether a family moved to its current school attendance boundary within the last two years. I created this measure using the block identifiers included in the Geomatch files, which allowed me to construct residential histories for families and observe whether households made residential moves into a new school attendance boundary in each study wave.

Neighborhood SES trajectory

My key neighborhood-related predictor is a typology of neighborhood SES trajectories: (1) gentrifying, (2) stable low/mid-SES, (3) socioeconomically declining, and (4) stable upper-SES. To define neighborhood SES trajectories, I drew on tract-level data from the long-form 1990 census and 2008–2012 five-year aggregate ACS on five indicators of neighborhood SES that capture housing and socioeconomic characteristics: median rent, median home value, median household income, percentage of residents 25 years and older with at least a college degree, and percentage of residents 16 years and older in a managerial, professional, or technical occupation (Owens 2012; Owens and Candipan 2019a). Because census tract geographies change over time, I normalized tracts to 2010 boundaries, using the Longitudinal Tract Database (Logan, Xu, and Stults 2017). I then used factor analysis, a method used to reduce several correlated variables into a set of linearly uncorrelated underlying dimensions, to construct neighborhood SES factor scores. I calculated SES factor scores using the entire universe of tracts for each MSA represented in my sample. I measured SES over a two-decade period, starting in 1990, to capture a neighborhood’s SES trajectory prior to the year of the dependent variable (2014). Each tract is assigned a standardized SES factor score, with a mean of 0 and standard deviation of 1 in 1990 and 2008–2012 (hereafter 2010), and I used these scores to assign an SES percentile rank for each tract relative to others within the same MSA. Relative SES rank is scaled from 0 to 100, with higher ranks representing tracts with the highest SES.

Next, I categorized tracts as gentrifying, stable low/mid-SES, declining-SES, or upper-SES based on changes in their relative SES rank from 1990 to 2010. Following prior work using 10-percentage-point cutoffs to define neighborhood change (Ellen and O’Regan 2011; McKinnish, Walsh, and White 2010; Owens and Candipan 2019a), I categorized tracts that began in the bottom four SES rank quintiles in 1990 and increased in relative SES rank by at least 10 percentage points as gentrifying (n = 224). Important for my analyses, gentrifying tracts are also defined as having above-average growth rates in white population from 1990 to 2010 relative to their MSA.⁶Declining-SES tracts are those that declined by 10 percentage points or more (n = 247). All remaining tracts that began in the bottom four SES quintiles in 1990 are considered stable low/mid-SES (n = 450). Finally, tracts categorized as upper-SES are those that began in the upper quintile in terms of SES rank (n = 173) and ended in the top two quintiles. Neighborhood types are mutually exclusive and exhaustive. The regional distribution is mostly proportional across neighborhood types, although fewer neighborhoods from the East (9.9 percent) and more from the South (45.0 percent) are represented in my sample.

Control variables

Motivated by past research, my analyses control for a battery of family characteristics, individual (child) attributes, and school factors that may shape whether a child attends the assigned neighborhood school. For family characteristics, number of children is a three-category variable capturing whether a family had one, two, or three or more children under the age of 18 during the 2013 survey wave. Models also include a continuous measure capturing the age of youngest child in a family. I have controlled for family income, a continuous measure constructed by averaging family income (divided by 10,000) in the 2013 and 2015 survey waves. Marital status is a binary measure indicating whether a child’s primary caregiver was married (in 2013), and homeownership indicates whether a household owned its home. All models also account for child-level factors. Grade level is a two-category variable capturing whether a child was in (1) elementary (i.e., Grades K–8) or (2) high school (Grades 9–12) during the 2013–2014 academic school year. Aside from operational differences and varying choice admissions processes, elementary levels tend to have smaller catchment areas than secondary levels, although secondary levels typically have more choice options. Some qualitative studies suggest that gentrifier parents of elementary-level children may choose public schools but reevaluate this decision as they progress through higher grades because these decisions are often contingent on access to selective enrollment schools (Billingham and Kimelberg 2013; Kimelberg and Billingham 2013). Motivated by existing work on school preferences that finds white parents particularly sensitive to minority (particularly black) school racial composition (Billingham and Hunt 2016), I controlled for child race using a binary indicator denoting whether a child was non-Hispanic white. I measured racial/ethnic school composition as percentage black, derived from the 2012–2013 Common Core of Data. I lagged this measure by one year to capture the racial/ethnic context of a child’s assigned school prior to making school enrollment decisions. Finally, I controlled for metro population density to ensure that my fixed-radius approach to measuring a neighborhood’s choice context was not driven by dense neighborhoods with substantially more non-neighborhood schooling options.

Analysis Plan

I perform logistic regression with robust standard errors (clustered by household) to predict the odds that a child opts out of the assigned neighborhood school based on household, neighborhood, and school characteristics. I begin with models examining how the availability of school choice shapes non-neighborhood school enrollment patterns. I estimate the equation

logit ((OptOut)) = α + β_{c} PubChoice + β_{p} Private + β_{k} X_{k} + ε,

(1)

where my dependent variable, OptOut, is a dichotomous indicator identifying whether a child enrolls in any type of school other than his or her neighborhood school (1 = opts out, 0 = attends neighborhood school). My key covariates of interest, PubChoice and Private, capture the number of nearby magnet, charter, and private schools to a child’s home residence. These coefficients indicate whether greater availability of nearby choice options is associated with the odds of opting out of the neighborhood school. All models include X, a vector of family, individual, and (assigned) school covariates (described previously).

Next, I investigate how residential tenure shapes school enrollment outcomes, specifically whether recent in-movers to neighborhoods are more likely to opt out:

logit ((OptOut)) = α + β_{c} PubChoice + β_{p} Private + β_{n} Newcome r + β_{k} X_{k} + ε .

(2)

Here, my second key predictor, Newcomer, is a binary measure designating a recent move (in the last two years) to the neighborhood. The coefficient for this term reveals whether recent in-movers to all neighborhoods are more likely than non-newcomers to bypass the assigned neighborhood school.

In the next set of models, I consider how school enrollment outcomes differ across neighborhoods with varying neighborhood SES trajectories, estimating the full model:

\begin{matrix} logit ((OptOut)) = α + β_{c} PubChoice + β_{t} Type + β_{r} Newcomer + \\ β_{rt} Newcomer \times Type + β_{ct} PubChoice \times Typ e + β_{p} Private + β_{k} X_{k} + ε . \end{matrix}

(3)

These analyses observe how gentrification plays into the decision to send a child to the neighborhood school, and I investigate this in two ways. First, I include Type, which categorizes neighborhoods as gentrifying (reference group), stable low/mid-SES, socioeconomically declining, or upper SES. The coefficient for neighborhood type indicates whether school enrollment patterns differ across neighborhoods with varying SES trajectories, controlling for the same individual, family background, and school characteristics in earlier models. Note that the coefficient for Type indicates the odds that a family opts out of the assigned neighborhood school conditional on residing in a particular neighborhood SES type. It does not predict whether a family moves into gentrifying or other types of neighborhoods. As such, the aim of these models is to document where choice is more likely to be activated. Taking findings of previous analyses into account, I include an interaction term between public choice availability and neighborhood type, PubChoice×Type, which indicates whether parents are more likely to opt out in gentrifying neighborhoods when there is more public choice. I control for private school proximity but focus on the role of public choice, which expanded significantly during this period (discussed in the following). Second, I interact my measure for newcomer status with neighborhood type, Newcomer×Type, to test whether newcomers are more likely to opt out of neighborhood schools compared with non-newcomers and whether these differentiated odds of opt-out by residential tenure differ between gentrifying and non-gentrifying neighborhoods.

Finally, building on regression analyses and continuing the focus on newcomers, I close with descriptive analyses observing the racial composition of assigned and enrolled schools as it relates to newcomers opting out of the neighborhood school. After establishing where newcomers are more likely to opt out in regression models, these descriptive analyses then aim to document particular features of schools (i.e., school racial composition) that potentially factor into in-movers’ decisions to bypass neighborhood schools.

Findings

Table 1 displays descriptive statistics for my dependent measure, key independent variables, and controls. The overall opt-out rate (i.e., non-neighborhood school enrollment) in my analytic sample is just over 44 percent, slightly higher than the national average for urban districts. Gentrifying neighborhoods have the lowest opt-out rate (40.6 percent) but also the fewest number of nearby choice options (1.3) on average. Only 38 percent of gentrifying neighborhoods in my sample had any nearby choice options, compared with two-thirds of stable low/mid-SES, declining-SES, and upper-SES neighborhoods. Stable low/mid-SES neighborhoods have the greatest average number of both public and private nearby choice options (1.4 schools of either type). Among neighborhoods that do have choice options, however, stable low/mid-SES and gentrifying neighborhoods have the most public and private choice options on average (public and combined public/private choice means are 3.3 and 4.4 for low/mid-SES and 3.2 and 3.5 for gentrifying, respectively). Gentrifying and upper-SES neighborhoods have the greatest proportion of white residents (71 and 76 percent in 2010), while socioeconomically declining neighborhoods have the greatest proportion of nonwhite residents (61 percent in 2010). Although the proportion of white residents declined among all neighborhood types from 1990 to 2010 (reflecting national demographic changes), it declined the least in gentrifying neighborhoods (where, by my definition, neighborhood white population growth exceeded the median growth rate of the corresponding MSA from 1990 to 2010).

Table 1.

Summary Statistics of Key Variables in Analysis Sample by Neighborhood Socioeconomic Status Type.

	Overall (N = 1,094)		Low/Mid-SES (n = 450)		Gentrifying (n = 224)		Declining-SES (n = 247)		Upper-SES (n = 173)
	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Dependent variable
% Opt-out rate	44.79		45.78		40.63		42.91		50.29
Family factors
Family income ($ in ten thousands)	8.3	10.8	5.9	4.8	11.1	9.4	6.1	4.7	13.8	21.7
Number of kids (under 18)	2.2	.8	2.3	.8	2.3	.8	2.1	.8	2.0	.8
Age of youngest child	6.8	3.9	6.6	3.9	6.5	3.6	7.0	3.8	7.2	4.3
% BA	31.8		21.3		43.3		21.5		59.0
% Married	64.5		61.8		79.9		49.8		72.8
% Owns home	51.0		42.7		62.5		41.3		71.7
Child factors
Grade level
% KG	71.5		72.0		77.7		67.2		68.8
% Grades 9–12	28.5		28.0		22.3		32.8		31.2
% White	53.6		48.2		72.8		36.0		67.6
School factors
Assigned school % black	28.3	33.3	33.2	36.8	13.6	22.0	41.2	34.9	16.1	20.6
Number private <2 miles	1.1	2.0	1.4	2.3	.7	1.9	1.1	1.5	1.1	1.7
Number magnet <2 miles	.2	.7	.3	.8	.1	.4	.5	1.1	.2	.7
Number charter <2 miles	.7	1.7	1.1	2.2	.5	1.7	1.1	1.5	.4	.8
Number public choice <2 miles	1.0	2.0	1.4	2.5	.6	1.8	.8	1.4	.6	1.3
Number all choice <2 miles	2.1	3.4	2.8	4.0	1.3	3.3	1.9	2.5	1.7	2.4
Number public choice <2 miles (among neighborhoods with choice)	2.9	2.6	3.3	2.9	3.2	3.1	2.3	1.6	1.8	1.8
Proportion any public choice	.336		.429		.183		.328		.306
Proportion any choice	.585		.638		.375		.623		.665
Residential tenure
% Newcomers	44.8		44.9		44.2		49.0		39.3
% White among newcomers	43.1		37.1		63.6		32.2		50.0
Neighborhood context
SES rank
1990	47		29		41		53		90
2010	47		31		66		32		86
Proportion college degree
1990	.190		.119		.158		.190		.417
2010	.264		.174		.341		.188		.507
Proportion professional occupation
1990	.243		.183		.219		.250		.421
2010	.328		.256		.418		.250		.511
Median home value (%)
1990	86,185		70,723		87,333		78,517		135,863
2010	195,347		163,244		237,614		145,375		295,472
Median rent ($)
1990	381		346		344		398		492
2010	783		725		887		699		919
Median household income ($)
1990	32,342		26,741		31,486		31,482		49,246
2010	55,770		45,593		69,905		43,379		81,629
% Non-Hispanic white
1990	70		58		79		69		90
2010	53		43		70		40		77
% Non-Hispanic black
1990	19.3		27.6		11.3		22.6		3.3
2010	24.3		30.6		11.0		36.4		8.1

Note: Analysis sample restricted to families with school-age children residing in census-defined metropolitan statistical areas with complete school assignment and neighborhood SES data. “Newcomers” refers to households that moved school attendance boundaries in the past two years. “Public choice” refers to magnet and charter schools. “All choice” refers to magnet, charter, and private schools. The number of public choice options (among neighborhoods with options) reports the average count of nearby public options for neighborhoods with any public choice. SES = socioeconomic status; KG = kindergarten.

In my sample, PSID children residing in gentrifying and upper-SES neighborhoods tend to be white (72.8 and 67.6 percent), while children in declining-SES neighborhoods are mostly nonwhite (64 percent). Just over two-fifths of children are recent in-movers (“newcomers”) to their current neighborhood. Notably, among newcomers to gentrifying neighborhoods, about 64 percent are white, the largest share of all neighborhood SES types. On the other hand, only 32 percent of newcomers to declining-SES neighborhoods are white, representing the lowest share among neighborhood types.⁷

Predicting Neighborhood School Opt-Out

I begin with logistic regression analyses examining school enrollment outcomes for families with school-aged children (Table 2). Model 1 presents the baseline model predicting the odds of opting out of the neighborhood school given family and child factors and school racial composition. A greater number of kids (p < .05) is associated with lower odds of enrollment in non-neighborhood schools—families with three or more children are less likely to bypass their assigned neighborhood school. Family income has a positive effect (p < .05) on the odds of opting out, indicating that class plays a role in decoupling neighborhoods and local schools.

Table 2.

Effects of School Choice Proximity and Residential Tenure on Non-neighborhood School Enrollment.

	Model 1		Model 2		Model 3
	Baseline Controls		School Choice		Newcomer
	Odds Ratio	SE	Odds Ratio	SE	Odds Ratio	SE
Family background
Family income (in ten thousands)	1.033*	.015	1.034*	.017	1.035*	.017
Age of youngest child	.952^†	.028	.956	.028	.967	.028
College degree	.967	.170	.979	.187	.995	.194
Married	1.019	.199	1.078	.210	1.127	.221
Owns home	1.114	.216	1.106	.215	1.368	.284
Number of kids (under 18)
1	Reference		Reference		Reference
2	.815	.153	.793	.150	.790	.150
3+	.553**	.120	.545**	.119	.551**	.121
Child factors
Non-Hispanic white (child)	.983	.200	1.049	.216	1.066	.221
Grade level
K–8	Reference		Reference		Reference
9–12	1.189	.236	1.250	.246	1.218	.243
School factor
School % black	1.691^†	.486	1.500	.434	1.606	.472
Metro control
Population density	1.096	.098	.970	.090	.969	.089
School choice
Number public choice <2 miles			1.089	.060	1.088	.061
Number private <2 miles			1.129*	.054	1.134**	.054
Residential tenure
Newcomer					1.704**	.300
constant	.891	.281	.718	.232	.438*	.161
n (clusters)	784		784		784
N (observations)	1,094		1,094		1,094

Note: Logistic regression results reported as odds ratios, with robust standard errors (clustered by household) in italics. All models include family, child, and school covariates. “Newcomer” refers to households that moved school attendance boundaries within the past two years. “School % black” is lagged by one year.

†

p < .10. *p < .05. **p < .01 (two-tailed).

Model 2 adds measures of school choice proximity, my main school-level predictors of non-neighborhood school enrollment, which capture the public and private choice context of a neighborhood. Consistent with expectation, greater availability of nearby private options increases the odds that parents will bypass the local neighborhood school. Each additional private school increases the odds of opting out by a factor of 1.13 (p < .001). Interestingly, the coefficient for public choice (i.e., magnet and charter) is not statistically significant. It could be the case that parents residing in different types of neighborhoods vary in their use of magnet and charter options, which I explore in later models.

Model 3 examines whether the odds of non-neighborhood school enrollment differ between recent movers and existing residents. Relative to longer-tenured residents, recent movers to a school attendance boundary are more likely to enroll in nonassigned neighborhood schools given school choice availability and model controls (odds ratio [OR] = 1.7; p < .01). Newcomers may avoid neighborhood schools because they are less familiar with them than longer-tenured residents, or it may be the case that newcomers only move to certain neighborhoods if they can opt out of the neighborhood school. Some newcomers may prioritize school stability when moving to a new neighborhood by continuing to send their children to their former neighborhood’s school (DeLuca and Rosenblatt 2010).

Do Parents Bypass Neighborhood Schools More Often in Gentrifying Neighborhoods?

The next set of models explores whether parents are more likely to opt out in gentrifying neighborhoods. Table 3 displays odds ratios for models examining whether school choice context and residential tenure shape school enrollment outcomes differently in gentrifying neighborhoods compared with other non-gentrifying neighborhood types. To be clear, these analyses do not support causal claims about parents opting out because of gentrification but rather, present a descriptive portrait of how patterns of non-neighborhood school enrollment play out across neighborhoods experiencing various SES trajectories.

Table 3.

Logistic Regression Predicting Non-neighborhood School Enrollment by Neighborhood Socioeconomic Status Type.

	Model 1		Model 2		Model 3
	Neighborhood Type		Public School Choice		Newcomer
	Odds Ratio	SE	Odds Ratio	SE	Odds Ratio	SE
Family background
Family income (in ten thousands)	1.036*	.018	1.038*	.018	1.039*	.018
Age of youngest child	.957	.027	.949^†	.027	.958	.028
College degree	.971	.187	.935	.182	.962	.192
Married	1.075	.211	1.103	.218	1.129	.223
Owns home	1.100	.215	1.164	.229	1.478^†	.312
Number of kids (under 18)
1	Reference		Reference		Reference
2	.798	.152	.784	.151	.778	.151
3+	.550**	.121	.532**	.118	.527**	.118
Child factors
Non-Hispanic white (child)	1.047	.217	1.014	.215	1.020	.218
Grade level
K–8	Reference		Reference		Reference
9–12	1.236	.243	1.285	.256	1.237	.250
Metro control
Population density	.967	.092	.965	.094	.957	.092
Neighborhood factors
Neighborhood type
Gentrifying	Reference		Reference		Reference
Low/mid-SES	1.219	.280	1.531^†	.383	2.062*	.676
Declining-SES	1.082	.278	1.498	.436	1.589	.604
Upper-SES	1.243	.338	1.807*	.530	2.683**	1.003
School choice
Number public choice <2 miles	1.086	.060	1.869**	.414	1.755**	.349
Number private <2 miles	1.126*	.054	1.129*	.054	1.133*	.053
Neighborhood type × public choice
Gentrifying × PubChoice			Reference		Reference
Low/mid-SES × PubChoice			.584*	.133	.622*	.128
Declining-SES × PubChoice			.521**	.127	.549**	.123
Upper-SES × PubChoice			.428**	.111	.455**	.110
Residential tenure
Newcomer					2.640*	1.071
Neighborhood type × newcomer
Gentrifying × newcomer					Reference
Low/mid-SES × newcomer					.565	.262
Declining-SES × newcomer					.886	.446
Upper-SES × newcomer					.382^†	.209
School factors
School % black	1.487	.441	1.350	.407	1.463	.452
constant	.627	.230	.529^†	.200	.267**	.124
n (clusters)	784		784		784
N (observations)	1,094		1,094		1,094

Note: Logistic regression results reported as odds ratios, with robust standard errors (clustered by household) in italics. All models include family, child, and school covariates. Reference neighborhood category is gentrifying neighborhoods. “Newcomer” refers to households that moved school attendance boundaries within the past two years. “School % black” is lagged by one year.

†

p < .10. *p < .05. **p < .01 (two-tailed).

Table 3, Model 1, presents the odds of opting out conditioning on school choice context and neighborhood type. Here, the main effect for public choice is positive but remains nonsignificant. Public choice options have proliferated since the mid-1990s, roughly the same period during which I observed neighborhood SES change. Qualitative work has found that public options, such as charter and magnet schools, are viewed as attractive options for gentrifier families. Therefore, I add interaction terms between nearby public choice and neighborhood type in Model 2. After doing so, the null main effect for public choice in Model 1 gains statistical significance. Here, the main effect for nearby public choice represents the odds for parents in gentrifying neighborhoods (because gentrification is my reference neighborhood category). The odds of opt-out in gentrifying neighborhoods increase with each additional public choice option that is located nearby (OR = 1.87; p < .01). Interestingly, when combining the main and interaction effects for public choice and neighborhood type, the odds of opting out are lower in non-gentrifying neighborhoods relative to gentrifying neighborhoods when there are nearby public choice options. Greater availability of nearby public options increases the odds of parents opting out of the neighborhood school, but it increases the odds the most in gentrifying neighborhoods—parents are most likely to bypass their assigned neighborhood school in gentrifying neighborhoods relative to low/mid-SES, upper-SES, and declining-SES neighborhoods. Taken together, these results suggest that opt-out increases the most in gentrifying neighborhoods as more public options are available.

Recall that fewer gentrifying neighborhoods in my sample have nearby choice options compared with non-gentrifying neighborhood types, so the null main effect for public choice in Model 1 is concealing key differences in terms of where the public choice context is more likely to break the link between home residence and neighborhood school enrollment (i.e., gentrifying neighborhoods). Although fewer gentrifying neighborhoods had public choice options, parents residing in those neighborhoods were far more likely than parents in non-gentrifying neighborhoods to opt out when those options were available. I also tested whether the odds of private school enrollment, a long-standing option that has not expanded in recent decades, varied between different types of neighborhoods, but I did not observe significant differences between gentrifying and non-gentrifying neighborhoods (results not shown). While there will always be some subset of families that choose private schools, thus breaking the neighborhood-school link in all neighborhoods with nearby options, the private school context of a neighborhood does not seem to be the main feature driving choices differently between families in gentrifying and non-gentrifying neighborhoods.

To better interpret interactions from Model 2 (Table 3), Figure 1 presents predictive probabilities of opt-out at various levels of public school choice availability among neighborhood types. On average, parents residing in gentrifying neighborhoods (square symbol) with at least some availability of public choice options are more likely to bypass neighborhood schools compared with parents in non-gentrifying neighborhoods, holding covariates at their respective means. While gentrifying neighborhoods have the greatest expected probability of opting out with just one public school choice option, the probabilities are similar across all neighborhood types. Significant differences, however, emerge as the number of public options increases—the upward curve for gentrifying neighborhoods clearly illustrates that the probability of opting out of neighborhood schools in gentrifying neighborhoods is higher relative to declining-SES, upper-SES, and low/mid-SES neighborhoods. Note that the average number of public choice options for gentrifying neighborhoods that do have nearby choice is about 3.2 schools, so the higher probabilities for gentrifying neighborhoods relative to other neighborhood types represent feasible outcomes. Moreover, the positive effect of public choice is greater in gentrifying versus non-gentrifying neighborhoods. For example, the probability of opt-out is about 15 percentage points higher for parents residing in gentrifying neighborhoods with one public school option (50), compared with those without any public choice (35), on average. In contrast, compared with neighborhoods without any public choice options, the probability of opt-out in low/mid-SES neighborhoods (circle symbol) is similar (44 vs. 46). In stable upper-SES neighborhoods, it could be the case that residents find their local school acceptable, so the availability of public choice does not decouple neighborhoods and schools in the same way as in gentrifying neighborhoods. Instead, when parents in upper-SES neighborhoods do activate choice options, they may invest in private schooling as a way to accrue additional advantages despite being zoned for high-quality neighborhood schools (Loeb et al. 2011; Mickelson et al. 2008).

Figure 1.

Predicted probabilities of non-neighborhood school enrollment in gentrifying and non-gentrifying neighborhoods given public choice options.

Do Newcomers to Gentrifying Neighborhoods Bypass Local Schools?

Results thus far indicate that children residing in gentrifying neighborhoods are most likely to opt out of their assigned neighborhood schools as the number of nearby school choice options increases. In previous analyses, I showed that newcomers are more likely to opt out of the neighborhood school. This may be particularly true in gentrifying neighborhoods because newcomers, who in my sample are mostly white and higher-income, may differ most from longer-tenured residents in terms of both their ability to access choice and their interest in doing so. For example, higher-SES newcomers to gentrifying neighborhoods may view charter options more favorably than neighborhood schools, which may still lag behind neighborhoods in terms of socioeconomic and demographic changes. On the other hand, newcomers to more socioeconomically (and demographically) stable neighborhoods (i.e., low/mid-SES and upper-SES neighborhoods) may not differ substantially from existing residents in either their school preferences or economic conditions.

Next, I explore heterogeneity in the odds of opt-out between newcomers and longtime residents across neighborhood type (Table 3, Model 3). Are the odds of opt-out for newcomer parents in gentrifying neighborhoods higher relative to parents who have resided in the neighborhood longer? In this interacted model, the main effect for newcomer represents the odds ratio of gentrifying newcomers to existing residents given that gentrification is my reference neighborhood category. Relative to existing residents, the odds of opting out for recent in-movers to gentrifying neighborhoods are 160 percent greater, all factors considered (OR = 2.6; p < .05). These results also indicate that residential tenure significantly shapes non-neighborhood schooling outcomes in declining-SES neighborhoods. After calculating odds ratios between newcomers and existing residents in declining-SES neighborhoods, newcomers in declining-SES neighborhoods are also more likely to enroll in non-neighborhood schools (OR = 2.3; p < .05).⁸

Figure 2 illustrates the results of Model 3 in a different way, comparing the average predictive probabilities of opting out between newcomers (squares) and longer-tenured residents (circles) at varying levels of nearby public choice options in gentrifying, low/mid-SES, upper-SES, and declining-SES neighborhoods. Trends illustrated in Figure 2 align with the hypothesis that newcomers to gentrifying neighborhoods are more likely to opt out of local schools compared with longer-tenured parents. Here, the expected probabilities for newcomers are most dissimilar from those of existing residents in gentrifying and declining-SES neighborhoods—the higher trend lines for newcomers suggest that they opt out more often than existing residents when nearby public choice options are available. It could be the case that newcomers to both types of neighborhoods rely on much different networks than existing residents for information about local schools. On the other hand, the adjusted probabilities for newcomers and existing residents map onto each other almost identically in low/mid-SES and upper-SES neighborhoods—two stable neighborhood types. That newcomers are most dissimilar from existing residents in socioeconomically and demographically changing neighborhoods (i.e., gentrifying and declining-SES neighborhoods) suggests that while recent in-movers may be contributing to changes in the demographic profile of their neighborhoods, they may not be contributing similarly to their neighborhood schools. Results here suggest that the main effect for newcomers from earlier models may be driven largely by parents moving into socioeconomically and demographically changing (i.e., gentrifying and declining-SES) neighborhoods.

Figure 2.

Predicted probabilities of opting out between residential newcomers and longtime residents by neighborhood SES type.

Descriptive Analyses: Are Newcomers in Gentrifying Neighborhoods Opting Out for Whiter Schools?

Findings thus far indicate that among places with at least one available nearby public school choice option, families residing in gentrifying neighborhoods (relative to non-gentrifying types) are more likely to bypass the local school, with newcomers to gentrifying neighborhoods the most likely to opt out. Gentrifying neighborhoods tend to have greater inflows of upper-SES and white residents, which contribute to the changing demographic and socioeconomic profiles of those neighborhoods (Owens and Candipan 2019a). Prior work finds that whites are particularly sensitive to school racial composition, preferring schools with greater representation of white students, and seek alternate schooling options when there is a perceptible share of nonwhite (particularly black) students. Building off regression analyses of newcomers, who are most likely to opt out, the following descriptive analyses further examine how school racial/ethnic composition, as one particular school feature, may be associated with non-neighborhood enrollment among newcomers. When newcomers to gentrifying neighborhoods opt out, are they enrolling in schools with a greater share of white students than their assigned neighborhood school?

Table 4 compares the racial composition of children’s assigned neighborhood schools with that of the schools in which they actually enroll.⁹ The first two rows indicate that when parents send their children to non-neighborhood schools, the average white racial composition of the enrolled school is higher than that of the assigned school, while the opposite is true for black racial composition—the share of black students is lower in enrolled schools relative to assigned schools. Newcomers in all types of neighborhoods opt out for schools with greater shares of white students and fewer black students, on average. Among newcomers who opt out, the difference in racial composition in assigned schools compared with enrolled schools is most evident in gentrifying and low/mid-SES neighborhoods. Moreover, the contrast in the racial composition of assigned and enrolled schools is greatest among white newcomers. While white newcomers to stable low/mid-SES and declining-SES neighborhoods also opt out for schools with more white students than their assigned neighborhood school, white newcomers to gentrifying neighborhoods opt out for schools with a far greater share of white students (64.7 to 78.2 percent) as well as a smaller proportion of black students (10.6 vs. 4.6 percent). This suggests that white newcomers to gentrifying neighborhoods may be more sensitive to the racial composition of their neighborhood school than to that of their neighborhood, aligning with recent work (Candipan 2019; Mader et al. 2018). These unadjusted analyses, although descriptive (and limited by a small sample size), reveal patterns suggestive of meaningful associations by race and neighborhood type, consistent with past work on school preferences. These results imply that the segregating choices of white and higher-SES newcomers may produce segregated social networks and separate educational experiences even among students from the same neighborhood (Burdick-Will 2018).

Table 4.

Mean School Racial Composition of Assigned and Enrolled Schools for Opt-Out Newcomers by Neighborhood SES Type.

	Low/Mid-SES (n = 450)		Gentrifying (n = 224)		Declining-SES (n = 247)		Upper-SES (n = 173)
	Assigned School	Enrolled School	Assigned School	Enrolled School	Assigned School	Enrolled School	Assigned School	Enrolled School
All opt-out students
% White	28.1	33.9	56.4	59.7	28.3	36.5	54.0	56.9
% Black	40.6	36.5	18.8	17.4	38.7	36.0	19.9	17.7
Opt-out newcomers
% White	21.5	28.9	49.2	56.0	30.4	33.1	52.3	51.9
% Black	46.0	42.1	25.6	22.0	36.5	39.9	24.9	22.2
Opt-out white newcomers
% White	41.9	50.2	64.7	78.2	44.5	54.6	66.2	64.9
% Black	8.8	7.9	10.6	4.6	8.4	11.8	13.1	9.8

Note: Sample restricted to opt-out students only. Homeschooled students (about 5 percent of opt-out students) and students with missing racial composition data for the enrolled school (n = 3) are excluded from these analyses. “Assigned school” refers to the neighborhood school to which each student was assigned based on grade and residence. “Enrolled school” refers the school a student actually attended. Data on school racial composition calculated from 2013–2014 Common Core of Data and 2013–2014 Private School Universe Survey. “Newcomer” refers to households that moved school attendance boundaries in the past two years. SES = socioeconomic status.

Interestingly, opt-out white newcomers to socioeconomically declining neighborhoods enroll in schools with a higher percentage of both white and black students relative to their assigned schools. Although the proportion of white opt-out newcomers to declining-SES neighborhoods is relatively small (~33 percent), the results nonetheless suggest nuance around typical “avoidance” explanations in the neighborhood and school choice literature. Future work should further explore how contextual features of schools vary between neighborhood types and how school characteristics shape enrollment decisions differently for parents in gentrifying and declining-SES neighborhoods.

Conclusion

Because neighborhoods have historically been segregated along economic and racial/ethnic lines, schools have also endured legacies of segregation. When neighborhoods change, do schools follow? Using a novel combination of individual, school, and neighborhood data that link children to both assigned and enrolled schools in the United States, this study set out to understand whether the availability of school choice shapes school enrollment outcomes in gentrifying neighborhoods—where the inflow of white and higher-SES parents to lower-SES and minority urban communities presents an opportunity to observe whether commonly understood narratives of race- and income-based school preferences are upheld even when families choose more diverse neighborhoods. I find that when there are nearby choice options to opt out of the neighborhood school, parents in gentrifying neighborhoods are more likely than parents in socioeconomically stable and declining neighborhoods to activate nonlocal options, bypassing neighborhood schools in the process. Because gentrifying neighborhoods are those experiencing change, this decoupling between neighborhoods and schools implies that whatever racial/ethnic and socioeconomic change is occurring in neighborhoods may not result in corresponding changes in schools. These findings are consistent with recent aggregate-level research describing growing dissimilarity between neighborhood composition and school composition in areas experiencing socioeconomic ascent (Bischoff and Tach 2018; Candipan 2019; Mader et al. 2018).

As past qualitative work has shown, processes of residential and school choice in gentrifying neighborhoods are complex and dynamic. Whether gentrifier parents opt out is thus highly contingent on neighborhood and school contexts, the geographic distribution of educational opportunities, and the ability of parents to actualize those opportunities. On the one hand, particular features of assigned local schools could draw gentrifier parents into gentrifying neighborhoods. On the other hand, gentrifiers opt out the most when there are nonlocal options, suggesting that the choice context of a neighborhood may also draw families into neighborhoods where they may hold reservations about the local school.

Residential newcomers opt out of neighborhood schools more than parents who have lived in the neighborhood longer, particularly in gentrifying neighborhoods. When newcomers, particularly white newcomers, opt out of neighborhood schools in gentrifying neighborhoods, they tend to seek non-neighborhood schools serving higher proportions of white students relative to their assigned neighborhood schools. These results imply that neighborhood integration does not guarantee school integration—the diversity produced via gentrification may also uphold school segregation as incoming white and higher-SES families choose more frequently to bypass neighborhood schools when nonneighborhood options are nearby. This has important implications for thinking about how urban policy and school policy are linked. Efforts to address school segregation via neighborhood integration may not always produce intended results.

Findings suggest that neighborhood schools in gentrifying areas may grow increasingly dissimilar from the neighborhoods they serve, particularly as school choice options proliferate. One implication of parents opting out in gentrifying neighborhoods is that while the neighborhoods may change, their key institutions may not. This results in nonintersecting social networks—even while neighborhood diversity increases, the segregated social networks within neighborhoods lead to separate educational (and everyday) experiences (Burdick-Will 2018). Another concern is that school choice may divert both money and political power away from traditional public schools in gentrifying neighborhoods (as Ladd and Singleton [2018] found with charter schools and local school funding in North Carolina) or that upper-SES and white parents may divert their social and economic resources to non-neighborhood schools. That said, while school integration is often a stated goal of districts, integrating schools along demographic lines alone does not guarantee meaningful inclusion, as other scholars have noted (Lewis and Diamond 2015; Lewis-McCoy 2014). While my data cannot speak directly to these nuanced dynamics, future work should consider examining within-school processes, such as whether specialized programming and curricula that draw gentrifier parents into demographically diverse urban schools ultimately sort students into segregated tracks within schools.

Partly due to data limitations, few quantitative studies have analyzed connections between residential choice and school choice nationally and from a microlevel perspective. In this study, I examined individual school enrollment outcomes among children residing in different types of neighborhoods to understand where and for whom school choice occurs. I focused on schooling decisions in gentrifying neighborhoods to understand whether parents moving into integrating neighborhoods embrace or avoid schools that may still be relatively diverse and less advantaged in some way. My findings suggest that while some families may welcome diversity in neighborhoods undergoing demographic change, that same embrace does not always extend to schools in changing neighborhoods.

This study presents a first step toward understanding how individual decisions about where to live and attend school aggregate to shape broader patterns of inequality across neighborhoods and schools, two key contexts for children’s well-being. The school choice “liberation” model posits that severing the neighborhood-school link is a way of expanding educational opportunities for lower-SES families anchored to lower-performing schools under a strict residence-based assignment system. Results from my study, however, find that the expansion of school choice breaks the neighborhood-school link more frequently in gentrifying neighborhoods, allowing white and higher-SES in-movers the opportunity to make residential decisions independently of school considerations. Future work should further explore whether some families consider a wider array of residential options in certain neighborhoods despite concerns about the zoned school given that school choice effectively decouples neighborhoods from the traditional public schools that serve them.

This study has limitations that future research should address. First, although I gathered information on families’ neighborhoods and residential moves over time, this study only examined school enrollment outcomes in a single year. Although school enrollment data are available in earlier CDS surveys, the lack of comprehensive SABS data for the 1997–2007 period prohibited my ability to match kids precisely to their assigned schools in earlier CDS years. As the collection of school attendance boundary data improves, future work should incorporate subsequent waves of CDS to address some of these limitations. Second, I lack comprehensive district school assignment policy data that would allow for more extensive examinations of how softer forms of school choice break the link between housing markets and education markets. Third, because PSID sample families rarely live in the same neighborhood, I was unable to directly compare newcomers and existing residents from the same neighborhood. Moreover, the data I used to construct residential histories were reported once every two years, so my analyses may be missing nuances regarding timing of moves. Additionally, the origin and design of the PSID have resulted in a largely black-white sample over time, limiting extensive examinations on how multiethnic diversity shapes neighborhood and school processes. Fourth, because census data are limited in their ability to capture the exact timing of neighborhood change, I lost some of the specific timing in terms of how school choice affects gentrification (and vice versa). Finally, this study was limited somewhat by sample size, including relatively smaller group sizes for gentrifying and (especially) upper-SES neighborhoods. A larger sample would allow for additional analyses.

Despite these limitations, this study makes several substantive contributions, combining multiple data sources in new ways to address previously unanswered questions with national-level quantitative data. While prior work demonstrates that choice happens for certain types of people, this study finds that choice also happens in certain types of places. This study engages with and extends theories of residential and school sorting and speaks more broadly to how individual-level processes, such as decisions about where to live and attend school, aggregate to create wider patterns of spatial inequality. Findings raise new questions about how race- and class-based sorting upholds existing racial and class hierarchies by way of neighborhood and school stratification. Although focused largely on gentrifying neighborhoods, this study sheds light more broadly on the nuanced and complex process of neighborhood change. Results bear on housing and school policies, including affordable housing, neighborhood revitalization, school choice, neighborhood and school integration, economic development, student assignment policies, and many other urban and education policies.

Research Ethics

This research uses restricted data files from the Panel Study of Income Dynamics and was conducted in accordance with guidelines set forth under a restricted-use contractual agreement designed to protect the anonymity of respondents. More information about restricted-use data files can be obtained via the Panel Study of Income Dynamics website at https://psidonline.isr.umich.edu.

Footnotes

Acknowledgements

I thank Ann Owens, Kim Goyette, members of the University of Southern California Neighborhood Working Group, participants at the 2017 Panel Study of Income Dynamics Data-Users Conference, and the editor and anonymous reviewers for helpful feedback on earlier drafts.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a National Science Foundation Doctoral Dissertation Research Improvement Grant (No. 1702765) and a National Academy of Education/Spencer Foundation Dissertation Fellowship.

ORCID iD

Jennifer Candipan

Notes

Author Biography

Jennifer Candipan is a postdoctoral research fellow in sociology at Harvard University and incoming assistant professor of sociology at Brown University. Her work broadly examines neighborhood change, links between housing and education markets, and how neighborhood and school processes shape inequality.

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