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Peabody Journal of Education

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Gentrification, Charter Schools, and Enrollment Patterns in Washington, DC: Shared Growth or New Forms of Educational Inequality?

Bryan Mann, Heather Bennett & Annah Rogers

To cite this article: Bryan Mann, Heather Bennett & Annah Rogers (2020) Gentrification, Charter Schools, and Enrollment Patterns in Washington, DC: Shared Growth or New Forms of Educational Inequality?, Peabody Journal of Education, 95:3, 211-228, DOI: 10.1080/0161956X.2020.1776070

To link to this article: https://doi.org/10.1080/0161956X.2020.1776070

Published online: 13 Jul 2020.

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Gentrification, Charter Schools, and Enrollment Patterns in Washington, DC: Shared Growth or New Forms of Educational Inequality? Bryan Manna, Heather Bennettb, and Annah Rogersa

aUniversity of Alabama, Tuscaloosa; bPennsylvania School Boards Association

ABSTRACT Cities in the United States and across the world have experienced gentrifica- tion at the same time as school choice policies have become more popular. This research examines the relationship between gentrification and charter schooling, seeking to understand how together they affect demographic composition of schools across Washington, DC. This study uses geographic information systems (GIS) mapping and statistical techniques to show that gentrified neighborhoods are more likely to have charter schools. Additionally, the demographic compositions of charter schools and traditional public schools differ depending on the gentrification classification of the census tract in which the schools are located. While a handful of diverse charter schools exist in gentrified neighborhoods and some diverse public schools exist in traditionally affluent neighborhoods, schools in Washington, DC remain racially and economically isolated overall.

Introduction

Gentrification and charter schooling have each changed urban educational ecosystems in the United States during the past 20 years (Davis & Oakley, 2013; Hwang & Lin, 2016). Charter schools first began opening across the United States in the mid-1990s. Many cities in the 1990s and 2000s also experienced the most recent wave of gentrification, which is defined generally as when wealthy residents migrate to city centers. This migration changes the sociodemographic dynamics of for- merly distressed neighborhoods. As urban schools and neighborhoods experience change, policy- makers must consider how these trends influence school racial segregation and isolation. It is not clear if or how the intersection of charter schools and gentrification change school enrollment demographics, or if patterns of racial isolation in schools persist in new forms.

This study examines school enrollment patterns in Washington, DC because the city has experienced both high levels of gentrification and charter schooling in the past two decades (Maciag, 2015; U.S. Department of Education, 2013). This context offers an ideal case to help understand what happens to school enrollment patterns when gentrification and charter schooling intersect. We examine how differences in levels of neighborhood gentrification in Washington, DC relate to the distribution of students within schools classified by type (charter/traditional). The questions at focus in this study are:

● How have school-aged racial demographics changed across tracts with different levels of gentrification in Washington, DC from 1990 to 2013?

● Are gentrified neighborhoods in Washington, DC more likely to have charter schools? ● How does school-level racial composition, both in charter and traditional schools, compare

across neighborhood types?

CONTACT Bryan Mann [email protected] University of Alabama, 408 Joseph R. Pearson Hall, 1122 West Campus Rd., Lawrence, Kansas 66045-3101 © 2020 Taylor & Francis Group, LLC

PEABODY JOURNAL OF EDUCATION 2020, VOL. 95, NO. 3, 211–228 https://doi.org/10.1080/0161956X.2020.1776070

These questions inform research about choice, racial isolation, and neighborhood change. Previous research suggests charter schools increase school segregation (Frankenberg, Siegel- Hawley, & Wang, 2010; Kotok, Frankenberg, Schafft, Mann, & Fuller, 2017), and scholars worry gentrification raises new concerns about how demographic changes shape fair enrollment patterns, especially in relationship to segregation and diversity (Erickson, 2016). Policymakers must determine how to manage neighborhood change in ways that eliminate racially and economically isolated schools. Ending racial isolation in schooling is a critical step toward mitigating educational inequity, especially as research shows benefits of diverse schools. These benefits include, but are not limited to, improved educational outcomes and life course chances for minority students, enhanced social cohesion and diverse interactions for all students, and the cultivation of a sense of community across racial and socioeconomic groups (Linn & Welner, 2007; Mickelson, 2008; Mickelson & Nkomo, 2012; Orfield & Ee, 2017; Wells & Crain, 1994).

In addition to the policy relevance of this study, it fits the theme of this issue of the Peabody Journal of Education: “School Choice Policies Shaping Neighborhoods, School Locations, and ‘Destinies.’” The issue expands knowledge on school choice policies and their influence on neigh- borhoods; this article explores the intersection of charter school policy and neighborhood change, uniquely exploring the influence they have together on racial and economic isolation. This study relates to educational opportunities across neighborhoods because it presumes that racial isolation and segregation have long posed barriers to expanding educational opportunity.

Strategic action fields

This study is guided by a strategic action fields (SAFs) understanding of organizations and a general field understanding of cultural and social reproduction (Fligstein & McAdam, 2011; Bordieu, 1977; Posey-Maddox, 2014, p. 9). This framework suggests organizations make strategic decisions based on their position in a schooling ecosystem while individuals navigate ecosystems in ways that reproduce their socioeconomic status and racial privilege. The rules of a given field alter strategic choices and behavior for both organizations and individuals.

This framework justifies our motivation to explore differences across organizational and neigh- borhood classifications. Enrollment patterns may change depending on a school’s type and the context of its geographic location. Schools target students in ways that are most likely to ensure their organizational survival as contextual factors shape market structures and market hierarchies shape decisionmaking (Jabbar, 2016; Lubienski, Gulosino, & Weitzel, 2009; Lubienski & Lee, 2016). Meanwhile, individuals enroll in schools that reflect their educational and cultural logic in ways that reproduce socioeconomic status (Holme, 2002). The results of these complex processes suggest that differences in school types and enrollment patterns rely on the relationship that organizations and individuals have with the schooling ecosystem in which they are situated.

The emergence of charter schools alters the rules of the educational ecosystem in Washington, DC because the new organizational type puts pressure on traditional schools. Charter schools and related choice policies allow families to select schools outside of catchment areas, reframing how home location relates to schooling options. Gentrification provides opportunities for organizations to open schools in new locations due to land use, zoning, and other policy changes.

Our research questions emerge directly from the SAFs and general field theories. The first question allows us to understand how gentrification trends relate to the school-aged population of a neighborhood. The second examines SAFs’ assumptions related to organizational behavior because it explores whether a certain type of organization (a charter school) is more likely to thrive in one ecosystem over another (a gentrified neighborhood versus a nongentrified neighborhood). The third question presumes the general field understanding of social and cultural reproduction suggesting that individuals make different choices in different environments, resulting in different enrollment patterns.

212 B. MANN ET AL.

Gentrification and schooling

The SAFs and general field understandings rely on the context and rules of the educational ecosystem. We turn to two strands of literature to help us explain the Washington, DC educational ecosystem, especially as it relates to gentrification and choice. The first considers the history of housing patterns and school segregation. The second considers the relationship between gentrifica- tion, charter schools, and segregation. In these sections, we first describe national movements and then shift to focus on Washington, DC.

Suburbanization, segregation, and gentrification

Historical housing policies catalyzed the development of the suburbs through White flight and exclusion of minority families. These policies began with the creation of New Deal agencies in the 1930s, such as the Federal Housing Administration (FHA) and the Veterans Administration (VA). The agencies financed home construction and mortgages in ways that created predominately White suburbs across the country (Hirsch, 2000; Rothstein, 2014; Wilson, 2015). Suburbanization strategies excluded minority residents through a number of policies and practices, including the denial of loans, redlining, blockbusting, racially exclusive agreements in contracts, government complicity in mob violence that ejected Black residents from newly purchased homes, and a host of others. Multiple layers of government designed these policies and practices to keep suburban spaces White (Rothstein, 2017).

As the creation of the suburbs exacerbated housing segregation, key Supreme Court decisions on school segregation, such as Brown v. Board of Education (1954), required school districts to desegregate. The court recognized that housing segregation inextricably relates to school segregation in Swann v. Charlotte-Mecklenburg Board of Education (1971). Due to this relationship, the court allowed busing and redistricting in desegregation plans. As a result, during the next 20 years, many districts in the United States began to desegregate (Orfield, Frankenberg, & Lee, 2003). However, later decisions hindered desegregation, especially Milliken v. Bradley (1974), which held that multidistrict remedies to desegregate were unlawful without evidence that suburban districts engaged in de jure segregation. Mandatory metro area integration plans are not permissible due to the Milliken decision.

Since metro area plans are tools not available for the purpose of increasing school integration, diversity, and equity, policymakers may seek alternative strategies to achieve these goals. One strategy could be to develop policies that leverage demographic changes related to gentrification. This strategy includes cross-city enrollment plans as cities experience influxes of wealthy residents to city centers, while also using the influx of tax funding from higher income residents to garner resources to enhance economic and social outcomes of urban schools (Byrne, 2003; Stillman, 2012). Efforts to foster inclusive and equitable school change alongside neighborhood change could also include the use of mixed-income housing strategies, magnet programs, school outreach, and weighted charter school lotteries (Mordechay & Ayscue, 2018).

A counterargument to the view of leveraging neighborhood trends to limit racial isolation is that gentrification and equity will never coincide because patterns of gentrification remain embedded in traditional structures of power, inherently limiting equitable outcomes (Blomley, 2004). Recent gentrifiers displace residents when they enter renovated neighborhoods (Hankins, 2007; Lees, 2000). White families sometimes use choice policies as tools to avoid integrating local schools (Renzulli & Evans, 2005). When neighborhood change leads to diverse neighborhoods, neighbor- hood diversity does not translate to school diversity (Candipan, 2019). When White families integrate schools, they often do so in ways that deemphasize the needs of minority residents (Diem, Holme, Edwards, Haynes, & Epstein, 2019; Posey-Maddox, Kimelberg, & Cucchiara, 2014).

Despite these concerning trends, state, local, and federal levels of government appear favorable to the idea of supporting gentrification. In fact, the federal government induced the most recent wave of gentrification in the 1990s and 2000s. The Department of Housing and Urban Development

PEABODY JOURNAL OF EDUCATION 213

(HUD) used grants and tax incentives to develop previously low-income neighborhoods in cities (Hackworth & Smith, 2001), while other HUD initiatives were used to eradicate severely distressed public housing locations and replace them with mixed-income housing (Davis & Oakley, 2013). From 1993 to 2010, there were 262 federal revitalization grants totaling approximately $6.2 billion. In addition to federal efforts, state and local governments contributed to gentrification through zoning, destroying public housing for real estate investments, and strategic placement decisions of public resources such as schools and parks (Lipman & Haines, 2007). Gentrification is prevalent in Washington, DC; it has the highest number of census tracts labeled as gentrifying out of any city in the United States (Maciag, 2015). Therefore, Washington, DC offers useful lessons on how gentri- fication relates to diversity and isolation in enrollment patterns.

Washington, DC’s history reflects the story of schools in the United States with a period of legal segregation followed by limited integration and then White flight. In the early twentieth century, the district enrolled even proportions of Black and White students, but they attended legally segregated schools. After Brown, there was a drastic White migration out of the District of Columbia Public Schools (DCPS) and into the suburbs and to private schools. The demographic distribution of students in 1954 was 57% Black/minority and 43% White with no integration. In 1990, at the start of the period of gentrification analyzed here, the proportions were 96% and 4%, respectively (History of DC Schools, 2006). These statistics show that Washington, DC’s schools were racially isolated both before and after Brown and into the 1990s.

Public school enrollment demographics have shifted in Washington, DC since 1990. The demo- graphic breakdown of public and charter students in 2013 was 9% White, 15% Hispanic,1 and 75% Black (NCES, 2018).2 Despite increases in diversity in the city’s public school population, reports show that school segregation has persisted (Orfield & Ee, 2017). However, the most rapidly gentrified census tracts seem to maintain a few diverse schools (Mordechay & Ayscue, 2017). A next step in understanding these trends is to expand the analysis to the entire city and consider patterns across gentrified and nongentrified tracts.

Gentrification and charter schools

While Washington, DC’s overall trends explain aspects of how gentrification relates to school enrollments, there is an added feature of the Washington, DC’s context that shapes enrollment patterns. School choice plans such as charter schools and choice public schools offer students the opportunity to attend schools outside of traditional catchment areas. This suggests that there is a potential for residents, no matter where they live in the city, to enroll in schools that are either more or less racially diverse than the city as a whole.

Despite these possibilities, past research raises questions about the effectiveness of using charter schools as integration tools. Charter schools have increased segregation on average (Frankenberg & Lee, 2003; Frankenberg et al., 2010; Garcia, 2008; Kotok et al., 2017; Rapp & Eckes, 2007). Despite some of the segregation in charter schools stemming from White families using them to avoid majority-minority traditional public schools, general patterns suggest charter schools typically enroll high concentrations of minority students (Rapp & Eckes, 2007; Stein, 2015).

Charter schools and neighborhood gentrification occur simultaneously in many circumstances (Pearman & Swain, 2017). This finding differs among cities in the United States (Davis & Oakley, 2013). Charter school creation has been used, at times, as an incentive to recruit middle-class, upwardly mobile families into urban spaces (Kennedy & Leonard, 2001), especially as charter school deregulation allows new residents to create schools that reflect particular educational and cultural values (Hankins, 2007).

1We use the label “Hispanic” (and not “Latinx”) because it aligns with the label used in census reporting. 2The NCES has released data since the process of publishing this study. The demographics of the DCPS, including charter schools,

remain similar. For example, in 2016–2017, there were about 11% White children. In 2015–2016, there were 10% White children. This suggests slow growth of White enrollment, but overall trends mirror what we report in our study that explores schools in 2013–2014. It would be worth reexamining these trends after the 2020 Census.

214 B. MANN ET AL.

Charter schools have a long history in Washington, DC but their relationship to gentrification and segregation has not yet been studied. The District of Columbia School Reform Act of 1995, which President Bill Clinton signed into law on April 26, 1996, established charter schools in Washington, DC. Charter schools are open to any student in the city; however, if a charter school is oversubscribed, enrollment is based on a lottery. Students may also attend out-of-zone public schools based on a lottery.

The lotteries are mostly random, but schools give preferences to students in a number of different special circumstances. These circumstances include those students previously enrolled in a DCPS school that converted to a charter school, those with siblings enrolled in their desired school, those who are children of charter school board members, and those who are children of public school teachers (DCSRA, 1996; National Alliance for Public Charter Schools, 2016). Some charters also give admission preference to students with disabilities and students with parents who are on active duty in the military; however, they must apply for and receive permission to by the Public Charter School Board. All DC residents have a “right-to-attend” traditional public school in their neighborhood. Non-DC residents may attend DC charter schools if space allows, but they must pay tuition to do so.

According to the SAFs and general field frameworks, one would expect the public school system in Washington, DC to have different student enrollment patterns and different distributions of schools by type across different neighborhood demographic trends. These patterns may or may not influence racial and economic isolation in schools. Since the default school for a child is a neighborhood school, there is a strong potential for neighborhood patterns and school patterns to remain. However, charter schools and choice programs may change these patterns, especially since some placements occur via lotteries that could randomize enrollment patterns.

Indeed, 8,500 students in the district were on waiting lists for one or more charter school in 2016, and nearly 7,000 students were on a waiting list for at least one traditional public school. This is about 18% and 15% of the student body, respectively, although the overlap among these is not clear (Chandler, 2015). These figures suggest that enrollment patterns in Washington, DC should not be random (at a minimum, more than 67% of students enrolled in the school they chose), allowing us to identify trends. As we reveal in the findings section, enrollments indeed relate to geography— thus strengthening our findings on the link between neighborhood type and school type.

Charter schools currently enroll about 47% of all DC public school students, a share that has consistently increased since charter schools were first established (DCPCSB, 2019). Three different types of schools are eligible to apply to become charters—existing public schools, existing private schools, and new schools. For existing public schools and existing private or independent schools to be converted into public charter schools, they must present a petition in which at least two-thirds of the parents of minor students, two-thirds of the adult students, and two-thirds of the full-time teachers in the school support the petition to have the school converted. New schools, on the other hand, must simply file an application with an eligible chartering authority.

Once petitions from any type of school are filed, a public hearing must be held, and the eligible chartering authority must decide to approve or deny the petition. In any given year, a single chartering authority may approve a maximum of 10 petitions. Once opened, eligible chartering authorities must use “demonstrable increases in student academic achievement for all groups of students as one of the most important factors when determining whether to renew or revoke a school’s charter” (DCPCSB, 2019).

The process for opening a traditional DC public school is quite different. For example, the most recent DCPS school opened after a five-year process. In 2014, an advisory committee proposed a recommendation for a new school. Then, the school district worked with the DC Office of Planning to determine site location and building plans while receiving input from residents at town halls. The new school opened in 2019 (DC School Planning, 2018).

When considering the social and policy history alongside of the conceptual framework of this study, it seems families and schooling organizations may navigate the Washington, DC schooling ecosystem (the “field”), and its emergent rules, in complex and strategic ways that continue to

PEABODY JOURNAL OF EDUCATION 215

perpetuate inequality. In traditionally affluent sections of the city, White residents may enroll in traditional public schools in ways to ensure perceived advantage, while in gentrified spaces new residents may wish to opt out of traditional public schools to avoid perceived disadvantage. Enrollment processes may allow for these strategies; however, the system is complex and contains a degree of randomness. Organizations follow similar patterns related to accruing advantage though their behaviors depend on their mission and strategies for survival. These strategies relate to rules for enrollment and recruitment as well as an organization’s ability to start new programs.

Methods

Data sources and classifications

The questions at focus in this study require multiple sources of data. School demographic data come from the 2013–2014 Common Core of Data (CCD) provided by the National Center for Education Statistics (NCES). This dataset includes school-level variables such as student enrollments disaggre- gated by racial composition and free and reduced-price lunch status. The NCES dataset includes school labels such as traditional public school (TPS) and charter school (charter).

The boundaries used to delineate areas of the city are census tracts retrieved from the US Census Bureau in the form of shape files. We used census tract boundaries and data from the 1990, 2000, 2010 Census and the 2009–2013 American Community Survey five-year estimates. The tracts were normalized based on 2010 designations (Logan, Xu, & Stults, 2012).

Defining gentrification

This study hinges on accurately capturing gentrification with quantitative data because the theore- tical frameworks assume different patterns along different contexts. The operationalization of gentrification relies on researcher discretion.3 Our study relies on Freeman’s (2005) definition because it allows for a consistent and reliable quantification of housing and economic variables. Using this definition, we identify tracts in Washington, DC with low socioeconomic traits in 1990 (education, wealth, and home values) and classify tracts as gentrifying if they experienced relatively high increases in those traits from 1990 to 2013.

The gentrification status of census tracts is derived from data created by The Governing (Maciag, 2015). This organization compiled gentrification data for 50 of the most populous cities in the United States and shared the data with researchers. We then replicated the designations. A census tract was labeled as gentrified if it met two standards, the first identifying it as a gentrification- eligible tract and the second if the eligible tract showed increases in specific socioeconomic variables.4

A tract was “eligible” for gentrification if it was located in a central city. The tract was also eligible if it started in 1990 with a population of 500 or more residents, a median household income in the bottom 40th percentile compared to tracts in its metro area, and a median home value in the bottom 40th percentile compared to all tracts within its metro area. A tract was labeled “not eligible” if it did not meet these criteria. Due to the criteria, the not eligible tracts are the traditionally affluent areas of the city. A tract was classified “gentrified” if it had a top-third percentile increase in educational

3For example, Marcuse (1985) defines gentrification as migrations of White, highly educated, and higher-income residents into spaces that replace low-income, minority residents in dilapidated inner-city housing (pp. 198–199). Zukin (1987) defines gentrification as the transformation of central city neighborhoods comprised of “socially marginal and working-class areas” into areas for middle-class use (p. 129). Smith (1996) defines gentrification as the displacement of working-class residents to remake class characterizations (p. 39). Slater, Curran, and Lees (2004) describe gentrification as both a “production and consumption of space” for and by an incoming more affluent population (p. 1142). Wilson (2015) explains gentrification as the “influx of middle-class, usually White, residents into cities with large minority populations” (p. 3). Freeman (2005) describes gentrification as the rapid reversal of socioeconomic status and disinvestment in inner-city neighborhoods (p. 463).

4For the report’s methods, see http://www.governing.com/gov-data/gentrification-report-methodology.html.

216 B. MANN ET AL.

attainment (percentage of residents age 25 and older holding bachelor’s degrees or higher), an increase in median home value when adjusted for inflation, and a top-third percentile increase in inflation-adjusted median home value. Census tracts with missing data for any of these variables were excluded. Of the 179 tracts in Washington, DC, we identified 59 as experiencing gentrification from 1990 to 2013.5

In an effort to add robustness to the findings and triangulation to the labeling of tracts, a researcher went on site visits to Washington DC, in July of 2018, December of 2019, and February of 2020. During these trips, the researcher shared blank maps with a snowball sample of participants and had participants label gentrification based on their perceptions. The first trip also included a walking tour of Washington DC, school locations with an employee of DCPS and interviews and conversations with school officials and residents about their perceptions of gentrifi- cation. The goal of these conversations was to triangulate perceptions of gentrification and correct blind spots in the analysis. General perceptions of gentrification, while containing some differences based on participants, mostly aligned with our gentrification labeling.

Labeling schools

Another feature of the study’s framework is that different organizational types relate to differences in demographic patterns due to organizational strategy and individual preference. To identify differ- ences of school characteristics, we classify schools by charter/traditional status and levels of racial isolation and poverty (Frankenberg et al., 2010). A school reflects an extreme poverty designation if it has 75% or more of its students free or reduced-price lunch eligible (FRL). A school receives a label of racially isolated if it has 90–100% minority students. A school receives a racially diverse label based on a benchmark for a critical mass, which is defined as having three or more demographic groups each constitute at least 10% of the school’s population (Jacobsen, Frankenberg, & Winchell Lenhoff, 2012, p. 825).

Analytical strategy

There were three questions at focus in this research. The first asks how the racial demographics of Washington DC, census tracts changed since 1990. For this question, we descriptively compare the demographic changes in each of the labeled census tracts (labeled according to if they gentrified by 2013). The demographics we report are total population, race, and race by child population. We used the children 15 years old or younger data (“residents <15”) because these data were consistent longitudinally across census reporting. The demographic data provide an estimate of the changes in the aggregate and younger-than-15 racial demographics in tracts that experience economic changes during the 20-year period.

The second question asks if gentrified neighborhoods are more likely to have charter schools. To answer this question, we used ArcGIS mapping software to merge the NCES 2013–14 school dataset and classified tracts. This allowed plots of schools in each neighborhood type by school type. After plotting the distributions of schools across census tracts, we conducted a chi-square test of independence to consider evidence of a dependent relationship between type of school and tract labeling. Upon establishing that this relationship was dependent, we created a chi-square goodness

5We ran alternate classifications with different variables. Using a race variable led to 82% similarity in tracts identified compared to the model used in our final analysis, whereas using a more restrictive income variable led to a 50% overlap. Based on the robustness checks, we note two key points: (a) The exclusion of eligibility criteria drastically changes results; and (b) school trend findings remained consistent when eligibility criteria were kept, but classification variables changed. For example, in the first classification, gentrified tracts had 87.5% both extreme poverty and racially isolated charters, 38.5% racially isolated only charters, and 77.8% neither extreme poverty nor racially isolated charters. In a classification with the 50% overlap of tracts, these percentages were 90%, 32%, and 64%. While different strategies alter labeling, school type and enrollment patterns remained consistent.

PEABODY JOURNAL OF EDUCATION 217

of fit test to compare expected proportions of charter schools and traditional public schools to actual proportions. Since gentrified tracts serve as the primary unit of analysis, their distribution of schools is the reference point. This analysis determines if the distribution of TPS and charter schools in the gentrified tracts statistically differed compared to the others. The formula for this method is:

X2 c ¼

X Oi � Eið Þ 2

Ei

In this equation, O is the observed value, E is the expected value, and i is the position on a created contingency table. This formula creates a statistic where zero is equal distribution.

The third question asks how school racial composition, in charter and traditional public schools, compare across gentrified tract classifications. The strategy includes understanding the overall demographics of traditional public schools and charter schools and comparing these demographics across tracts. Then, we use the Exposure Index, commonly employed in school segregation and diversity research. This index reflects the isolation and interaction indices and provides an under- standing of the racial exposure, on average, of a student of each racial demographic group (Iceland & Weinberg, 2002). The formulas for these indices are:

Isolation ¼ Xn

i¼1

xi

XT

� � xi

ti

� �

Interaction ¼ Xn

i¼1

xi

XT

� � yi

ti

� �

In the first, n is number of schools; xi is population of a demographic (e.g., White) in school i; ti is the total population in school i; and XT is the total population of a demographic in all schools in the city. The second is similar, the only difference being that yi is a demographic comparison group in school i (e.g., Black or Hispanic if xi is White). Isolation is interpreted as the percentage of the same demographic group encountered, on average, by an individual of that group. Interaction is the percentage of a different demographic group, on average, encountered by the average individual of another group. For example, a Black isolation index of 0.88 would mean that a Black student, on average, attends a school where 88% of the students are Black. A Black-White interaction index of 0.43 means a Black student, on average, attends a school where 43% of students are White.

The Exposure Index combines both indices to understand the school racial demographic population encountered, on average, by a student of a given race. The presentation may include the following for a White student: White isolation, Black interaction, Hispanic interaction. The numbers could be 0.75, 0.10, 0.15. This distribution would mean a White student, on average, attends a school comprised of 75% White students, 10% Black students, and 15% Hispanic students.

In reporting the findings, we show the indices for Washington DC, as a whole. While we ran the Exposure Index by tract, there were so few White children in two of the three tracts (as shown in Table 1) that the Exposure Index in these tracts is skewed by a few outlier schools. Instead, to gain an understanding of the distributions of students in schools across tract types, we show the placement of isolated and diverse schools on a map. These maps show the extent of racial diversity and isolation, how isolation and diversity trends compare across school types, and how isolation and diversity across school types relate to gentrification labeling of tracts.

218 B. MANN ET AL.

Findings

Uneven changes in Washington, DC

Washington, DC experienced demographic change during the past three census counts. The changes have not occurred evenly across the city. As shown in Table 1, the White population in the year 2010 comprises 24% of the population in the tracts labeled as gentrifying. While the share of White residents younger than 15 also increased in gentrifying tracts, White younger-than-15 residents make up less than 10% of the under-15 population of gentrifying tracts.

The median younger-than-15 resident distribution is 309 Black children, 24 White children, and 30 Hispanic children in gentrifying tracts. The median younger-than-15 resident distribution is 604 Black children, 2 White children, and 15 Hispanic children in tracts not gentrifying. These not gentrifying tracts have declining younger-than-15 resident populations, especially of Black children. In tracts not eligible for a gentrification classification, the median younger-than-15 resident dis- tribution is 112 White Children, 43 Black children, and 39 Hispanic children. The population grew in all categories in the not eligible tracts.

The demographic changes reveal a few trends. The first is that the economic changes in gentrified tracts bring higher shares of White residents. The share of White residents younger than 15 years old also grew, but much less extensively than the overall White population. In addition, the tracts not eligible for gentrification classification, which primarily include the traditionally wealthiest tracts in the city, continue to grow across all ages of White residents.

Gentrified neighborhoods have the highest proportion of charter schools

The maps of traditional public schools and charter schools in Washington, DC depict spatial relationships with tract classification and location. Figure 1 shows that traditional public schools are located across all tract designations in the city. Figure 1 also shows that charter schools cluster on the eastern (right) portion of the map where gentrification has occurred as well as in low-income areas in the east and south.

There are higher proportions of charter schools in gentrified tracts. This finding is statistically significant in the chi-squared analyses. Table 2 shows school type by tract descriptively in the 2013–2014 school year and reveals a higher proportion of charter schools in gentrified tracts (the chi-squared

Table 1 Demographic change across tract designations in Washington, DC, based on the 1990, 2000, and 2010 Census counts.

1990 2000 2010

Tracts that gentrify by 2013 Median Population Count 3,284 2,947 3,030 Percent White 11% 11% 24% Median White Residents Younger than 15 13 5 24 Median Black Residents Younger than 15 510 452 309 Median Hispanic Residents Younger than 15 12 17 30 Tracts that do not gentrify by 2013 Median Population Count 3,114 2,919 2,974 Percent White 7% 5% 6% Median White Residents Younger than 15 3 2 2 Median Black Residents Younger than 15 785 736 604 Median Hispanic Residents Younger than 15 7 8 15 Tracts not eligible for gentrification classification Median Population Count 3,051 3,131 3,299 Percent White 53% 53% 60% Median White Residents Younger than 15 90 62 112 Median Black Residents Younger than 15 105 69 43 Median Hispanic Residents Younger than 15 28 32 39

Note. Data from the US Census Bureau. The tracts not eligible are labeled as such because they were the wealthiest areas of the city in 1990.

PEABODY JOURNAL OF EDUCATION 219

analyses tables are Table A1 and Table A2 in Appendix A). The analyses provide evidence to suggest that school type is dependent on tract designation, and charter schools are more likely to be found in gentrified tracts.

In addition to analyzing school distribution in 2013–2014, we plotted changes of school type across tracts since 2001, which is the earliest available data on charter schools in the NCES dataset. These plots reveal that tracts that gentrify by 2013 began with a higher share of charter schools in 2001 (Figure 2). Throughout the next 12 years, the share of charter schools in the tracts that gentrify increased at about the same rate as tracts that do not gentrify, showing that higher levels of charter schools preceded gentrification trends.

Racial demographics differ across school type and tract gentrification designation. Table 3 details the distributions of students across the city in traditional public schools and charter schools. Table 3 shows traditional public schools have higher percentages of White and Hispanic students and lower percentages of Black and FRL students compared to charter schools. Traditional public schools have more diverse student populations than charter schools overall. Traditional and charter schools in tracts that have not gentrified (the consistently low-income tracts) tend to have extremely isolated Black student populations. Charter schools in gentrified tracts tend to have slightly higher percentages of White students than

Figure 1 Traditional public school and charter school locations in Washington, DC, 2013–2014.

Note. Basemap from Esri, HERE, Garmin, OpenstreetMap contributors, and the GIS user community.

Table 2 Gentrification tract and school types in Washington, DC, 2013–2014.

Neighborhood Type Total Schools Percent TPS Percent Charter

Gentrified 72 40.28% 59.72% Did Not Gentrify 77 51.94% 48.16% Not Eligible 58 69% 31% Data Not Available 3 100% 0% Total 210 53.33% 46.67%

Note. We classified a neighborhood as gentrified if it experienced gentrification from either 1990–2000 or 2000–2013. The majority of gentrified tracts occurred in the second wave. Data from the National Center for Education Statistics and the US Census Bureau. TPS = traditional public school.

220 B. MANN ET AL.

traditional public school enrollments in gentrified tracts. Enrollments are diverse across school types in tracts not eligible to gentrify (the traditionally high-income tracts).

These demographic trends suggest White residents are more likely to enroll in traditional public schools in wealthier areas of the city. There are fewer shares of White residents in either type of school in less affluent or gentrifying areas of the city. Meanwhile, Black students are overrepresented in charter schools, and this overrepresentation occurs at the greatest levels in gentrified areas of the city. Across all areas of the city, charter schools have higher proportions of students receiving free and reduced-price lunch. However, as shown in a later findings section, these patterns are not true of every charter school. In gentrified tracts, there are a few diverse charter schools, but racially isolated charter schools are much more common.

Racial isolation and diversity by tract type and school type

Schools in Washington, DC, in 2013–2014 had high levels of racial isolation with limited cross-racial interaction, as shown in Table 4. Charter schools have the greatest amount of racial isolation. Black students have less interracial contact with White, Asian, and Hispanic students in charter schools than they do in TPS. This suggests, on aggregate, that charter schools lead to greater racial isolation in Washington, DC.

Analyzing interaction at the tract level proved to be problematic due to the extremely limited number of younger-than-15 White residents in most tracts (except those designated as not

Share of Charter Schools by Tract Classification in Washington, DC, 2001-2013

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Tracts that gentrify

Tracts that do not gentrify

Tracts not eligible for gentrification classification

Figure 2 Share of charter schools by tract classification in Washington, DC, 2001–2013.

Table 3 Racial and economic demographics by school and tract type in Washington, DC, 2013–2014.

Schools in all tracts White Black Hispanic FRL

Charter 4.01% 83.46% 10.36% 73.03% Traditional Public 11.73% 68.13% 16.28% 53.71% Schools in gentrified tracts Charter 3.12% 81.99% 13.34% 77.25% Traditional Public 2.95% 69.52% 25.11% 61.50% Schools in tracts that have not gentrified Charter 1.47% 93.59% 3.03% 76.25% Traditional Public 2.24% 94.11% 2.97% 68.78% Schools in areas not eligible to gentrify Charter 15.71% 51.95% 27.10% 47.66% Traditional Public 24.57% 46.87% 21.40% 37.27%

Note. Data from the National Center for Education Statistics and the US Census Bureau.

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gentrification eligible). To account for this, we examined and plotted individual schools with diverse populations and those with minority student isolation. Figure 3 depicts the racially isolated and diverse schools in the city. The majority of schools in the eastern tracts of Washington, DC are racially isolated, be they gentrified or not, charter or traditional public. The few racially diverse schools in the not eligible tracts (higher income) are traditional public schools while the few racially diverse schools in gentrified tracts are charter schools.

Discussion and conclusion

This study reveals intriguing findings related to the intersection of community sociodemographic tract classifications and charter school implementation. In our discussion, we consider reasons for differing

Table 4 Racial exposure in schools in Washington, DC, 2013–2014.

White Black Hispanic Asian

Charters White 0.236 0.497 0.191 0.027 Black 0.024 0.900 0.061 0.004 Hispanic 0.074 0.491 0.402 0.011 Asian 0.179 0.534 0.184 0.038 Traditional Public Schools White 0.474 0.298 0.131 0.046 Black 0.051 0.832 0.094 0.011 Hispanic 0.094 0.394 0.462 0.032 Asian 0.268 0.373 0.257 0.069

Note. Example interpretation: In Column 2, Row 1 under the Charters heading, it shows a White- Black interaction of .497. This suggests a White student, on average, attends a charter school comprised of 49.7% Black students.

Figure 3 Racially isolated and diverse school locations in Washington, DC, 2013–2014.

Note. Basemap from Esri, HERE, Garmin, OpenstreetMap contributors, and the GIS user community.

222 B. MANN ET AL.

compositions of schools based on neighborhood demographic changes. Then, we connect the study to past literature and theory and weigh policy options.

Neighborhood change and the composition of schools across neighborhoods

Census tracts in Washington, DC have seen shifting racial demographics during the past 20 years. Neighborhoods in the northeastern portion of the city have seen economic shifts that have led to increasing shares of White residents. While the overall share of White residents has increased, the share of White children has lagged. The result of these trends is that schools in Washington, DC have remained racially isolated with few exceptions. The diverse exceptions in gentrified neighborhoods tend to be charter schools. The diverse exceptions in historically affluent areas of the city tend to be traditional public schools.

Charter schools are more likely located in the gentrified tracts than other tracts in Washington, DC. This finding follows the literature as it relates to the relationship between urban renewal efforts, gentrification, and the rise of school reform policies such as charter schools (Davis & Oakley, 2013; Hankins, 2007; Pearman & Swain, 2017; Wilson, 2015). These differences were apparent at the start of the period measured in this study, suggesting that charter schools preceded the most recent wave of gentrification. After the beginning point, the share of charter schools grew at similar rates over time across gentrifying and nongentrifying tracts.

The reasons charter schools are more prevalent in gentrified spaces may relate to many factors. Gentrified areas, by definition, are areas with the greatest amount of change in capital, real estate, and demography. It could be that these changes offer opportunities and incentives for charter school operators to locate their new programs. At times when and in areas where storefronts and housing options change with new businesses and apartment buildings, charter school developers may acquire space more easily. The influx of new residents and capital may introduce a new set of school logics that reflect new neighborhood development logics. As the landscape of space and residents change, so too may the landscape of schooling options.

These findings also raise the possibility that charter schools serve as a tool to induce gentrification. If new residents seek to avoid sending their children to public schools, they might prefer charter schools. More research is needed to understand the complex dynamics of resident choices in these spaces. It is not clear if charter schools are helping create the demographic shift, or if demographics are causing charter schools to emerge. Greater shares of charter schools preceded the most recent wave of gentrification. Although previous research by Pearman and Swain (2017) links gentrification and charter schooling, Davis and Oakley (2013) show these patterns are context dependent. Both studies suggest urban renewal efforts have influenced the growth of the charter sector. If charter schools help drive gentrification, then the next tracts to gentrify in Washington, DC may be low-income tracts with high numbers of charter schools.

Gentrification, charter schools, school diversity, and integration

Another key finding of this study relates to enrollment distributions. Some scholars expressed hope that an influx of White residents to the urban core would lead to more integration and diversity in communities and schools (Byrne, 2003; Stillman, 2012). It is difficult to argue that gentrification is causing school integration in Washington, DC. As gentrifiers move into urban spaces, they are not integrating most schools, especially traditional public schools. This finding likely stems from the lack of White children in these spaces and White residents moving away when they have school-aged students. The few diverse charter schools in gentrified spaces reflect a potential to increase school diversity, but these patterns have yet to happen at any level of scale.

The emergence of a few diverse charter schools in gentrified spaces raises a policy dilemma. While the few diverse schools in gentrified neighborhoods are charter schools, the trend across the rest of the city is that charter schools are more isolated along the lines of race and FRL status. If policymakers seek to use charter schools to increase the number of diverse schools in gentrifying neighborhoods, they might lose benefits of potential diversity, especially economic diversity, in other areas of the city. On average,

PEABODY JOURNAL OF EDUCATION 223

gentrification and charter schooling have yet to mitigate problems of racial and economic exclusion and isolation, despite succeeding in a few small cases. Policymakers continue to seek intradistrict tools to enhance school diversity due to a lack of interdistrict possibilities resulting from of the Milliken (1974) decision. Leveraging charter schools and gentrification to achieve racially and economically diverse schools overall proves to be a complex and possibly untenable strategy.

Connecting findings to choice and organizational theory

While these findings are unfortunate for those who hope gentrification may decrease racial and economic isolation in schools, the findings are not surprising when considering the history of segregation and White families’ resistance to school integration (Erickson, 2016). The SAFs and general field understandings help show that organizations and individuals act strategically in ways that reflect field-level logics. Behaviors change depending on context. This organizational under- standing, taken with the understanding that parents make choices based on a variety of logics (Holme, 2002), leads to heterogeneity among school enrollments based on race and class.

Different enrollments based on different preferences that change across tract contexts were evident in this study. In traditionally affluent areas of Washington, DC, White residents chose traditional public or perhaps private schools, so fewer charter schools developed in these areas. In gentrified tracts, several types of schools emerged, including a few diverse charter schools and charter schools for low-income residents. The patterns adhere to the theory that the few remaining White residents with school-aged children tend to avoid majority-minority schools. Diverse charter schools emerge only in areas where White residents are the minority. Meanwhile, the growth of the sector creates charter schools with heightened levels (compared to traditional public schools) of racial and FRL isolation in low-income areas of the city.

Policy possibilities

For those uncomfortable with gentrification and its influence on urban space, a policy suggestion could be to limit charter schools. Billingham (2015) explains, gentrification and its connection to market-based reform policy is complex and, at times, gentrifying families “advocate constraining, rather than expanding, the educational marketplace” (p. 696). The momentum of gentrification will be difficult to stop, especially in the growing population of Washington, DC. This can be viewed as both a problem and an opportunity. The problem is that thus far in the stages of gentrification, evidence suggests gentrification exacerbates social and educational inequality. The opportunity is that since trends of gentrification are relatively new, policymakers could possibly leverage demographic changes to achieve diverse and equitable schools.

Striving for educational equity through a separate-but-equal ideology has shown to be unfeasible throughout history (Scott & Quinn, 2014). Based on the view of gentrification as an opportunity, policy recommendations could heed the suggestions made by Kucsera, Siegel-Hawley, and Orfield (2015) in Berkeley, CA. The authors suggest creating microneighborhoods where each neighborhood has a diversity code. Then, school leaders assign students to schools based on the diversity code to ensure diverse schools across the district (p. 565).

Another option to prompt more diverse schools in Washington, DC is to create more diverse charter schools. For example, one strategy would be to push for “intentionally diverse” charter schools (Zubrzycki, 2012). One way to achieve this goal is to add diversity-based weighting to choice policies. These suggestions are a start, but policymakers and advocates should come together, look at models like Berkeley, and develop strategies to leverage gentrification in a way that promotes diverse schools. While creating diverse charter schools is an option, it should be approached with caution. The evidence from Washington, DC suggests that increasing the number of charter schools across the city may lead to some diverse charter schools in gentrified areas, but also to greater isolation in other areas. Policymakers should recognize the dual distributions of charter schools as they are implemented.

224 B. MANN ET AL.

Policymakers could also consider other possibilities. These include strategies mentioned earlier, such as mixed-income housing strategies, magnet programs, and student outreach and recruitment (Mordechay & Ayscue, 2018). As the fallout from Milliken has shown throughout the past several decades, these strategies rely on intradistrict plans where White residents remain in or migrate to the urban core. This trend is rare, and when cities attempt to incentivize it, they tend to do so in ways that further marginalize minority residents (Diem et al., 2019; Posey-Maddox et al., 2014). There is the option to pursue mergers with suburban districts such as the merger that occurred in Wake County, NC (Holme & Finnigan, 2013). However, the Washington, DC context poses additional challenges. A metro area plan would require interstate agreements because suburban districts are in Maryland and Virginia.

Past patterns suggest that as policymakers weigh their options, they need to remember that diverse schools for diversity’s sake do not guarantee educational equity. As they consider neighbor- hood and school trends, their goals must center on equity for marginalized communities, an objective easily lost as they seek to encourage different neighborhood and school trends with gentrification. As Washington, DC changes, historically marginalized communities are most vulner- able to being left behind. If school leaders wish to undo the legacy of segregation in a time of limited options, they need to remember to include everyone in the sharing of any benefits that may accrue as capital investment and expansion continue to reshape schooling in our nation’s capital.

Acknowledgments

The research reported in the article was made possible in part by a grant from the Spencer Foundation [#201900236]. The views expressed are those of the authors and do not necessarily reflect the views of the Spencer Foundation.

Author bios Bryan Mann is an assistant professor in the Department of Educational Leadership and Policy Studies at the University of Kansas. He completed the research for this article in his previous role as an assistant professor of educational foundations and policy at the University of Alabama. Dr. Mann’s research focuses on education policy, including topics related to geography, school enrollment patterns, and school choice.

Heather Bennett is the Director of Equity Services at the Pennsylvania School Boards Association (PSBA). In this role, she assists public school boards and districts with equity focused tools, programming and research. Dr. Bennett received her PhD in educational leadership at Penn State University. Her research focus is on the intersection of law, race and education. She also obtained her juris doctorate from Penn State, Dickinson School of Law and is barred to practice law in New Jersey and Pennsylvania. She was a high school social studies teacher in Fulton County, GA.

Annah Rogers is a PhD candidate in the Political Science Department at the University of Alabama. Her primary research interest is in education policy with a focus on school choice policies and educational inequities.

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Appendix A. Chi-squared tests

Table A1 Chi-squared test of independence: Expected and actual distributions of charter and traditional public schools across census tracts in Washington, DC, 2013–2014.

Gentrified Did Not Gentrify Not Eligible Totals

Traditional Public School 29 (37.91) 40 (40.55) 40 (30.54) 109 Charter 43 (34.09) 37 (36.45) 18 (27.46) 98 Totals 72 77 58 207*

χ2 = 10.629, df = 2, P (χ2 > 10.629) = 0.0049

Note. Expected values (based on an even proportional distribution of charter schools) are displayed in parenthesis. *Three schools in tracts listed as “no data available” are not included. Data from the National Center for Education Statistics and the US Census Bureau.

Table A2 Chi-squared goodness of fit: Distributions of charter and traditional public schools in Washington, DC, compared to gentrified tract distributions, 2013–2014.

Did Not Gentrify Not Eligible

Traditional Public School 40 (31.01) 40 (23.36) Charter 37 (45.98) 18 (34.64) χ2 3.9 18.68 Degrees of Freedom 1 1 P (χ2 > value) 0.048 <.0001

Note. Expected values based on a reference point of gentrified school proportions are displayed in parenthesis. Data from the National Center for Education Statistics and the US Census Bureau.

228 B. MANN ET AL.