Gentrification and Student Achievement: a Quantitative Analysis of Student Performance on Standardized Tests in Portland's Gentrifying Neighborhoods

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Gentrification and Student Achievement: A Quantitative Analysis of Student

Performance on Standardized Tests in Portland’s Gentrifying Neighborhoods

Justin Joseph Ward

A thesis submitted in partial fulfillment of the

Requirements for the degree of

Master of Urban Studies

Urban Studies

Thesis Committee

Lisa Bates, Chair

Michael Smith

Liming Wang

Portland State University

2019

ABSTRACT

Across the United States one would be hard pressed to find an urban center that has been

unaffected by the phenomenon known as gentrification. From substantial economic

growth to the displacement of long-term residents, the benefits and criticisms of the

process of gentrification are wide ranging and extend over a thorough body of literature.

Commonly associated with increasing levels of education and higher resident incomes,

gentrification should be a boon to struggling public schools that are continually plagued

by generational poverty. Unfortunately, the continued widening of the education gap and

increasing racial segregation in our public schools (G. J. Duncan & Murnane, 2014)

suggest that any benefits of gentrification are not translating to equity in our public

schools. By looking at the city of Portland, this paper attempts to quantitatively explore

the complicated relationship among gentrifying neighborhoods, school performance on

the 3

grade standardized Math and Reading tests, and racial demographics of the

students. This paper will follow the methods established by Keels et al. in their work on

gentrification and school achievement in Chicago (2013). By using 2000 Census and the

2015 ACS data and spatial analysis and mapping in GIS, gentrifying school

neighborhoods in Portland will be identified and analysis of student test performance and

racial demographics will be conducted to determine if any relationship exists. By

exploring how these schools have changed both academically and racially we can expand

educational and urban theory around the process of gentrification.

TABLE OF CONTENTS

ABSTRACT .......................................................................................................................................... i

LIST OF TABLES ................................................................................................................................ iii

LIST OF FIGURES .............................................................................................................................. iv

INTRODUCTION AND LITERATURE REVIEW ..................................................................................... 1

Defining and Measuring Gentrification ....................................................................................... 4

Setting the Stage for Gentrification ............................................................................................. 5

Gentrification and Academic Capital ........................................................................................... 9

School Choice ............................................................................................................................. 11

No Child Left Behind and Standardized Testing ......................................................................... 13

Conceptual Model ...................................................................................................................... 15

METHODS ....................................................................................................................................... 16

Figure 2: Method Flow Chart ..................................................................................................... 17

Spatial Analysis........................................................................................................................... 18

Data Sources and Indicators of Gentrifying School Neighborhoods ................................... 18

Spatial Data and Maps .......................................................................................................... 19

Statistical Analysis ...................................................................................................................... 21

Data Sources and variables ................................................................................................... 21

FINDINGS ........................................................................................................................................ 22

CONCLUSION .................................................................................................................................. 29

REFERENCES ................................................................................................................................... 31

APPENDIX A: Geographic Boundary Changes ................................................................................ 35

APPENDIX B: Census and School Change Data .............................................................................. 37

iii

LIST OF TABLES

Table 1: Portland Indicators of Neighborhood Change ................................................................. 23

Table 2: Performance on Standardized Tests by Neighborhood Type (2000-15) .......................... 27

Table 3: Change in Performance on OAKS: Gentrifying vs. Stable Neighborhoods ....................... 28

LIST OF FIGURES

Figure 1: Conceptual Model ........................................................................................................... 15

Figure 2: Method Flow Chart ......................................................................................................... 17

Figure 3: Gentrifying Neighborhoods and Landing Zones ............................................................. 25

Figure 4: Stable/constant Schools vs. Gentrifying School - Math .................................................. 26

Figure 3: Stable/constant Schools vs. Gentrifying School - Reading ............................................. 26

INTRODUCTION AND LITERATURE REVIEW

In the last half-century, urban revitalization efforts in city centers have attracted suburban

middle-class residents back to the city with the cost of displacing low-income, and often

minority, residents to the fringes. This “urban revitalization” or “urban redevelopment” is

a rebranding of gentrification, in order to allow planners and city developers to distance

themselves from the negative impacts that are historically synonymous with gentrification

(Slater, 2013). Despite the displacement and cultural dispossession associated with

gentrification (Harding & Blokland, 2014), advocates for gentrification cite the

development of local services and amenities as a result of the influx of social, cultural, and

economic capital that follows gentrifiers (Freeman & Braconi, 2004; McKinnish, Walsh,

& Kirk White, 2010; Vigdor, 2002). As resources and economic capital relocate to

traditionally disinvested regions of a city a question arises, what effect if any does this have

on our local public schools? The focus of this research aims to answer whether

gentrification has influenced student academic outcomes in Portland’s public elementary

schools? If so, in what ways?

Nationwide, our urban K-12 public schools are plagued with low attendance and graduation

rates (Balfanz, Herzog, & Mac Iver, 2007; Catterall, 1987; Rumberger, 2001), high levels

of crime and student dropout rates (Chen, 2008), and a long history of unsatisfactory test

scores amongst students from low-income families and students of color (Coleman, 1988;

EOGOAC, 2015; Farkas, 2008; Sandy, Duncan, & Cede, 2010). The stark disparity

between our suburban and urban schools, high-income and low-income students, and white

and black students has raised questions of systemic inequity in our current education

system. While the relationship between social class and education attainment has been well

understood and documented, the status quo appears to continuously be reinforcing this

divide. In a society that is increasingly mobile, it is necessary for city planners, policy

makers, and education specialists to understand how these instances of neighborhood

change can transform our public schools.

By using the city of Portland as a unit of analysis, this research will investigate the

measurable effects of gentrification on student academic outcomes. Only a few notable and

similar studies, like the one conducted by Keels et al. (2013) in Chicago, have been done

to quantitatively measure this relationship. While many researchers have theorized about

this relationship and academics like DeSena (2006 & 2009), Cucchiara (2008), and

Silverman (2014) have written extensively on this subject, there still seems to be a gap in

the available research that documents a quantitative correlation between these changing

neighborhoods and public schools. This study aims to add some understanding to that gap

With the growing awareness of the phenomenon of gentrification, it is important that we

expand our understanding about the effect gentrification has on education outcomes. The

dualism of gentrification as a process of displacement, disruption of social capital, and

cultural dispossession as well as a process of investment, improving safety, and economic

growth have been well documented (Slater, 2006), and critiques or challenges on all of

these claims have been made numerous times.

Although research on gentrification has been extensive, especially in the past couple

decades, there is a surprising lack of research on its effect on local public schools (Jordan

& Gallagher, 2014; Silverman, 2014; Warren, 2005). A better understanding of this

relationship is important for city planners and education policy makers to anticipate any

influence gentrification might have on community public schools. While at one time it has

been argued that, community builders and school reformers act as if urban schools and

communities are not linked (Warren, 2005), the growing work of local organizations like

SUN community schools, STAND for children, and the Multnomah Youth Commission

serve as examples that this is not necessarily the case in Portland. However, what is still

missing is an understanding of the measurable effects that communities have on schools.

While a complete understanding of this relationship is incredibly complex, this paper will

attempt to uncover the quantitative and measurable change the process of gentrification has

on school demographics and performance on standardized tests by 3

grade students. If we

are to continue to think of education as one of the great indicators of social mobility it is

necessary for community builders, policy makers, and education reformers to understand

the direct effect that the process of gentrification has on local public schools.

Defining and Measuring Gentrification

Gentrification can be understood as an influx of residential and commercial investments in

previously disinvested urban neighborhoods, followed by the arrival of higher

socioeconomic households with the cost of displacing low-income households from the

neighborhood. As explained by Smith, “It is this combination of social, physical, and

economic change that distinguishes gentrification as an identifiable process” (1987, pg.

463). However, to apply this universally is not particularly easy. As pointed out by many,

the discussion on this topic is long and conflicted (Bates, 2013; Redfern, 2003; N. Smith,

1987). The interpretation and definition of gentrification is continually amended and

redefined throughout the expansive literature on this topic. This is largely due to

gentrification not being a discreet event or observation, but rather a process that extends

over time and transforms based on the context of the city or communities it is manifesting

in. According to Tom Slater we need to get away from the obsession of redefining

gentrification and instead place greater efforts on understanding the underlying causes and

theories that explain gentrification as a process (2013). In the years since Slater aired this

grievance, it seems that the literature on gentrification as indeed come a long way.

Instead of wading into the murky process of defining gentrification this study relies on

established literature to identify instances of gentrification across the city of Portland.

Spatially, gentrification can be identified by increasing rental prices or home values,

increased income through a substantial shift to white collar employment, and an increase

in the population of residents that hold a four year degree or higher, improve local

amenities, and racial demographic change (Ley, 1986; Patrick Heidkamp & Lucas, 2006;

Wyly & Hammel, 1998). For the purposes of this study I will be using the four variables

for measuring and identifying gentrification: (a) Household Income—Perhaps the most

notable variable associated with gentrification is the arrival of new wealth into a gentrifying

neighborhood. In a 1990-2000 study, national census data determined that gentrifying

neighborhoods experienced an average median household income increase of over $10,000

(McKinnish et al., 2010; Zuk et al., 2015). (b) Home Value—Because revitalization of

housing and creation of mixed income developments are typically the most outwardly

noticeable characteristics of a gentrifying neighborhood (Slater, 2013; Sullivan, 2007). (c)

Higher Levels of Education—Because of its close correlation with social class, levels of

education is commonly used to identify areas that are becoming more affluent (Freeman &

Braconi, 2004; Ley, 1986; Sullivan, 2007; Zuk et al., 2015). (d) Change in Racial

Demographics—deeming displacement a necessary part of gentrification it is imperative

that this study look at the change in racial demographics in neighborhoods to create a robust

method of identifying gentrification.

Setting the Stage for Gentrification

Gentrification is not an accidental process that manifests in random neighborhoods or

cities. Instead, like most urban phenomena it was created through practices facilitated by

urban politics and structural systems. Things like: Neighborhood ghettoization, redlining,

discriminatory housing practices, and predatory banking are a few are of the more

prominent causes that create conditions ripe for gentrification.

Ghettoization: Deep rooted racism and a strong desire of the affluent to protect their

property values, ghettos were formally and informally created through local housing

policies to centralize poverty and minorities in order to geographically maintain a class

divide. Black and minority communities in Portland were further marginalized and

stigmatized by the crime and vice that was purposefully ignored by law enforcement and

allowed to prosper in neighborhoods like Albina (Serbulo & Gibson, 2013). This

stigmatization can have destabilizing effects on these communities that further marginalize

the occupants and subject them to further discriminatory actions and justify purposeful

neglection and divestment through redlining (Wacquant, 2007). By centralizing poverty in

urban ghettos and once severe spatial stigmatization was accomplished, urban society and

outsiders could use them as dens of vice without fear of the negative externalities

associated with such behavior rolling over into their own communities. If this kind of illicit

behavior could be eradicated it was simply easier to gain control by encouraging it in the

ghettos so that it was contained, without care to the way this pathway to control negatively

disrupted health, education, safety, and general livelihood of those trapped in the ghetto.

Redlining: Motivated by capitalistic interests, redlining is the process in which investors

and banks adopt guidelines that deny funding in the form of loans for investment in urban

areas that are experiencing economic decline (Byrne, 2003). Because economic capital and

ethnicity are so closely related, redlining was as much a deprivation of resources for the

poor as it was for minority communities. In their study on Portland, Serbulo and Gibson

determined that redlining was not merely motivated by invisible motivators, but was

intentional and an established guideline for lenders and investors to follow (2013). The

withholding of public and private funds from impoverished neighborhoods magnified

problems of education, violence, and drug use that are typically found in areas of extreme

poverty. Redlining, especially in Portland’s Albina, Alberta, and Chinatown

neighborhoods formally separated residents in these areas from economic capital and social

mobility.

Discriminatory Housing in Portland: A history of overt and institutionalized racism

permeated Portland so thoroughly that until 1956 Portland realtors followed a National

Realtors Code which stated, “A Realtor should never be instrumental in introducing into a

neighborhood a character of property or occupancy, members of any race or nationality, or

any individuals whose presence will clearly be detrimental to property values in that

neighborhood.” Even into the 1960’s it was generally understood that 90% of realtors still

would not sell a home to a black individual or family in a white neighborhood (Gibson,

2007). Historically minority residents of Portland have not only faced difficulties acquiring

fair loans, but they were restricted from living in certain many neighborhoods, thus creating

city sponsored segregation and further intensifying the concentration of poverty.

Predatory banking practices: Although banking institutions have been guilty of predatory

practices for generations (and many would argue since their inception), we saw in the 2007

market crash how these practices are targeted toward low-income communities and

communities of color (Davidson & Martin, 2014). Amongst many other dishonest policies,

substandard loans were disproportionately extended to low-income borrowers that had

much higher chances of defaulting on the loans. As we saw this had a devastating effect on

our global economy as well as those that were victimized by these practices. Relaxed

regulation of banking under neoliberal reforms allowed for private banking institutions to

take advantage of and profit from the already precarious financial positions of many of

these communities.

While many of these practices are restricted by the 1968 Fair Housing Act, an inability or

unwillingness to enforce these laws prevented that state from effectively regulating these

kinds of behaviors (Lipsitz, 1994). These practices have helped set the stage for

gentrification in urban centers like Portland across the country by collectively contributing

to the further depreciation of land value in areas of concentrated poverty. This depreciation

of value, especially of land that is centrally located creates what is known as a bid rent gap,

which is a difference between the lands current value and its potential value (Anas, Arnott,

& Small, 1998). It is this gap between the actual and potential value of land, that creates a

market ripe for gentrification. In his theory of gentrification, Neil Smith argues that

gentrification is primarily motivated by capital gains, through investment in the housing

market (1979). When a large rent gap appears gentry and other outside investors are

motivated by capital gains to move into the area. This ultimately serves two ends, first it

transforms these areas into a neighborhood that meets the wants of those with the capital

(the gentry), second it quickly drives up the land value which can force long term residents

from their homes and neighborhoods. The cost of redevelopment and revitalization in

gentrifying neighborhoods is felt disproportionately on the lower income and minority

residents as they are often priced out of these neighborhoods(Defilippis & Fraser, 2010;

Slater, 2013; A. Smith & Timar, 2010; N. Smith, 1979), and those that are able to remain

often lack the social capital to participate in the changing community the way they once

might have (Lees, 2008).

Gentrification and Academic Capital

There is a rich base of research that connects high academic achievement to many of the

characteristics of families that participate as gentry. In this way gentry bring a kind of

academic capital to their new neighborhoods. This academic capital is a combination of

economic, human, and social capital that all have positive relationships with student

academic achievement and performance on standardized tests.

Perhaps the most commonly understood connection, is the overwhelming influence that

family income plays on a student’s academic achievement. From Early childhood

education to higher education, several scholars give a thorough overview of the many ways

in which socio-economic background can dictate their academic success (Coleman et al.,

1966; G. Duncan & Murnane, 2011). Education and income are so closely linked that it

comes as no surprise that the widening education gap is closely mirroring that of the income

gap. The education gap has been growing for the past fifty years and it was found that,

“The achievement gap between children from high- and low-income families is roughly 30

to 40 percent larger among children born in 2001 than among those born twenty-five years

earlier” (Reardon, 2011). Given an understanding of this relationship, some have argued

that the promise of a mixed or higher-income neighborhood would be a boon for struggling

and traditionally low-income school (Byrne, 2003).

Literature also argues for the direct connection between academic pedigree and student

achievement. Higher levels of parental education is directly connected to lower student

dropout rates (Rumberger, 2001), as well as child hood brain development, early skills

development, and educational attainment (Davis-Kean, 2005; G. Duncan & Murnane,

2011). In his study of the widening education gap, Sean Reardon found that parental

education was a much more powerful indicator of student academic achievement than

income (2011).

When controlling for both socio-economic and educational backgrounds, there still seems

to be yet another indicator of student success: ethnicity. Biased tests, white cultural norms,

and systemic racism further disadvantage minority students in our educational system.

Even when controlling for things like income, it has been argued by some that minority

students still tend to perform worse academically and are disciplined more frequently than

their white peers (Gregory, Skiba, & Noguera, 2010). This suggests that as neighborhoods

become whiter and we might expect to see an increase in standardized test performance

because teaching styles and tests are carried out and written with a white bias.

With education theory supporting the claim that the human, social, and economic capital

associated with the gentry should have a net benefit to our local public school, what I plan

on investigating is how great are these benefits? The displacement and loss of social control

that low-income and minority residents face is too great a cost. This research is proposing

to understand if those residents that do stay in these changing neighborhoods are benefitting

from improved public school? Unfortunately, according to education scholars and school

choice scholars this may not be the case where public education is concerned (Davis &

Oakley, 2013; Desena, 2006; Desena & Ansalone, 2009; Jordan & Gallagher, 2014;

Lipman, 2008). If high academic achievement has long been associated with the higher

income and levels of education why might gentrification be failing our public schools?

School Choice

Good Schools are real estate anchors in gentrifying communities (Lipman, 2004). The

promise of a great education is a strong motivation for medium and high-income parents

to relocate and stay in gentrifying neighborhoods. Unfortunately, the investments in

schools seem to be made largely in charter and private schools rather than our public

schools. While top performing schools have been theorized to be agents of gentrification,

these schools are rarely public and oftentimes don’t serve a population that is representative

of the larger neighborhood. In her analysis of school attendance behavior Judith Desena,

examines the many ways in which gentry families participate with the education system

and matriculate their students (Desena, 2006). Perhaps most importantly she argues that

local schools are often rejected by gentry (2006, pg. 248). Instead of participating in local

schools Desena, reports that the wealthier families take advantage of their mobility and

send students out of district to a better school, or enroll their students in private schools.

This kind of attendance behavior can be incredibly important in Portland because of the

freedom of inter-district and school transfer afforded by Oregon law (HB 3681, 2012). One

problem that struggling public school face as a result of this attendance flexibility is the

loss of revenue, “Every student who leaves, takes along at least $5,000 in state funding”

(Owen, 2011).

Amongst gentry parents that do participate with local public schools, Desena reports that

they are able to use their social or economic capital to leverage the schools to serve their

needs (2006). While this may be ultimately beneficial to the school and not directly harm

long term residents, there is a worry that this behavior further distances minority and

low-income families from having a voice in school decisions. This research does not

seek to make an argument for or against school choice, rather it simply intends to discuss

and investigate the possible challenges that school choice presents for neighborhood

public schools and the possible role they may play in spurring gentrification.

No Child Left Behind and Standardized Testing

While testing has been established as the standard for measuring student academic

performance of scale, it has also been criticized for a number of limitations and inherent

biases (Vargyas & Connor, 2013). The cultural, gender, and language bias in these tests

have been well documented, and research suggests an inherent disadvantage for those that

speak English as a second language or those that don’t easily identify with mainstream

white culture. In his discussion on alternatives to traditional testing, Howard Gardner

argues that we have moved away from apprenticeship style assessment and fully embraced

and completely validated the more formal style of testing that we have become so familiar

with in our schools (Gardner, 1992). While always a standard of evaluation in our

education system, testing took on an entirely more substantial role in our education with

the passage with No Child Left Behind (NCLB) act of 2001.

NCLB is widely considered to be the most far reaching education policy since the 1970’s

(Dee & Jacob, 2010). NCLB’s most drastic feature was connecting testing performance

and yearly progress of students to federal and state dollars. It required States to conduct

annual assessments that measured a student’s yearly progress and grant rewards or

implement sanctions on districts based on the yearly progress of tested students. If testing

had been a substantial part of our education system before, it was placed squarely at the

heart of it with the passage of NCLB. By threatening funding and potential resources,

NCLB forced schools to prioritize testing in a way that was never previously done. With

the passage of Every Student Succeeds Act (ESSA) our schools might see a shift away

from this prioritization of testing, but for the time being testing continues to be the standard

by which we measure student growth.

Other measures, such as GPA, attendance, discipline, and graduation rate are less reliable

because they are not all measured consistently across class rooms or schools. Standardized

testing is a uniform test that allows for a more normative comparison of student

performance across schools, cities, and counties. Because testing has, and will most likely

continue to be, the standard by which we measure student success, this paper will rely on

the Oregon Assessment of Knowledge and Skills (OAKS) as the best metric for student

growth.

Conceptual Model

The purpose of this thesis is to conduct a Quantitative exploration of the

relationship between changing school neighborhoods and school test

performance and racial demographics. Below is a conceptual model used to

connect established urban and education theory to my research design.

Figure 1: Conceptual Model

Gentrification – neighborhood change

Variables for identification

Δ Median Household Income

Δ Median home value

Δ % population with bachelor degree

Δ % population that identify as white

Public School Change

↑ Increase in test

performance

= no significant

change on test

performance

↓decrease in test

performance

Applied Urban and Education Theory

• The relationship between the different forms of capital

(economic, human, and social) and student test performance.

• Concentration of poverty (ghettoization)

• Commodification of the housing market.

• Location as a good for consumption.

• Gentry school choice behavior

• Improved public amenities associated with gentrification

• Schools as initiators of gentrification

METHODS

The research necessary for the completion of this thesis was broken into two distinct parts.

The first part is a spatial analysis component that identified gentrifying school

neighborhoods in the city of Portland. The second part was a quantitative analysis of

student test scores in the identified gentrifying neighborhoods.

Because this thesis is meant to add to the limited but growing amount of literature on this

topic, rather than reinvent the wheel, this research will be relying heavily on research

methods that have come before. The methods in this study will closely mirror those used

by Keels et al. in their paper “The Effects of Gentrification of Neighborhood Public

Schools” (2013). Because this paper will be using Portland neighborhoods as the unit of

analysis rather than Chicago, there will be adjustments made to the methods to account for

these very different contexts. Refer to fig.1 for a flow chart that outlines the steps intended

for this study.

Figure 2: Method Flow Chart

Use Portland as a baseline by establishing percent

change between 2000-2015 in the following indicators:

• Median household income

• Median house value

• Population 25 years and over with a Bachelor’s

degree

Using ArcGIS find the mean of the percent change of the

census tracts in each school catchment zone. This will

give me an approximate percent change in the indicators

of gentrification by school neighborhood.

Note: School catchment zones

that have changed dramatically

and have completely changed the

geographic area they serve will

be excluded from this analysis.

Compare school catchment area change to the baseline

change. Catchment areas with variables that demonstrate

a percent change greater than the baseline change are

treated as indicators of possible gentrification

School catchment areas that exceed the baseline change in

all 4 of the indicators of gentrification will be labeled as

“likely gentrifying.”

Note: “likely gentrifying” areas

are places that demonstrate

characteristics that suggest they

might have gentrified or were

likely gentrifying during the

2000-2015 period, these areas

will denote the schools of

interest for this study

After establishing schools that reside in

neighborhoods that are “likely

gentrifying,” determine academic

growth or decline in student

performance on the 3

grade standardized

reading and math tests

Note: Pre-data will come from the

2000 Census and post-data will

come from 2015 5-year ACS

estimates.

Note: Data will come from the

Oregon Department of Education

After establishing schools that reside in

neighborhoods that are “likely

gentrifying,” determine racial

demographic change in the student body

of the school.

Note: Data PPS enrollment

profiles

Statistical analyses: To be determined upon review of

preliminary findings.

Establish percent change, in same variable as above,

between 2000

2015 by census tract

Spatial Analysis

First, a spatial analysis was conducted using census data to interpolate census tract data

onto Portland’s public-school attendance areas. To do this, a comparison between 2000

and 2015 census tract data was used to determine gentrifying neighborhoods around

Portland elementary public schools. For the purposes of this research, education, housing

value, income, and racial demographic change were used as the variables to identify

gentrifying communities. The variation in changes of these four indicators indicated

varying degrees of gentrifying school neighborhoods. The changes in these variables were

then mapped to school catchment areas to give us an idea of the change in the school

neighborhood.

Data Sources and Indicators of Gentrifying School Neighborhoods

1. 2000 Decennial Census tract data

2. 2015 ACS Census tract data

Indicator 1: Change in Median household income

Indicator 2: Change in Median house value

Indicator 3: Change in Population 25 years and over with a Bachelor’s degree

Indicator 4: Change in Percent of population identified as non-Hispanic white

To establish a baseline, percent change between these variables from 2000 through 2015

will be done on Portland citywide. Portland served as the baseline score to be used in

comparison to the change between the variables on the census tract level. Possible

gentrification will be indicated if the percent change in the indicators on the school

neighborhood level is greater than the city baseline. Once percent change from 2000-2015

in each census tract was established, the change in each school catchment zone was be

aggregated by finding the average amount of change in the census tracts that fall within a

shared catchment zone. School catchment zones that demonstrate a greater change than the

city of Portland in three or four of the independent variables were identified as “likely

gentrifying.”

Using ArcGIS these likely gentrifying school neighborhoods were identified, and all

schools that are indicated as existing in gentrifying neighborhoods will be used as the

experimental group which was been influenced by gentrification. While those schools that

are identified as existing in neighborhoods that are not gentrifying act as a control group

that are not influenced by the independent variable. School neighborhoods with

dramatically shifted catchment zones, recently opened schools, or recently closed schools

will be excluded from the study.

Spatial Data and Maps

Data was collected from the U.S. Census and the American Community Survey (ACS) on

the census tract level. A comparison of the 2000 and 2015 census tracts (appendix A:

Geographic Boundary Changes) reveals new and changed geographical boundaries

amongst various tracts. In cases of tract changes data from the 2000 census was split and

merged in order to create appropriate comparison groups to the 2015 census tracts.

To identify gentrifying school neighborhoods, school catchment zones (also known as

attendance areas) were used to define a school neighborhood. Using areal interpolation

data from the census tract level were mapped to the catchment zones.

Census tracts and school catchment zones can be found in appendix A: Geographic

Boundary Changes. A complete list of census tract and school changes and mergers can

be found in the appendix B: Census and School Change Data).

Statistical Analysis

The second part of the analysis in this paper uses data on the public school within Portland

to investigate the relationship between school neighborhood change and student

performance on standardized tests. Student-level data on standardized test scores obtained

from the Oregon Department of Education (ODE) during the 2000-2001 school year

through the 2015-2016 school year were analyzed for change over time. Longitudinal data

through that period was then used to perform an analysis of the effects of gentrification on

the local schools over time.

Data Sources and variables

1. PPS Enrollment Profiles

Independent Variable: Gentrifying (Yes or No?)

Dependent Variable: Percent of enrolled white students

2. Oregon Department of Education (ODE)

Independent Variable: Gentrifying (Yes or No?)

Dependent Variable A: 3

grade math proficiency on the Oregon assessment

of knowledge and skills (OAKS)

Dependent Variable B: 3

grade reading proficiency on the OAKS

Because the effects of gentrification on schooling will first become evident in younger

children (G. Duncan & Murnane, 2011; Keels, Burdick-Will, & Keene, 2013), and because

parents are more involved in the younger children’s daily routines (Joseph & Feldman,

2009), this analysis relies on data from 3

grade standardized assessments. In addition to

these reasons, in contrast to older students, the younger students are also a better indication

of how the neighborhood is changing.

FINDINGS

Fifteen school catchment zones were identified as likely gentrifying because they

demonstrated uncharacteristically quick growth in median income, median home value,

percent of white residents, and percent of residents over 25 years old with a bachelor’s

degree. Further longitudinal analysis of student performance on the standardized

assessments in these schools revealed that there was no statistically significant difference

between the outcome between gentrifying and non-gentrifying school neighborhoods.

These findings in Portland pushes against many leading theories that gentrification results

in positive academic outcomes in local public schools. While these neighborhoods might

be benefiting one of the many externalities associated with gentrification, it is not clear that

the gentrification of these neighborhoods has any effect on student performance of state

standardized assessments.

Like findings by local scholars such as Bates (2013) and Gibson (2017), this study

identified that gentrifying schools were typically found in the North-East and South-East

neighborhoods of the city. In review of 2000-2015 census data 18.2 percent (26) of the 143

census tracts within Portland were identified as having a growth significantly higher than

Portland in the following four criteria: Median Household income, median house value,

population 25 and over with a bachelor’s degree, percent of population that identifies as

white. These census tracts were largely found centralized in the North East, Albina and

Alberta neighborhoods.

To measure change on a school neighborhood level, a baseline for comparison needed to

be created. To create a baseline, Portland city averages on the four indicators were used as

the comparison tool for determining census tract change. As seen in Table 1, it was

discovered that of the four variables of interest in this study, levels of higher education

grew at a much faster rate than the other three variables. While incomes stayed stagnant,

the boom in the housing market can be seen across the city. We can see widespread trends

in the city level data, but by breaking it down to the census level we get a more refined

picture of what is happening on a neighborhood level.

Table 1. Portland Indicators of Neighborhood Change

Indicator 2000 2015

Percent Change

with MoE

Median income* 55,141 55,003 -0.3% (+/-) 1.5%

Median home value* 212,480 295,100 +39.1% (+/-) 1.4%

Percent of population that is white 77.1% 77.6% +0.5% (+/-) 0.4%

Percent of population with bachelor’s

degree 32.6% 45.5% +12.9% (+/-) 0.9%

Data Source: 2000 Census and 2015 ACS Survey.

*adjusted to 2015 dollars

Using aerial interpolation, census tract data was aggregated by school catchment zones to

find the change across the four indicators in each school neighborhood. Once a percent

change for each indicator was determined for every public-school neighborhood in

Portland, a direct comparison could be made between the school neighborhood and city

level. School neighborhoods were given a score based on whether their average for each

indicator fell below the baseline margin of error, within the baseline margin of error, or

above the baseline margin of error. Each neighborhood could have a score within the range

of -4 to +4 based on how they compared to the city baseline.

For example, The Bridger elementary school neighborhood demonstrated the following

key neighborhood changes:

1. Percent Change in Median Income: -1.3%

2. Percent change in median home value: +49.5%

3. Percent change in population that is white: +1.9%

4. Percent change in population with bachelor’s degree : +25%

Because three of the four criteria are above the city average (median home value,

population that is white, and population with a bachelor’s degree) and 1 of the four criteria

(median income) is not significantly different from the city average, Bridger Elementary

school neighborhood was identified with a gentrification score of 3.

Figure 3: Gentrifying Neighborhoods and Landing Zones

Using this method, it was determined that 17 (25%) of the 68 public K-12 schools in

Portland reside in neighborhoods that have a gentrification score of 4 indicating that they

have very likely experiencing gentrification. In Figure 1. we see a tendency for gentrifying

school neighborhoods to be clustered in the near-eastside regions of the city. Not

surprisingly, the historically black neighborhood of Albina and nearby neighborhoods such

as Alberta, which have rich histories of redlining and disinvestment, have showed strong

indicators of gentrification. Further East we see neighborhoods with scores of -4, indicating

Stable Neighborhoods

Likely gentrifying

Possible landing zone

that they are likely landing zones for those residents that have been displaced from the

gentrifying communities.

This clustering of gentrifying school neighborhoods leads naturally to the question at the

root of this paper; what effect if any does this concentration of neighborhood change have

on the academic performance of students that attend the local public schools?

Regression analysis of the change in test performance in gentrifying neighborhoods when

compared to those that show little to no evidence of gentrification, reveals no significant

difference. In Figure 3 and Figure 4, regression lines for both the constant and gentrifying

Figure 4: Stable/constant schools vs.

gentrifying schools – math

Figure 5: Stable/constant schools vs.

gentrifying schools – reading

Gentrifying School Neighborhoods

Stable/Constant School Neighborhoods

neighborhoods shows a negative relationship between time and student performance on the

math and reading state standardized tests.

Table 2: Performance on Standardized test by Neighborhood Type (2000-15)

Math Reading

Neighborhood Type Slope (b) P-value Slope (b) P-value

Stable/Constant

Neighborhood

-3.33 0.0001 -3.09 0.0001

Gentrifying

Neighborhood

-2.70 0.0002 -2.39 0.007

Data Source: 2000 Census and 2015 ACS Survey.

As seen in table 2, the negative trends of performance on the standardized test are

significant findings. The trend lines show that each year results in an approximate decrease

of 2.39-3.33 points on the standardized tests depending on neighborhood and content area.

One can speculate as to why this trend is being experienced in Portland. Could it simply be

that students are not exiting the third grade as prepared and competent as they have in

previous years, or could it be more easily explained by an increased rigor in the assessments

each year? The introduction of common core standard in 2014-15 suggests that increased

rigor has at least something to do with the quick drop in test scores for the 2014-15 year,

this outlier year fails to account for the negative trend before these years. While finding the

explanation to the negative trend in data might be considered nearly impossible, an

investigation into the difference between gentrifying school neighborhoods and stable or

constant school neighborhoods can be quantitatively explored.

Table 3: Change in Performance on the OAKS: Gentrifying vs. Stable

Neighborhoods

Data Source: 2000 Census and 2015 ACS Survey

In a two sample T-test (Table 2), the average performance over time of gentrifying school

neighborhoods and stable neighborhoods was compared to investigate if the changing

neighborhoods have had any significant effect on the state standardized math and reading

test. Although the regression lines in figure 2 and figure 3 illustrate a possible trend. A T-

test analyzing the difference of means left us with P-values of 0.776 and 0.734 and a

conclusion that the difference in means between gentrifying and non-gentrifying

neighborhoods is not statistically significant. These results prohibit us from drawing any

correlation between the performance on the OAKS standardized test and neighborhood

change.

While gentrification could have a very dramatic effect on community and school dynamics,

statistical analysis has failed to show any correlation between the gentrifying communities

and school performance on the state standardized test. The influx of a higher educated

population and higher incomes reveals a marginal and statistically insignificant difference

in academic outcomes for local public schools.

Subject Pair

Paired Differences

t df

2-tailed

p-val Mean

Math

(x)Gentrifying neighborhoods-

(y)Stable neighborhoods

(x) -2.29

(y) -3.30 0.288 20 0.776

Reading

(x)Gentrifying neighborhoods-

(y)Stable neighborhoods

(x) -2.14

(y) -3.13 0.344 20 0.734

CONCLUSION

When considering the findings in this study it is important to keep in mind that this analysis

only considers averages across neighborhoods. By trying to capture the larger community

trends in the data, finer detailed analysis of students and schools was lost. The data gathered

at this level did not allow us to explain a wider range of school outcomes and limited this

study to general claims and observations.

The findings confirm that while, we might see measurable change in demographics,

income, or home value in specific school neighborhoods, the change we see in academic

performance on the OAKS is negligible. Expected benefits associated with gentrification

fail to materialize in the test scores of local schools. While changing neighborhoods do

undoubtedly have drastic effects on the schools within them, we cannot definitively say the

performance of the students within these schools are changed in any statistically relevant

way.

With the introduction of common core standards, the growing popularity of charter schools,

and the continued presence of private schools in Portland it is hard to definitively say

gentrification is having no effect on our education system. While the effects may not be

witnessed in our public schools, this anomaly could be hidden by funneling of high

performing students to nearby charter and private schools. Meaning the effects might be

felt in gentrifying neighborhoods, but they are simply not spilling over into our public

schools.

The growth of middle- and upper-income households and families in gentrifying

neighborhoods is not connected to any meaningful change in student outcomes. It is only

through neighborhood effects and the associated externalities, that come with increased

economic capital in a community, that low-income children attending public schools in

gentrifying neighborhoods will see any benefits. In this analysis it appears that the variables

associated with gentrification and neighborhood change are not the panacea for our

struggling public-school system.

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APPENDIX A: Geographic Boundary Changes

Figure 1A: 2000 Census Tracts

Pictured below is the geography of the census tracts during 2000, for more detail on how

the census tracts changed over the course of the study please refer to Table 1B in

Appendix B: Tables

Figure 2A: 2015 Census Tracts

2000 Census Tracts

2015 Census Tracts

Figure #A: School Catchment Zone

Because census tracts do not map neatly over school catchment zones, a method of aerial

interpolation was used to aggregate census tract data to the school boundary areas

throughout the study area.

2015 School Catchment

Zones

APPENDIX B: Census and School Change Data

Table 1B: Census Tract Changes and Mergers

Due to the change in geography of certain census tracks throughout the study area,

several census tracts in the 2000 data set had to be merged or split in order for a direct

comparison to 2015 census tract data.

2000 Tracts Action 2015 Tracts

44 renamed 9800

22.01 & 23.01 merged 22.03

22.01 & 23.02 merged 23.02

53 & 54 merged 106

64.01 split 64.03 & 64.04

Table 2B: School Name Change and Mergers

Over the 2000-2015 school years there were several changes made to the schools in the

study area. The table below details the year a change occurred, the name of the school

and the action that was taken.

Year School Action

2005 Ball Elm Was closed (removed from

analysis)

2007 Clarendon-Portmouth Reopened as César Chávez

2007 Clark Reopened as Harrison Park

2007 Hoolyrood & Fernwood Merged into Beverly Cleary

2007 Humboldt Merged with Boise-Eliot

2007 Rose City Reopened as Roseway heights

2013 Ockley Green Merged with Chief Joseph

Table 3B: Portland 2000-2015 Indicators of Change

Below are the key variables of neighborhood change used in this study. These values

were used to create a baseline for neighborhood comparison.

Indicator

2000

2015

Percent Change

with MoE

Median income*

55,141

55,003

-0.3% (+/-) 1.5%

Median home value*

212,480

295,100

+39.1% (+/-) 1.4%

Percent of population that is white

77.1%

77.6%

+0.5% (+/-) 0.4%

Percent of population with

bachelor’s degree

32.6%

45.5%

+12.9% (+/-) 0.9%

Data collected from the 2000 Census and 2015 ACS survey

*Median Income and Median home values adjusted to 2015 dollars

Table 4B: Indicators of Neighborhood Change by School Neighborhood

School Name

%Change

Population

%Change

Population

of White

Residents

%Change

Median

Income

%Change

in Median

Home

Value

%Change

Population

with

Bachelor’s

degree

Gentrification

Score

Abernethy

Elementary 20.7% 1.2% 10.0% 56.6% 32.0% 4

Ainsworth

Elementary 14.9% -7.0% 8.0% 16.5% 39.7% 0

Alameda

Elementary 8.5% 2.4% 15.5% 59.4% 35.8% 4

Arleta

Elementary -0.5% 0.2% -2.1% 37.6% 26.8% -1

Astor

Elementary 24.5% -1.4% -0.7% 58.4% 21.4% 1

Atkinson

Elementary 0.9% 3.0% 3.3% 54.0% 29.5% 4

Beach

Elementary 7.0% 13.5% -65.2% -37.7% 32.9% 0

Beverly

Cleary 11.2% 2.2% 8.0% 56.0% 35.5% 4

Boise-Eliot

Elementary 32.3% 26.4% 27.8% 68.3% 35.0% 4

Bridger

Elementary 0.7% 1.9% -1.3% 49.5% 25.0% 3

Bridlemile

Elementary 11.5% -2.7% 9.6% 31.4% 37.7% 0

Buckman

Elementary 6.8% 3.5% 8.2% 52.5% 29.6% 4

Capitol Hill

Elementary 33.4% -2.4% -2.7% 30.9% 33.4% -2

César Chávez

K-8 36.9% 4.5% -12.1% 32.0% 13.3% -1

Chapman

Elementary 37.3% -2.1% 4.7% 17.6% 34.6% 0

Cherry Park

Elementary 21.4% -11.8% -32.6% 8.0% 11.0% -4

Chief Joseph

Elementary 11.9% 9.0% 5.5% 60.7% 31.7% 4

Creston

Elementary -0.3% 5.2% 6.2% 41.2% 30.5% 4

Duniway

Elementary 7.6% -0.6% 17.1% 45.3% 36.5% 2

Earl Boyles

Elementary 17.5% -9.1% -24.7% 5.9% 8.4% -4

Faubion

Elementary 9.9% 4.9% -14.1% 25.3% 19.9% 0

School Name

%Change

Population

%Change

Population

of White

Residents

%Change

Median

Income

%Change

in Median

Home

Value

%Change

Population

with

Bachelor’s

degree

Gentrification

Score

Forest Park

Elementary 66.3% -11.3% -8.3% 10.1% 34.2% -2

Gilbert

Heights

Elementary 38.7% -12.5% -23.4% -1.6% 9.3% -4

Gilbert Park

Elementary 50.0% -14.2% -12.5% 4.7% 10.8% -4

Glencoe

Elementary 10.2% 0.1% 3.0% 51.9% 28.0% 2

Glenfair

Elementary 36.9% -6.5% 1.0% 0.6% 7.0% -3

Grout

Elementary 11.1% 1.9% -2.5% 53.6% 23.4% 2

Harrison Park 33.7% -20.0% -24.5% 18.3% 9.9% -4

Hayhurst

Elementary 12.9% -4.3% 0.7% 35.2% 26.6% -1

Irvington

Elementary -0.2% 11.7% 15.2% 58.9% 35.1% 4

James John

Elementary 12.5% 4.7% 0.1% 37.4% 25.4% 1

Kelly

Elementary 17.3% -4.6% -10.9% 3.5% 9.3% -4

King

Elementary 6.1% 35.6% 56.6% 91.9% 41.1% 4

Laurelhurst

Elementary 8.4% 1.0% 4.9% 62.4% 35.0% 4

Lee

Elementary 12.3% -5.0% -17.2% 22.1% 22.2% -2

Lent

Elementary 15.9% -1.1% -11.4% 7.9% 11.7% -4

Lewis

Elementary 12.8% -0.1% 13.5% 32.4% 28.0% 0

Lincoln Park

Elementary 27.3% -13.1% -25.9% -6.6% 6.0% -4

Llewellyn

Elementary 13.5% -1.1% 7.8% 56.4% 27.0% 2

Maplewood

Elementary 16.3% 3.4% 7.8% 42.3% 30.3% 4

Markham

Elementary

School 12.2% -3.3% -3.2% 26.0% 21.0% -2

Marysville

Elementary 7.7% -1.1% -6.6% 27.4% 18.6% -2

School Name

%Change

Population

%Change

Population

of White

Residents

%Change

Median

Income

%Change

in Median

Home

Value

%Change

Population

with

Bachelor’s

degree

Gentrification

Score

Menlo Park

Elementary 19.3% -9.8% -21.6% -6.1% 6.0% -4

Mill Park

Elementary 43.1% -14.3% -24.2% 2.8% 8.3% -4

Parklane

Elementary 16.8% -10.1% -25.2% -4.5% 5.7% -4

Peninsula

Elementary 12.7% 8.8% 3.3% 42.4% 21.9% 4

Pleasant

Valley

Elementary 41.3% -10.6% -16.1% 2.7% 8.4% -4

Prescott

Elementary 5.6% -9.5% -15.5% 6.0% 15.4% -2

Rieke

Elementary 9.8% -2.0% 7.8% 30.7% 33.4% 0

Rigler

Elementary 12.0% 3.2% -24.5% 27.5% 20.3% 0

Rosa Parks

Elementary 21.2% 7.8% 1.2% 38.6% 17.6% 2

Roseway

Heights -4.3% 7.0% -2.4% 49.6% 26.6% 2

Russell

Academy 12.9% -9.3% -17.9% 5.6% 11.1% -4

Sabin

Elementary 8.4% 21.2% 30.9% 80.5% 35.6% 4

Sacramento

Elementary 4.7% -4.5% -6.2% 5.1% 9.3% -4

Scott

Elementary -1.2% 6.0% -1.9% 38.9% 20.1% 1

Shaver School 1.6% -5.8% -29.0% 8.0% 9.8% -4

Sitton

Elementary 3.5% 11.4% 6.4% 32.1% 26.5% 2

Skyline

Elementary 51.6% -10.0% 4.0% 13.0% 32.4% 0

Sunnyside

Environmenta

l 10.8% 0.0% 11.4% 52.4% 31.4% 2

Ventura Park

Elementary 29.4% -16.1% -21.6% 6.4% 10.1% -4

Vernon

Elementary 6.1% 15.0% 20.2% 66.9% 34.2% 4

Vestal

Elementary 4.6% -2.3% 8.3% 35.6% 25.9% 0

School Name

%Change

Population

%Change

Population

of White

Residents

%Change

Median

Income

%Change

in Median

Home

Value

%Change

Population

with

Bachelor’s

degree

Gentrification

Score

West

Powellhurst

Elementary 31.0% -10.5% -31.2% -1.3% 1.6% -4

Whitman

Elementary 24.5% 6.1% 1.0% 17.4% 11.9% -1

Woodlawn

Elementary 5.7% 21.4% 5.5% 54.0% 34.3% 4

Woodmere

Elementary 12.8% 0.4% -2.2% 23.9% 19.1% -1

Woodstock

Elementary 7.4% 2.8% 15.5% 46.3% 29.6% 4