Witte_georgetown_0076M_13272

CAN SCHOOL INTEGRATION INCREASE STUDENT ACHIEVEMENT?
EVIDENCE FROM HARTFORD PUBLIC SCHOOLS
A Thesis
submitted to the Faculty of the
Graduate School of Arts and Sciences
of Georgetown University
in partial fulfillment of the requirements for the
degree of
Masters of Public Policy
in Public Policy
By
Lucretia Anne Witte, B.A.
Washington, DC
April 12, 2016

ii
Copyright 2016 by Lucretia Anne Witte
All Rights Reserved

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CAN SCHOOL INTEGRATION INCREASE STUDENT ACHIEVEMENT?
EVIDENCE FROM HARTFORD PUBLIC SCHOOLS
Lucretia Anne Witte, B.A.
Thesis Advisor: Erica Johnson, Ph.D.
ABSTRACT
Since Brown vs. Board of Ed prohibited de jure school segregation, the national black-
white achievement gap has reflected trends in school integration. Hundreds of programs
and policies since then have had only incremental impacts on the disparities in
educational outcomes based on race and socioeconomic status. In Hartford, CT, the 1999
Sheff vs. O’Neill case required the State of Connecticut to create an integration program
to reduce racial isolation in Hartford’s segregated inner city schools. As a result,
Hartford implemented a proactive integration policy. Between 2008 and 2013 the
number of poor black and Hispanic students attending an integrated school in Hartford
increased twenty percentage points. A difference-in-difference approach was used to test
whether this policy had an effect on reading, writing and math proficiency rates, holding
demographic factors constant. The study finds that integration status has a positive and
highly significant effect on proficiency rates in math and reading and no effect on writing
proficiency.

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O, let my land be a land where Liberty
Is crowned with no false patriotic wreath,
But opportunity is real, and life is free,
Equality is in the air we breathe.
Langston Hughes, “Let America Be America Again”

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TABLE OF CONTENTS
Introduction..........................................................................................................................1
Background..........................................................................................................................4
Literature Review: Does Integration Impact What Students Achieve? ............................12
Conceptual Model: How Can Integration Improve Student Outcomes? ..........................19
Empirical Strategy: Connecticut District Level Data and The Difference In Differences
Model.................................................................................................................................22
Results................................................................................................................................27
Potential Limitations..........................................................................................................35
Directions for Further Research.........................................................................................36
Policy Implications ............................................................................................................37
Conclusions........................................................................................................................38
References..........................................................................................................................39

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LIST OF FIGURES
Figure 1: White Flight across America, between 1960 and 1970........................................2
Figure 2: The Black-White Achievement Gap Between 1971-2012...................................3
Figure 3: Map of Integration Efforts in 1966.......................................................................7
Figure 4: Hartford Public Schools Regional School Choice Program.................................9
Figure 5: Towns in the Sheff Open Choice Region...........................................................18
Figure 6: Conceptual Model for How School Integration Impacts Student Outcomes.....20
Figure 7: Population Density of Connecticut Counties, with City Names ........................24
Figure 8: Regression Equation...........................................................................................26
LIST OF TABLES
Table I: Percent Of Black Students In Schools With More Than Half Minority Students
By Region, 1968-1980.........................................................................................................4
Table II: Percent of Minority Hartford Residents Attending Integrated Schools..............10
Table III: Descriptive Statistics on Connecticut Counties.................................................23
Table IV: Student Demographics in Comparison Cities....................................................25
Table V: Fully Controlled Model Using Math, Reading, and Writing Proficiency Scores
............................................................................................................................................28
Table VI: The Impact of Integration on Proficiency Rates in Suburban Open Choice
Districts..............................................................................................................................31
Table VII: The Relationship Between School Type on Math Proficiency Rates ..............33

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INTRODUCTION
America is a diverse society in which educational differences have the
potential to become a progressively larger source of inequality and social
conflict. Many people now recognize that eliminating these differences
has become a moral and pragmatic imperative. (Miller, 1995)
In the US we believe that education is the great equalizer, but we have acted on
that belief inconsistently throughout our nation’s history. Today, the black-white
achievement gap persists despite broad school reform efforts ranging from increased
spending to higher accountability to better teacher education. This thesis will use a
difference-in-difference analysis of an integration effort in Hartford, Connecticut to
determine whether purposeful integration increased student achievement, with the hope
that Hartford’s outcomes can support policy decisions in other cities.
On May 7, 1954, the Supreme Court ruled that segregation in schools was illegal
(Brown v. Board of Ed, 1954). This case marked a promise of better opportunity for the
next generation. During the 1960s, schools across the country integrated in compliance
with this ruling. Integration did reduce the black-white achievement gap: between 1970
and 1988, the black-white achievement gap narrowed by 46% (NCES, 2012). Further
studies determined that higher achievement led to better life outcomes: blacks who spent
five or more years in desegregated schools earned 25% more than those that did not
(Johnson, 2011). They were less likely to be incarcerated, and more likely to be healthy
as adults (ibid). Integration had no negative effect on whites’ earnings, incarceration, or
health.

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Yet over the years, segregation crept back. Detroit’s 1974 Mililken v. Bradley
case decided that segregation was constitutional as long as it was not a product of the
district’s explicit policy (Miliken v. Bradley, 1974). In the early 2000s, federal judges
struck down mandates that had ordered deliberate integration policies (Greenberg,
Grewal, Kalogrides, Reardon, 2011). Meanwhile, between 1960-1970 whites began
moving out of cities, leaving minorities behind in the urban core. Neighborhoods and
schools became resegregated (Fry and Taylor, 2012, Frankenberg, Lee, and Orfield 2003;
Orfield, Kucsera, and Siegel-Hawley 2012). Even though integration was shown to have
positive effects on minority students and no negative effects on white students, many
districts did not pursue desegregation efforts once federal mandates were removed.
Within these re-segregated schools, expectations of students, resources, and traditions
were different (Kozol, 2005). These inequities perpetrated an achievement gap, which
Figure 1: White flight across America, between 1960 and 1970. Source:NYSTROM/Herff Jones,
2015

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contributes to a gap in social outcomes between between whites and minority students
(Hanushek, Kain and Rivkin, 2009, Perry 2010).
Figure 2: The Black-White Achievement Gap between 1971-2012. Source: NCES, 2014.
Myriad public and non-profit efforts have been targeted at the racial achievement
gap.1
Head Start, Title I, local at-risk funding, teacher evaluations, instructional reforms,
and non-profits like Teach for America all focus on providing more equitable educational
opportunities for minority students.2
But despite this multi-sector, multifaceted push for
1
The Department of Education lists 22 nationwide initiatives and projects; there are more than two million
non-profits in the US, according to the National Center for Charitable Statistics at the Urban Institute. If
one-twentieth of those non-profits did work related to education, that would be over one hundred thousand
programs related to education reform. In addition to these, there are also state and local education
initiatives and programs.
2
Mission statements for these organizations each mention equalizing educational opportunity for students
from all backgrounds. Head Start: “The National Head Start Association (NHSA) is a nonprofit

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educational equity, the black-white student achievement gap has actually widened
slightly since 1988, when integration was at its peak (NCES, 2012).
Table I:
Percent Of Black Students In Schools With More Than Half Minority Students
By Region, 1968-1980
Total
U.S.
Southern
States
Border
States
Northeast
States
Midwest
States
West
States
1968 76.6 80.9 71.6 66.8 77.3 72.2
1972 63.6 55.3 67.2 69.9 75.3 68.1
1976 62.4 54.9 60.1 72.5 70.3 67.4
1980 62.9 57.1 59.2 79.9 69.5 66.8
Change
1968-80
-13.7 -23.8 -12.4 +13.1 -7.8 -5.4
Can renewed integration efforts increase student achievement? A few cities have
recommitted to integration efforts, with mixed results. This thesis will examine whether
school integration efforts in Hartford, Connecticut reduced the achievement gap for
minority students between 2007-2013.
BACKGROUND
The unique story initiated by Connecticut’s Supreme Court is of great
regional and national importance, not as a grand solution to a very deeply
embedded problem but as an important example of what can be done
through the wise use of choice programs with clear civil rights objectives
and major educational innovations. (Orfield, 2015)
organization committed to the belief that every child, regardless of circumstances at birth, has the ability to
succeed in life.” Title I: “Title I… provides financial assistance to local educational agencies (LEAs) and
schools with high numbers or high percentages of children from low-income families to help ensure that all
children meet challenging state academic standards.” Teach for America: “Our mission is to enlist, develop,
and mobilize as many as possible of our nation's most promising future leaders to grow and strengthen the
movement for educational equity and excellence.”

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Sheff vs. O’Neill: The Catalyst for Integration Efforts in Hartford, CT
In 1989 in Hartford, Connecticut, the parents of 4th grader Milo Sheff and
seventeen other children began a civil action against the state for providing a racially
isolated and therefore unequal education to their children. Some plaintiffs were black,
some were Latino or white. Even though segregation in their schools was no result of
state policy—rather as a result of racially homogenous neighborhoods— the plaintiffs
claimed that the state was responsible for proactively integrating these schools.
In 1995, Judge Harry Hammer ruled in favor of the state, writing in his decision
that Connecticut was not responsible for correcting educational inequities due to de facto
segregation, only for ensuring that no de jure segregation took place.3
In 1996, John
Brittain, a civil rights lawyer, appealed Hammer’s decision. The initial ruling was
overturned by the Connecticut Supreme Court on the basis that Connecticut had an
obligation to provide students with equal educational opportunities.4
According to their
decision, that meant the state had to provide students an education that was not impaired
by racial and ethnic isolation (Sheff vs. O’Neill, 1999). In compliance with the court’s
ruling, the legislature called for additional spending on magnet schools, voluntary
interdistrict transfer programs, and interdistrict coop programs. It prohibited redrawing
of district boundaries (ibid).
3
Precedent for Hammer’s decision could be found in Miliken v. Bradley, among other cases.
4
While there was some language in the Connecticut State Constitution about equitable school funding, the
Supreme Court’s decision deviated from other US Supreme Court precedents that have held that only de
jure segregation is unconstitutional. Relevant language from the eigtht article of the Constitution of the
State of Connecticut reads, “The interest of [the school fund] shall be inviolably appropriated to the support
and encouragement of the public schools throughout the state, and for the equal benefit of all the people
thereof….and no law shall ever be made, authorizing such fund to be diverted to any other use than the
encouragement and support of public schools, among the several school societies, as justice and equity
shall require.”

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In 2003, a legal settlement between the plaintiffs and the state formalized the
definitions, integration targets and programs the state would support to comply with the
Sheff vs. O’Neill ruling. The state funded two main programs: the Interdistrict Magnet
Program and the Project Choice Transfer Program (Dougherty, Estevez, Wanzer and
Tatem, 2006). The goal was that at least 30% of poor black and Hispanic students living
in downtown Hartford would attend integrated5
schools by June 2007.
This settlement effectively created a natural experiment in Hartford. Because the
ruling concerned Hartford and its suburban ring but not the other segregated school
systems in Connecticut, any change in achievement trends in Hartford that did not occur
in other cities could be attributed to the integration policy. If integration efforts narrowed
the achievement gap, integration could begin to be investigated as a strategy for school
reform.
Hartford: An Ethnographic and Political History of Inequality in Connecticut’s Capitol
Due to a strong manufacturing and shipping industry, Hartford was the wealthiest
city in the country by the late 19th
century (Clark, 1876). Today it is the second-
wealthiest city in the nation (Lee, 2015). Attracted by its wealth and booming
manufacturing sector, immigrants flooded to Hartford during the first half of the
twentieth century, including a tide of Puerto Rican immigrants right after World War II.
But in the late 1950s, as suburbs around Hartford sprang up to make room for its new
5
A note about language: For the purpose of this study, “integrated” and “desegregated” mean different
things. While all schools in the US were ordered to be desegregated by Brown vs. Board of Ed in 1954,
being desegregated is necessary but not sufficient for being integrated. If a desegregated school’s
neighborhood has only black and Hispanic residents, then it is, in the language of the Sheff case, racially
isolated. This distinction can also be thought of in terms of de jure and de facto segregation. While
segregation in Hartford was de facto, the court ruled that the Department of Education had a responsibility
to create integrated educational environments. According to the terms of the settlement agreement, schools
were considered integrated if they had at least 25% but not more than 75% black and Hispanic children in
attendance.

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residents, the city’s core began to decline. Businesses moved to less expensive locations
in the suburbs and wealthy whites followed them at increasing rates. In the 1990s,
Hartford’s population shrank by 11 percent (1999 Census).
Figure 3: Map of Integration Efforts in 1966, The Hartford Times.
Source: Hartford Public Library.
Despite its historic wealth, a series of recent economic policies, such as high taxes
on everything from income to sales to unemployment insurance, have made Connecticut
one of the most hostile climates to small business in the country (Powell, 2013). By
2012, Connecticut was last in the country for economic growth; that year it was the only
state whose economy actually shrunk (BEA, 2012). This took a toll on employment and
economic mobility prospects for residents. That year, 17.3% of the workforce was
unemployed and more than a quarter lived below the poverty line (De Avila, 2012).
Between 1996 and 2006, the number of small businesses in the state declined by 2.2%,
when the average change in all other states was a 10% increase (ibid). As a result of a

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barren employment landscape, crime is pervasive in some parts of the urban core. In
2013, Hartford was the 24th
most dangerous city in the US and the 4th
most dangerous
city under 200,000 people (Warner, Fuchs and Lubin, 2013; FBI, 2013).
Limited economic opportunity and high crime rates contribute to the state of
housing and school segregation between inner-city Hartford and its suburban neighbors.
In 1987-88, the school year before Sheff vs. O’Neill was filed, there were 25,000 students
in the Hartford Public School District, 90.5% of whom were ethnic minorities, while the
surrounding suburbs were 2-30% minority (Brenz, 2013). Virtually all students who
attended segregated schools were also poor, and achieved at much lower rates than their
white affluent peers (Orfield, 2015). This led to Elizabeth Sheff’s 1989 decision to sue
the state on grounds that isolated education is not equal education, a conclusion that
much subsequent econometric research has justified (NCES report 2015-018, among
others). Importantly, due to its longstanding history of anti-slavery sentiment, Hartford
as a city was open to Sheff’s view on the state’s responsibility: teachers and principals
testified against the state in the Sheff proceedings, giving evidence that was crucial to
both the Supreme Court’s decision and to public sentiment about the case (Green, 1993).
The 2003 and 2008 Sheff Settlements: Goals, Programs and Results
In the initial 2003 Sheff settlement, the goal was that 30% of public school minority
students living in Hartford would have an integrated educational experience by June
2007. The settlement defined “integrated” schools as schools where minorities
constituted more than 25% and less than 75% of students. There were three main
initiatives through which the state sought to achieve this goal:

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1) Interdistrict Magnet Schools. These schools, mostly located in the urban core,
were designed to attract suburban students to attend school in the city. They
offered specialized curricula, often with new facilities like a performing arts
center or a planetarium.
2) The Open Choice Program. This program allows inner city minorities to transfer
to a suburban district school.6
The hosting district specifies how many Open
Choice students they will accept each year.
Figure 4: Hartford Public Schools offers students the opportunity to choose from a
variety of school options through the Regional School Choice Office Lottery. HPS
partners with other magnet and charter providers as well as suburban open choice
districts.
6
Open Choice districts include Avon, Berlin, Bolton, Bristol, Canton, Cromwell, East Granby, East
Windsor, Ellington, Enfield, Farmington, Glastonbury, Granby, Hartford, Newington, Plainville, Portland,
Region 10, Rocky Hill, Simsbury, Somers, South Windsor, Southington, Suffield, Vernon, West Hartford,
Wethersfield, Windsor, and Windsor Locks. Seats are available in elementary, middle and high schools.

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3) The Reverse Choice Program. This much smaller program allows students from
the suburbs to transfer in to an urban district school.
Table II:
Percent of Minority Hartford Residents Attending Integrated Schools
Year Goal Actual
2002-2003 30% 10%
2004-2005 30% 13%
2006-2007 30% 17%
2008-2009 41% --
2010-2011 41% --
2012-2013 41% 37%
2015-2016 47.5% 45.5%
Data for this table was compiled from settlement documents and the Connecticut
Mirror’s reports on integration levels. Empty boxes represent years when integration
was not measured.
Students from either the Hartford urban core or the hosting districts were eligible to
participate in both programs. Spots were allotted through the Greater Hartford Regional
School Choice Lottery.
In 2003 when Sheff and the State of Connecticut reached this initial settlement,
only 11% of Hartford minority students were receiving an integrated education (Orfield,
2015). By 2007 that number had grown to 16.9%, but it had still fallen far short of its
goal of 30% (Orfield, 2009). Some magnet schools had too many minorities (>75%)
while others had too few (<25%) (Rioual, 2013). Further, many minorities attending
magnets were actually coming into Hartford from the suburbs, where their home districts
would have been more integrated than the options available to city residents (ibid). As a
result, the state and the plaintiffs negotiated a new settlement in 2008. This time, they
aimed for 80% of lottery applicants to be accommodated in an integrated education
environment by June 2013 (Connecticut General Assembly, 2008), with a fallback goal

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of 41% of poor black and Hispanic students from the urban core being enrolled in
reduced-isolation settings. The state promised more resources and greater political
efforts to meet the increased goals. There was dramatic improvement—by 2013, 43% of
Hartford minority students attended a racially integrated school (Orfield, 2015).
Demand for the programs operated under the integration efforts is high. Many
more families enter the lottery than can be accommodated in either of the programs
(Dougherty, 2014). Even though Hartford surpassed its 2013 fallback goal of 41% of
minority students in reduced isolation schools in 2013, it did not succeed in placing 80%
of lottery applicants. Sheff and the state are currently in ongoing negotiations about
future requirements, with another ruling expected in the next 1-2 years (Megan, 2015).
There may be future settlements which require the city to meet this growing demand.
Several studies have been done using Hartford data from 2003 to 2008, which
find a positive effect of integration on student achievement scores in math and reading
(e.g., Bifulco, 2009; Cobb, 2011). Further, positive effects of magnet schools on student
achievement have been found in Hartford (Orfield, 2015). But no study has yet been
done to evaluate the impact of integration on academic outcomes since the Phase II
settlement efforts in 2008. Because the percent of students attending an integrated school
increased from just below 17% to 43% in the five years following those renewed
integration efforts, any impact on student achievement in Hartford during those years can
add to the body of evidence for policymakers considering whether integration can work
as a citywide education reform strategy.

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LITERATURE REVIEW:
DOES INTEGRATION IMPACT WHAT STUDENTS ACHIEVE?
In the 1966 Coleman Report, formally titled Equality of Educational Opportunity,
researchers from Johns Hopkins concluded that poor black students learn better in well-
integrated classrooms (Coleman et al, 1966). Over the subsequent 50 years, researchers
explored and tested this conclusion to inform policy decisions, particularly in light of the
growing achievement gap amongst our country’s high and low achievers. Research has
shown that school segregation impacts student achievement when it concentrates race,
when it concentrates poverty, and when it reduces students’ self-worth.
Racial Diversity Promotes Achievement: Theory and Evidence
According to sociological theory, attending a racially diverse school develops
self-awareness and critical thinking as students learn about people who are different from
them and prepares them to succeed in a racially integrated world. This exposure may
stimulate their creativity, ability to see diverse perspectives, and curiosity about the
world, leading to higher academic achievement through the cultivation of attitudes and
mindsets (Schofield, 1995, and Wells & Crain, 1994).
There are mixed results on whether higher levels of integration lead to higher
student achievement. Cook and Evans (2000) found that little of the black-white gap in
NAEP scores can be attributed to segregation level. Card and Rothstein (2007) found
that neighborhood segregation had consistently negative effects on student achievement,
but that school segregation had no independent effect. The most recent result, Hanushek,
Kain and Rivkin (2009), found that the more segregated a school was, the worse its black

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students performed academically, while segregation had insignificant effects on white
achievement. The magnitude of their finding was significant: according to their analysis,
if all Texas schools had the mean share of black enrollment, the black-white achievement
gap for 7th
graders would be reduced by 10% (Hanushek, Kain & Rivkin, 352). One
potential explanation why these studies found such different results is that it was hard to
find exogenous variation in racial composition in the data used for these studies.
Socioeconomic Status is the Best Predictor of Student Success: Theory and Evidence
Theory about poverty and its influence on student achievement suggests that low
socioeconomic status (SES) influences student preparation for school, parental
involvement in education, and student aspirations beyond school (American
Psychological Association, 2014). Students in low-SES households are less likely to
have developed academic and social skills before starting school (Lacour, 2011). Once
they start school, they are less likely to receive help from their parents with homework or
to have parents engage in school activities. (US Department of Health and Human
Services, 2000). As they reach high school, they are less likely to stay enrolled and
graduate and are more likely to feel as though material is either not relevant or not
interesting to them (Yazzie-Mintz, 2007).
The empirical evidence on the implications of poverty on student achievement is
far less mixed than the effects of integration. Studies have shown that students’ own
socioeconomic status and that of their peers is positively related to achievement. The
Coleman Report found that the best predictor of student academic achievement was
family socioeconomic status, and the second-best predictor was the socioeconomic status

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of the other students in the school (Coleman, 1966). Rusk (2002) studied public schools
in Madison-Dane County, WI, and found that a 1% increase in middle class classmates
was associated with a 0.64 percentage point gain in reading and a 0.72 percentage point
gain in math. Rumberger and Palardy (2005) found that a school’s SES was as impactful
as a family’s SES on student achievement growth in math, science, reading and history.
The PISA 2006 report found neighborhood effects of socioeconomic status: students
from any socioeconomic background performed better in a high SES school than a low
SES school (PISA, 2006). Perry (2010) found that increases in a school’s mean SES
were associated with consistent increases in student achievement, and that this
relationship held regardless of students’ individual SES.
Self-Worth: The Psychological Theory Behind an Omitted Variable
The final avenue through which segregation impacts achievement is students’ self
worth. Sociological theory postulates that students who attend segregated schools
develop an attitude that they are not “worth it” when they see how run-down their school
is or how little their teachers care (Epps, 1975). Recent ethnographies of race relations in
the US have cited broken windows and trash left out on the street in poor minority
communities as physical manifestations of the world’s value for them (Coates, 2015,
among others).
While this theory does not have the empirical evidence that the other two do, it is
included here because psychological studies of black children that connected low self-
esteem to segregated schooling were the main social science evidence of harm from
segregation cited in Brown vs. Board of Education (Hanushek, 2009). In spite of

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numerous qualitative studies of the self-worth effect, quantitative social science research
has not explored this theory or its relationship to race and SES. Many studies instead try
to isolate the effects of schools’ racial and socioeconomic composition, when in fact the
two constructs are very much interwoven in the lived realities of schools (Mickelson,
2010). Without explicit studies of the impact of segregation on students’ self image, the
psychological effects should be considered an omitted variable. When students attend
schools in both racially and socioeconomically isolated neighborhoods, the self-worth
effect most likely amplifies the negative effects of race and SES on student achievement.
Results of Integration Efforts on Student Achievement
Brown vs. Board of Ed’s prohibition of de jure segregation was correlated with an
increase in student achievement. In 1988 when integration rates peaked, the black-white
achievement gap had closed 54% (The Nation’s Report Card, NCES 2012). After 1988,
the trends in de facto segregation began to undo the progress made by Brown vs. Board of
Ed and the achievement gap began to widen again.
Based on the substantial empirical evidence that integration is positively
correlated with achievement growth, a number of cities have used proactive integration as
an education reform strategy. Some have found positive effects on student achievement
or psychological metrics, but others have demonstrated no or negative impact.
A few examples of integration with positive results include Louisville and
Jefferson County, Kentucky; Seattle, Washington; LaCrosse, Wisconsin; and Cambridge,
Massachusetts (Semuels, 2015; Kahlenberg, 2012; and Cowen Institute, 2011). In 2008,
Orfield, et. al., reported that students who attended integrated schools in Jefferson County

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and Seattle “have reported that interracial schooling experiences have been valuable and
have made them better prepared to live and work in diverse communities.” (Orfield, 104).
Kahlenberg (2000) shows that through peer effects, economic integration in LaCrosse
supports academic growth.
Those cities where integration efforts were ineffective include Detroit, which only
made it two years before being released from its court order by the Milliken v. Bradley
case, and Metco in Boston, where public opposition shut the program down. Detroit did
not collect enough data for an empirical evaluation of the program’s effect. Boston lasted
long enough that economists could evaluate impact: Angrist and Lang (2004) found that
there was little positive impact on student achievement, but that blacks in receiving
districts could be negatively impacted by the arrival of lower-achieving black students.
Research finds that integration methods and public opinion are crucial to the
positive relationship with student achievement. Today, the most successful form of
integration has been the magnet school, which pulls parents, students and faculty from
diverse communities into a new school (Kahlenberg, 2000). Following that, open choice
programs with friendly receiving districts have been correlated with positive effects on
student achievement. Receiving districts which did not voluntarily extend spaces to
urban students, as in Boston’s Metco and the Detroit program, fare worst.
The comparison between Louisville, KY and Detroit, MI shows how two
originally similar districts have diverged since their decisions about integration. In 1972
when the courts ordered these two districts to integrate, their demographics looked
similar: they had equally segregated schools and proportionally-sized racial ghettos
(Orfield, 2015). In 1974 their paths diverged: Miliken v. Bradley freed Detroit from the

17
integration mandate, while Louisville kept theirs. In 2000, the average black student in
Detroit attended school with less than 2% white students, whereas black students in
Louisville were likely to attend schools that were half white (ibid). A comparison of
student achievement on NAEP fourth grade reading tests in 2011 showed that twice as
many students scored at goal or higher in Louisville as in Detroit (NAEP District
Summary Tables, 2011). Further, there are stark economic differences: in 2010 Detroit
had 28 jobs per hundred residents while Louisville had 61 (Orfield, 2015). Between
2005-2015, Detroit lost 25% of its population while Louisville’s increased by 7% (ibid).
There are many factors besides their school integration policies that have influenced these
cities’ evolution, but this case study invites researchers to consider and investigate
whether school integration policies can impact housing, race relations, economic
opportunity and growth of enterprise in a city that chooses to integrate.

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The Impact of Segregation in Hartford
Figure 5: Towns in the Sheff Open Choice Region.
Source: CT Regional School Choice Office, 2015.
In Hartford, research on the impact of integration on student achievement was
done prior to 2012 when integration levels were less than 30% (e.g., Bifulco, 2009, and
Cobb, 2011). These analyses determined that the effects of integration programs were
positive and statistically significant on reading and math scores of middle and high
school students (Cobb, 2009). In the years since those studies came out, integration rates
have risen from 30% of minority students in Hartford attending integrated schools to
45.5% (Thomas, 2013). The data from the past three years thus provides more variation
in integration rates. This thesis will use the Hartford data from the years between 2007-
2014 to show the impact of the integration policy on student test scores.

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CONCEPTUAL MODEL
HOW CAN INTEGRATION IMPROVE STUDENT OUTCOMES?
I hypothesize that higher levels of school integration improve academic
achievement for black and Hispanic students without affecting white students. I expect
that the more even the ratio of black and Hispanic students to white and Asian students,
the greater the improvement in student outcomes for blacks and Hispanics.
To derive the conceptual model, I returned to the research, which suggests that
integrating schools changes student outcomes predominantly through school quality, peer
effects, and psychological effects. Some researchers have found that integration mainly
acts through improved school quality (Rumberger & Palardy, 2005). Others have found
that the effect comes from peer effects; that is, minorities benefit from being in a school
with whites or Asians (Hanushek, Kain & Rivkin, 2000). The following figure presents a
conceptual model for how racial integration could catalyze improved educational
outcomes. Consistent with the existing literature, this model suggests that a more even
ratio of blacks and Hispanics to whites and Asians will improve student outcomes for
blacks and Hispanics without significant impact on whites and Asians.

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Figure 6: Conceptual Model for How School Integration Impacts Student Outcomes.
School quality, the first channel through which integration impacts achievement, can be
determined by racial composition through increased funds or better advocacy. In the
context of Hartford, white and Asian students attending integrated schools are almost all
from a middle or high SES households (Orfield, 2015). High-SES households pay more
in taxes, which means more money is available for school budgets. Further, more than
40% of low-income students don’t get a fair share of state and local education funding
(US Department of Education, 2011). While low-income schools receive supplements
from federal programs like Title I, this money often comes with restrictions that could
impact how effectively it is deployed (Formula Fairness Campaign, 2015).
School quality could also be impacted through parent involvement. Parental
advocacy can include demanding better information about what students are learning,
pushing to recruit and retain higher quality teachers, volunteering at school activities, or
even working on homework with one’s child. These behaviors have been shown to have

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strong positive effects on student achievement (Hill & Taylor, 2004). According to the
U.S. Department of Health and Human Services, only 36% of low-income parents were
involved with three or more school activities on a regular basis, compared with 59% of
parents above the poverty line (HHS, 2015). When students from low social-capital
families attend an integrated school, they benefit from the increased parent engagement.
Rumberger and Palardy (2005) found that the most important channels through which
school quality impacts achievement for black and Hispanic students are increased teacher
expectations, more homework assigned, more rigorous courses taught, and students
feeling safer at school.
Peer effects are the second channel through which integration impacts student
academic outcomes. Children from affluent families are more likely to be academically
advanced coming into school, while children from low SES families begin kindergarten
with less linguistic capability than their middle-class and affluent peers (Purcell-Gates,
McIntyre & Freppon, 1995). Students from affluent families are more likely to
demonstrate constructive learning behaviors, whereas low SES is associated with higher
levels of emotional and behavioral difficulties like anxiety, depression and ADD/ADHD
(Weissman et al., 1984; Goodman, 1999; Spencer et al., 2002), and higher levels of
aggression (Molnar et al., 2008). Research suggests that low SES students benefit from
the academic preparation of their peers and assimilate to standards of behavior
demonstrated by high SES students (Coleman, 1966; Rusk, 2002; Perry, 2010).
Peer effects can also impact student achievement via social skills. Attending an
integrated school has been shown to make students more comfortable around others of
different backgrounds and less racially prejudiced (Wells, Holme, Revilla, & Atanda,

22
2004). In the 2006 Parents Involved case, 553 social scientists signed an amicus brief
stating that integrated education exposes students to cultural knowledge and social
perspectives and promotes critical thinking (Amici Brief for the American Council on
Education, 2007). Black and Hispanic students also graduate and attend college at a
higher rate when attending integrated schools (Bachman, 1971). These skills and further
education in turn sets them up for better job prospects beyond graduation (Garda, 2011).
The third channel through which integration impacts student outcomes for low-
income black and Hispanic students is via psychological effects. Kenneth and Mamie
Clark found that black children were likely to view black as “bad and ugly” and white as
“good and pretty” (Clark and Clark, 1939). This trend is combined with the impact of
low SES. According to the American Psychological Association, lower SES is linked to
negative psychological outcomes like depression, learning disabilities, and a tendency to
give up on tasks, while high SES corresponds with positive outcomes like optimism, self-
esteem and perceived control (2015).
EMPIRICAL STRATEGY:
CONNECTICUT DISTRICT LEVEL DATA
AND THE DIFFERENCE IN DIFFERENCES MODEL
A difference in difference approach can be used to test the hypothesis about
whether the integration program changed the trajectory of student outcomes in Hartford.
The study will compare academic achievement trends in Hartford integrated schools (the
“treatment group”) to achievement trends in other similar cities in Connecticut (“the
control group”), before and after the integration efforts. Based on the difference between

23
treatment and control before 2008 and the difference after, an integration effect on
achievement can be estimated.
In late 2008, the state of Connecticut reached a new settlement where the state
agreed to increase the proportion of inner-city minority students attending integrated, or
“reduced isolation,” schools. The pre-integration years are 2006-2008. Between 2008
and 2011 the proportion of inner city minority students in integrated schools increased by
20 percentage points. The post-integration years will be considered 2011-2013.
All school districts in Connecticut for which the state reports achievement data
are included in the sample. Below are some descriptive statistics at the county level.
Table III:
Descriptive Statistics on Connecticut Counties7
County Name 2010 Pop Avg Adult Ed % FRL % Black/Hisp AvgInc
Fairfield 916,829 11.81 22.6 25.6 $82,283
Hartford 894,014 11.56 25.9 30.2 $64,967
Litchfield 189,927 11.87 17.8 7.8 $71,338
Middlesex 165,676 11.93 13.3 10.5 $76,994
New Haven 862,477 11.80 31.4 30.6 $61,996
New London 274,055 11.93 24.6 17.5 $66,583
Tolland 152,691 11.93 13.5 8.4 $80,529
Windham 118,428 11.93 29.4 12.4 $59,333
Fairfield, Hartford and New Haven counties are home to the state’s largest cities but also
to some of the most affluent and well-educated suburbs. For example, Fairfield County
has nearly 23% of students that qualify for free and reduced lunch, yet the mean family
income there is over $82,000. The poor black and Hispanic families in Bridgeport, a city
7
Full variable names: Column I: 2010 county population, Column II: Average Adult Educational
Attainment, Column III: Percent of students receiving free or reduced lunch, Column IV: Percent of
students that are black or Hispanic, and Column V: 2013 Average Income.

24
in Fairfield County, earn much less than $82,000 a year, but these disparities are muted
by the pockets of extreme wealth that exist in other parts of the county. In Fairfield,
Hartford and New Haven counties, the percent of students receiving free and reduced
lunch tracks the percent of black and Hispanic students quite closely, whereas in counties
without big cities, such as Litchfield or Windham, there are poor white and Asian
students, too. The combination of urban poverty and racial segregation in these cities has
sparked a charter movement, independent of the Sheff integration settlement.
Figure 7: Population Density of Connecticut Counties, with City Names
Bridgeport and New Haven are of particular interest as comparison districts.
They have similar attributes to Hartford: urban cores surrounded by suburbs, similar
crime rates and similar economic opportunities (Rizzo, 2013). Hartford has a greater poor
black and Hispanic population that New Haven, but a smaller one than Bridgeport.
Importantly, all three were trending similarly during the period of interest. All three
cities are in close proximity and thus subject to similar shocks from the recession. All

25
three experienced an increase in the presence of charter schools and Teach for America
(Connecticut State Department of Education, 2015). All three implemented Common
Core State Standards in 2010 (CT SDE, 2010). One notable difference was that Hartford
built a cadre of magnet schools during the early 2000s to serve as the primary integration
strategy for the Sheff settlement, while magnet growth in other cities stagnated.
Table IV:
Student Demographics in Comparison Cities8
City % BH %FRL %SpecEd %ELL %Mag %Char
Bridgeport 92.4 87.2 9.8 7.7 1.9 44.8
Hartford 64.0 53.9 14.3 4.6 18.4 36.0
New Haven 54.7 44.7 7.1 3.8 10.1 34.0
Stamford 62.5 53.9 9.1 10.9 5.7 20.0
Waterbury 73.9 77.3 16.3 11.4 11.2 0.0
All other CT 12.4 29.4 14.6 2.6 0.6 0.0
The state of Connecticut sponsors multiple data sets which can be combined to
give a picture of how integration impacted student achievement.
The dependent variable is student proficiency, measured as percent of students in
the district scoring proficient or above on the Connecticut Mastery Test and the
Connecticut Academic Performance Test in grades 3, 5, 8 and 10 during the years
observed. Students take these tests in math, reading and writing once a year. The test
represents a standard metric of student achievement that is consistent over time. Ideally,
the regression could have been done using 2014 and 2015 data, but the most recent data
available was for 2011-12 and 2012-13. As integration did increase after 2012-2013, any
8
Full variable names: Column I: Percent Black and Hispanic, Column II: Percent of students receiving Free
and Reduced Lunch, Column III: Percent of students classified as Special Education, Column IV: Percent
of students classified as English Language Learners, Column V: Percent of LEAs that are Magnets,
Column VI: Percent of that are Charters.

26
effect shown on the 2012 and 2013 years would be expected to be stronger in 2014 and
2015 as integration levels continued to rise.
Control variables are obtained from the Common Core of Data. These include
racial composition of the district, percent of students qualifying for free and reduced-
price lunch, the percent of Special Education students, the percent of English Language
Learner students, the average adult educational attainment, and the average school staff
per pupil ratio.9
Each of these controls will make the estimate for the impact of
integration on achievement more accurate by controlling for the well-documented effects
of being minority, poor, ELL or special education on student achievement.
Indicator variables were created for the treatment group (integrated schools in Hartford),
for post-2010 (after the 2008 resettlement of the Sheff v. O’Neill integration went into
effect), and for charter/magnet status. The coefficient of interest will be on the
interaction between an indicator variable for treatment and for post-2010.
Regression Equation
Figure 8: Regression equation of interest.
For each variable, I use data from district i and year t.
9
While these data were available at the school level, achievement data was only available at the district
level. To perform the analysis, school-level observations were collapsed to generate district averages. This
means that the controls are less accurate, because not all schools within a district have the same staff per
pupil or the same rates of poverty. The Sheff settlement requires integration at the school level because
having an even number of white and non-white students promotes the learning objectives discussed earlier;
having controls at the district level makes it harder to control for the things that make individual schools
different.
ProficiencyRateit = β0 + β1 (IntegrationStatus) + β2 (Post2010) +
β3(IntegrationStatus*Post2009) + β4 (PercentBlack/Hispit) + β5(PercentFRLit) + β6
(AdultEducAttainmentit) + β7 (ELLit) + β8 (SpecialEdit) + εit

27
I hypothesize that integrating a district will increase student proficiency rates.
While academic, social and labor market outcomes are all important metrics for the
success of an integration effort, only academic data is available at this time. A positive
result in academic gains bodes well for social and labor market indicators when this
population of integrated students graduates.
RESULTS
According to a difference-in-difference analysis, increased integration rates in
Hartford have a positive and statistically significant effect on reading and math
proficiency rates. Implementing integration policy in Hartford as a result of the Sheff
settlement moved integration levels in Hartford from 17% to 37% (see Table I). This 20
percentage point increase in the percent of inner-city black and Hispanic students in
integrated schools is associated with a 5.7 percentage point increase in math proficiency
and a 10.9 percentage point increase in reading proficiency (see the “Integration x Post
2010” coefficients in Table V). Writing proficiency increases were also positive but not
statistically significant.10
10
This may have been because not all grades in the sample reported test scores for writing. Therefore,
although each district has a writing proficiency rate, this rate was generated from fewer grades reported.

28
Table V:
Fully Controlled Model Using Math, Reading, and Writing Proficiency Scores
1 2 3
VARIABLES Math Proficiency Reading Proficiency Writing Proficiency
Integration Status -7.529*** -10.55*** -5.434**
(2.371) (2.811) (2.550)
Post-2010 4.557*** 6.113*** 3.165***
(0.399) (0.419) (0.334)
Integration*Post-2010 5.720** 10.89*** 2.279
(2.694) (3.190) (3.008)
% Black/Hisp -7.575*** -9.363*** -3.202
(2.746) (2.786) (2.201)
% FRL -26.11*** -31.37*** -23.41***
(3.137) (3.666) (2.748)
% SpEd -46.25*** -46.35*** -42.92***
(6.476) (5.258) (4.814)
% ELL -52.93*** -39.83*** -33.52***
(9.909) (8.250) (7.765)
Avg Adult Ed -0.417 -0.688 -1.211**
(0.656) (0.599) (0.496)
School Staff Per Pupil -12.52* -7.905 -2.923
(7.238) (6.162) (3.757)
Constant 105.9*** 104.3*** 113.6***
(7.606) (6.971) (5.764)
Observations 2,211 2,207 2,213
R-squared 0.656 0.667 0.587
Robust Standard Errors in Parentheses
*** p<0.01 ** p<0.05 * p<0.1
In all three models, the controls on student characteristics are negative and
typically highly statistically significant. A 1 percentage point increase in special
education or ELL population tends to have the largest negative effect on predicted

29
proficiency rates, followed by increasing the percent of poor students. A 1 percentage
point increase in black and Hispanic enrollment also has a negative and statistically
significant impact on math and reading proficiency rates, but at a much smaller
magnitude. Average adult education and school staff per pupil did not have statistically
significant effects on proficiency rates.
Attending school in an integrated LEA in Hartford is associated with a negative
and statistically significant effect on proficiency rates. Because race, socioeconomic
status and other factors are already controlled for, this effect can be interpreted as the
effect of being located in Hartford on student proficiency rates. Schools located in
Hartford have lower proficiency rates, on average, than the rest of Connecticut.
Similarly, there is a positive and statistically significant impact of time on student
test scores. Students in the after-policy period of 2011-2013 scored better, on average,
than students tested between 2006-2008. This could be a result of the rising trends in
charter schools, which were not controlled for in this model. It could also be a product of
improved teacher quality due to the increasing presence of Teach for America teachers. It
could even be a product of greater awareness and advocacy for public education in
Connecticut due to changes like the Sheff settlement or implementation of the Common
Core.
Overall, a high level of variation is explained by the model, with an R2
of
approximately 0.65 for both reading and math proficiency rates.
Table IV captures students who attend integrated schools in Hartford city. It does
not include students in the Open Choice component of the integration program. Many
students who get bussed from the inner city out to a suburban school are among only a

30
few transferring Hartford students in their whole school. Before SY 2015-16, the State
added 325 Open Choice seats for Hartford students, but that is distributed over 28
receiving districts in the Hartford suburban ring. That means about 10 new students from
Hartford joined each district last fall, which is at most a handful of new students per
school. These schools do not meet the integration definition set out in Sheff, and from a
statistical perspective, there is likely very little difference on average from the addition of
these few inner-city students.
Studies of the Metco integration program in Boston found that integration had no
effect on incoming students but actually hurt students in receiving districts. Table VI
shows the impact of integration on suburban choice districts. For math, reading and
writing, impacts are negative and of very small magnitude, and none are statistically
significant. Hosting Hartford urban students through the open choice program had no
effect on proficiency rates in the receiving districts.

31
Table VI:
The Impact of Integration on Proficiency Rates in Suburban Open Choice Districts
1 2 3
VARIABLES Math Proficiency Reading Proficiency Writing Proficiency
Suburban Open Choice 0.767** 0.929* 0.972***
(0.375) (0.530) (0.371)
Post 2010 4.521*** 6.277*** 3.136***
(0.414) (0.433) (0.354)
Suburban Open Choice*Post 2010 -0.232 -0.655 -0.670
(0.553) (0.709) (0.519)
% Black/Hisp -8.321*** -10.23*** -3.858*
(2.744) (2.777) (2.214)
% FRL -25.20*** -30.50*** -22.53***
(3.136) (3.664) (2.739)
% SpEd -47.38*** -47.44*** -43.97***
(6.514) (5.138) (5.013)
% ELL -52.08*** -39.03*** -32.57***
(9.917) (8.213) (7.831)
Avg Adult Ed -0.0335 -0.452 -0.730
(0.570) (0.528) (0.461)
School Staff Per Pupil -12.70* -8.049 -3.178
(7.231) (6.168) (3.773)
Constant 101.3*** 101.4*** 107.8***
(6.649) (6.249) (5.403)
Observations 2,211 2,207 2,213
R-squared 0.655 0.665 0.586
*** p<0.01 ** p<0.05 * p<0.1
Some critics of Hartford’s integration policy point to the fact that it rests upon
magnet schools to make integration appealing (Courant Editorial Staff, 2016). Magnet
schools are costly: they cost approximately $2,500 more per student than traditional

32
public schools (Megan, 2015). Yet they are a key tenet of Hartford’s Open Choice
integration strategy. Even though Table II shows that other counties in Connecticut are
wealthier, on average, than Hartford, median household income in Hartford is still 121%
of the median household income nationwide (American Community Survey, 2015). It
may be that on some level, the city’s wealth makes it possible to “afford” integration.
Cities that could not afford to build 19 new magnet schools in the course of a decade
might not be able to execute a voluntary integration strategy. This represents a threat to
the study’s external validity of the model.
To test whether magnet schools accounted for the positive relationship between
integration and achievement, Table VII examines the impact of non-district status (either
charter or magnet) on student achievement. First a controlled model was run with an
indicator for non-district (meaning the LEA is run by either a charter or a magnet
network). Next the model was run with an indicator for charter-only LEA, and last
magnet-only LEA. Non-district status has a positive and statistically significant effect on
math and reading proficiency rates (regressions 1 and 4). In regressions 2-3 and 5-6, this
effect decomposes into an even stronger and highly statistically significant effect of
charter status on proficiency rates, and a small negative but not statistically significant
effect of magnet status on proficiency rates. This suggests that that magnet schools are
not the reason for integration’s positive effects in Hartford as critics have claimed.
It is unclear why charters have such a high and statistically significant effect on
proficiency rates. The most obvious explanation is that their instructional and
organizational practices may actually be better than the district schools’ practices.
Another possibility is non-random selection of students into charter schools. In the past,

33
Table VII:
The Relationship Between School Type on Math Proficiency Rates
1 2 3 4 5 6
VARIABLES Math Math Math Read Read Read
% Black/Hisp -7.539*** -8.645*** -5.002* -7.849*** -8.114*** -5.926**
(1.511) (2.890) (2.664) (2.757) (2.780) (2.747)
% Poor -25.15*** -25.46*** -23.36*** -29.32*** -29.25*** -28.06***
(1.568) (2.929) (2.929) (3.397) (3.372) (3.327)
% SpecEd -34.82*** -34.53*** -50.63*** -37.93*** -39.99*** -48.49***
(4.282) (6.750) (6.329) (5.273) (4.821) (5.741)
% ELL -53.33*** -47.44*** -65.76*** -47.74*** -46.17*** -57.28***
(5.882) (10.97) (9.949) (9.387) (9.489) (8.890)
Avg Adult Ed 2.536*** 2.455*** 2.377*** 2.988*** 2.920** 2.900***
(0.383) (0.496) (0.506) (0.515) (0.515) (0.519)
Staff/Student -13.97*** -5.977 -6.553 -7.573 -1.987 -4.426
(2.924) (7.644) (7.542) (6.850) (6.374) (7.001)
Non-District 6.557*** 5.181**
(1.195) (2.067)
Charter 10.34*** 6.392**
(3.188) (2.738)
Magnet -3.938 -0.323
(2.510) (2.275)
Constant 71.78*** 71.54*** 74.15*** 62.26*** 62.46*** 63.88***
(4.592) (5.934) (5.991) (6.171) (6.217) (6.207)
Observations 2,211 2,211 2,211 2,207 2,207 2,207
R-squared 0.634 0.640 0.630 0.628 0.628 0.625
*** p<0.01 ** p<0.05 * p<0.1
some charters have been accused of “cream-skimming” to get the best students (Altonji,
Huang and Taber, 2010, and Cullen and Rivkin, 2003). Another possibility is that
charters classify a high level of students as special education so as to exempt them from
reporting the lowest student scores. These hypotheses could be tested using longitudinal

34
achievement data at the student level to examine whether, on average, students that
matriculated to charters scored higher than their district peers before entering charters, or
whether attending a charter school increases a student’s likelihood of being classified as
special education. Other controls, such as teacher education and salary levels, per pupil
spending, and parent engagement could be included to isolate whether charters
themselves are correlated with higher achievement.
Table VII also demonstrates a potential source of negative bias muting the
coefficient of integration on achievement. During the period in which magnet schools
and integration were used as a reform strategy in Hartford, charter schools were also used
statewide to provide options for poor and minority families.11
Charters improve
academic scores, as is shown by regressions 2 and 5 above, but are highly segregated
(National Association for Public Charter Schools, 2014). In interpreting Table VII and
considering the impact of increased charter presence in Hartford, it is important to
consider that charter schools increase racial isolation (in other words, decrease
integration) but also increase student achievement. This likely muted the effect of
integration on achievement found in Hartford in the regressions from Table V. It also
likely improved the schooling options for minority children in other cities, making the
comparison benchmark higher during years after the proliferation of the charter
movement.
11
According to the National Alliance for Public Charter Schools’ 2014 State-by-State analysis, charters
became legal in 1996 and as of 2013–14, there were 18 public charter schools and 6,981 students enrolled
in them. 88% of these were located in urban areas, while only 49% of all schools are located in urban
areas.

35
POTENTIAL LIMITATIONS
The greatest limitation of this study is that the data is at the district level, which
mutes the effect of integration on individual schools and individual students. Future
studies should use student-level longitudinal data, if possible, to evaluate whether
students performed better in integrated settings than racially-isolated ones.
Another potential threat to the external validity of this study is that magnet
schools are expensive. Even Connecticut is currently engaged in a school funding
lawsuit that may threaten the future of its magnet programs (Courant, 2015). Can other
cities that have lower tax revenues than Hartford afford to create good magnet schools?
If not, are there alternatives that would allow them to follow a voluntary integration
strategy that works?
Attending an integrated school is highly correlated with attending a magnet
school. This opens the internal validity of the study up to a number of threats due to
omitted variable bias. It may be that students who opt into Hartford’s Open Choice
programs have parents who are more committed to their educational outcomes. It may be
that they are smarter, so their parents are more committed to their education. It may be
that they are second and third children of parents who had negative experiences in the
Hartford Public Schools and have committed to ensuring better for their other kids. If any
of these stories were true, it would bias the estimated effect of integration downwards,
towards zero. None of these omitted variables could be controlled for with the data
available.

36
DIRECTIONS FOR FURTHER RESEARCH
Further research should be done to improve the precision and expand the validity of
these results. To confirm that integration has a positive and statistically significant
impact on achievement data, this difference-in-difference model could be replicated using
the following adjustments:
1) Use 2014 and 2015 data to understand the effect of a longer lag and higher levels
of integration. Some of the ways in which integration impacts achievement may
take time to bear out their full effect.
2) Use school- or individual-level achievement results. District-level achievement
data limited the nuance of this analysis. Looking at the effect of attending an
integrated school on school averages or individual scores could be far more
instructive.
3) Change the dependent variable to another metric of student success. Test scores
are one of many metrics of achievement. The effect of integration could be
explored differently by using high school graduation rates, college outcomes,
civic engagement outcomes, or earnings outcomes as the dependent variable
instead.
Based on the positive effect found in this study, another potential question is, what is
the best method for integration? One way to study this relationship would be to look at
the impact of LEA type on achievement. If, as this study found, charters have a consistent
positive association with achievement, such a finding could opt that integration be done
using charters instead of magnets. This question has significant financial sustainability
ramifications as well.

37
Finally, to determine the external validity of school integration as a reform policy, it
would be helpful to study whether city characteristics, such as wealth, size, racial
composition and urban concentration influenced integration’s relationship with student
achievement. This could be done by comparing achievement gains in all cities with
integration policies, and controlling for the characteristics mentioned above. Such a
study could illuminate whether, perhaps, it might be easier to integrate a highly
concentrated city like New York, or harder to integrate a racially dichotomous one.
POLICY IMPLICATIONS
There are three main policy recommendations for the state of Connecticut:
1) Integrate schools using school choice. Based on the positive impacts on
achievement in Hartford, Connecticut should support integration through school
choice. Policies could include easing the regulatory environment for new charter
networks, providing financial or logistical supports to open choice programs in
other cities, and funding new magnet schools.
2) Track school level data in more readily available formats. One key to
understanding the return on investment in Hartford schools is precise, school- or
student-level data on achievement. Connecticut’s data reporting is fragmented
and imprecise. Graduation rates are not available before 2010 and student
achievement scores are missing after 2013. By collecting better data and
improving access for researchers, Connecticut can get a clear picture of how
various policies impact student achievement.

38
3) Commit school funding to support equal opportunities for all children. In a school
funding case currently on the Connecticut Supreme court docket, magnets and
other special programs risk losing their funding. Connecticut should commit to
spending equal amounts on schooling opportunities regardless of a child’s zip
code.
CONCLUSIONS
Increasing integration levels in Hartford, CT by twenty percentage points between
2008 and 2013 had a positive and statistically significant impact on student proficiency
rates in math and reading. Small changes in integration level (suburban districts) had no
effect on proficiency rates. LEA type had an important impact on student achievement,
and the increase in charters in CT likely muted the impact of integration in Hartford
alone. These findings have important policy implications for Connecticut: sustain its
integration efforts through funding and conducive policies, and increase data
transparency for further evaluations. Further research should be done to understand the
longer-term educational and civic outcomes of integration and how its relationship with
achievement varies as city characteristics change.

39
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