THE IMPACTS OF CHARTER - Am MIT spezialisierte KI-Forschung

THE IMPACTS OF CHARTER

SCHOOLS ON STUDENT

ACHIEVEMENT: EVIDENCE FROM

NORTH CAROLINA

Abstrakt
Using an individual panel data set to control for student
ﬁxed effects, we estimate the impact of charter schools
on students in charter schools and in nearby traditional
public schools. We ﬁnd that students make consider-
ably smaller achievement gains in charter schools than
they would have in public schools. The large negative
estimates of the effects of attending a charter school are
neither substantially biased, nor substantially offset, von
positive impacts of charter schools on traditional public
Schulen. Endlich, we ﬁnd suggestive evidence that about
30 percent of the negative effect of charter schools is
attributable to high rates of student turnover.

Robert Bifulco

(Korrespondierender Autor)

Assistant Professor of Public

Policy

University of Connecticut

Department of Public Policy

1800 Asylum Avenue

West Hartford, CT

06117-2697

Email:

robert.bifulco@uconn.edu

Helen F. Ladd

Professor of Public Policy

Studies and Economics

Duke University

214A Terry Sanford Institute

Kasten 90243

Durham, NC 27708

C(cid:1) 2006 American Education Finance Association

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

1. EINFÜHRUNG
Charter schools have been one of the fastest-growing forms of school choice
during the past decade. While school voucher programs have faced legal chal-
lenges and political opposition, charter school programs have been adopted
in thirty-nine states and the District of Columbia. As of January 2004, 2,966
charter schools were in operation, serving approximately 638,000 students
nationwide (CER 2005).

Although the provisions of charter school programs vary widely from state
to state, most charter schools share several characteristics. They typically have
more autonomy than traditional public schools, are exempted from selected
state and local regulations, and are schools of choice, which means parents
must actively choose to enroll their children in a charter school. They are free
and open to all parents within a given jurisdiction and, if oversubscribed, Sind
typically required to select students by lottery. Endlich, they are publicly funded,
and the amount of their funding is directly linked to the number of students
they enroll.

Charter school programs are intended not only to increase student learning
but also to promote educational innovation, diversiﬁcation of educational pro-
grams and learning environments, and expanded opportunities for teachers to
become more involved in program design and school governance.1 Nonethe-
weniger, improving student learning is among the most important goals of charter
school programs, and scholars and policy makers alike have been awaiting
evaluations of how charter schools have affected student achievement.

Charter schools might improve academic achievement in several ways.
Erste, they may increase the performance of the students who choose them
by providing more effective learning environments than traditional public
schools do. Charter schools might achieve this goal by hiring more effective
Lehrer, by using resources more efﬁciently, or by attracting a more moti-
vated set of students who provide positive spillover beneﬁts to other students.
Zweite, even if charter schools are no more effective than traditional public
schools for the typical student, they might beneﬁt some students by providing
alternative educational environments and programs. Students at risk of failure
in traditional school settings, Zum Beispiel, might do better in charter schools
to the extent that those schools offer smaller, more intimate environments,
specialized curricula, or targeted support services. Endlich, the achievement
of students in traditional public schools could rise if the competition from
charter schools for students and funding induced traditional public schools to
become more productive.

1. Goals similar to these are included in a model charter school law developed by Ted Kolderie, founder
of the Charter Friends National Network (Nathan 1996), and similar goals are articulated in many
of the charter school laws.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Alternativ, charter school students might achieve at lower levels than
they would in traditional public schools if charter schools are less well funded,
are operated by less experienced or less qualiﬁed ofﬁcials, provide a peer
environment that is less conducive to achievement, or for some other reason
are unable to provide an effective educational program. Charter schools might
also diminish the quality of traditional public schools by drawing away funding,
motivated students, and/or quality teachers.

In diesem Artikel, we use an extensive, individual-level panel data set to evaluate
the impact of charter schools in North Carolina on the math and reading
performance of students in grades 4 durch 8. We use student-level ﬁxed-
effects models together with auxiliary analyses to address three questions:
(1) Do students who attend charter schools make larger achievement gains, An
average, than they would have in the absence of charter schools? (2) Do students
who attend traditional public schools located near charter schools, and thus
subject to competition from charter schools, make larger achievement gains
than they would have in the absence of charter schools? (3) What accounts for
quality differences between charter schools and traditional public schools?

After paying close attention to potential biases in our impact estimates,
we ﬁnd that students make considerably smaller achievement gains in charter
schools than they would have in traditional public schools. We also conclude
that the large negative estimates of the effects of attending a charter school
are neither substantially biased, nor substantially offset, by positive impacts
of charter schools on traditional public schools. Endlich, we ﬁnd suggestive
evidence that about 30 percent of the negative effect of charter schools is
attributable to high rates of student turnover.

The next section provides a brief review of previous efforts to evaluate the
impact of charter schools on student performance. Abschnitt 3 describes the
North Carolina charter school program, and section 4 describes the sample
and the data. Abschnitte 5, 6, Und 7 examine the impacts on students who attend
charter schools, measure the effect of competition from charter schools on
students in nearby public schools, and brieﬂy explore the effect of student
turnover on the quality of charter schools. A concluding section discusses the
implications of our ﬁndings.

2. PREVIOUS RESEARCH
In principle, the best way to determine how effective charter schools are in
raising student achievement would be to use a random experiment. One could
imagine two types of experiments. One type would take the students interested
in attending a particular charter school and randomly assign them either to
the charter school or to a control group of students who would not have access

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

to that school. One could then measure how effective that charter school is for
the types of students who apply to it by comparing the average achievement
of the students who were admitted to the school with that of the students in
the control group. That approach would be useful for determining whether
that particular charter school (oder, more generally, the educational program
it offered) was effective. A second approach, one that is more similar to the
experimental designs for investigating the effect of voucher programs, würde
be to start with all the students interested in attending any charter school and
to randomly assign them either to a treatment group that would give them
the right to attend any charter school or to a control group of students who do
not have access to charter schools.2 This latter type of experiment would be
useful for determining the average impact of the presence of a whole system
of charter schools on student achievement. To the best of our knowledge, NEIN
one has attempted to do an experiment of the latter type, and only a few efforts
have been made to implement the ﬁrst strategy.3

More common are studies based on administrative data that are designed
to determine the effects of a whole system of charter schools. Because these
studies are not based on random experiments, users of this approach must
be particularly attentive to the issue of selection bias. The most convincing
estimates from administrative data of the impact of charter schools on the
students who attend them are found in Hanushek, Kain, and Rivkin’s (2002)
analysis of charter schools in Texas. Drawing on student-level panel data sim-
ilar to the data employed in this study, they use a model with student ﬁxed
effects to isolate the average impact of charter schools on charter school stu-
dents. The authors ﬁnd that students in state-sponsored charter schools show
signiﬁcantly smaller test score gains than they would have exhibited had they
remained in traditional public schools but that these negative effects dimin-
ish as charter schools gain more operating experience. The differences from
traditional public schools become statistically insigniﬁcant for charter schools
operating for three or more years.

Using similar data and methods, Gronberg and Jansen (2001) ﬁnd a similar
pattern of effects for Texas charter schools.4 These authors also ﬁnd that

2. These voucher experiments are described in Howell and Peterson (2002).
3. One such study is that by Hoxby and Rockoff (2004), which approximates the random assignment
approach by comparing achievement outcomes for students who were accepted or not accepted
through a lottery process to three oversubscribed charter schools in Chicago. At best, the positive
achievement effects that they report apply to the programs offered by, and to the types of students
that applied to, these three oversubscribed schools.

4. One difference between the models estimated by Gronberg and Jansen (2001) and by Hanushek,
Kain, and Rivkin (2002) is worth noting. Hanushek, Kain, and Rivkin estimate a model where
the change in test scores, Ai , between year t and t − 1 is on the left-hand side: Ait − Ai,t−1 =
αCit + Xit B + γi + εit , where Cit is an indicator of charter school status, Xit is a vector of control
Variablen, and γi is the effect of unobserved, time-invariant student characteristics. Although it is
not entirely clear from their presentation, it appears that Gronberg and Jansen (2001) estimate a

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

students in charter schools serving mostly at-risk students make slightly larger
gains than the average student in traditional public schools. Because charter
schools have fewer resources on average than traditional public schools, Die
authors conclude that charter schools are more efﬁcient than traditional public
schools in generating student achievement.

Results comparable to those of Hanushek, Kain, and Rivkin (2002) Auch
emerge from a similar study on the performance of charter schools in Florida
(Sass, this issue). Sass ﬁnds that student achievement in math and reading
is initially lower in charter schools. After operating for four years, Jedoch,
charter schools generate achievement at levels comparable to those of the
traditional public schools.

Additional support for the view that students in charter schools do less well
than students in traditional public schools emerges from the experience of
Michigan, where Eberts and Hollenbeck (2001) found that students attending
charter schools there had lower test scores than other students even after the
authors controlled for student, building, and district characteristics, einschließlich
measures of past achievement levels. Im Gegensatz, Solmon, Paark, and Garcia
(2001) conclude that students enrolled in charter schools in Arizona for two
or more consecutive years made larger gains on standardized tests of reading
than students who attended traditional public schools.5 Unlike the studies
in Texas and Florida, Jedoch, neither the Michigan study nor the Arizona
study controlled for student ﬁxed effects on achievement gains. Daher, ob
differences across these studies reﬂect differences in charter school policy
across states, methodological differences, or differences in actual outcomes
is not clear. One goal of the present study is to shed light on this issue by
replicating with North Carolina data the approach used by Hanushek, Kain,
and Rivkin (2002) for Texas.

Ähnlich, mixed results emerge from existing research on how competi-
tion from charter schools has affected the performance of students in tradi-
tional public schools.6 Using school-level data from Michigan and Arizona in

model with the test score from year t on the left side and the test score from t − 1 on the right
Seite: Ait = α Ai,t−1 + βCHit + Xit (cid:5) + γi + εit . Controlling for student ﬁxed effects in this model
is similar to estimating the following differenced equation:

Ait − ¯Ai = α(Ai,t−1 − ¯Ai ) + β(Cit − ¯Ci ) + (Xit − ¯Xi )(cid:5) + (γi − γi ) + (εit − ¯εi ), where each vari-
able value is expressed as a deviation from the individual mean. We can see from this last formulation
that in the Gronberg and Jansen (2001) model a component of the differenced error term is corre-
lated with a component of the differenced lagged dependent variable, which biases the ﬁxed-effect
estimator (Baltagi 1995).
Estimated effects on gains in math were not robust across model speciﬁcations, and thus the authors
do not draw conclusions about charter school impacts on math.

6. The articles discussed here focus speciﬁcally on the effects of competition from charter schools.
Belﬁeld and Levin (2002) provide a more comprehensive review of the effect of competition on
school performance and ﬁnd that any positive impacts are either substantively small or subject to
question based on subsequent studies.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

separate analyses, Hoxby (2001) examines changes in mean test scores be-
fore and after the introduction of charter schools. For both states, she ﬁnds
that schools that face competition from charter schools show larger improve-
gen (by about 1 Zu 3 percentile points) in average performance levels than
schools not facing signiﬁcant charter school competition.7 Her analysis does
nicht, Jedoch, address the possibility that changes in the student composition
of schools might confound the estimated effects of charter school competition.
In another study of Michigan schools, Bettinger (1999) uses an instrumental
variable strategy to address the possibility that the location of charter schools
is inﬂuenced by the performance of the public schools and ﬁnds no evidence
that competition from charter schools improves the performance of students
in traditional public schools.

Using school-level panel data from North Carolina and distance from a
charter school to measure competition, Holmes, DeSimone, and Rupp (2003)
ﬁnd that average test scores increased about 1 percent more in schools facing
competition from charter schools than in other schools. In a companion study,
Holmes (2003) uses individual-level data to examine the same question and
ﬁnds that any gains to students in schools located near charter schools are small
at best—no more than one- to two-tenths of a percentile. Because Holmes and
his colleagues do not use a full student-level panel, they are not able to account
fully for potential differences between students in schools located near charter
schools and those in schools located elsewhere. The results we present below
are not subject to this limitation.

3. CHARTER SCHOOLS IN NORTH CAROLINA
Legislation authorizing charter schools in North Carolina was passed in 1996,
and the ﬁrst charter schools opened in fall 1997.8 Figur 1 summarizes charter
school policy in North Carolina. Though less permissive than Arizona and
Michigan, North Carolina has taken a moderately permissive approach to
charter schools compared to most other states.9 Of particular importance for
this study are that the North Carolina charter schools receive operating funding
at the same level as the traditional public schools and that the students in

7. Hoxby (2001) counts a school as facing charter school competition if at least 6 Prozent der
students enrolled in its district (in the case of Michigan) or municipality (in the case of Arizona) Sind
enrolled in charter schools.
Except where noted, the description of the North Carolina charter school program that follows is
based on the charter school legislation.

9. Annual rankings by the Center of Education Reform (CER) have rated North Carolina among the
states with “strong charter laws” and have ranked its law from the seventh strongest to the twelfth
strongest in the nation. “Stronger” laws by CER’s measures place fewer restrictions on establishing
a charter school, place fewer regulations on charter school operations, and provide more funding
for charter schools than “weaker” laws. The most recent report by CER ranks the charter school law
in North Carolina as “stronger” than those in California, Wisconsin, and Texas, among other states
(CER 2003).

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Figur 1

Timing and number

First charter schools established in 1997; limit of 100 Schulen, with no more than 5 new charter
schools in any district in a single year.

Sponsors and approval of charters

Eligible sponsors are local school districts, the state university or the State Board of Education, Aber
final approval must come from the State Board of Education; local districts can comment on how
the charter school would affect them but their approval is not required; charter is renewable for five-
year periods.

Regulations and restrictions on charter schools

No affiliation with a religious institution; subject only to regulations related to the health safety and
discipline of students and specific regulations that apply to charter schools; mindestens 75 percent of
teachers in grades K–5 and 50 percent in grades 6–12 must be certified.

Funding

Access to full per pupil state support for schools in the state plus prorated share of locally financed
supplements for education; no state-supported access to start-up funding; access to federal start-up
funds.

Accountability

Charter schools are subject to the state testing requirements and to the state’s accountability
Programm.

Charter School Policy in North Carolina

charter schools are subject to the same state testing requirements as other
students. Because North Carolina has been testing all students in grades 3–8
in math and reading since 1992–93, test results can be matched for individual
students over a long period of time.

Charters can be revoked for a number of reasons, including poor student
performance and ﬁnancial mismanagement. Between 1997 Und 2002 the state
board of education revoked seven charters, and seven more relinquished their
charter voluntarily or closed due to low enrollment or ﬁnancial problems.
Gesamt, um 12 percent of the charter schools that have been opened are now
closed. Jedoch, in no case was the decision to revoke a charter or to close
due primarily to low student performance (Manuel 2002).

Tisch 1 details the growth in charter schools in North Carolina. By 2000–1
there were 90 charter schools and over 15,000 charter school students. Growth
in the number of charter schools has slowed since 2000–1, primarily because
the state law caps the number of charter schools at 100. Charter schools in
North Carolina are more likely to be elementary or middle schools than high
Schulen, and most charter schools serve at least some students between grades
4 Und 8, the grades examined in this study. Der 93 charter schools in 2001–2
are spread across 46 of North Carolina’s 100 counties. During the 2001–2
school year, Wake County (home to the state capital, Raleigh) and nearby
Durham County had the highest concentration of charters: 12.4 Und 18.2
percent of public schools, jeweils. In Charlotte-Mecklenberg, the state’s

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

most populous urban area, nur 6 von 130 public schools were charters in 2002.
As of 2002, only seven states had more charter schools than North Carolina;
and of those, only ﬁve had a greater concentration of charter schools: Arizona,
Florida, Wisconsin, Michigan, and California.

Tisch 2 shows how the mix of students in North Carolina charter schools
differs from that in traditional public schools. Compared to traditional public
Schulen, charter schools have a larger percentage of black students and lower
percentages of Hispanic and white students. Gleichzeitig, charter schools
serve a higher percentage of students whose parents are college educated
and a lower percentage of students whose parents are high school dropouts.
Despite the higher education level of their parents, these students exhibit lower
levels of performance on both end-of-grade (EOG) reading and math tests. Der
analysis below is designed to determine how much of this difference in student
performance can be attributed to the charter schools themselves.

4. THE NORTH CAROLINA DATA
The data for this study come from the North Carolina Education Research
Data Center. A collaborative effort involving the North Carolina Department
of Public Instruction (DPI), Duke University, and the University of North
Carolina, the Data Center collects a wide range of administrative data from
the DPI and other sources and prepares the data for use by researchers. Der
data in this study come primarily from individual-level EOG test score ﬁles
maintained by the DPI.

For purposes of this study, student-level panels were assembled for ﬁve
cohorts of students—the cohorts of students in third grade in 1996, 1997,
1998, 1999, Und 2000. Each cohort contains the universe of students in third
grade in North Carolina during the speciﬁed year. Each student has a unique
identiﬁer that is consistent over time, allowing us to follow students from
third grade through the last year that they remain in North Carolina pub-
lic schools, the year they complete eighth grade or the 2001–2 school year,
whichever comes ﬁrst. Figur 2 depicts the structure of the data set. We ob-
serve most of the students who were in third grade in 1995–96 (cohort 96)
each year from 1995–96 through 2000–1 as they move from third grade
through eighth grade.10 Similarly, we follow the cohort in third grade in
1996–97 through eighth grade. We are able to follow subsequent cohorts
only through 2001–2, which is before these students reach eighth grade.11

10. We are unable to observe all students in all years because we cannot observe those students who leave
North Carolina schools before they reach eighth grade. Regardless of whether a student proceeds
as expected from third through eighth grade, we observe the student as long as he or she remains
in North Carolina public schools.

11. Because not all students progress through grades as expected, some students are part of more than
one cohort. Zum Beispiel, students from the cohort of third graders in 1995–96 who are held back in

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

5
8

ICH

E
D
U
C
A
T
Ö
N
F
ICH
N
A
N
C
E

A
N
D
P
Ö
L
ICH
C
Y

Tisch 1 Number of Charter Schools and Charter School Students in North Carolina, by Grade Level and Year

GRADES K–8

HIGH SCHOOL

UNITARY

TOTAL

Schools

Students

Schools

Students

Schools

Students

Schools

Students

1997–98

(2.0%)

1998–99

(2.6%)

1999–2000

(3.1%)

7,249

(0.8%)

9,667

(1.1%)

2000–1

12,371

(3.6%)

(1.3%)

(2.1%)

2001–2

13,517

(3.7%)

(1.4%)

(2.3%)

(0.3%)

270

(5.8%)

(1.3%)

(0.1%)

(9.1%)

(1.9%)

526

(0.2%)

783

(0.2%)

1,263

(0.4%)

(15.2%)

(19.2%)

(1.9%)

(2.8%)

8,555

(0.7%)

12,691

(3.6%)

(1.0%)

15,523

(4.1%)

(1.2%)

18,235

1,036

(3.0%)

2,498

(8.1%)

2,369

(9.8%)

3,455

(17.0%)

(11.6%)

(4.1%)

(1.4%)

Notes: Enrollment ﬁgures are taken from the NCES Common Core
of Data, which does not provide information on charter schools
for 1997–98. Enrollment counts are for schools in the identi-
ﬁed category, not for students in the grade ranges indicated.

Figures in parentheses are the percentages of all North Carolina
schools in the category that are charters and the percentage of
all students in the category that are in charter schools.

ICH

M
P
A
C
T
S

Ö
F

C
H
A
R
T
E
R

S
C
H
Ö
Ö
L
S

ICH

N
Ö
R
T
H

C
A
R
Ö
L
ICH

N
A

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Tisch 2 Descriptive Statistics on Charter and Traditional Public Schools, 2001–2

Average enrollmenta

% femalea

Ethnic compositiona

% black

% Hispanic

% Weiß

Parent educationb

% less than high school

% high school grad

% some college, but did not graduate

% two-year college degree

% four-year college degree

% graduate school degree

% that changed schools in last yearb

Avg. performance on EOG readingb,C

Avg. performance on EOG mathb,C

Charter
Schools

Traditional
Public Schools

196

48.9

574

48.8

39.9

2.1

55.5

3.9

34.6

4.8

11.6

36.6

8.6

47.4

31.2

5.3

60.0

10.6

43.7

4.1

13.4

22.8

5.3

13.2

–0.057

–0.133

0.001

0.002

Notes: a Figures are calculated using Common Core data and are based on
entire population of schools. b Figures are computed using individual student
end-of-grade (EOG) ﬁles maintained by the North Carolina Education Research
Data Center and thus are based only on students in grades 3–8. c EOG test
scores converted to standard scores with mean of zero and standard devi-
ations of one. Grade-speciﬁc means and standard deviations were used to
make the conversions.

Figur 2

cohort 96
cohort 97
cohort 98
cohort 99
cohort 00

1995–96
Grade 3

1996 –97
Grade 4
Grade 3

Cohorts of Students Examined in This Study

1997–98 1998 –99 1999 –2000 2000–1 2001–2
Grade 7
Grade 5
Grade 6
Grade 4
Grade 5
Grade 3
Grade 4
Grade 3

Grade 8
Grade 7 Grade 8
Grade 6 Grade 7
Grade 5 Grade 6
Grade 4 Grade 5

Grade 6
Grade 5
Grade 4
Grade 3

The information available for each student in each year includes their
scale scores on the EOG reading and math tests, their school, whether their
school is a charter, their grade, their gender, their ethnicity, and the highest
level of education completed by their parents. EOG reading and math tests are

third grade are also part of the cohort of third graders in 1996–97. In the analyses presented here,
the ﬁve cohorts are combined. In cases where combining cohorts resulted in duplicate observations
for a student in a single year, one of the two observations was eliminated by applying a set of decision
rules, which are available from the authors upon request.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

multiple-choice tests that measure the achievement of competencies described
in the North Carolina Standard Course of Study and are administered in the
spring of each year to students in grades 3–8. Individual results are reported as
developmental scale scores, which are designed to measure growth in reading
and math and thus are expected to increase as students move from lower
grades to higher grades.12 In order to ensure comparability of test scores
and test score gains for students from different grades, we use grade-by-
year-speciﬁc averages and standard deviations to convert the developmental
scale scores to standard scores with a mean of zero and standard deviation of
eins.

To distinguish the effects of charter schools themselves from the effects of
movement among schools, we created two indicator variables: one to denote
whether the student changed schools in the current year, and the other to
denote whether the student made a structural school change (d.h., moved from
elementary to junior high school). A student is counted as having changed
schools if the school identiﬁer in the current year differs from the school
identiﬁer for that student in the previous year and the change was not a
structural change. A change of schools is considered structural if the student
moved to a school in the same district and more than 10 percent of the students
in the same grade and school as the student in the previous year are also in
the same grade and school as the student in the current year.

The school identiﬁer allows us to link each observation to school-level data
from the Common Core of Data. Zusätzlich, we have created variables for
each school that indicate its distance from the nearest charter school and
the number of charter schools within various radii of the school. To cal-
culate these distances we matched addresses from the Common Core13 to
latitude and longitude coordinates using a geocoding service provided by
Teleatlas. The distances calculated are straight-line distances (with an ad-
justment for the curvature of the earth) between the latitude and longitude
coordinates.

Tisch 3 provides information on the number of students and observations
in each cohort. Cohorts range in size from 93,349 In 1996 Zu 106,106 In
2000. The average number of times we observe students in a given cohort is
determined by two factors: the number of years between when the students in
the cohort are in third grade and either when they are in eighth grade or 2001–2,

12. For more information on the end-of-grade tests see http://www.ncpublicschools.org/reportsstats.html.
13. Addresses for charter schools provided in the Common Core were checked against address infor-
mation taken from materials on the North Carolina Department of Public Instruction (DPI) web
site. In case of a conﬂict or missing address data in the Common Core, addresses from DPI were
gebraucht.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

R
Ö
B
E
R
T

ICH
F
U
L
C
Ö

A
N
D

H
E
L
E
N

F
.

L
A
D
D

Tisch 3 Number of Students and Observations in Each Cohort

Number of students

93,349

98,404

102,869

105,292

106,106

495,943

Cohort 96

Cohort 97

Cohort 98

Cohort 99

Cohort 00

Totala

Avg. observations/student

Avg. observations/student with

valid reading scores

Avg. observations/student with

valid math scores

Number of students observed at
least once in a charter school

Students with reading gains

observed in both charter and
traditional public school

Students with math gains

observed in both charter and
traditional public school

4.9

4.8

5.3

5.1

4.5

4.3

4.4

3.7

3.5

3.6

2.8

2.7

4.3

4.1

4.2

1,145

1,603

2,009

2,181

2,035

8,745

1,103

1,360

1,461

1,270

644

5,724

1,106

1,363

1,467

1,277

645

5,741

Notiz: aTotal counts are not equal to the sum of each cohort because a small percentage of students appear in more than
one cohort.

6
1

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

and the percentage of missing observations. The percentage of missing
observations can be determined as the ratio of the average observations per
student to the number of times we would observe a student in the absence of
missing observations. This calculation implies that the percentage of missing
observations falls from 19 percent in the ﬁrst cohort to 7 percent in the ﬁnal
cohort and is about 9.5 percent of the total sample.14 Test scores from a par-
ticular year might be missing for a student because that student left the North
Carolina public school system, was exempted from taking the test, or has a
missing or an invalid test score for some other reason.

The bottom three rows of Table 3 provide information on the number of
charter school students in our sample. We observe 8,745 students who spent at
least one year in a charter school. As we explain below, our preferred estimates
of the impact of charter schools are based on students for whom we observe
test score gains at least once in both a traditional public school and a charter
Schule. High percentages of charter school students in the ﬁrst two cohorts are
observed in a traditional public school at some point. For cohorts who were
in third grade after the charter school program started, the percentages are
smaller. Gesamt, we observe measures of test score gains in traditional public
schools for approximately 65 percent of the charter school students in our
sample.

Tisch 4 compares three groups of students in our sample: students who
are observed only in traditional public schools, students observed at least once
in a charter school, and students for whom either reading or math gains are
observed at least once in a charter school and at least once in a traditional public
Schule. Compared to the students observed only in traditional public schools,
the charter school students in our sample exhibit the same patterns as in
Tisch 2: they are more likely to be black, less likely to be white or Hispanic,
less likely to have parents with a high school education or less, and more likely
to have college-educated parents. They also have lower student test scores,
where test scores have been normalized to have a mean of zero and standard
deviation of one.

Note that the characteristics of the students observed in both a charter and
a traditional public school (last column) are very similar to those for the larger
sample of all charter school students (middle column). Daher, we have some
assurance that our preferred estimates of charter school impacts reported
below are based on a subsample of charter school students that is similar
demographically to the larger group of all students in charter schools. Der

14. The weighted average of the number of times we would observe a student in the absence of missing
observations across all ﬁve cohorts is 4.75. The percent of missing observations in the total sample
is then 1 − (4.30/4.75) = 0.095.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Tisch 4 Descriptive Statistics for Study Sample

Students observed
only in traditional
public schools

Students observed
at least once in
a charter school

Students observed
in a charter and
traditional public
schoola

Ethnicity

% black

% Hispanic

% Weiß

Parent education

% less than high school

% high school graduate

% some college

% 2-year college degree

% 4-year college degree

30.4

3.4

63.0

10.5

43.6

5.0

13.6

21.9

% graduate school degree

5.2

Average reading scoreb

Average math scoreb

Average reading gainb

Average math gainb

Grade ﬁrst observed

in a charter school

0.002

0.004

–0.010

–0.011

Grade 3c

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

44.0

1.4

52.7

4.7

37.1

5.5

13.6

31.5

7.2

–0.122

–0.215

–0.025

–0.020

2,224

1,642

1,827

2,209

625

218

43.6

1.3

53.5

4.5

37.2

5.2

13.6

31.2

8.2

–0.095

–0.181

–0.029

–0.034

386

629

1,787

2,149

589

206

Notes: a Students with either reading or math gains observed at least once in a charter school and
at least once in a traditional public school. b EOG test scores converted to standard scores with
mean of zero and standard deviations of one. Grade-speciﬁc means and standard deviations were
used to make the conversions. c Students ﬁrst observed in a charter school in grade 3 might have
ﬁrst entered a charter school anytime from kindergarten to grade 3.

subsample does, Jedoch, differ from the larger group in important ways. Der
subsample has slightly higher average test scores and shows smaller average
gains. The differences in math gains are particularly marked. Zusätzlich, stu-
dents who ﬁrst entered a charter school before grade 5 are underrepresented.
An important question is whether the effects of charter schools are markedly
different for students who enter at early grades than those who enter at later
grades, an issue we discuss below.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

5. IMPACTS OF CHARTER SCHOOLS ON CHARTER SCHOOL STUDENTS
In this section we report estimates of the average difference between the
achievement of charter school students and what those students would have
achieved in the absence of charter schools.

Estimation Strategy

The primary challenge in estimating this charter school effect arises from the
fact that charter school students are self-selected and are likely to differ in
unobserved ways from otherwise similar students who choose to remain in
traditional public schools. To address this challenge, we follow the strategy
used by Hanushek, Kain, and Rivkin (2002) and use repeated observations on
individual students to control for individual ﬁxed effects. Essentially we are
comparing the test score gains of students in charter schools to the test score
gains made by the same students in traditional public schools.

Interpretation of our estimates draws on the following model of educational

production:15

YiGT = αCHiGT + XiGTBG
t=T −1(cid:1)

λt (αCHigt + XigtBg ) + γiG +

t=1

g =G−1(cid:1)

g =4

(γig) + ηGT + εiGT

(1)

where Y is a test score for student i in grade G in year T , X is a set of
individual student characteristics, including variables indicating whether or
not student i made a structural school change, a nonstructural school change,
or no change during year t.16 The variable of interest in this study is CH,
which indicates that student i attended a charter school in year t.17 In this
general form of the model, the effects of the control variables on student test
scores are allowed to vary by grade, so that parent’s education, zum Beispiel,
might matter more (or less) in later grades than in earlier grades. The effects
of school and student characteristics from previous years carry over to year T
but degrade at a rate given by (1 − λt ). This form also contains the effects of

15. The discussion here draws on a long-standing literature on educational production. For examples
from this literature see Summers and Wolfe (1977), Hanushek (1979), and Ferguson and Ladd
(1996). The general form of the production function presented here, and its usefulness in identifying
potential biases in our analysis, was suggested to the authors by Robert Kaestner.

16. Typically a production function includes measures of school inputs as well as student characteristics.
In this case, Jedoch, we are interested in estimating the total charter school effect, including any
effect that might be due to input differences between charter and traditional public schools, so it is
not appropriate to control for school inputs.

17. Charter school status is a school characteristic. The combination of school-level and individual-level
variables in any production function study, including ours, calls for the use of robust standard
Fehler. All of our standard errors are adjusted for clustering within schools using the “cluster”
option in STATA, which makes use of a generalization of the Huber/White/Sandwich estimator of
variance.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

unobserved student characteristics, γ , which are assumed to accumulate in
an additive fashion from year to year and which might also vary by grade, Und
grade-by-year effects, η, which capture systematic differences across exams.
The ﬁnal term represents random error. The coefﬁcient α is the average effect
of attending a charter school in year T , and the sum of that effect across all the
years a student has attended a charter school is the cumulative charter school
Wirkung.

This general form of the production function cannot be estimated because
the number of grade-speciﬁc individual effects is coincident with the number
of observations. Zusätzlich, a complete set of explanatory variables from
previous years is not available. Folglich, restrictions have to be placed on
the general form of the model to obtain estimates. dennoch, this general
formulation is useful for clarifying the identifying assumptions of and potential
sources of bias in the various estimates we present.

We estimate three empirical models, for both reading and math test scores,
which place different restrictions on the general model. The third model pro-
vides our preferred estimates of charter school impacts. The ﬁrst model, welche
we refer to as a “levels model,” can be written as:

YiGT = αCHiGT + XiGTB + ηGT + εiGT.

(2)

This model, which we estimate using ordinary least squares (OLS) and robust
standard errors, yields the difference in levels of performance between charter
school students and traditional public school students controlling for observ-
able student characteristics and grade-by-year effects. This formulation places
severe restrictions on the general form of the model in equation (1). The effects
of the control variables are assumed to be the same across different grades,
and the effects of the student’s educational experiences in previous years are
restricted to zero, as are the effects of other unobserved student characteristics.
Because the past educational experiences of the student and other unobserved
factors such as the student’s motivation are likely to inﬂuence both student
test scores and the choice to enroll in a charter school, omitting these variables
is likely to bias the estimates of the charter school effect.

A second approach restricts the effects of student characteristics to be the
same across years and replaces the additive individual effect with a one-time
ﬁxed student effect. This yields:

YiGT = αCHiGT + XiGTB +

t=T–1(cid:1)

t=1

(αCHigt + XigtB) + γi + ηGT + εiGT.

(1A)

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Taking the ﬁrst-difference of (1A) produces our second model, which we esti-
mate using OLS and robust standard errors:

(cid:8)YiGT = YiGT − Yi(G−1)(T −1) = αCHiGT + XiGTB + λGT + νiGT

λGT = ηGT − η(G−1)(T −1)

νi GT = εiGT − εi(G−1)(T −1).

(3)

We call this the “gains model” because it estimates the difference between the
average test score gain made by charter school students and traditional public
school students controlling for observable student characteristics and grade-by-
year effects. By focusing on gains in student achievement during a given year,
this model eliminates the need to adjust estimates of the charter school effect
for educational experiences prior to year t. Jedoch, if unobserved differences
between charter school and traditional public school students affect the rate
of growth in student performance as well as its level—that is, if unobserved
student characteristics have additive effects (as in (1)) rather than a one-time
Wirkung (as in (1A))—then estimates of α from the gains model will generate
biased estimates of the effect of attending a charter school.

The third model can be derived by restricting λt in equation (1) to one,
taking the ﬁrst-difference, and restricting the effects of student characteristics
to be the same across grades:18

(cid:8)YiGT = YiGT − Yi G(T −1) = αCHiGT + XiGTB + γi + λGT + νiGT
λGT = ηGT − η(G−1)(T −1)
νiGT = εiGT − εi(G−1)(T −1).

(4)

This is the gains model with an individual ﬁxed effect, γi , and the coefﬁcients
are estimated using the “within” student estimator (Baltagi 1995) and robust
standard errors. Using the ﬁrst-difference formulation eliminates the need to
control for previous educational experiences, and the ﬁxed-effects estimation
controls for any unobserved differences between charter school students and
traditional public school students that remain constant over time. Estimation
of this model requires three or more observations for each student, welche,
with the exception of the Texas and Florida studies discussed above, has not
been available in previous quasi-experimental evaluations of school choice
programs.19

18. This last restriction is empirically investigated and partially relaxed below.
19. An alternative ﬁxed-effects strategy, used by Rouse (1998) in her evaluation of the Milwaukee
voucher program and by Solmon, Paark, and Garcia (2001) in their study of charter schools in

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

This third model, which we refer to as the ﬁxed-effects model, provides
powerful protection against self-selection bias. Jedoch, this protection comes
at a cost. Note that the estimated effects of charter schools from this model
are based on the experiences of only those students who have test scores gains
observed at least once in a charter school and at least once in a traditional
public school.20 The estimator could provide biased estimates of the average
charter school effect if the subsample of students used to identify the charter
school effect is not representative of all charter school students. We discuss
this issue further below.

Primary Results

The ﬁrst three columns of Tables 5A (reading) and 5B (math) present our
estimates of the levels, gains, and ﬁxed-effects models. Turning ﬁrst to the
control variables, we ﬁnd results that are generally consistent with expectations.
Females exhibit higher levels of achievement in both math and reading, Und
larger annual gains, although the difference in gains is signiﬁcant only for
math. Blacks and Hispanics exhibit lower levels of achievement than whites.
Hispanics, Jedoch, make larger annual gains in both reading and math than
either blacks or whites. Both the level of achievement and annual gains in
achievement are higher for students with more educated parents. Children of
college graduates, Zum Beispiel, score more than one standard deviation higher
than children of high school dropouts. Endlich, students who change schools,
either because of a move or because they are transitioning to middle school,
make smaller gains during their transition year than students who remain in
the same school.

Emerging from all three models and for both subjects are negative and
statistically signiﬁcant coefﬁcients on the charter school indicator variable. Sei-
cause the dependent variable is expressed as a standard score with a mean of 0
and a standard deviation of 1, the coefﬁcients can be interpreted as proportions
of a standard deviation. In the levels models, charter school students score,
on average, 0.16 of a standard deviation lower in reading and about 0.25 von
a standard deviation lower in math than observationally similar students in
traditional public schools. From the gains models, we see that students in

Arizona, regresses test score levels on the treatment indicator controlling for individual ﬁxed effects.
The ﬁxed-effects estimates in those studies do not provide as much protection against self-selection
bias as this method because they do not control for the additive effects of unobserved characteristics
on test score gains, d.h., they do not control for effects of unobserved individual characteristics on
test score growth.

20. Only the impacts of variables that change over time can be distinguished from the individual student
ﬁxed effects. For the same reason, we cannot obtain estimates of the impacts of gender, ethnische Zugehörigkeit,
and parental education from the ﬁxed-effects models.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Table 5A Estimated Impacts of Attending a Charter School on Reading Test Scores

Charter school

–0.158∗∗(0.044) –0.062∗∗(0.009) –0.095∗∗(0.014)

Levels

Gains

Fixed Effects Fixed Effects II

Charter school (for students

observed entering
charter only)

Charter school (for students

observed exiting
a charter school)

Gender (male = 0, female = 1) 0.174∗∗(0.002) 0.001 (0.001)

Ethnicitya

Black

Hispanic

White

Parent educationb

High school grad

Some college

–0.351∗∗(0.023) –0.029∗∗(0.004)

–0.002 (0.025) 0.041∗∗(0.005)

0.235∗∗(0.023)

–0.011∗∗(0.004)

0.444∗∗(0.005) 0.005∗ (0.002)

0.679∗∗(0.006) 0.016∗∗(0.003)

2-year college degree

0.784∗∗(0.006) 0.016∗∗(0.002)

4-year college degree

1.130∗∗(0.008) 0.022∗∗(0.002)

Graduate school degree

1.419∗∗(0.011) 0.027∗∗(0.003)

–0.062∗∗(0.015)

–0.155∗∗(0.021)

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

Changed schools in last year

–0.133∗∗(0.005) –0.018∗∗(0.003) –0.013∗∗(0.004) –0.013∗∗(0.004)

Made structural change

in last year

–0.048∗∗(0.007) –0.065∗∗(0.006) –0.056∗∗(0.007) –0.056∗∗(0.007)

Total observations

1,527,157

1,512,587

1,494,885C

Total students

445,562

441,863

424,066C

Notes: All models include grade/year ﬁxed effects. Dependent variable is EOG developmental scale
score expressed as a standard score. Figures in parentheses are robust standard errors calculated
using generalization of Huber/White/Sandwich estimator and are robust to clustering within schools.

a Reference category is Asian and Native American. b Reference category is high school dropouts. c Sample
count includes only those observations of students with at least three valid test score measures, welches ist
the minimum required to identify ﬁxed effects and effect estimates for nonconstant variables.

∗ statistical signiﬁcance at .05 Ebene, ∗∗ statistical signiﬁcance at the .01 Ebene.

charter schools also make smaller annual gains, on average, than observation-
ally similar students. In neither case can the lower performance necessarily
be attributed to being in a charter school. The estimates from the ﬁxed-effects
Modelle, Jedoch, indicate that the smaller gains made by charter school stu-
dents are indeed due to enrolling in a charter school rather than to any ﬁxed,
unobserved differences between charter school students and students in tra-
ditional public schools.

The negative effects of attending a charter school are large. Wie gezeigt in
column 3 of both tables, charter school students exhibit gains nearly 0.10

EDUCATION FINANCE AND POLICY

ROBERT BIFULCO AND HELEN F. LADD

Table 5B Estimated Impacts of Attending a Charter School on Math Test Scores

Charter school

–0.255∗∗(0.073) –0.076∗∗(0.021) –0.160∗∗(0.021)

Levels

Gains

Fixed Effects Fixed Effects II

Charter school (for students

observed entering
charter only)

Charter school (for students

observed exiting
a charter school)

Gender (male = 0, female = 1) 0.036∗∗(0.002) 0.009∗∗(0.001)

Ethnicitya

Black

Hispanic

White

Parent educationb

High school grad

Some college

–0.464∗∗(0.023) –0.019∗∗(0.005)

–0.046 (0.024) 0.020∗∗(0.006)

0.155∗∗(0.023)

–0.020∗∗(0.005)

0.386∗∗(0.005)

–0.007∗∗(0.002)

0.603∗∗(0.006) 0.005 (0.003)

2-year college degree

0.705∗∗(0.006) 0.004 (0.003)

4-year college degree

1.076∗∗(0.008) 0.029∗∗(0.003)

Graduate school degree

1.404∗∗(0.014) 0.058∗∗(0.004)

–0.097∗∗(0.022)

–0.272∗∗(0.030)

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

Changed schools in last year

–0.160∗∗(0.005) –0.030∗∗(0.004) –0.027∗∗(0.005) –0.028∗∗(0.005)

Made structural

change in last year

–0.044∗∗(0.008) –0.068∗∗(0.008) –0.061∗∗(0.010) –0.061∗∗(0.010)

Total observations

1,533,367

1,520,132

1,502,339C

Total students

446,855

443,548

425,654C

Notes: All models include grade/year ﬁxed effects. Dependent variable is EOG development scale
scores expressed as a standard score. Figures in parentheses are robust standard errors calculated
using generalization of Huber/White/Sandwich estimator and are robust to clustering within schools.

∗ statistical signiﬁcance at .05 Ebene, ∗∗ statistical signiﬁcance at the .01 Ebene.

standard deviations smaller in reading and 0.16 standard deviations smaller
in math, on average, than the gains those same students had when they were
enrolled in traditional public schools. If such losses compounded annually,
a student enrolled in charter schools for ﬁve years would score nearly one-
half of a standard deviation lower in reading and eight-tenths of a standard
deviation lower in math than they would if they remained in traditional public
Schulen. The difference in achievement growth due to being enrolled in charter
schools appears to be considerably larger than differences in growth between
children of high school dropouts and the children of parents with graduate

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Figur 3

H
T
A
M
N
Ö
T
C
A
P
M

ICH

D
e
T
A
M

ich
T
S
E

0.600

0.400

0.200

0.000

-0.200

-0.400

-0.600

-0.800

-1.200

-1.000

-0.800

-0.600

-0.400

-0.200

0.000

0.200

0.400

0.600

0.800

Estimated Impact on Reading

Statistically Significant Reading Only
Statistically Significant Math and Reading

Statistically Significant Math Only
Neither Reading nor Math Statistically Significant

Distribution of Estimated Charter School Impacts across Charter Schools

degrees and between blacks and whites—differences that are the object of
considerable concern. The negative impacts of enrolling in a charter school
are also substantially larger than the negative impacts of changing schools or
making the transition from elementary school to junior high.21

This ﬁnding of a negative average effect need not mean that all char-
ter schools are unsuccessful in raising the achievement of their students.
dennoch, wie in der Abbildung gezeigt 3, many of them appear to exhibit negative
impacts on achievement in both math and reading. The ﬁgure depicts our
estimates of charter school impacts for each of the charter schools in each
of the two subjects. Marks in the southwest quadrant represent schools with
negative estimated impacts in both subjects. Those in the northeast quadrant
exhibit positive impacts in both subjects. The fact that so many schools are in
the southwest quadrant indicates that the negative average impact of charter
schools on student achievement is not driven by a few atypical outliers. Wie-
immer, it is also worth noting that a handful of charter schools in North Carolina
do appear to provide signiﬁcant achievement beneﬁts for their students.

21. Each of the models presented in Tables 5A and 5B was also estimated with ﬁxed effects for school
districts. Including the district ﬁxed effects had negligible impacts on estimates of charter school
impacts and the other coefﬁcients.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Possible Biases Related to Peculiarities of the Identifying Sample

Despite its advantages in addressing self-selection bias, the ﬁxed-effects es-
timator could provide biased estimates if the sample of students used to
identify the charter school effect is not representative of all charter school
students.22 As we have already noted (siehe Tabelle 4), the identifying sample
is demographically similar to the larger group of all charter school students
in the tested grades. dennoch, two differences between the full sample of
charter school students and the subsample that is used to identify charter
school effects in the third columns of Tables 5A and 5B are worth noting.

One concern is that students who leave charter schools to return to public
schools may be overrepresented in the identifying sample. This overrepre-
sentation of exiters would bias downward the effect of charter schools (Das
Ist, make it more negative than the true effect for all charter school students)
to the extent that these students leave because of an unsatisfactory academic
experience in charter schools. The size of this bias depends both on the extent
to which exiters are overrepresented in the sample and on the size of the dif-
ference in outcomes between charter school students who exit and those who
do not.23 Among students whose test score gains are observed at least once in
both sectors, 37.1 percent are exiters (d.h., are observed in a traditional public
school after they were in a charter school). This percentage exceeds the 30.4
percent exit rate for all charter school students in our sample. Daher, exiters are
overrepresented in the identifying sample of switchers by nearly 25 Prozent.
This overrepresentation matters because, as shown in the ﬁnal columns of
Tables 5A and 5B, the negative effect of attending a charter school is larger
for exiters than for those students who are observed only entering charter
Schulen. Notiz, Jedoch, that even for the students who are not observed exit-
ing a charter school, the estimated impacts of charter schools are still negative,
statistically signiﬁcant, and substantial. Daher, the overrepresentation of exiters
cannot explain away the estimated negative effect of charter schools.

22. Hanushek, Kain, and Rivkin (2002) acknowledge that their ﬁxed-effects estimates cannot be inter-
preted as how enrollment in a charter school would affect the achievement of the average student.
Because the beneﬁts of charter school enrollment are likely to be different for those who choose to
enroll than they would be for a randomly selected student, any study that estimates charter school
impacts based on the experience of students who choose charter schools cannot be generalized to
the broader school population (Heckman, LaLonde, and Smith 1999). The issue here is different. Wenn
the set of students on whom we base our estimates of charter school impacts is not representative
of the larger charter school population, then we might not be able to generalize to the population of
charter school students, let alone the broader population of all public school students.

23. This issue is counterpart to the issue faced by past evaluations of voucher programs in Milwaukee
and elsewhere, in which impact estimates were based on stayers. Since those who remain in voucher
schools are likely to have had a more positive experience than the average voucher recipient, diese
earlier evaluations have had to address the possibility that impact estimates are biased upward
(Rouse 1998; Howell and Peterson 2002).

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

To determine the size of the bias created by overrepresentation of exiters,
we calculated “true” effect estimates as weighted averages of the estimated
effects for students only observed entering charter schools and those observed
exiting charters, with the weights set equal to the proportion of each group
in the overall sample of charter school students (0.696 for enterers only and
0.304 for exiters). The weighted effect estimates are −0.090 for reading and
−0.150 for math, which implies that our original ﬁxed-effects estimates are
biased downward by only 5.5 Und 6.5 percent of the “true” effect.

A second difference between our identifying subsample and the larger
group of charter school students is that students who entered charter schools
in the younger grades are underrepresented in the identifying subsample. In
contrast to the case for exiters, there is no clear a priori reason for charter
school effects to differ between students who enter charter schools in different
grades. The data suggest, Jedoch, that the gains of students who entered
charter schools in the later grades are smaller than those of students who
entered in earlier grades. These differences emerge from a modiﬁed form of
the gains model (not the ﬁxed-effects model, for which such an estimation is
not possible) in which we allow the effects of attending a charter school to vary
by the grade that the student ﬁrst entered the charter school. Konkret, für
students entering a charter school in third or fourth grade, the estimated effect
is −0.052 for reading and −0.041 for math, and for those entering a charter in a
later grade the estimated effects are −0.069 for reading and −0.103 for math.24
Given the underrepresentation of students who enter during early grades,
the smaller negative effects for early entrants, particularly for math, vorschlagen
that the true average effects of attending a charter school across all charter
school students might be less negative than indicated by the ﬁxed-effects
estimates. dennoch, the true effects are almost certainly negative. Wir sind
quite conﬁdent in making this assertion, for the following reasons. Erste, Die
estimated charter school effects obtained from the gains model are negative
and statistically signiﬁcant even for students entering a charter school in the
early grades. Zweite, we have reason to believe that those estimates are biased
toward zero because they emerge from a model that does not control for
student ﬁxed effects.25

24. The estimated effects for early grade entrants are statistically signiﬁcantly at the 0.01 level for
reading and at the 0.10 level for math; and for later grade entrants both reading and math estimates
are statistically signiﬁcant at the 0.01 Ebene. The difference between estimated effects for the early
grade entrants and for the later grade entrants is not statistically signiﬁcant in the case of reading
but is statistically signiﬁcant at the 0.05 level in the case of math.

25. The estimates of charter school impacts from the models that control for individual ﬁxed effects are
substantially larger (more negative) than those obtained from the gains model without individual
ﬁxed effects. The difference between the two sets of estimates reported in Tables 5A and 5B might
reﬂect either self-selection bias in the estimates from the gains model without ﬁxed effects or
differences in the sample of charter school students on which the effect estimates are based. Wann
we reestimate the gains model using only those charter school students that help to identify charter

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Other Potential Biases

Another potential source of bias is the possibility that competition from char-
ter schools may improve the performance of traditional public schools. To the
extent that this occurs, the estimates from the ﬁxed-effects approach would
understate the true impact of the charter schools. We address this issue empir-
ically below and conclude that this form of downward bias is inconsequential.
Zusätzlich, the ﬁxed-effects estimates in Tables 5A and 5B would be biased
downward if students with declining test score gains were more likely than
other students to transfer to a charter school. The gains of those students while
they were in traditional public schools would systematically overestimate the
gains they would have made in subsequent years in the absence of charter
schools.26 To assess the likelihood of this source of bias, we estimated the
following equation for math and reading using observations on charter school
students while they were in public schools but before they transferred to a
charter school:

(cid:8)AiGT = AiGT − Ai(G−1)(T −1) = αt + ηGT + εiGT.

(5)

(cid:8)AiGT is, as before, the achievement gain of student i in year T ; t is a year
counter, taking a value of zero in 1997 and increasing by one for each year
nach; ηGT represents grade by year ﬁxed effects; Und (cid:10) is a random error term.
Because the test scores have been scaled to have a mean of zero in each grade
and year, if gains for all students were observed they would have a mean of
zero each year and thus no time trend. The estimated value of α indicates
whether the trend in test score gains is signiﬁcantly different for students

school effects in the ﬁxed-effects model, we get statistically signiﬁcant impact estimates of −0.068
for reading and −0.104 for math. Both of these estimates are signiﬁcantly less negative than the
estimates provided by the ﬁxed-effects model, which provides evidence that the estimates from the
gains model do suffer from self-selection bias and that the differences between the gains models
and ﬁxed-effects models in Tables 5A and 5B cannot be attributed solely, or even primarily, Zu
differences in samples.

26. This argument assumes that our estimates are based primarily on charter school students who are
observed in a traditional public school prior to being observed in a charter school. Tatsächlich, of the
students for whom we observe test score gains in both charter and traditional public schools 62.9
percent are observed ﬁrst in traditional public schools, then in charters; 20.4 percent are observed
ﬁrst in charter schools, then in traditional public schools; Und 16.8 percent are observed ﬁrst in
traditional public schools, then in charter schools, and then again in traditional public schools. Less
als 1 percent are observed in charter schools, then in traditional public schools, and then again in
charter schools.

Hanushek, Kain, and Rivkin (2002) consider the possibility that students experiencing a tempo-
rary dip in test score gains in a given year might be more likely to transfer to a charter school. In
this case, test score gains of charter school students while they were in traditional public schools
would underestimate what would have been observed for those students in the absence of charter
Schulen, and the ﬁxed-effects estimator would overestimate the positive impacts of charter schools
(Ashenfelter 1975). Jedoch, this type of selection cannot provide an alternative explanation for
ﬁndings of negative charter school impacts.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

who subsequently enroll in a charter school. For reading, the estimate of
α is very small, negative (−0.002), and statistically insigniﬁcant. For math,
α is a larger, positive number (0.022), but still statistically insigniﬁcant.27
These results indicate virtually no trends, on average, in the test score gains of
students who subsequently enroll in a charter school, which suggests that the
estimates in the last column of Tables 5A and 5B are not biased.

Endlich, our estimates might be biased downward if the characteristics that
distinguish charter school students from traditional public school students
have more negative impacts on student achievement in later grades than in
earlier grades. This possibility led us to relax the restriction in our ﬁxed-effects
model that the effects of observed student characteristics are constant over
Zeit. In regressions that include interactions between student characteristics
and grade levels (not shown), we ﬁnd that the achievement effects of individual
characteristics do in some cases differ across grades. Jedoch, the variation in
effects across grades for a given student characteristic often does not follow an
obvious pattern; Und, more important, allowing the effect of observed charac-
teristics to vary by grade has virtually no impact on the estimated charter school
Wirkung. Daher, our estimates of charter school impacts are robust to assump-
tions about variation across grades in the inﬂuence of student characteristics
on achievement.

Extensions

As we discussed above, Hanushek, Kain, and Rivkin (2002) and Gronberg
and Jansen (2001) ﬁnd that the negative impacts of charter schools in Texas
disappear for charter schools that have been operating for three or more years.
To examine whether that pattern also emerges in North Carolina, we report
in the ﬁrst and fourth columns of Table 6 (Modell 1) ﬁxed-effects estimates
that allow the estimated impact of attending a charter school to vary with
the number of years the charter school has been open. As was the case for
Texas, we ﬁnd that the negative effects of charter schools are larger for newly
opened charter schools than for more established charter schools. Jedoch,
in contrast to the Texas studies, the negative effects of charter schools in North
Carolina remain statistically signiﬁcant and large even for schools that have
been operating for ﬁve years.28

27. The results of these regressions as well as those referenced in the next two paragraphs are available

from the authors upon request.

28. The dip in charter school performance during the ﬁfth year is anomalous. There is some support
for the explanation that schools that opened during the 1997–98 school year, and thus observed
into their ﬁfth year, are of lower quality than the charter schools opened subsequently. Konkret,
the ﬁfteen charter schools that we observe into their ﬁfth year of operation, on average, have
more negative impacts on student test score gains than the other charter schools, even when
observations during the ﬁfth year of operation are excluded. Jedoch, when we allow the impact of
the ﬁfteen schools observed into their ﬁfth year to vary by year of operation, we ﬁnd a similar dip

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Tisch 6 Variation in Estimated Impacts of Attending a Charter School by Years of Operation

READING

MATH

Modell 1

Modell 2

Modell 1

Modell 2

First year of charter school

–0.184∗∗(0.027)

[–0.174∗∗]A

–0.312∗∗(0.051)

[–0.284∗∗]A

Students obs. entering only

Students obs. exiting

–0.144∗∗(0.044)

–0.243∗∗(0.027)

–0.233∗∗(0.058)

–0.401∗∗(0.048)

Second year of charter school –0.064∗∗(0.019)

[-0.071∗∗]A

–0.131∗∗(0.028)

[–0.117∗∗]A

Students obs. entering only

Students obs. exiting

–0.061∗∗(0.025)

–0.093∗∗(0.032)

–0.081∗∗(0.035)

–0.200∗∗(0.039)

Third year of charter school

–0.056∗∗(0.021)

[–0.039∗]A

–0.081∗∗(0.037)

[–0.079∗∗]A

Students obs. entering only

Students obs. exiting

–0.020 (0.022)

–0.084∗∗(0.039)

–0.050 (0.045)

–0.147∗∗(0.051)

Fourth year of charter school

–0.064∗∗(0.021)

[–0.056]A

–0.092∗∗(0.030)

[–0.093∗∗]A

Students obs. entering only

Students obs. exiting

–0.040∗ (0.024)

–0.094 (0.097)

–0.067∗∗(0.024)

–0.152∗∗(0.073)

Fifth year of charter school

–0.159∗∗(0.050) –0.110∗∗(0.053)

–0.198∗∗(0.060) –0.123∗∗(0.053)

Change in schools

–0.011∗∗(0.001) –0.012∗∗(0.001)

–0.025∗∗(0.002) –0.027∗∗(0.003)

Structural change in schools

–0.044∗∗(0.001) –0.044∗∗(0.001)

–0.035∗∗(0.001) –0.034∗∗(0.002)

Notes: Both estimates include grade/year and individual student ﬁxed effects. Dependent variables
are EOG scale scores converted to a standard score with a mean of zero and standard devia-
tion of one. Figures in parentheses are robust standard errors calculated using generalization of
Huber/White/Sandwich estimator.

a Coefﬁcient marked by (A) are not directly estimated in the model but, eher, weighted averages of the
coefﬁcients for students observed entering only and students observed exiting. Note that the weighted
average of these coefﬁcients is a linear combination; inferences for these ﬁgures are based on Wald tests
(Griffiths, Hill, and Judge 1993, P. 453).

∗ statistical signiﬁcance at 0.10 Ebene, ∗∗ statistical signiﬁcance at the 0.05 Ebene.

More reﬁned estimates of the effects by year are presented in the sec-
ond and fourth columns of Table 6 (Modell 2). These estimates account for the
potential bias that arises from the overrepresentation of students who exit char-
ter schools.29 We also report the weighted average effects, where the weights,
as above, are the proportions of the exiters and the nonexiters observed in the
entire sample of charter school students. Consistent with our earlier analysis,
the estimated weighted impacts from Model 2 for each year in both math and
reading are slightly less negative than those for Model 1. The weighted impact

in impact estimates during the ﬁfth year. For some reason, it appears that these schools performed
substantially worse during 2002 than during 2001.

29. All observations of students in a ﬁfth-year charter are from 2002, which is the last year we observe
students. Folglich, none of the students observed in a charter during its ﬁfth year are subse-
quently observed in traditional public schools, and thus we cannot separate the effects on charter
school exiters from the effects on those observed entering only.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

estimates are still negative, Jedoch, in all eight cases and are statistically sig-
niﬁcant in seven. Daher, even after several years of operation, charter schools
apparently continue to reduce student learning relative to what it would have
been in the traditional public schools.

A second extension involves disaggregating the average charter school
impacts not only by the age of the charter school but also by whether it is the ﬁrst
year a student has been in the school and by whether the student is observed
exiting a charter school at some point. Analytically, this disaggregation is
achieved by replacing the single charter school indicator in equation (4) with a
separate indicator variable for each type of charter school experience. Der Schlüssel
results are shown in Tables 7A and 7B. Erste, the large negative impacts on
average appear to be driven largely, but not entirely, by students during their
ﬁrst year in a charter regardless of the age of the school. Remember that these
estimates control for the generic effect of changing schools, which is identiﬁed
primarily by transfers between traditional public schools. For some reason, Die
year a student newly transfers into a charter has much more negative impacts
than transferring into a traditional public school.30 Second, as indicated by the
generally nonsigniﬁcant coefﬁcients in the next-to-last column, students who
choose to remain in charter schools do not continue to accumulate negative
impacts after their initial year in a charter school. This ﬁnding is reassuring in
that it justiﬁes the decision of many parents to keep their children in charter
schools once they are there. Jedoch, it is also clear that the initial hit these
students take is not offset by gains in subsequent years, so that even this
Gruppe, which is harmed least by their choice to attend a charter school, still
has lower levels of achievement as a result of that choice. Dritte, the students
who ultimately leave charter schools typically exhibit poorer performance in
math relative to what they would have done in a traditional public school, beide
during their ﬁrst year in a charter school and in subsequent years.

6. IMPACTS OF CHARTER SCHOOLS ON TRADITIONAL PUBLIC SCHOOL
STUDENTS
Although the charter school sector has grown rapidly over the last decade, Es
is still a marginal share of the public school system and is likely to remain so
for a number of years. Auch so, charter school programs have the potential
to have broader impacts on student achievement if traditional public schools
respond to the threat of losing students by improving the quality of their
own educational programs. To the extent, Jedoch, that charter schools draw
more motivated students away from traditional public schools and that peer

30. We also ran the models presented in Tables 7A and 7B with controls for whether or not the student’s

school change interacted with his or her grade, and we obtained virtually identical results.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Table 7A Disaggregated Effects of Charter Schools on Reading

STUDENTS’ FIRST YEAR IN
CHARTER SCHOOL

STUDENTS’ SUBSEQUENT
YEARS IN THE SCHOOL

Age of Charter

Nonexiter

Exiter

Nonexiter

Exiter

First year

Second year

Third year

Fourth year

Fifth year

–0.243∗∗

(0.052)

–0.176∗∗

(0.087)

–0.537∗∗

(0.114)

–0.388∗∗

(0.171)

–0.127∗∗

(0.042)

–0.074∗∗

(0.033)

–0.061∗∗

(0.030)

–0.105∗∗

(0.036)

–0.140∗∗

(0.062)

–0.038

(0.053)

–0.114

(0.081)

–0.108

(0.112)

–0.015

(0.040)

0.025

(0.033)

0.000

(0.024)

–0.195

(0.130)

Notes: Estimates are from models that include grade/year and individual student ﬁxed effects,
as well as controls for whether or not student made a structural change or other change of
school in the current year. Figures in parentheses are robust standard errors calculated
using generalization of Huber/White/Sandwich estimator with correction for clustering within
Schulen.

∗ statistically signiﬁcant at 0.10 Ebene, ∗∗ statistically signiﬁcant at 0.05 Ebene.

Table 7B Disaggregated Effects of Charter Schools on Math

STUDENTS’ FIRST YEAR IN
CHARTER SCHOOL

STUDENTS’ SUBSEQUENT
YEARS IN THE SCHOOL

Age of Charter

Nonexiter

Exiter

Nonexiter

Exiter

First year

Second year

Third year

Fourth year

Fifth year

–0.475∗∗

(0.069)

–0.294∗∗

(0.074)

–0.294∗∗

(0.134)

–0.185

(0.255)

–0.209∗∗

(0.048)

–0.106∗∗

(0.047)

–0.090

(0.065)

–0.156∗∗

(0.038)

–0.189∗∗

(0.049)

–0.193∗∗

(0.064)

–0.218∗∗

(0.075)

–0.135

(0.129)

–0.011

(0.058)

0.003

(0.042)

–0.005

(0.034)

–0.117∗

(0.063)

Notes: Estimates are from models that include grade/year and individual student ﬁxed effects,
as well as controls for whether or not student made a structural change or other change
of school in the current year. Figures in parentheses are robust standard errors calculated
using generalization of Huber/White/Sandwich estimator with correction for clustering within
Schulen.

∗ statistically signiﬁcant at 0.10 Ebene, ∗∗ statistically signiﬁcant at 0.05 Ebene.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

effects matter, the quality of education at those traditional public schools may
suffer.

Measuring Charter School Competition

To estimate the competitive effects of charter schools, we must ﬁrst measure
the amount of competition provided by charter schools. Two approaches appear
in the literature. Hoxby (2001) identiﬁes schools located in districts that have
mindestens 6 percent of their students enrolled in charter schools as facing charter
school competition. This measure is not appropriate for North Carolina, Wo
most districts cover relatively large geographic areas. That measure would
miss the competition that occurs for some schools when charter schools are
concentrated in one area within a district, and it would overstate competition in
other parts of the district. Holmes, DeSimone, and Rupp (2003) and Bettinger
(1999) both use distance from a charter school to develop indicators of whether
or not schools face competition from charter schools. This approach has the
advantage of capturing within-district variation in the amount of charter school
competition schools face.

How close does a charter school have to be located to a traditional public
school to provide substantial competition for students? We observe 6,576
transfers from traditional public schools to charter schools in our data. Für
89.7 percent of these transfers the distance between the charter school where
the student enrolled and the school the student attended the previous year is
fewer than 10 miles. If the threat of losing students is what motivates traditional
public schools to respond to charter schools, then only those charter schools
located within 10 miles of a given school are likely to exert much effect on the
Schule.

Tisch 8 helps us further assess the intensity of competition from charter
Schulen. This table summarizes the distribution of the percentages of students
who transfer to a charter school in a given year for schools that are various
distances from charter schools. Even among schools within 2.5 miles of a
charter school, only slightly more than 1 percent of students are lost to charter
schools each year, and only a small percentage of schools have lost as many
als 5 percent of their students in any year. If the likelihood of losing students
to charter schools indicates the intensity of the competition, the amount of
competition provided by charter schools in North Carolina is small.

Tisch 8 also indicates that whatever competition there is varies reasonably
systematically with distance up to the 10-mile radius. Insbesondere, Schulen
innerhalb 2.5 miles of a charter school lose a higher percentage of students to
charter schools and hence appear to face more competition, on average, als
do schools 2.5 Zu 5 miles from the nearest charter, und so weiter. Daher, in diesem

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Tisch 8 Annual Transfers to Charter Schools by Distance to the Nearest Charter School

Miles to Nearest
Charter School

Avg. Jährlich % von
Students Lost to
Charter Schools

% of Schools
Losing More
Than 1%
Annually

% of Schools
Losing More
Than 2%
Annually

% of Schools
Losing More
Than 5%
Annually

0–1

1–2.5

2.5–5

5–7.5

7.5–10

10–12.5

12.5-15

15–20

>20

1.02%∗

1.23%∗

0.87%∗

0.58%∗

0.46%∗

0.33%

0.23%

0.28%

0.24%

38.1%

35.9%

26.1%

17.7%

12.4%

8.4%

6.9%

7.2%

6.4%

17.2%

18.3%

10.6%

6.8%

6.4%

4.6%

2.0%

3.9%

2.9%

0.9%

3.8%

2.6%

1.7%

0.9%

0.8%

0.4%

1.2%

0.8%

Notiz: ∗ The reported average is signiﬁcantly different at a 0.05 signiﬁcance level than the average
for schools located more than 20 miles from any charter school (the last column).

section we estimate the separate effects of being within 2.5 miles, zwischen 2.5
Und 5 miles, and between 5 Und 10 miles of a charter school.

Tisch 9 indicates that the threat of losing students to a charter school
depends not only on the distance to the nearest charter school but also on the
number of charter schools within a given radius of the school. Zum Beispiel,
the average percent of students lost to charter schools is twice as high for
schools with more than two charter schools located within 5 miles than it is for
schools with only one charter school within 5 miles. A school with more than
two charters within 5 miles is also more than twice as likely to lose more than
2 percent of its students to a charter school in a given year as a school with
only one charter school within 5 miles. Daher, we investigate how the effect of
charter schools on traditional public schools varies with the number of nearby
charter schools as well as with the distance to the nearest charter.

Estimation Strategy

The location of charter schools is not randomly determined. If charter schools
were primarily established in response to dissatisfaction with traditional public
Schulen, charter schools would tend to be located in areas with low-quality
traditional public schools where students would tend to make below-average
test score gains. Alternativ, charter schools might be more likely to attract
students in areas where parents tend to be more motivated and better informed.
In those areas, gains in student test scores might be higher than in other areas,
even in the absence of charter schools.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Tisch 9 Annual Transfers to Charter Schools by Number of Charter Schools within Various Distances

Avg. Jährlich % von
Students Lost to More Than 1%
Charter School

% Losing

Annually

% Losing More
Than 2%
Annually

% Losing More
Than 5%
Annually

# of charters
innerhalb 2.5 miles

# of charters
innerhalb 5 miles

# of charters
innerhalb 10 miles

1.15%

1.33%

1.03%

0.79%

1.34%

1.63%

0.51%

0.95%

1.40%

35.5%

40.9%

39.0%

26.7%

36.4%

47.2%

17.5%

23.4%

41.4%

16.4%

23.7%

22.0%

10.9%

19.0%

24.2%

6.8%

12.1%

20.2%

2.7%

4.3%

1.7%

2.1%

5.0%

2.8%

1.1%

3.0%

3.9%

To protect against potential bias created by the selection of charter school
Standorte, we rely again on individual student ﬁxed effects. Konkret, we esti-
mate equations similar to equations (2), (3), Und (4), with two key differences.
Erste, we compute our estimates using observations only of the students in
traditional public schools. Zweite, we replace the variable indicating charter
school status with three dichotomous variables indicating whether or not the
school attended by the student is within 2.5 miles of a charter school, zwischen
2.5 Und 5 miles of a charter school, and between 5 Und 10 miles of a charter
Schule. Estimates from the modiﬁed versions of equations 2 Und 3 are sus-
ceptible to selection bias, while the ﬁxed-effects estimates, which are based
on within-student comparisons, effectively control for any unobserved student
characteristics that remain constant over time.

The estimates from the ﬁxed-effects equations are identiﬁed primarily
by students who attend schools located within the speciﬁed distance of a
charter school and whose test score gains we observe in that school both
before and after the nearby charter school opens. Jedoch, students who
move from a traditional public school not located near a charter school to a
school that is located near a charter school (und umgekehrt) also contribute to the
identiﬁcation. If charter schools tend to locate near low-quality schools, Dann
we would expect to see a drop in the test score gains of students moving into
schools located near charter schools, regardless of any charter school impacts.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Daher, estimates from student ﬁxed-effects models, which are based in part
on the change in test score gains of students moving into or out of schools
located near charters, might be biased downward. To address this possibility,
we estimate a fourth equation, for both math and reading, which controls for
school ﬁxed effects as well as student ﬁxed effects.

More speciﬁcally, we begin with the following model:

(cid:8)YijGT = YijGT − Yi j G(T −1) = αCjGT + XiGTB + γi + θ j + λGT + νijGT

(6)

where each term is deﬁned as in equation (4), except subscript j indexes
Schulen, CjGT represents measures of charter school competition, and θj is
a school ﬁxed effect. Unbiased estimates of α and β in equation (6) can be
obtained by differencing each variable from its individual student mean and
by including a set of explicit school dummy variables. For our sample this
would require 1,885 school dummy variables, which creates computational
Herausforderungen.

To avoid these difﬁculties, we deﬁne an individual spell as the set of obser-
vations on a particular student in a particular school. Let s(ich,J) index individual
spells, and set ηs(ich,J) = γi + φj. Note that ηs(ich,J) is the same for each observa-
tion within the same spell. Folglich, substituting ηs(ich,J) into equation (6)
and differencing each variable in the resulting equation from that variable’s
within-spell mean effectively sweeps out the sum of the effect of unobserved
individual and school heterogeneity (γi + φ j ). OLS estimates of the resulting
equation identify the effect of charter school competition using students who
remain in the same school (and thus within the same individual spell) as the
extent of charter school competition faced by the school changes over time.
The OLS estimates of α effectively control for both school and individual ﬁxed
effects.31

Ergebnisse

The results of our estimations are presented in Tables 10A and 10B. Für
reading, estimates from the student ﬁxed-effects models suggest that charter
school competition reduces student test score gains in schools located within
2.5 miles of a charter school and has no effect on gains in schools located
zwischen 2.5 Und 10 miles from a charter school. For math, none of the es-
timates from the student ﬁxed-effects model are signiﬁcantly different from
null.

31. For further discussion of estimating panel data models that include individual and group ﬁxed effects
see Andrews, Schank, and Upwad (2004) and Abowd, Kramarz, and Margolis (1999). Obwohl
this estimation strategy provides unbiased estimates of the parameters of interest, the estimates are
less efﬁcient than the alternative of including explicit school dummies.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Table 10A Estimated Impacts of Charter Schools on Reading Scores of Traditional Public School Students

Levels

Gains

Student &

Student Fixed School Fixed
Effects

Effects

Within 2.5 miles of a charter

0.023 (0.013) −0.002 (0.004) −0.013∗∗(0.006)

0.010 (0.017)

2.5–5 miles from a charter

0.035∗∗(0.012)

0.004 (0.004)

0.000 (0.006)

0.021 (0.018)

5–10 miles from a charter

0.026∗∗(0.009)

0.006∗ (0.003)

0.002 (0.004)

0.016 (0.016)

Gender (male = 0, female = 1)

0.174∗∗(0.002)

0.001 (0.001)

Ethnicitya

Black

Hispanic

White

Parent educationb

High school grad

Some college

−0.351∗∗(0.023) −0.028∗∗(0.004)

−0.004 (0.025)

0.042∗∗(0.005)

0.235∗∗(0.023) −0.010∗∗(0.004)

0.443∗∗(0.005)

0.005∗ (0.002)

0.678∗∗(0.006)

0.016∗∗(0.003)

Two-year college degree

0.784∗∗(0.006)

0.016∗∗(0.002)

Four-year college degree

1.124∗∗(0.007)

0.022∗∗(0.002)

Graduate school degree

1.411∗∗(0.011)

0.027∗∗(0.003)

Change schools in last year

−0.139∗∗(0.005) −0.016∗∗(0.003) −0.011∗∗(0.004) −0.022∗∗(0.004)

Made structural change in last

Jahr

Beobachtungen (students)

−0.049∗∗(0.007) −0.064∗∗(0.006) −0.055∗∗(0.008) −0.067∗∗(0.003)

Total

1,512,892

1,498,460

Within 2.5 miles of a charterc

166,077

163,929

(443,514)

(439,841)

(87,379)

(86,179)

2.5–5 miles from a charterc

274,977

272,087

5–10 miles from a charterc

324,432

321,460

(163,518)

(161,748)

(160,470)

(158,912)

1,475,833

(420,036)D

161,408

(81,641)D

265,705

(153,337)D

317,621

(155,079)D

Notes: All models include grade/year ﬁxed effects. Dependent variable is EOG developmental scale
score expressed as a standard score. Figures in parentheses are standard errors computed using
generalization of Huber/White/Sandwich estimator and are robust to clustering within schools.

a Reference category is Asian and Native American. b Reference category is high school dropouts.
c Observations count number of times students are observed in a school during a year when school was
in speciﬁed category, which is less than the number of times the students are observed overall. d Sample
count includes only those observations and students with at least three valid test score measures, welche
is the minimum required to identify ﬁxed effects and effect estimates for nonconstant variables.

∗ statistical signiﬁcance at .05 Ebene, ∗∗ statistical signiﬁcance at the .01 Ebene.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Table 10B Estimated Impacts of Charter Schools on Math Scores of Traditional Public School Students

Levels

Gains

Student &

Student Fixed School Fixed
Effects

Effects

Within 2.5 miles of a charter

0.020 (0.016)

0.012 (0.006) −0.007 (0.009) −0.006 (0.024)

2.5–5 miles from a charter

0.026 (0.014)

0.015∗ (0.006)

0.003 (0.009)

0.018 (0.020)

5–10 miles from a charter

0.020 (0.011)

0.021∗∗ (0.005) 0.010 (0.007)

0.013 (0.016)

Gender (male = 0,
female = 1)

0.036∗∗ (0.002) 0.009∗∗ (0.001)

Ethnicitya

Black

Hispanic

White

Parent educationb

High school grad

Some college

−0.463∗∗ (0.023) −0.019∗∗ (0.005)

−0.048 (0.024)

0.019∗∗ (0.006)

0.155∗∗ (0.022) −0.019∗∗ (0.005)

0.386∗∗ (0.005) −0.007∗∗ (0.002)

0.603∗∗ (0.006) 0.004 (0.003)

Two-year college degree

0.705∗∗ (0.006) 0.004 (0.003)

Four-year college degree

1.071∗∗ (0.008) 0.026∗∗ (0.003)

Graduate school degree

1.398∗∗ (0.013) 0.054∗∗ (0.004)

Change schools in last year −0.164∗∗ (0.005) −0.028∗∗ (0.004) −0.024∗∗ (0.005) −0.034∗∗ (0.003)

Made structural change in

last year

Beobachtungen (students)

−0.044∗∗ (0.008) −0.066∗∗ (0.008) −0.059∗∗ (0.011) −0.063∗∗ (0.003)

Total

1,519.078

1,498,460

1,483,186

Within 2.5 miles of a charterc

166,839

164,823

(444,806)

(439,841)

(87,724)

(86,658)

2.5–5 miles from a charterc

275,951

273,223

5–10 miles from a charterc

325,650

322,984

(164,021)

(162,328)

(160,979)

(159,608)

(421,904)D

162,322

(84,105)D

266,820

(155,894)D

319,120

(151,730)D

∗ statistical signiﬁcance at .05 Ebene, ∗∗ statistical signiﬁcance at the .01 Ebene.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Tisch 11 Estimated Impacts of Charter Schools on Traditional Public School Students by Number of
Charter Schools within Five Miles

READING

MATH

Student &

Student Fixed School Fixed
Effects

Effects

Student &

Student Fixed School Fixed
Effects

Effects

1 charter within 5 miles −0.001 (0.005)

0.014 (0.012)

−0.006 (0.007)

0.001 (0.016)

2 charters within 5 miles −0.014 (0.010) −0.004 (0.027)

0.004 (0.014)

0.031 (0.031)

>2 charters within 5 miles −0.038∗∗ (0.009) −0.020 (0.036)

−0.020 (0.014)

0.052 (0.044)

Change schools in last year −0.010∗∗ (0.004) −0.022∗∗ (0.004) −0.023∗∗ (0.005) −0.034∗∗ (0.003)

Made structural change in

last year

Beobachtungen (students)

−0.055∗∗ (0.008) −0.067 (0.003)

−0.059∗∗ (0.011) −0.064∗∗ (0.003)

Total

1,475,833 (420,036)

1,483,186 (421,904)

1 charter within 5 milesa

281,144 (151,425)

282,418 (152,579)

2 charters within 5 milesa

81,603 (51,760)

>2 charters within 5 milesa

64,366 (35,793)

82,047 (51,311)

64,677 (36,109)

Notes: All models include grade/year ﬁxed effects and individual ﬁxed effects. Dependent variable
is EOG developmental scale score expressed as a standard score. Figures in parentheses are
standard errors computed using generalization of Huber/White/Sandwich estimator and are robust
to clustering within schools.

a Observations count number of times students are observed in a school during a year when school is
in speciﬁed category, which is less than the number of times the students are observed overall. Sample
count includes only those observations and students with at least three valid test score measures, Die
minimum required to identify ﬁxed effects and effect estimates for nonconstant variables.

∗ statistical signiﬁcance at .05 Ebene, ∗∗ statistical signiﬁcance at the .01 Ebene.

Once school ﬁxed effects are controlled for, the coefﬁcients of some of the
charter school competition variables become positive. Jedoch, in no case are
the estimates statistically signiﬁcant. Weiter, it is unclear why students in
schools located between 2.5 Und 10 miles of a charter school would beneﬁt
from charter school competition, but not students in schools located within
2.5 miles. The anomalous pattern of point estimates reinforces the conclusion
that any apparent positive effects should be attributed to chance rather than to
the beneﬁcial effects of competition from charter schools.

To examine whether charter school effects are larger when there are mul-
tiple charter schools located near a traditional public school, we replaced the
three variables indicating a school’s distance from the nearest charter school
with three new variables indicating, jeweils, whether the school had one,
zwei, or more than two charter schools located within 5 miles. The results of
this analysis are presented in Table 11.

Wieder,

it appears that models omitting school ﬁxed effects provide
downward-biased estimates of the impact of charter schools. The preferred

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

results for reading suggest no beneﬁts from charter school competition. Nicht
only are none of the estimated impacts statistically different from zero, Aber,
contrary to the expectation of a larger impact when nearby charter schools are
more numerous, the estimated effects are more negative for schools exposed
to competition from larger numbers of charter schools. The results for math
are more consistent with expectations. The estimated effects of charter school
competition on math gains are all positive, and they grow larger as the number
of charter schools within 5 miles increases. Jedoch, none of the estimates is
statistically different from zero.

We conclude that charter schools appear to have no statistically signiﬁcant
effects on the achievement of the traditional public school students in North
Carolina. We emphasize, Jedoch, that the intensity of competition is not
very great. Even schools located close to a number of charter schools are
unlikely to lose a substantial percentage of students to charter schools. Daher,
our ﬁnding that charter schools have no effects on traditional public schools
in North Carolina should not be interpreted as a general statement about the
potential of charter school competition to inﬂuence traditional public schools.
dennoch, the ﬁnding that the effects of charter schools on students in
traditional public schools are small and statistically insigniﬁcant implies that
competitive effects generate essentially no bias in our estimates of charter
school impacts on charter school students.

7. WHY DO STUDENTS MAKE SMALLER GAINS IN CHARTER SCHOOLS?
Our estimates indicate that North Carolina students who transfer into charter
schools make smaller gains than they would have had they remained in tra-
ditional public schools, even when the charter schools they attend have been
operating for ﬁve years. Several factors could account for these smaller gains.
The mix of peers that students encounter in charter schools might negatively
affect test scores, resources might be less adequate in the average charter
school than in traditional public schools, and/or charter schools might be less
efﬁcient than traditional public schools.

Another reason that charter schools might have difﬁculty providing effec-
tive educational programs is student turnover. Tisch 12 zeigt, dass, on average,
the percentage of students in a school between grades 4 Und 8 that have made
a nonstructural transfer in the last year is higher in charter schools than in
traditional public schools. Wie erwartet, the average rate of student turnover
is lower in charter schools that have been open longer. Jedoch, average
turnover rates in charter schools remain twice as high as those in traditional
public schools, even for charter schools that have been open for ﬁve years.
Changing student populations makes student grouping and scheduling more

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Tisch 12 Average Percent of Students in Grades 4–8
That Have Made a Nonstructural Transfer in the Last
Year, by Charter School Status

Traditional public schools

First-year charters

Second-year charters

Third-year charters

Fourth-year charters

Fifth-year charters

13.7%

100.0%

46.1%

37.2%

39.5%

25.4%

Figures represent unweighted school-level averages.

challenging, intake of new students can distract administrators from other
tasks, and assessing and helping new students can place extra demands on
teachers’ time. Hanushek, Kain, and Rivkin (2001) ﬁnd that higher student
turnover harms all students in school regardless of whether they themselves
are movers.

To determine the role that high student turnover rates play in explaining
the poor performance of charter schools, we add two school-level variables
to our ﬁxed effects model of student achievement (equation (4)): the percent
of students in the school who have made a nonstructural school change and
the percent who have made a structural change during the last year. As we
saw in Table 6, students in charter schools in their ﬁrst year of operation
show especially small test score gains. In order to focus on the role student
turnover plays in explaining quality differences between charter and traditional
public schools that remain after start-up challenges have been met, we exclude
observations of students in ﬁrst-year charter schools from these estimations.
As in our earlier analyses, we use the Huber/White/Sandwich estimator to
compute standard errors that are robust to clustering within schools.

The results are presented in Table 13. The ﬁrst and third columns show
the estimates from our original student ﬁxed-effects model, without con-
trols for student turnover. These estimates differ from those reported in
Tables 5A and 5B because observations of students in ﬁrst-year charter schools
are excluded. The second and fourth columns show estimates from models
that include measures of student turnover. For both reading and math, beide
the percent of students in a school who have made a nonstructural transfer and
the percent who have made a structural transfer have statistically signiﬁcant,
negative effects on student achievement. Zusätzlich, including these controls
for student turnover reduces the coefﬁcients on the charter school indicator,
von 29 percent in the case of reading and by 30 percent in the case of math,
suggesting that high student turnover rates account for almost one-third of

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

Tisch 13 Estimated Impacts of Attending a Charter School on Reading Test Scores Controlling for the
School’s Student Turnover Rate

Charter schoola

−0.062∗∗ (0.014) −0.044∗∗ (0.015) −0.106∗∗ (0.021) −0.074∗∗ (0.022)

Reading

Math

Individual-level controls

Change schools in last year −0.011∗∗ (0.004) 0.010∗∗ (0.003) −0.025∗∗ (0.005) 0.002 (0.004)

Made structural change in

last year

School-level controls

Percent of students making
nonstructural change

Percent of students making

a structural change

Observationsb

Studentsb

−0.055∗∗ (0.007) −0.015∗∗ (0.006) −0.060∗∗ (0.010) −0.016∗∗ (0.007)

−0.106∗∗ (0.015)

−0.168∗∗ (0.027)

−0.057∗∗ (0.011)

−0.060∗∗ (0.014)

1,488,498

420,521

1,495,885

422,060

Notes: All models include grade/year ﬁxed effects and individual ﬁxed effects. Dependent variable
is EOG developmental scale score expressed as a standard score. Standard errors are calculated
using generalization of Huber/White/Sandwich estimator and are robust to clustering within schools.

a Observations of students attending charter schools in their ﬁrst year of operation are excluded. b Sample
count includes only those observations of students with at least three valid test score measures, welches ist
the minimum required to identify ﬁxed effects and effect estimates for nonconstant variables.

∗ statistical signiﬁcance at .05 Ebene, ∗∗ statistical signiﬁcance at the .01 Ebene.

the negative impact charter schools have on student performance.32 However,
the coefﬁcients on the charter school variable remain statistically signiﬁcant,
suggesting that some combination of peers, resources, and efﬁciency also play
a role in the poor performance of charter schools.

That high student turnover rates play a signiﬁcant role in explaining the
poor performance of charter schools has potentially important implications
for debates about school choice. Because school choice plans lower the costs
to families of switching schools, it is plausible that such plans will increase the
movement of students across schools and thereby increase student turnover
Tarife, to the detriment of students in those schools.

8. CONCLUSIONS AND FUTURE DIRECTIONS
Our estimates imply that students in North Carolina do less well in charter
schools than they would have done in traditional public schools. Even in charter
schools that have been open for more than one year, students gain on average

32. We also estimated the models presented in Table 13 with observations of students in ﬁrst-year
charters included. In these estimations, adding the school-level measures of student turnover also
reduced the coefﬁcient on the charter school indicator by about one-third for both reading (−0.095
to −0.062) and math (−0.160 to −0.103).

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

0.062 of a standard deviation less in reading and 0.106 of a standard deviation
less in math than they would have had they remained in traditional public
Schulen. Because they are based on a subsample of charter school students
that overrepresents students who exit charter schools and underrepresents
students who enter charter schools during the early grades, these estimates
might overstate the magnitude of the true average impact of charter school
across all charter school students. dennoch, all indications are that average
charter school impacts across all charter school students are negative.

In contrast to the ﬁndings from comparable studies of charter school sys-
tems in Texas and in Florida, negative effects of charter schools hold even for
charter schools that have been operating for several years. When we disaggre-
gate the average charter school impact by the length of time the charter school
student has been in the school and whether or not the student is observed exit-
ing charter schools, we ﬁnd that the large negative impacts of charter schools on
average are driven largely, although not entirely, by students during their ﬁrst
year in a charter school regardless of the age of the school. Although negative
impacts for charter school students who choose to remain in charter schools
do not continue to accumulate after the ﬁrst year, even this group of students
shows lower achievement levels as a result of transferring into charter schools.
The effects of charter school competition on the achievement of students
in traditional public schools appear to be negligible. That may well reﬂect
the fact that North Carolina charter schools provide only a limited amount
of competition for traditional public schools. Infolge, the North Carolina
charter school program does not yet provide a deﬁnitive test of the potential
effects of charter school competition on traditional public school students.

Why students make smaller test score gains in charter schools than in
traditional public schools is worth investigating. We provide evidence that
high student turnover rates explain about 30 percent of the difference between
test score gains made in charter schools and what we would expect the same
students to make in traditional public schools. This ﬁnding suggests that
student turnover can be an unintended negative side effect of school choice.
Jedoch, charter schools in North Carolina still show negative impacts on
student achievement even after controlling for student turnover rates. Weiter
investigation to determine whether the remaining negative impacts are due to
peer effects, resource inadequacies, or inefﬁciencies would be useful.

Funding for this research was provided by the Smith Richardson Foundation. Der
authors wish to acknowledge the valuable research assistance provided by Shana Cook,
and to thank Tim Sass, participants at the University of Connecticut’s Department of
Public Policy “brown-bag” series, Die 2003 Association of Public Policy Analysis and
Management Research Conference, the New England Study group, and anonymous
reviewers who commented on earlier versions of the article.

EDUCATION FINANCE AND POLICY

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

ROBERT BIFULCO AND HELEN F. LADD

VERWEISE
Abowd, John M., Francis Kramarz, and David N. Margolis. 1999. High wage workers
and high wage ﬁrms. Econometrica 67:251–333.

Andrews, Martyn, Thorsten Schank, and Richard Upwad. 2004. Practical estimation
methods for linked employer-employee data. Unpublished paper, University of Manch-
ester.

Ashenfelter, Orley. 1975. The effect of manpower training on earnings: Preliminary
results. Proceedings from the Twenty-Seventh Annual Winter Meeting of the Industrial
Relations Research Association, Madison, WI.

Baltagi, Badi H. 1995. Econometric analysis of panel data. New York: John Wiley & Sons.

Belﬁeld, Clive R., and Henry M. Levin. 2002. The effects of competition on educational
outcomes: A review of the U.S. evidence. Review of Educational Research 72:279–341.

Bettinger, Eric. 1999. The effects of charter schools on charter students and public
Schulen. National Center for the Study of Privatization in Education Occasional Paper
NEIN. 4.

Center for Education Reform (CER). 2005 Charter school highlights and statistics.
Zugriff 20 Januar 2005. Available http://edreform.com.

Eberts, Randall W., and Kevin M. Hollenbeck. 2001. An examination of student achieve-
ment in Michigan charter schools. W. E. Upjohn Institute for Employment Research
Staff Working Paper 01–68.

Ferguson, Ronald, and Helen F. Ladd. 1996. How and why money matters: An anal-
ysis of Alabama schools. In Holding schools accountable: Performance based reform in
Ausbildung, edited by Helen F. Ladd. Washington, Gleichstrom: Brookings Institution Press,
S. 265–98.

Griffiths, William E., R. Carter Hill, and George G. Judge. 1993. Learning and practicing
econometrics. New York: John Wiley & Sons.

Gronberg, Timothy J., and Dennis W. Jansen. 2001. Navigating newly chartered waters:
An analysis of Texas charter school performance. Austin: Texas Public Policy Foundation.

Hanushek, Eric A. 1979. Conceptual and empirical issues in the estimation of educa-
tional production functions. Journal of Human Resources 14:351–88.

Hanushek, Eric A., John F. Kain, and Steven G. Rivkin. 2001. Disruption versus Tiebout
improvement: The costs and beneﬁts of switching schools. NBER Working Paper No.
8479.

Hanushek, Eric A., John F. Kain, and Steven G. Rivkin. 2002. The impact of charter
schools on academic achievement. Unpublished paper, Universität in Stanford.

Heckman, James J., LaLonde Robert J., and Smith, J. A. 1999. The economics and
econometrics of active labor market programs. In Handbook of Labor Economics, Bd. 3,
edited by Orley Ashenfelter and David Card. New York: North-Holland, S. 1865–2097.

Holmes, George M. 2003. Do charter schools increase student achievement at tradi-
tional public schools? Unpublished paper, East Carolina University.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

IMPACTS OF CHARTER SCHOOLS IN NORTH CAROLINA

Holmes, George M., Jeff DeSimone, and Nicholas Rupp. 2003. Does school choice
increase school quality? NBER Working Paper No. 9683.

Howell, William G., and Paul E. Peterson. 2002. The education gap: Vouchers and urban
Schulen. Washington, Gleichstrom: Brookings Institution Press.

Hoxby, Caroline M. 2001. How school choice affects the achievement of public school
students. Paper prepared for Koret Task Force meeting, Hoover Institution, Stanford,
CA.

Hoxby, Caroline M., and Jonah E. Rockoff. 2004. The impact of charter schools on
student achievement. Unpublished paper, Harvard Universität.

Manuel, John. 2002. The good, the great, and the struggling: An up close look at charter
schools across North Carolina. North Carolina Insight 20:21–55.

Nathan, Joe. 1996. Charter schools: Creating hope and opportunity for American education.
San Francisco: Jossey-Bass.

Rouse, Cecilia E. 1998. Private school vouchers and student achievement: An evaluation
of the Milwaukee parental choice program. Vierteljährliches Journal of Economics 113:553–602.

Sass, Tim R. 2004. Charter schools and student achievement in Florida. Education
Finance and Policy, this issue.

Solmon, Lewis, Kern Paark, and David Garcia. 2001. Does charter school attendance
improve test scores? The Arizona results. Phoenix, AZ: The Goldwater Institute.

Summers, Anita A., and Barbara L. Wolfe. 1977. Do schools make a difference? Amer-
ican Economic Review 67:639–52.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
e
D
P
A
R
T
ich
C
e
–
P
D

F
/

1
1
5
0
1
6
9
2
9
8
9
e
D
P
2
0
0
6
1
1
5
0
P
D

B
j
G
u
e
S
T

Ö
N
0
8
S
e
P
e
M
B
e
R
2
0
2
3

EDUCATION FINANCE AND POLICY
PDF Herunterladen