1. No category

Why Do Charter Schools Fail? - An Analysis of Charter School
Survival in New Jersey
Julia Schwenkenberg
James VanderHoff
*
#
Rutgers University, Newark Working Paper #2013-002
March 2013
ABSTRACT
The reasons for charter school failure may determine if charter school competition improves
public education. We estimate survival regressions to assess the effects of various factors on the
probability of school failure. We find that students’ test scores are the most important
determinant of survival: a one standard deviation increase reduces the probability of failure by at
least 72%. Higher expenditures on facilities and a longer waitlist result in smaller but significant
reductions in the probability of failure. Factors like administrative and class room expenditures,
total enrollment, and student demographics do not have significant effects on school survival.
JEL: I21, H75
Key words: Charter schools, Education policy, School choice, Charter school failure
#Department of Economics, Rutgers University, Newark. jhv@rutgers.edu
*Department of Economics, Rutgers University, Newark. julia.schwenkenberg@rutgers.edu
I. Introduction
The public education systems are failing a large segment of the population. Official data
on test scores shows that poor and minority students are persistently performing below grade
level. Some people have argued that the best way to reform the education system is to create
competition for traditional public schools (TPS) by distributing private school vouchers and by
allowing charter schools to operate alongside TPS. Charter schools are the most widely available
1
choice schools because they are public schools, though they are not operated by the local
school district. The number of charter schools has increased substantially in the last decade.
2
Now forty one states issue charters to schools which specify the philosophy, goals, and methods
for achieving their mission and exempt these schools from some regulations that govern other
public schools. State governing agencies periodically review charter schools and may revoke the
charter if a school does not attract enough students, does not meet its specified goals, or is
mismanaged. Hence, like firms in a competitive market, charter schools can fail due to fiscal
mismanagement or due to the inability to satisfy customer demands for a quality education which
might include raising students’ cognitive and non-cognitive skills or providing a safe or, in other
ways, enhanced learning environment. But only failures driven primarily by the schools’ failure
to improve skills will spur the desired improvement in TPS. Not only will underperforming
charter schools be removed but, perhaps more importantly, the threat of failure and the loss of
the charter incentivize teachers and administrators to provide an effective and efficient education
in order to meet the expectations and goals of the parents and students. Thus, the question of
1
2
Sutton and King (2011) discuss the legal issues associated with vouchers to private schools.
In The State of Charter Schools, The Center for Education Reform [2011] reports the number of charter schools
increased from 1,651 in 2001 to 5,761 serving 1.9 million students in 2012.
why schools fail is important for the assessment of the likely success of charter school
competition.
3
There have been a numerous studies that analyze whether charter schools are in fact
improving educational outcomes. For many years researchers have investigated if charter
schools enhance students’ cognitive skills, usually by comparing charter school students’
standardized test scores with scores of TPS student after controlling for socioeconomic factors.
4
Some recent studies conclude that charter school students have higher graduation rates [Booker,
et al (2011)] and score higher on standardized tests than TPS students [Booker et al., 2007;
Abdulkadiro˘Glu , et al (2011) ], while others conclude charter school students do not score
higher on tests [Hanushek, et al., 2007]. Studies investigating the effects of charter schools on
the scores of students who remain in TPS also show mixed results [Sass [2007]]. Welsh [2011]
concludes that competition from charter schools causes increases in resources to teaching
personnel in TPS. Scott Imberman [2011] concludes that charter school students improve in
noncognitive skills compared to students at traditional public schools. Another line of research
reports estimates of production functions to ascertain whether charter schools make efficient use
of their resources and concludes that charters do not use resources efficiently but are more
efficient than traditional schools. [Gronberg, et al. [ 2012]
A study particularly relevant to our work was released in October 2012 by the Center for
Research on Education Outcomes [CREDO]. Researchers analyze New Jersey (NJ) elementary
3
Loeb, et all. (2011) discuss effects of competition and analyze on Milwaukee public schools. Barr (2007) finds
effects of New Jersey charter school competition on TPS performance.
4
“Virtually all the articles in this special issue highlight that the existing literature is inconclusive about the
aggregate effect charter schools have on student achievement,” write Toma and Zimmer [2012] in their introductory
article in the special charter school volume of Economics of Education Review.
5
and middle grade charter schools for the academic years 2006/2007 to 2010/2011. Their
analysis of student records indicates that NJ charters’ students made larger gains than similar
TPS students in both reading and math on standardized state required tests. Their analysis of
charter school average scores indicates that, in math, 40% of charter schools have significantly
larger student gains than TPS and 13% have significantly lower gains than TPS. In reading, 30%
of NJ charter schools had statistically significant gains exceeding those of TPS and 11% had
significantly smaller gains. The researchers conclude that the results for NJ charter schools are
“notably more positive” than the 16 states analyzed previously, as detailed in their 2009 report.
6
Unlike many other charter schools studies, the CREDO researchers do not implicitly assume all
charter schools are the same, but they do not analyze the differences between ‘good’ and ‘bad’
charter schools and whether these schools survive or fail.
We look at the characteristics of individual charters to determine causes of failure using
data on New Jersey charter schools. NJ has some of the top performing charter schools in the
7
country, while NJ charters fail at almost twice the national failure rate. We estimate a survival
model that specifies the instantaneous probability of failure (the hazard rate) as a function of
students’ test scores (cognitive skills), non-cognitive skills, and financial mismanagement. We
also account for the effects of student demographics, competition from other charters and TPS,
the length of the student waitlist, as well as district and school size. We find strong evidence that
failure is caused predominantly by low student test scores. Our results are robust across models
5
They calculate changes in test scores from the previous year. Since NJ tests grades 3-8 and 11, they cannot
analyze high schools.
6
Zimmer, et al. (2012) present a tabular summary of some aspects of charter school differences by states and other
geographical location but they do not analyze differences in charter schools within a location.
7
CRE reports that 15% of charter schools fail. 25% of NJ charters have failed since 1996.
and statistical methods. Our results also indicate that schools that spent more on facilities have a
higher probability of survival, which might indicate that charter schools are underfunded.
II. Charter Schools in New Jersey
In 1996, New Jersey became the 19th state to allow the formation of charter schools. The
charter school legislation authorizes the Department of Education to issue four-year, renewable
charters to the founders of charter schools with the consent of the host school district, which does
not oversee the charter schools. All New Jersey charter schools are startups; they do not replace
8
existing public schools.
New Jersey, like other states, issues charters to schools with varied
philosophies and methods. For example, their mission statements include “environmental based
education,” “culturally immersed educational experience,” and “academically rigorous
curriculum.” Also, the charter schools offer different educational services: some provide an 11month school year, others operate an 8 a.m. to 5 p.m. school day and others conduct Saturday
classes. Every charter school is open to any student in the state, although nearly all students
reside in the home school districts because these students receive preference. Charter schools
cannot enroll students based on standardized tests or other characteristics. If the number of
applications exceeds the number of openings, enrolled students are chosen by a lottery and the
unsuccessful lottery participants are put on a waiting list. In 2012, 96% of NJ charter schools
report a waiting list while, nationally, about 65% of the schools have a waiting list.
The New Jersey Department of Education (NJ DOE) has an annual multistage approval
process. Applications are due to the DOE by March 31 and approvals/denials are given by
September 30. An expedited schedule is available for experienced charter school operators
8
Recently changes will allow NJ private schools to convert to public charter schools but no schools in our sample
are conversions.
(applications are due in October with an approval by February). Approved schools must pass the
School Preparedness review which allows the DOE to assess organizational leadership and
capacity by June 30 of the following year and the final charter granting decision is made by July
9
15th. The school can then elect to open in September or take an additional planning year. The
NJ DOE approved 125 charter school applications since 1996 but 24 of the approved schools did
begin operating: five were denied their final charters and 17 charters were surrendered before the
school opened, usually due to roadblocks put up by the local district, to site location problems or
to funding problems. Charter School Laws Across the States [2012] details the problems that
Princeton International Charter School experienced in 2010 and reports: “This is not an
uncommon story in the Garden State. Many charters are approved but never open because of
district opposition and the state, as authorizer, does not step in to advocate for its schools as
strong authorizers do. (p 55)” From 1996 to 2012, over 102 new schools were opened and in the
fall of 2012, 86 charter schools were operating and serving over 30,000 students. Of those failed
schools that became operational, 7 charters were not renewed, 12 charters were revoked and 6
charters were surrendered.
Since 1996 the NJ DOE has issued an annual New Jersey School Report Card (SRC),
which details school and district level data for all New Jersey public schools (both charter and
traditional). The Report Card data are sent to parents, summarized in local newspapers, and
reported on the websites of major newspapers and of the Department of Education. The Report
Cards provide parents with the information they need to make informed decisions: test scores,
characteristics of the schools’ students, the schools’ resources and learning environment, school
9
http://www.state.nj.us/education/chartsch/app/
finances, and teacher/staff information.
10
Most of the variables are reported at the school level
for three academic years. The Report Cards also includes comparable, averaged data for other
public schools in the home district, in similar socioeconomic school districts and throughout the
state and, for charter schools, provides three years of wait list and enrollment data, which
provides information to evaluate the likelihood of a successful charter school application.
III.
The Survival Model
New Jersey charter schools ‘die’ when a school voluntarily surrenders their contract or
when the NJ DOE revokes or fails to renew the school’s charter. To analyze determinants of
charter school survival, we estimate a hazard function specified as follows:
h(t|x) = h0(t) exp(β1x1+..+ βkxk).
The hazard rate h(t) is the instantaneous probability of failure conditional on survival
until time t, and xk are the determinants of survival. The baseline hazard, h0(t), is the hazard rate
with all covariates set to zero. The baseline hazard can be left unspecified, which would yield the
Cox survival model, or can be parameterized using a specific distribution. We found that a
11
Weibull specification provided the best fit for the data.
In the Weibull case the baseline hazard
depends on time as follows:
h0(t) = p tp-1,
where p is a parameter that is jointly estimated with the coefficients. To facilitate interpretation
of the estimation results, it is customary to present the exponents of the coefficient estimates,
10
Hastings and Weinstein (2008) show that access to information about test scores is very important for parents
decisions on school choice.
11
Estimates using the Cox proportional hazard model, and a log-normal, or log-log specification for the hazard rate
are very similar. We selected the Weibull specification using the AIC score, as well as Cox-Snell residuals to check
the fit.
which are equal to the hazard ratios for a unit change in the covariate. A ratio above unity
indicates a higher probability of failure and therefore a shorter survival time. In addition to
parameter estimates of the survival model, we will also present unconditional and conditional
estimates of the survivor function. The survivor function gives the probability of surviving just
beyond time t, and it is defined as follows:
S(t|x)=exp(-H(t|x)),
where H(t) is the cumulative hazard, that is, the total number of expected failures until time t, or
the integral of h(t).
Even though charter schools are not-for-profit enterprises, their entrance and exit from
12
the education market is similar to a new firm’s performance in a competitive industry.
Demand for charter schools not only depends on charter school quality but also on school
competition in the school district. We assume that the quality of a charter school’s product is
primarily determined by its students’ cognitive and non-cognitive performance. Parents directly
observe a measure of product quality in the form of student test scores that are reported by the NJ
DOE. Unfortunately, we do not observe good measures of non-cognitive skills, but we use
attendance rates as a proxy. A direct measure of parental demand for a charter school’s product,
which we will also include in one of our specifications, is the school’s waitlist. This variable also
captures unobserved quality attributes that are not measured by test scores, like safety.
A charter school’s finances are also an important determinant of survival.
13
Mismanagement is an often cited reason for school closure.
But the effects of higher spending
12
Firm survival is, for example, affected by establishment size, and the life-cycle stage and the type of the firm’s
industry. Small firms generally face survival disadvantages. Industry characteristics determine the level of
competition and of innovation (see Audretsch and Mahmood, 1995).
13
The State of Charter Schools [2011] attributes 42% of failures to financial reasons, including low student
enrollment, and 24% due to mismanagement, including fraud.
are not obvious, since higher expenditures per student may improve the final product, the child’s
education, which in turn determines parental demand for the school. We decompose total
expenditures into class room expenditures, administrative expenditures and other expenditures.
We would expect class room expenditures to increases survival, while high administrative and
other costs might signal mismanagement.
Survival may depend on how well charter schools compete with other schools, primarily
those in the host school district(s). As detailed below, we include measures of competitiveness
with other charter schools and TPS in the host district. The effect of school size is not clear a
priori. Often schools start with one grade and then scale up their operations as the initial student
population matures and new students get admitted which may enhance survival by enabling the
school to learn how to best educate their students. Moreover, a smaller scale might allow a
stronger focus on the student population. On the other hand, a small scale might be associated
with higher overhead costs, and large schools might receive more political support. Finally, we
also consider that schools with higher percentages of poor or minority students might by less
likely to survive as they might face more challenges. The next section details our data and the
variables we use in the estimation.
IV.
Data
Our primary data set consists of more than 600 observations over a thirteen-year period
14
(2000 to 2012) for 102 charter schools, which operated between 1 and 15 years.
The number
of observations per charter school differs for several reasons. New schools were started during
each year in our sample and schools were closed in 10 of our sample period years. Also, tests
14
The 1999/2000 academic year is denoted 2000.
were not administered in every school during each year of operation because most startup
schools chose to expand by adding grades as the first students progressed to higher grade levels.
Because the state initially started testing in the 4th grade, schools that started with 1st grade
students and added a grade every year would not have test score data for the first three years of
operation. We have all data on closed schools for their last year of operations (their age of
death), with the exception of one school that closed at age 3
15
and those schools that shut down
during their first year of operation. As discussed in the next section, we estimate our survival
regressions conditional on surviving the first year.
The scores on the state mandated tests of all public school students measure the school’s
students’ cognitive skills. Starting in 1996, the state of New Jersey tested all 4th grade, 8th grade
and 11th grade public school students in math, language and, sometimes, in science. Starting in
2004, tests have been phased in for grades 3, 5, 6, and 7. The Report Card summarizes the results
of the annual spring grade 3 to grade 8 tests in three categories: advanced proficient, proficient,
and partially proficient. The Report card also reports the results of the October High School
Proficiency Test given to 11th graders throughout the sample period. The state reports more
detailed school-level information, including the mean score, in an annual assessment report, the
source for our test data. We standardized the school mean scores using the state school mean
and standard deviation; therefore, our data measure standard deviations from the state mean. For
each charter school, the annual effectiveness measure [TESTS] is the average of the standardized
results of all tests given at the school for the given year.
16
15
Paterson Charter School of Urban Leadership never reported test scores and was closed due to “deficiencies
related to its fiscal operations, governance and compliance with education laws and regulations” according to NJ
DOE.
16
A school with students in grades 1 to 8 would administer math and verbal tests to students in grades 3,4,5,6,7 and
8, a total of 12 tests that would comprise the set used to calculate the school average test score.
Noncognitive skills include completing assignments on time and satisfactorily,
punctuality, and behavior that is respectful of others. We measure this type of skill with
attendance rates [ATTEND] at the schools because this measure depends on the actions of the
17
students, not the administrators.
We considered using the schools’ suspension rate but it
18
depends on student behavior and the degree that administrators punish disruptive behavior.
Mismanagement includes some financial problems and inappropriate action of administrators
towards students and staff. Lacking any measure of the later mismanagement,
19
we assume
mismanagement will be manifest as high administrative costs relative to class room related costs.
We include overall expenditures per student [ EXPEN], classroom expenditures [
EXPENCLRM], administration expenditures [ EXPENADM] and other expenditures which
primarily are facility costs [ EXPENOTH].
Some of our models include variables to control for student and school characteristics
that may also affect the survival of the charter schools. Charter school students are more likely
to be a member of a minority ethnic group or from a low income family than TPS students. We
use two student demographics variables: the percent of students identified as Black or Hispanic
[MINORITY] and the percent of students who qualify for free or subsidized lunch [POOR].
Scale may be important, so we include enrollment [ENROLL] to control for size effects. Charter
schools may have unmeasured characteristics that appeal to students and parents, such as safety,
17
Academy High Charter School attributes is relatively low attendance rate of 91% due to lower attendance to the
once a month Saturday school session and the students’ family and other weekend commitments. So attendance
does depend somewhat on school administration policies.
18
The percent of students suspended varied a lot year to year within schools and is not easily interpreted. Are
higher suspension rates an indication of poorly behaved students or administrators attempting to increase
noncognitive skill enhancement? Suspension rates was not statistically significant in unreported model estimates.
19
We considered faculty turnover but high turnover could be a measure of good management if excellent teachers
were hired but left to take other jobs because charter school teachers on average earn 15% lower salaries. Also,
charters are startups and hire many new teachers so would naturally have higher teacher turnover.
nearness to public transportation or extracurricular activities. To represent these factors, we use
the number of students who applied for admission but did not win a placement in the admissions
lottery and are on a wait list [WAIT].
We measure competition within the host district with three variables. We include the
number of charter schools in the district [CHARTERS]. Also, we include two measures of other
schools’ test scores. These are the percentage of charter schools out of all schools within the
district with average test scores above the charter school mean in a given year [COMPCS]. This
measure is defined relative to schools with the same grades levels. In particular, schools are
grouped into elementary (reporting test scores for grades 3-5), middle (grades 6-8), and high
school (reporting HSPA). For charter schools with multiple host districts these measures use the
totals across these districts. Also we compute competition from TPS by the percentage of
relevant grade level TPS in the host districts with test scores above the state average
[COMPTPS].
Our data primary derives from the SRC but other data sources provide missing values.
The primary reason for missing data is the state stopped reporting statistics on charter schools
that failed. For example, the 2011 report card reports data for the 2010/2011 academic year and
was released during the summer of 2012. However three charter schools were closed during the
2011/2012 school year and were dropped from the 2011 report card, so data was not reported for
the ‘death’ year for these schools. Also, sometimes values were just left out of the report card,
perhaps because the charter schools missed the reporting deadline. We were able to replace
missing values with data from the NJ DOE issued annual reports on test scores, enrollment
statistics and budget numbers. Also, we found missing enrollment and race data in the Common
Core of Data from the National Center for Education Statistics.
TABLE 1
MEANS FOR OPEN AND CLOSED SCHOOLS
Mean
Mean
Standard
Difference
N open
open
closed
error
# schools
77
TESTS
-0.77
-1.93
1.16
0.11
559
ATTEND
94.32
92.79
1.53
0.45
541
ENROLL
287.00
323.70
-36.70
20.68
567
WAIT
264.70
62.83
201.80
53.59
539
POOR
62.02
73.21
-11.19
2.94
524
MINORITY
88.80
97.69
-8.89
2.34
565
1
EXPEN
131.20
124.50
6.69
3.00
647
1
EXPENCLRM
58.98
53.44
5.54
1.61
646
1
EXPENADM
22.77
24.00
-1.24
0.84
646
EXPENOTH 1
49.66
46.81
2.85
2.14
646
COMPCS
9.62
6.73
2.89
1.43
559
COMPTPS
22.81
5.88
16.93
3.38
559
CHARTERS
4.44
3.85
0.59
0.46
658
NUMBER OF TPS
31.10
21.46
9.64
2.40
658
AGE
11.76
8.78
2.98
0.39
658
1
Expenditures are in $100 (2012 dollars) per student.
N closed
25
94
94
99
90
90
101
110
111
111
110
94
94
120
120
120
Table 1 reports means and the number of observations for the schools that are open and those
that closed sometime during the sample period. Also the difference in means is reported along
with the standard error of the difference. The number of schools that closed is 25 out of 102
schools, which is a 25% failure rate, substantially higher than the national rate of 15%. Closed
schools report tests scores 1.15 standard deviations lower than schools that did not close. Closed
schools report lower expenditures and face less competition from the number of charter schools,
from the number of TPS in the district and from the number of schools with higher test scores.
Closed schools also report more minority and poor students. Schools that remain opened during
the sample period have wait lists over 4 times that of closed schools and are in operation 3 years
longer, on average. Most differences are statistically significant.
V.
A.
Survival Analysis
Kaplan Meier survivor function
This section presents estimates of the Kaplan Meier survivor function for NJ charter
schools that have become operational. The top half of table 2 presents the unconditional Kaplan
Meier survival function estimates. The number right censored is equal to the number of schools
that are alive at this age after the 2011/2012 school year. The estimates indicate that the
probability of surviving the first year is 93%, as about 7% of the schools fail during the first year.
The probability of surviving past age 5 is 80% and schools have an estimated 64% survival
probability beyond age 15, which is the longest possible survival time for charter schools in New
20
Jersey.
The lower portion of table 2 shows estimates of the Kaplan Meier survivor function for
charter schools with test scores below and above the overall charter school mean of -0.94.
Because many schools are not reporting test scores for their initial years of operation and
because schools may move between the two groups, the 4th column (net lost) is negative when
more charter schools enter the analysis at the specified age than are right censored or failed. Only
one charter school with test scores above the charter school mean failed, therefore the survivor
function is constant after age 4 (the age when this schools failed) and significantly higher than
the survivor function for schools with scores below the mean. Figure A.1 in the appendix
illustrates the two survivor functions.
20
The Kaplan Meier non-parametric maximum likelihood estimate of the survivor function is defined as follows:
Sˆ (t ) 

j |t j t
nj  d j
nj
, where nj is the number at risk at age tj (schools that are operating and have survived until
then) and dj is the number of deaths at age tj. If no school dies at any particular age the survivor function remains
constant.
Age
All schools
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
TABLE 2
Charter School Survival
Number of
Right
Survivor Standard
Failures
schools
Censored Function Error
102
7
9
0.9314
0.025
86
3
7
0.8989
0.0304
76
4
5
0.8516
0.0369
67
3
5
0.8134
0.0413
59
1
3
0.7997
0.0428
55
0
3
0.7997
0.0428
52
1
3
0.7843
0.0447
48
1
2
0.7679
0.0466
45
1
1
0.7509
0.0486
43
0
2
0.7509
0.0486
41
0
3
0.7509
0.0486
38
1
4
0.7311
0.0512
33
0
9
0.7311
0.0512
24
3
12
0.6397
0.0666
9
0
9
0.6397
0.0666
Number of
Survivor Standard
Age
Failures Net Lost
schools
Function Error
Schools with test scores below the Charter school mean
1
27
2
-12
0.9259
0.0504
2
37
3
-9
0.8509
0.0622
3
43
3
6
0.7915
0.0667
4
34
2
0
0.7449
0.0704
5
32
1
8
0.7217
0.0719
6
23
0
-4
0.7217
0.0719
7
27
1
-3
0.6949
0.0741
8
29
1
5
0.671
0.0753
9
23
1
-2
0.6418
0.0775
10
24
0
5
0.6418
0.0775
11
19
0
3
0.6418
0.0775
12
16
1
3
0.6017
0.0824
13
12
0
6
0.6017
0.0824
14
6
3
1
0.3008
0.1295
15
2
0
2
0.3008
0.1295
Schools with test scores above the Charter school mean
1
5
0
-8
1.00
4
26
1
-1
0.9615
0.0377
15
7
0
7
0.9615
0.0377
Because tests are given during the spring and reported late summer after the end of the
academic year, charter school failures during the first year of operations likely do not occur
because of testing results. First year failures are presumed due to management incompetency
21
and unpreparedness that did not become apparent earlier during the approval process.
For
example, the inability to get the school operational, to hire sufficient certified staff or to deliver
22
the curriculum and services outlined in the charter.
Therefore we estimate the survival
23
regressions conditional on surviving the first year.
B.
Estimations Results
The estimation results are presented using the exponentiated coefficients, the hazard ratios, as
described above. All specifications of our model include enrollment, test scores, and the different
types of expenditures per student. Only two variables are significant in all specifications. The
estimated effect of test scores is both large and highly significant in all specifications. The
estimated hazard ratios range from 0.14 to 0.23, which means that a one standard deviation
increase in test scores decreases the probability of dying by 77%-86%. Also, higher
expenditures on predominantly facilities are statistically significant in all models. The hazard
ratio of 0.95 indicates that a $100 increase in spending per student reduces the probability of
21
After 2008 the NJ DOE extended the approval timeline which is described in section 2. We also do not have any
schools failing during the first year after 2008. Therefore, it also makes sense to exclude the first year of operation
from the survival regressions and view the initial year as part of the initial selection process together with the
approval proceedings.
22
For example, REACH Charter School was ordered to close during the first year of operations and transfer
students back to their home districts due to “[..] the school's inability to resolve a growing number of serious fiscal
and programmatic problems [..]The commissioner said the charter school has incurred a significant debt, making it
fiscally insolvent, and it is now experiencing a loss of certified staff.[...] Prior deficiencies identified by the
department at the school included using uncertified teachers, having an insufficient number of mentors for teachers
and enrolling students in unapproved grade levels.”. http://www.state.nj.us/education/news/2000/0419reach.htm
23
Including the schools that do have data for the first year does not significantly alter the results presented below.
Performing the analysis conditional on surviving the first two years also preserves the results.
failure by 5%.24 Charter school size does not affect the probability of charter school failure in
any model.
Other variables are included selectively in different model specifications. The first
specification in table 3 analyzes the effect of non-cognitive skills, as measured by attendance.
Attendance does not significantly affect the probability of failure. The second specification
shows that the percentages of minority and poor students have no effect on charter school
survival, after controlling for expenditures and test scores. In the third specification we include
the number of students on the waitlist, which could pick up unobserved quality differences that
increase parental demand for the school. This variable is highly significant, and the coefficient on
25
test scores falls (the hazard ratio increases) but remains large and significant.
In the two final specifications we include the competition variables. Charter school
competition, either measured by the percentage of higher performing charter schools or by the
number of other charter schools in the same district, has no effect on the survival of charter
schools. This might indicate that there is sufficient demand for good schools. Although, the
percentage of TPS with test scores above the state mean is generally lower in districts with
charter schools, competition from TPS has a positive effect on survival at the 10% significance
level. This result may occur because districts with some higher performing TPS encourage
parents to seek alternatives if their children go to lower performing TPS. Or it may be due to
reverse causation—charter school competition motivates TPS teachers and administrators to
increase student test scores. In the final specification we also estimate that there is a positive
effect of district size, as measured by the number of TPS, also significant at the 10% level.
24
25
Note that the units for the financial variables are $100 per student.
The number of students on the wait list is primarily determined by schools’ test scores and academic quality
VanderHoff [2006] and thus weakens the estimated effect of SCORE but it adds independent information.
Larger districts might benefit charter schools because they have a greater capacity for additional
public schools. The graphs of the estimated survival function in Figure 1 illustrate the impact of
test scores. The survival predictions are conditional on the mean values of the other covariates
and are made for values of TESTS at the 25%, 50% and 75% percentile.
Table 3
Weibull Regressions
Student
Performance
ENROLL
TESTS
EXPENOTH
EXPENADM
EXPENCLRM
ATTEND
0.999
(1.14)
0.187
(4.75)***
0.949
(2.78)***
1.008
(0.16)
0.986
(0.48)
0.989
(0.12)
POOR
Student
Demographics
0.999
(1.07)
0.178
(4.96)***
0.945
(2.86)***
1.022
(0.48)
0.981
(0.63)
Waitlist
1.000
(0.16)
0.227
(4.32)***
0.947
(3.00)***
1.035
(0.72)
0.998
(0.06)
Competition
0.998
(1.39)
0.218
(4.89)***
0.946
(2.94)***
1.002
(0.04)
0.988
(0.44)
0.999
(1.12)
0.169
(5.12)***
0.954
(2.45)**
1.005
(0.12)
0.975
(0.84)
0.985
(0.70)
1.017
(1.02)
MINORITY
WAIT
0.988
(2.64)***
COMPCS
1.005
(0.15)
0.975
(1.84)*
COMPTPS
CHARTERS
TPS
N
no. of subjects
no. of failures
District Size
595
78
17
585
82
17
588
78
17
599
82
17
* p<0.1; ** p<0.05; *** p<0.01
The table displays hazard ratios with t-statistics in parentheses
1.089
(0.55)
0.953
(1.67)*
599
82
17
Figure 1
VI.
Conclusion
Our analysis indicates that the predominant reason for charter school failure is the poor
cognitive skills of their students, as measured by school average scores on state mandated tests: a
one standard deviation increase in test scores reduced the probability of failure by at least 72%.
If charter schools are to provide the competition necessary to improve the scholastic performance
of students in traditional public schools, the choice schools that do not improve student
performance must be removed from the menu of choices in the education market. Our analysis
indicates this competitive pressure is occurring, at least in New Jersey charter schools.
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Appendix 1
Figure A.1