competition and public school ... - university of toronto
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Competition and Public School Performance:
An Empirical Analysis using Data from Ontario
Winnie P. C. Chan
University of Toronto
November 2003
Abstract
There is widespread concern over public school performance which initiated policy in-
terest in competition as a means to improve public school productivity. This paper studies
the Ontario Equity in Education Tuition Tax Credit program implemented in 2002 to test
such policy claim. The available data suggests no significant change in overall performance
of public schools and school average score gain is not significantly different between high-
competitive and low-competitive school district. Empirical strategy aims to measure public
school productivity response is discussed in light of data limitation. More control variables are
needed to determine how much competition contributes to the overall performance change.
1
1 Introduction
Public concern about education quality has initiated a broad-ranging debate over the policy of
increasing school choice. To promote higher academic excellence, a popular proposal is to inten-
sify competition between schools. Mechanisms to promote competition between schools include
vouchers, charter schools and tax credit. There are different experiments1 using these mechanisms
with the goal of improving education quality. Ontario introduced the Equity in Education Tax
Credit (EiETC) in 2002 in response to public dismay of the falling student’s performance. EiETC
is a refundable tax credit2 for parents and legal guardians in respect of tuition costs at eligible
independent schools in Ontario.
Such proposal calls for treating schools like firms in Industrial Organization. In a more compet-
itive environment, students are less captive and schools are forced to raised productivity to retain
student enrollment. When public schools improve performance, students staying in public schools
also benefit. Pro-competition advocates claim that increasing competition can raise production
productivity of the whole schooling market. The ideal test for such claim involves large-scale
randomized experiment set-up. Test students at the status-quo system and use longitudinal com-
parable tests to track individual student achievement. Then randomly assigned some schools to
face increased competition from private schools while insulating some schools as control groups.
Test the students again at the new equilibrium. Simple difference-in-difference estimation would
allow us to test whether public schools increase productivity. Such neat large scale experiment
has never been implemented. Small voucher programs have been implemented in the US but it is
difficult to learn about the general equilibrium effects on public school system.
There are some important questions remained unsolved in the literature: First, how to define
1Example: Six US states — Arizona, Florida, Illinois, Iowa, Minnesota and Pennsylvania — have enacted tax
credits; Alberta has charter schools and Chile has a large-scale national voucher scheme.2Details of the tax credit are discussed in section 3.
2
competition between schools? As pointed out by Hoxby (1994, 2000), simple measures of choice
(e.g. number of private school students in the metropolitan area) are erroneous as private school
enrollment is likely to be endogenous to public school quality. Instrumental variable methods
can be used to counteract the endogeneity problem. However, different papers3 using Catholic
instrument show no conclusive result4. It is necessary to develop other methodology to measure
competition5 to determine which public schools are going to respond.
As noted in Chubb and Moe (1990), student achievement depends on school’s input as well as
student’s peer. With an overall performance change, can we isolate the impact of changing school
productivity from the changing student peer effect after more competition? Hsieh and Urquiola
(2003) points out clearly that with increased school choice, there is a productive incentive from
the school side and a sorting effect from the student side. Both effects affect student achievement
and are both unobservable in education production function. The problem becomes harder as full
population of students (both public and private schools) is rarely followed over time. It is also
important to analyze what happens to private school entry, enrollment and tuition after private
school competition has increased. If tuition tax credit leads to rent-seeking behavior of private
schools, e.g. increase in private school tuition without increase in private school enrollment, then
the competitive impact on public schools will be greatly reduced.
Empirical analysis using the standardized test results of Grade 9 students in Ontario before
and after the introduction of EiETC helps us deal with the first question. No matter what market
3Hoxby (1994) uses the proportion of Catholics at the county level as an instrument for private school enrollment
and concludes that competitiveness raises public school quality. McMillan (2002) replicates the same strategy with
the same dataset suggested in Hoxby (1994). Adding more controls for sorting, the school productivity effect
disappears.4See Altonji, Elder and Taber (2000).5See Bayer, McMillan and Rueben (2003). This paper explores empirically the linkages between demand, as
determined by the residential location and public-private schooling decisions of the households of an urban area, and
supply, as determined by the response of schools to the competitiveness of the environment in which they operate.
3
structure local schools in Ontario face, with EiETC, there is greater competition between public
and private schools as more parents can send their children to independent schools. This ‘natural
experiment’ allows us to analyze the pre- and post- schooling result with an exogenous change
in competitive level. This paper aims to see what we can learn from Ontario’s EiETC with the
data available. Focusing on student achievement of public schools, we would like to address three
questions:
1. Is there any overall performance improvement under a more competitive schooling market?
2. How much can competition explain the change in performance?
3. Can we isolate the sorting effect and measure the productive effect of public schools?
2 Literature Review
Mechanisms like tuition tax credit or voucher increase the choice set of parents. With a larger
choice set, parents can afford alternative schoolings to children other than public schools. Facing
greater competition from affordable private school alternatives, public schools have to increase
productivity to retain enrollments. In terms of welfare, with bigger choice set, parents can make
school choice decisions that attain higher utility level than before. Both channels are welfare-
enhancing with productivity increase in the whole schooling market. Hoxby (1994) supports the
notion that increased competition raises public school productivity. Her paper points out that
common competition measures (e.g. private school enrollment share) are potentially endogenous
to public school quality and thus, the OLS estimates are biased. She proposes to use instrument to
account for competitiveness within schooling market and uses Catholic population of a county as
an instrument for private school enrollment. The IV estimates show that public school performance
improves significantly with greater competition from private schools. Hoxby (2000) supports the
4
same idea that choice raises productivity by simultaneously raising achievement and lowering
spending using natural boundaries, streams, of the metropolitan area as instrument.
Other papers have questioned the validity of using Catholic instrument to identify competi-
tion within school market. McMillan (2002) replicates the same instrumental variable approach
of Hoxby (1994) and shows that the findings are sensitive to the choice of dependent variable,
the measure of competition and neighborhood controls. Student achievement does not increase
significantly with more competition identified by Catholic instrument. By adding sorting controls
to account for the observable differences between public and private schools, the remaining effect
of productivity is close to zero. McMillan (2003) shows that in well-defined circumstances, rent-
seeking public schools find it optimal to reduce productivity when a voucher is introduced. So,
there are both theoretical and empirical arguments suggesting that increased school choice does
not necessarily raise school productivity.
Hsieh and Urquiola (2003) analyze the impact of voucher in Chile. They point out that with the
provision of vouchers, there are both productivity response from public schools and sorting effect
of students which affect the student achievement. If choice leads to sorting, then the proper way
to measure the effect of choice on productivity is to consider its effect on aggregate achievement in
entire educational markets. They also argue that the use of instrument to compare regions with
different competition levels or experiment that randomly assigned a student to study in private
schools will not allow us to separate the productivity and sorting effect. Their conclusion in Chile
nationwide voucher is that the first order impact was ‘middle-class flight’ into voucher private
schools. This shift does not seem to have resulted in achievement gains. This result calls for future
research in alternative choice designs6 which could preserve the competitive effects of vouchers but
6One year result of the Florida’s voucher plan suggests positive result for such ‘increased competition’ scheme.
In a recent article of the Wall Street Journal (Europe), Florida’s Governor Jeb Bush discussed the school voucher
program implemented since 2001. Only students at schools that receive failing grades for two years in a row are
eligible for the ‘opportunity’ vouchers which allow students to study in private schools. So far, fewer than 1,000 of
the state’s 2.5 million students have exercised the vouchers. Of the 64 Florida schools graded ‘F’ last year, only nine
5
force schools to increase productivity instead of selecting better students.
This paper agrees with Hsieh and Urquiola (2003) concern and analyzes the Ontario Equity
in Education Tax Credit experiment to see if we can know more about public school response
under an increased choice environment. But instead of looking at the average school achievement
of private and public schools, we focus on the performance of public school students.
3 Background
3.1 Equity in Education Tuition Tax Credit
EiETC is in effect starting from the tax year 2002 during the Progressive Conservative era. It
was announced in the 2001 Ontario budget and passed into law in June 2001. The tax credit rate
applied to the first $7,000 of eligible annual tuition fees7 paid per student for full-time elementary
or secondary studies. The full credit was supposed to phase-in in 5 years8. But with the Liberal
government sworn in Queen’s Park on October 23, 2003, EiETC will soon be an outgoing policy
as the Liberal view in education is to invest in public system instead of subsidizing private-school-
goers. The rest of this section is to provide some basic information of the claiming policy during
2002 - 2003. For year 2002, 10% of eligible tuition fees can be claimed, so parents or legal guardian
can claim a maximum of $700 for one child going to elementary or secondary school. The credit is
continued to fail this year. One had become an ‘A’ school, three had become ‘B’ schools, and 27 had become ‘C’
schools. The grades are calculated by measuring learning gains recorded on the state’s comprehensive assessment
test. The article was published on November 4, 2003.7Fees related to accommodation or boarding, child care, and separate charges for meals, computers, books,
clothing, travel, sports and equipment do not qualify as tuition fees.8The tax credit rate is 20 percent of eligible tuition fees for independent schools in 2003 and was supposed to
move to 30 percent in 2004, 40 percent in 2005 and 50 percent in 2006 and beyond.
6
calculated separately for each child and credits are added together to determine the total allowable
credit. There is no family limit on the amount of credit available. The credit is refundable. The
total credit will first reduce any taxes owing on the return and any credit in excess will be refunded
to the taxpayer. A school is eligible if it is located in Ontario and registered as a private school
under the Education Act. According to the bulletin list9 issued by Ministry of Finance in January
2003, there are 580 eligible independent schools for the purposes of the tax credit for the period
January 2002 to July 2003.
3.2 EQAO
Education Quality and Accountability Office (EQAO) is an independent agency of the Ontario
government. It was established based on a recommendation made by the Ontario Royal Commis-
sion on Learning in 1995. EQAO administers standardized tests for students in Grade 3, Grade
6, Grade 9 and Grade 10. The results provide information about student achievement and the
quality of publicly funded education in Ontario. Students receive Individual Student Report (ISR)
and the results are also aggregated into individual school, school board and provincial level.
3.3 Standardized test scores
The Grade 3 and Grade 6 Assessments of Reading, Writing and Mathematics are based on the
reading, writing, and mathematics expectations in The Ontario Curriculum, Grades 1-8. The
Grade 9 Assessment of Mathematics provides individual and system data on students’ knowledge
and skills based on the expectations for students in Grade 9 applied and academic programs in
The Ontario Curriculum, Grades 9 and 10: Mathematics. All public school students10 in these
9The list is available on the Ministry of Finance webpage http://www.gov.on.ca/fin/english/taxbeng.htm10Except students in locally developed programs. Such programs are developed within school boards and meet
education needs that are not met by courses outlined in the provincial curriculum policy documents. Since the
7
programs are required to participate in the assessment11. Ontario Secondary School Literacy Test
(OSSLT) is introduced to ensure that students have acquired the essential reading and writing
skills that apply to all subject areas in the provincial curriculum up to the end of Grade 9. All
students in public and private schools who are working towards an Ontario Secondary School
Diploma (OSSD) are required to write the OSSLT in Grade 10. Successful completion of the
OSSLT is a graduation requirement.
3.4 Focus of the analysis
To analyze the impact of EiETC in Ontario, this paper focuses on the Grade 9 mathematics test.
EQAO started administering Grade 9 test in the academic year 2000-2001. With EiETC passed
into law in June 2001, parents can make related school decision for the academic year 2001-2002. So,
test score in 2000-2001 can serve as the pre-tax credit student achievement and test score in 2001-
2002 serves as the post-tax credit student achievement. Students in Grade 9 may choose applied
or academic courses which has a number of different curriculum expectations. EQAO develops
separate assessments for the two programs. In 2000-2001, there were 95,669 students participating
in the academic assessment and 41,973 students participating in the applied assessment. In 2001-
2002, there were 99,094 students participating in the academic assessment and 47,220 students
participating in the applied assessment. The empirical analysis in this paper will focus on scores
of the academic assessment of English-language public schools12. To accomplish comparability
across years, some items are administered over consecutive years which enable EQAO to ensure
the results are comparable and can report all students in the same program on the same scale.
assessments are based on Ontario curriculum, it is not fair to have students in locally developed programs write
them.11Private school students are optional in participation of EQAO Grade 3, 6 and 9 tests.12As suggested by EQAO, it is not fair to compare scores between Anglophone and Francophone schools. The
assessments are developed in parallel. Forty percent of the English-language and French-language assessments are
the same. However, because there are curriculum differences, these two assessments are not comparable.
8
4 Performance Effect
For the 2000-2001 dataset, there are 96,558 observations of the English-speaking public school in
the academic stream and 99,776 observations for 2001-2002. 7,407 observations in 2000-2001 and
2,651 observations in 2001-2002 are dropped from the dataset due to missing or other administrative
reasons that make the student score non-comparable. The dataset used has 89,151 observations
for the 2000-2001 and 97,125 for the 2001-2002. Aggregating the individual data to school and
district board level, in 2000-2001, the observations include student scores for 649 schools and 59
school boards. In 2001-2002, there are 656 schools. For comparison, there are 643 schools13 which
exists in both year and thus students in these schools are used for the main analysis.
4.1 School and board level
Using the individual student scores provided by EQAO dataset, we can analyze the pre- and post-
result aggregated to school and board level. In Figure 1, the weighted14 school average scores are
graphed. The schools are arranged in order of their 2000-2001 weighted average school scores. The
w grade 1 series shows the weighted school average score of the corresponding school in 2001-2002.
From the graph, we can observe that the schools in lower quartiles show greater improvement in
scores than schools in higher quartiles. Table 1 summarizes the result of regressing the weighted
school average gain15 with the quartile dummies16 . The regression coefficients support the claim
13There are 6 schools which only exists in 2000-2001 and 14 new schools enter in the year 2001-2002.14The weight in the analysis is the number of students taken the test in the school divided by the total number
of observations.15Individual school weights vary between the two years as there are different number of students writing the test.
For each year, we know the number of students of each school in the sample. After getting an average from the two
numbers, we calculate an average weight to each school. This weight is then multiplied to the school average score
difference to calculate the weighted school average gain.16The ordered schools are divided into quartiles with 161 schools in quartile 1 - 3 and 160 schools in quartile 4.
9
Table 1: Linear regression of school average gain by quartiles
Coefficient t-test 95% Confidence Interval
Dependent variable School Average Gain
mean = 0.0060615 std. dev. = 0.0571286 min = -0.2791665 max = 1.094335
quartile 2 -0.0083373 -1.31 -0.0208432 0.0041687
(0.0063686)
quartile 3 -0.000019696 -0.31 -0.0144755 -0.0105364
(0.0063686)
quartile 4 0.008271 -1.30 -0.0018573 -0.0195434
(0.0063786)
Constant 0.0107004∗ 2.38 0.0018573 0.0195434
(0.0045033)
n = 643
AdjustedR2 = 0.0043
* indicates significant coefficient at 5%
that schools in the top quartiles produce less gain in the average school score under the new regime.
This result suggests that schools in the lower quartiles work harder to show improvement. This
can be a response simply from greater accountability with standardized test score published. ‘Bad’
schools have to work harder as published test score allows parents and students to infer school
productivity. Tuition tax credit allows parents to switch to private schools and this creates bigger
threat to the schools in lower quartile. So, intensifying competition and accountability measure
work in the same direction to discipline schools. Also, since there is a maximum for individual
student score, schools in the top quartiles are harder to get the same extent of improvement as
schools in the lower quartiles.
10
Figure 1: Weighted school average score
Grade 9 Weighted School Average (Math, academic), wscore ordered
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
1
20
39
58
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1
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Wei
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ted
sch
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l ave
rag
e
overall_wscore
overall_wscore_1
11
To isolate the effect of competition, it will be necessary to disentangle the effect of accountability
from it. This would be possible if we have more years of data after the standardized tests are
implemented and before changing competition sets in. In that case, we can estimate a trend related
to the accountability measure and can be more clear about the magnitude of the competition.
However in Ontario case, standardized test of Grade 9 started in 2000-2001 and tuition tax credit
started to affect the 2001-2002 Grade 9 students. We won’t be able to estimate the trend for the
accountability side and thus any effect we estimate for the school productivity will be from school
response to both the competition and the accountability17.
In Figure 2, the schools are arranged with weighted scores of both year ranked. The weighted
score in 2001-2002 for the middle section exhibits a parallel shift up from the weighted score of
2000-2001. This means that the improved score may come from a relative easy exam in the 2001-
2002 compared to the one in 2000-2001. It is not easy to control for the level of difficulty of the
exam across years as there is no ranking of such level for EQAO test papers. Another interesting
observation of this ordered graph shows that top scores in the 2001-2002 are lower than that in
the 2000-2001. One possible explanation for this drop in top scores is that the best students in the
public system have switched to private schools. The average school scores of top schools with the
adversely affected student body are lower. Another possible explanation involves the target of the
accountability measure imposed on schools. If we assume that teachers in the higher quartiles work
harder than teachers in the lower quartiles without any accountability measure. Tuition tax credit
is likely to induce some better students to leave the public system. To maintain a high score for
‘good’ schools, teachers have to work even harder as the average student quality they face is now
worse than before. This may affect the participation constraint of teachers in ‘good’ schools. If
good teachers exit the market, this would affect the student’s performance in an adverse direction.
Future work can be done to bring this hypothesis into empirical test as we can actually track the
teacher’s characteristics via the teacher’s questionnaire of EQAO.
17This means that the productivity estimate obtained is likely to be overestimated.
12
Figure 2: Weighted school average score, both ranked
Grade 9 Weighted School Average (Math, academic), both ordered
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
1 22 43 64 85 106
127
148
169
190
211
232
253
274
295
316
337
358
379
400
421
442
463
484
505
526
547
568
589
610
631
Wei
gh
ted
sch
oo
l ave
rag
e
overall_wscore
overall_wscore_1
13
In Figure 3, the weighted scores are aggregated up to the school board level. Similar to the
school average graph, this is ranked according to the weighted score in 2000-2001. As shown in
the figure, the school board averages have not improved significantly. Only school districts in the
top quartile show considerably more improvement. They include Toronto DSB, Peel DSB, York
Region DSB, Ottawa-Carleton DSB, Toronto Catholic DSB and Dufferin Peel Catholic DSB and
Windsor-Essex Catholic DSB.
Table 2 summarizes the result of regressing the weighted average board score gain18 with the
quartile dummies19. The regression coefficients show that significant gain is in quartile 4 only.
In Figure 4, the board average scores are ordered in both years. The result conveys similar
idea as in Figure 3 that the board average scores have not changed a lot except in the top quartile.
The board that shows the largest improvement is Toronto District School Board.
The board average score suggests that these school boards may face higher competition20
from the tuition tax credit and public schools have to be more productive. Without controlling
for student characteristics, this may also reflect that these school district boards attract better
students.
Toronto District School Board is a big outlier in the board average comparison. To see whether
schools in Toronto District School Board drive the results in the school level, the weighted school
average gain analysis is repeated and as seen in Figure 5 and in Table 3, schools in the lower
quartiles still shows bigger weighted school average gain. By regressing the board average gain
with quartiles again without Toronto DSB, Table 4 shows that there is still significant gain in
18The weighted average board score gain is calculated using the similar method of the weighted average school
score gain.19There are 59 boards and the ordered boards are divided into quartiles with 15 boards in quartile 1 - 3 and 14
boards in quartile 4.20In Section 5, an empirical strategy to divide school district boards into high- and low- competition area is
discussed.
14
Figure 3: Weighted board average score
Grade 9 Board Weighted Average Score (Math, academic), 2000-2001 VS 2001-2002
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59
Wei
gh
ted
bo
ard
ave
rag
e sc
ore
w_grade_0
w_grade_1
15
Table 2: Linear regression of board average net gain by quartiles
Coefficient t-test 95% Confidence Interval
Dependent variable Board Average Gain
mean = 0.0035293 std. dev. = 0.034833 min = -0.0000729 max = 0.0164214
quartile 2 0.0014123* 2.01 -0.00000294 0.0028216
(0.0007032)
quartile 3 0.0033106* 4.71 0.0019013 0.00472
(0.0005728)
quartile 4 0.007823∗ 10.93 0.0063887 0.092572
(0.0007157)
Constant 0.0004723 0.95 -0.0005242 0.0014689
(0.0004723)
n = 59
AdjustedR2 = 0.6943
* indicates significant coefficient at 5%
16
Figure 4: Weighted board average score, both ranked
Grade 9 Weighted Board Average (Math, academic) 2000-2001 VS 2001-2002, both ranked
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59
Wei
gh
ted
bo
ard
ave
rag
e
w_grade_0
w_grade_1
17
Table 3: Linear regression of school average net gain by quartiles (with schools in Toronto DSB
dropped
Coefficient t-test 95% Confidence Interval
Dependent variable School Average Gain
mean = 0.0069563 std. dev. = 0.0611723 min = -0.2791665 max = 1.094335
quartile 2 -0.097582 -1.33 -0.0241484 0.0046321
(0.0073261)
quartile 3 -.0025263 -0.34 -0.0169165 0.0118639
(0.0001206)
quartile 4 .0096265 -1.32 -0.0239913 -0.0047383
(0.0073132)
constant 0.0124512∗ 2.40 0.0022575 0.0226449
(0.0051896)
n = 560
R2 = 0.005
* indicates significant coefficient at 5%
quartile 4. This shows that the outlier does not affect the general observation of the school
average scores comparison.
4.2 Simple regression results
Student achievement is affected by school characteristics, student characteristics, family charac-
teristics and neighborhood characteristics. Each student has to take a student questionnaire while
taking the Grade 9 tests which could provide us with student characteristics control variables. The
18
Figure 5: Weighted school average score (with schools in Toronto DSB dropped)
Grade 9 Weighted School Average (Math, academic), wscore ordered, Toronto DSB dropped
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
1 20 39 58 77 96 115
134
153
172
191
210
229
248
267
286
305
324
343
362
381
400
419
438
457
476
495
514
533
552
Wei
gh
ted
sch
oo
l ave
rag
e
overall_wscore
overall_wscore_1
19
Table 4: Linear regression of board average net gain by quartiles (with Toronto DSB dropped)
Coefficient t-test 95% Confidence Interval
Dependent variable Board Average Gain
mean = 0.0035293 std. dev. = 0.0034833 min = -0.0000729 max = 0.0164214
quartile 2 0.0014123* 2.47 0.0002638 0.0025607
(0.0005728)
quartile 3 0.0033106* 5.78 0.0021622 0.0044591
(0.0005728)
quartile 4 0.0071979∗ 12.11 0.0060061 0.00838987
(0.0005944)
constant 0.0004723 1.17 -0.003398 0.0012844
(0.000405)
n = 58
AdjustedR2 = 0.7376
* indicates significant coefficient at 5%
20
relevant variables include gender, whether the student is born outside Canada, if yes, the number
of years the student has stayed in Canada, language speak at home, percentage of missing classes,
accessibility of computer and time spent with Math homework. Unfortunately, such important in-
formation from the student’s questionnaire has not yet arrived and it creates extra complications
in interpreting the results. From the current dataset, there are only a few individual student infor-
mation available: gender, the semester structure of the Math class students enrolled in, whether
the student is in ESL (English as a Second Language) or special education program21 and whether
the students take the Grade 9 tests with special provision22 or accommodation23. There are two
school-specific factors available: whether the school is a Catholic school24; whether the school is
in Northern Ontario25.
From the Canadian Census 2001, there are some geographical variations across schools that
can be used as neighborhood control variables26. The local geographical control variables used are
proportion of population with college or above education, total unemployment rate, proportion
of recent immigrants, median income of couple families with children below 18 and proportion
of families with children below 18. Table 6 provides some descriptive statistics of the control
21In Ontario, children who have behavioral or communication disorders, or intellectual, physical or multiple
disabilities, or who are gifted, may require special education services or special education programs in order to
enable them to attend school and to benefit fully from their school experience.22Students in ESL or ELD (English Literacy Development) are allowed to have special provisions when taking the
EQAO tests. These include allowing extra time, periodic supervised breaks, providing individual or group-setting
or verbatim reading of instructions and/or questions.23Accommodations are provided according to the student’s needs and the EQAO policy. These include allowing
extra time, assisting with pacing, providing a quiet work place, providing assessment tasks in a different format
(e.g., Braille text) or allowing the use of various technological resources (e.g., voice-activated computer).24Ontario provides constitutionally guaranteed, publicly funded Roman Catholic school districts. Publicly subsi-
dized Catholic school is a guaranteed right established in the 1867 British North America Act and in 1984 onwards,
the public funding was extended from Grade 10 to the end of high school. There are 167 Catholic schools in
2000-2001 and 172 in 2001-2002.25There are 58 schools in 2000-2001 and 57 in 2001-2002.26More about data matching will be discussed in Section 5.
21
Table 5: Descriptive statistics of student control variables, mean and standard deviation (in paren-
thesis)
2000-2001 2001-2002 change
First 0.451704 0.428281 -0.23423
(0.2708614) (0.02358642) (0.2331676)
Second 0.367253 0.309538 -0.05771
(0.2426823) (0.2275051) (0.2621589)
Full 0.17247 0.114531 -0.05794
(0.373439) (0.2911935) (0.233879)
Unknown 0.008573 0.14765 0.139078
(0.0658149) (0.22828201) (0.237667)
With accommodation and provision 0.002878 0.003658 0.00078
(0.0096285) (0.0114693) (0.0122955)
With accommodation 0.0205 0.019566 -0.00093
(0.0328639) (0.0301964) (0.034434)
With provision 0.011901 0.013173 0.001272
(0.0351267) (0.0395156) (0.0338556)
No accommodation nor provision 0.939263 0.959251 0.019989
(0.1023052) (0.0660689) (0.104211)
Unknown 0.025458 0.004351 -0.02111
(0.0905143) (0.0398811) (0.0983635)
Special education 0.039937 0.023111 -0.01683
(0.0343999) (0.0220891) (0.035037)
ESL 0.017941 0.021617 0.003676
(0.039671) (0.0485696) (0.0339479)
Boy 0.474895 0.485101 0.010206
(0.1071978) (0.1030662) (0.0934132)
Girl 0.47782 0.508126 0.030306
(0.1083542) (0.1026816) (0.1016168)
Unknown 0.428281 0.009718 -0.03757
(0.0989696) (0.053725) (0.1121845)
22
Table 6: Descriptive statistics of neighborhood control variables
Mean Std.Dev. Max. Min.
Proportion of Population with College or above education 0.426044 0.091944 0.606 0.199
Unemployment Rate (age 15 or above) 0.06484 0.018468 0.143 0.026
Recent Immigrant 0.076737 0.079936 0.222 0
Average Earnings 34,108.63 5,166.95 56,070 23,547
Median Income of Couple Families with children below 18 70,961.57 10,308.47 108,456 46,657
Proportion of Families with children below 18 0.746946 0.063056 1.141304 0.557214
variables.
Result 1 summarizes the regression result of weighted school average with student character-
istics, school-specific controls and these neighborhood local controls. The year dummy is not
significant but the point estimate is negative showing that school average score is lower in 2001-
2002. Students in the first semester course have lowered score compared to students in full year
course. Students with special provision during the tests also have lower score. Among the control,
recent immigrant which aims to capture the diversity of the population close to the schools and me-
dian income of couple families are significant. For the median income control, parents with higher
income are proxied as family with higher socio-economic status. This confirms that students with
higher SES have higher score though the point estimate is small. For the recent immigrant control,
it is harder to interpret. It can be interpreted as with more immigrants, the schooling demand is
more diverse which supports more private schools and thus public schools respond by increased
productivity and students have higher score. Or it can be that the new immigrants are stronger in
Math and thus raised public schools math test scores. The interpretation will be clearer once we
can control for more student’s characteristics. The immigration status of the students will allow
us to learn more about the effect of immigrants on score in our sample to distinguish from the
23
two possible stories. Schools in Northern Ontario have lower score than students in Southern part.
This may suggest that students are better in the Southern part or schools in Southern part have
more resources. To interpret this properly, again, we need more control in students and schools
characteristics.
24
Result 1: Weighted school average regression with year dummy
Dependent Variable School average
mean = 0.0045567 std.dev. = 0.0025916
min = 0.0000416 max = 0.0160092
Coefficient Robust Std. Errora t test 95% Confidence Interval
year -0.0003 0.00042 -0.71 -0.00114 0.000544
sem first -0.00157 *b 0.000563 -2.79 -0.0027 -0.00045
sem second 0.000212 0.000543 0.39 -0.00087 0.001298
sem notprovided -0.00045 0.000434 -1.04 -0.00132 0.000417
assess accandprov 0.000521 0.008417 0.06 -0.01633 0.01737
assess acc -0.00177 0.002443 -0.72 -0.00666 0.003125
assess prov -0.00438* 0.001229 -3.56 -0.00683 -0.00192
assess noSIF -0.00568* 0.002177 -2.61 -0.01004 -0.00133
special ed -0.00191 0.002673 -0.71 -0.00726 0.00344
ESL -0.00202 0.003185 -0.64 -0.0084 0.004352
gender boy 0.000138 0.000523 0.26 -0.00091 0.001184
gender unknown 0.004019 0.002327 1.73 -0.00064 0.008676
Catholic 0.000129 0.00041 0.32 -0.00069 0.00095
North -0.00131* 0.000435 -3.01 -0.00218 -0.00044
College or above -0.0028 0.002917 -0.96 -0.00864 0.003038
Unemployment -0.00082 0.00732 -0.11 -0.01547 0.013836
Immigrant 0.012642* 0.004507 2.81 0.003621 0.021663
Median income 1.15E-07* 2.53E-08 4.55 6.45E-08 1.66E-07
Families with kids 1.76E-08 1.14E-08 1.54 -5.31E-09 4.05E-08
Constant -0.00266 0.001444 -1.84 -0.00555 0.000227
R2 = 0.3383 n = 1, 278
aStandard errors are clustered at the school district levelb* indicates significant coefficient at 5%
25
5 Impact from competition
Ontario’s EiETC provides us with an exogenous change in the competition environment of schools.
In section 4, the existing data suggests that there is improvement in student achievement after
tuition tax credit is in effect. But as discussed before, the increase may be from better student’s
characteristics or other favorable factors. If the tax credit were administered randomly to different
localities, then we can compare the performance gain in treatment versus control groups. Such
comparison allows us to measure the performance effect from the increasing competition of private
schools. However, the tax credit is available universally to all students in Ontario, there is no
natural control group. To analyze the impact of competition from tuition tax credit, we need to
use cross-sectional variations in Ontario to divide school districts into high- and low- competition
area.
5.1 Competition index
With EiETC, public schools face greater competition from private schools in the same district27
as the private schooling alternatives become financially more accessible to families. Within a
school district, there will be more demand for alternative schooling options when the population
is more wealthy and more diverse. The more diverse the population, the harder public schools in
the district can satisfy diverse demand from the different groups. Private schools can operate in
different niche markets of schooling. The Canadian census subdivisions (CSDs) data is used to
measure the average characteristics of a school district board. CSD usually corresponds to a city
or a town division of a province. One school district board spans over several CSDs and there are
27This empirical competitiveness measure does not capture competition from private schools of other school
districts. More information on the average distance traveled by Ontario private school students is needed to see
how big this bias would be. But this possibility suggests that the competition measure is a lower bound of the
actual competition public schools face.
26
sometimes more than one school existing in a CSD. We use five variables to measure the affluence
and the diversity of the school district board: average earnings of the population, number of families
with kids below 18, total unemployment rate, percentage of recent immigration28 and percentage
of population with college or above education attainment. Average earnings and unemployment
rate can control for the affluence level of the district, number of families with kids below 18 can
measure the demand of schooling, recent immigration capture the diversity of the district and
education attainment can proxy for the parental education level of the district. A competitive
score for each school district can be calculated29 and we can define 18 school districts to be in
the high-competitive environment. This approach assumes that the geographical characteristics of
the whole school district are exogenous to individual public school performance and thus can be
used as instruments to predict competitiveness. To test the validity of using these cross-sectional
characteristics to instrument for competitiveness of a school district, information of the private
school enrollment share for each district is needed. If we know the private school enrollment
share, then we can use the school district controls to project a predicted private school enrollment
share. By comparing the predicted and actual values, we can learn more about the significance
of each district controls and can get a more convincing rule. Since there is no private school
information available, the present criteria is a bit arbitrary but nevertheless it captures the idea
of using district controls to proxy for competitiveness while local characteristics are controlled in
the regression. The rule is sensitive to the district control chosen and in the present set-up, they
are taken up from the hypothesis of when tuition tax credit is most likely to have great impact.
By changing the characteristics from average earnings to median income of couple families with
children under 18 and number of families with children below 18 to proportion of families with
children below 18, the predicted high-competitive district set is affected. Instead of 18 districts
that is predicted in the first rule, in this tighter measure to families of the students, Toronto DSB
and Toronto Catholic DSB are ruled out. This is partly due to the aggregation of CSD information
28Population who immigrated to Canada between 1991-2001.29The competitive index construction will be discussed more in detail in the Appendix.
27
where Toronto represents all the six cities: Toronto, North York, Scarborough, York, East York
and Etobicoke. Variations across these cities are averaged out in CSD census statistics and this
lowers the predicted competitiveness of Toronto school district. This problem would be overcome
once we extend the mapping information down to enumeration area (EA) which could make the
socio-economic variables closer to the student families in the school. With EA information, we
can extend our competitiveness measure to capture high- and low- competitive CSD within school
districts. The CSD information becomes the exogenous characteristics and the local controls can
use EA information which is a much tighter measure.
High-competitive school districts
Dufferin Peel Catholic District School Board*a Peel District School Board
Durham Catholic DSB Durham DSB
Greater Essex County DSB Windsor-Essex Catholic DSB*
Halton Catholic DSB Halton DSB
Hamilton-Wentworth Catholic DSB Hamilton-Wentworth DSB
Ottawa-Carleton District Catholic School Board Ottawa-Carleton DSB*
Toronto Catholic DSB*b Toronto DSB*b
Waterloo Catholic DSB Waterloo Region DSB
York Catholic DSB York Region DSB*
a* indicates district school boards which shows improvement in the board average chart, Table 3bToronto is not high-competitive district using the second district controls
Geographical characteristics of the whole school district are assumed to be exogenous to indi-
vidual public school performance. However, such characteristics close to where the public schools
locate will affect the public school performance. Thus, we are going to control for geographical
characteristics of the school at the CSD level in the regression.
28
5.2 Econometric framework
With existing data, Equation 1 is used to test whether schools in high-competitive school districts
raise productivity and results in higher score of students. Comparing the gains of the weighted
school average score, Result 3 has shown that the overall year effect is insignificant and the point
estimate suggests a reduction in score. In this section, we would like to focus on those high-
competitive school district and see if schools in these districts will have a higher score which may
come from increasing school productivity30.
ln4school averagei = β0 + β1di + β′24Xi + β′
3Zi + εi (1)
where the dependent variable is the gain of weighted school average, di is the dummy for schools
in the high-competitive school districts and zero otherwise, X denotes the student and school
characteristics which are variable between two years and Z denotes the local geographical controls
at CSD level which is the same for both years. Effectively, we compare the means of the test score
before and after the shock.
For the school characteristics, there are observable characteristics of teachers in a teacher’s
questionnaire that the ninth grade teachers have to fill in. They include the teacher’s experience
in teaching Grade 9, teacher’s comfort with the curriculum and teacher’s teaching practices. Similar
to the situation of student’s questionnaire information, these control variables are not available at
the moment.
EQAO collects data on factors relating to school environment that affect student achievement
in the province of Ontario under its Education Quality Indicators Program, EQUIP. EQUIP has
identified seven themes, 13 indicators of school quality at the secondary school level. These data
are collected to control for the factors affecting student achievement. Indicators like family income
30Section 6 will discuss the relationship between sorting and productivity effect and the problem in getting a
clean estimate of school productivity effect.
29
levels within a school and parental involvement can be a good proxy for socio-economics status
of the students. EQAO has administered two EQUIP studies in for 1999-2000 and 2000-2001.
However, it is still unclear when the EQUIP data can be made available for public use. The
alternative we use is the census information of school districts which is less precise than the school
aggregate information collected by EQUIP. As discussed before, the local controls can be tighter
once we assemble the EA census data.
Result 2 and 3 summarize the results of the regression with existing variables using both
competitive score. The local controls used in the analysis is percentage of population with college
or above education, total unemployment rate, proportion of recent immigrants, median family
income of couple families with children below 18 and proportion of families with children below
18.
Using different competition criteria produce similar results as shown in Result 2 and 3. Under
both rules, the high competitive dummy is insignificant. This suggests that student’s score in
the high- and low- competitive districts are not significantly different. This suggests that tuition
tax credit may not have created significant competitive impact. Since it is a one-year result,
the impact on student achievement may not have reflected. With a tighter competition index
developed later with EA mapping, the competitive dummy may be able to gauge out significant
competitive impact. The discrete school district rule may average out a lot of high-competitive
effect in certain CSDs within the school district. So, this result is likely to be underestimated.
Catholic schools have significant higher weighted gain in the sample. Figure 6 plots the unweighted
gain of the school score against the average number of students in the schools. It shows that most
of the schools with large number of students are having an average gain between 0 to 0.5. This
may explain why the weighted average school score gain is so small.
With tuition tax credit, students in Catholic schools are less likely to be affected compared
to public schools as the religion factor in switching to other religion private schools disappear in
Catholic schools. This suggests that students are less likely to suffer from adverse sorting effect
30
Figure 6: Unweighted school average gain and the average number of students
Grade 9 Unweighted School Average Difference VS School size (Math, academic)
0
50
100
150
200
250
300
350
400
450
-1 -0.5 0 0.5 1 1.5 2 2.5
Unweighted School average gain
Ave
rag
e n
um
ber
of
stu
den
ts
avg_freq
31
with better students switching to private system. To validate this result, we need to track switching
student characteristics in Catholic school to see. Further on sorting and student tracking will be
discussed in Section 6.
32
Result 2: Competitive effect estimation with the average earnings criteria set
Dependent Variable School Average Gain
mean = 0.060615 std.dev. = 0.0571286
min = -0.2791665 max = 1.094335
Coefficient Robust Std. Err.a t test 95% Confidence Interval
High comp. -0.01756 0.012235 -1.44 -0.04205 0.006932
4sem first -0.00826 0.014058 -0.59 -0.0364 0.01988
4sem second -0.00064 0.006216 -0.1 -0.01308 0.011805
4sem notprovided 0.016318 0.015589 1.05 -0.01489 0.047523
4assess accandprov -0.04335 0.102018 -0.42 -0.24756 0.160864
4assess acc -0.01891 0.038263 -0.49 -0.0955 0.057677
4assess prov 0.04279 0.036726 1.17 -0.03073 0.116305
4assess noSIF 0.033413 0.04958 0.67 -0.06583 0.132658
4special ed -0.04374 0.04677 -0.94 -0.13736 0.049876
4ESL -0.03993 0.060593 -0.66 -0.16121 0.081364
4gender boy 0.028087 0.028589 0.98 -0.02914 0.085315
4gender unknown 0.027084 0.025163 1.08 -0.02328 0.077453
Catholic 0.020649*b 0.008762 2.36 0.003111 0.038188
North -0.03127 0.022298 -1.4 -0.07591 0.01336
College or above -0.09778 0.097407 -1 -0.29276 0.097199
Unemployment 0.746284 0.48899 1.53 -0.23254 1.725104
Immigrant 0.064268 0.100203 0.64 -0.13631 0.264847
Median income 1.28E-06 1.08E-06 1.19 -8.74E-07 3.44E-06
Families with kids -0.01429 0.040868 -0.35 -0.0961 0.067514
Constant -0.081 0.062734 -1.29 -0.20658 0.044575
R2 = 0.0842 n = 643
aStandard errors are clustered at the school district levelb* indicates significant coefficient at 5%
33
Result 3: Competitive effect estimation with the median income criteria set
Dependent Variable School Average Gain
mean = 0.060615 std.dev. = 0.0571286
min = -0.2791665 max = 1.094335
Coefficient Robust Std. Err.a t test 95% Confidence Interval
High comp. -0.01836 0.013939 -1.32 -0.0462592 0.0095433
4sem first -0.00782 0.013616 -0.57 -0.0350766 0.0194334
4sem second -0.00201 0.006623 -0.3 -.0152709 0.0112435
4sem notprovided 0.014318 0.014279 1 -.0142645 0.0429001
4assess accandprov -0.06206 0.10796 -0.57 -0.278166 0.1540452
4assess acc -0.02271 0.038994 -0.58 -0.1007676 0.0553437
4assess prov 0.040127 0.036629 1.1 -0.0331929 0.1134472
4assess noSIF 0.033281 0.049345 0.67 -0.0654938 0.1320556
4special ed -0.03939 0.044272 -0.89 -0.1280149 0.0492262
4ESL -0.04267 0.062669 -0.68 -0.1681166 0.0827744
4gender boy 0.027395 0.028183 0.97 -0.0290192 0.0838086
4gender unknown 0.026543 0.024694 1.07 -0.0228879 0.0759731
Catholic 0.020873*b 0.008867 2.35 0.0031251 0.0386216
North -0.03276 0.023519 -1.39 -0.0798344 0.0143204
College or above -0.12219 0.115864 -1.05 -0.3541151 0.1097379
Unemployment 0.743224 0.486978 1.53 -0.2315684 1.718017
Immigrant 0.025128 0.086654 0.29 -.1483287 0.1985855
Median income 1.63E-06 1.36E-06 1.2 -1.10e-06 4.36e-06
Families with kids -0.00838 0.039478 -0.21 -0.0874007 0.0706453
Constant -0.09935 0.077003 -1.29 -0.2534933 0.0547834
R2 = 0.0872 n = 643
aStandard errors are clustered at the school district levelb* indicates significant coefficient at 5%
34
Since we are looking at the weighted school average difference, if we assume that this differenc-
ing takes away most of the fixed school characteristics, Result 4 summarizes the regression with
competition dummy and neighborhood characteristics. The competitive dummy is still insignifi-
cant. The neighborhood controls are not significant too and this may suggest the current mapping
to CSD level is not precise enough.
Result 4: Competitive effect estimation with local neighborhood controls
Dependent Variable School Average Gain
mean = 0.060615 std.dev. = 0.0571286
min = -0.2791665 max = 1.094335
Coefficient Robust Std. Err.a t test 95% Confidence Interval
High comp. -0.01286 0.009636 -1.33 -0.0321517 .0064253
College or above -0.05197 0.073333 -0.71 -0.1987589 0.094823
Unemployment 0.358751 0.279261 1.28 -0.2002507 0.9177522
Immigrant 0.065037 0.092907 0.7 -0.1209373 0.251011
Median income 8.78E-07 8.41E-07 1.04 -8.05e-07 2.56e-06
Families with kids 0.01603 0.034939 0.46 -0.0539072 0.085967
Constant -0.06762 0.051944 -1.3 -0.1715959 0.0363602
R2 = 0.0192 n = 643
aStandard errors are clustered at the school district level
6 Sorting vs Productivity
To pin down the productivity effect from public schools in response to greater competition as
predicted by the school choice advocates, we need to single out the impact from sorting of students
with tuition tax credit. Assume that with tuition tax credit, students in the top-end31 switch to
31This is often known as ‘cream-skimming’ effect with the best students leaving the public system.
35
the private system and the student body in public school is adversely affected. So even if public
schools respond with higher productivity, the overall student score may still be lower than before
because of the worse peer effect after tuition tax credit is introduced. Sorting can also go in the
opposite direction in which students from the bottom-end, with tuition tax credit, move to private
schools that specialize in helping them. So, we need to understand which direction predominates
in Ontario experiment before signing the direction of school productivity induced by tuition tax
credit.
To control for the Grade 9 sorting effect is not easy as students move from Grade 8 in the
elementary schools to Grade 9 in the secondary schools. This means students have pre-sorted
when they come to Grade 9 public schools. So, the only way is to compare the average student
characteristics to see if the student body has been affected by the tuition tax credit. Comparing
the student characteristics in this way, however, only controls for the observable characteristics of
the students. The unobservable characteristics of students due to sorting are remained unknown
and thus stays in the our ‘residual’ estimate.
With the dataset, to track the same student between the two years, the only group is students
in Grade 9 in 2000 - 2001 and Grade 10 in 2001 - 2002 as they took both the Grade 9 math test
and the Grade 10 OSSLT. We can follow which students moved to private school in 2001-2002 as
all students32 in Grade 10 has to take the OSSLT. Students who move to private schools in Grade
10 will still show up in the dataset. By analyzing the characteristics of students who switch, we
can get a clearer direction of sorting within public school. The link could be done only if EQAO
provides the suitable student identifier across years and the request is still under review at the
moment. Ideally, if students take comparable tests across years, then this group will allow us to
compare the same student achievement before and after the competition shock. Since they are
taking different tests that are not comparable, we can only infer the sorting pattern from this
32All students working toward an Ontario Secondary School Diploma (OSSD) are expected to participate in the
OSSLT.
36
group. Even if this link could be done, it can only be suggestive as there is no reason to believe
that the sorting pattern across grades is the same. With our current dataset, there is not much we
could infer for the sorting impact in Ontario and this hinders our ability to isolate the productive
effect of public schools when facing greater competition.
After we get a more precise estimate of the competition estimate, we can use the sorting pattern
of the Grade 10 batch to see whether productivity of public schools increases or not. Assuming the
sorting direction is that with tuition tax credit, the ‘better’ students are more likely to move to
private schools and the ‘left behind’ in public school suffers from worse peer effect than before. If
α1 is positive and significant, we can argue that the improved test score must come from positive
productivity response from public school.
If test score decreases after EiETC, then it gets more complicated in interpretation as the
decreased test score can come from worse peer effect and/or increased/decreased33 of public school
productivity. The worsened score, nevertheless, casts doubt on the pro-competition literature and
its relevant policy implication. If EiETC cannot improve the test score of public school students,
then the tax revenue loss from the tuition tax credit simply goes as a subsidy to the ‘privileged’
families34 who have children in private schools. Even if students switching to private schools do
benefit, the benefit gained by such group is not likely to compensate the cost of lowered score in
the public school and the loss tax revenue as the target efficiency35 is unlikely to be a big number
33McMillan (2003) shows that in well-defined circumstances, rent-seeking public schools find it optimal to reduce
productivity when a voucher is introduced. So, there is a possibility that public schools will exert lower effort in a
more competitive environment with EiETC.34In Ontario, about 37% of all children attending private schools come from households with incomes of $100,000
or more, the high income group. 42 % comes from the middle-income group with income from $50,000 to $100,000.
Only 21% comes from the low-income group. These figures are taken from Daily Statistics of Statistics Canada on
July 4, 2002.35Target efficiency is a criterion used to judge the efficiency of public spending programs. In tuition tax credit,
target efficiency measures the proportion of the total cost that moved children from public schools to private schools.
37
as predicted by other similar programs36.
The logic of increasing school choice is to increase the threat that public schools will lose student
if they do not perform well. Student enrollment is tied-in to funding received by each school. So,
it is important to see how big this threat is for public schools as public schools can simply choose
not to respond if the threat created is not big enough. EQAO collects school aggregate enrollment
level by grade and by looking at the changes in between years, we should be able to see if there
is a significant decline of public school enrollment that would force public schools to improve.
While we are still waiting for this variable, some inferences can be made through board aggregate
enrollment level of Grade 9 provided by the Ministry of Education.
Table 7 shows the Grade 9 enrollment figures of the two years. The drop in the enrollment
number is not very drastic. The biggest decrease is 252 in the Waterloo Region District Board but
Toronto District School Board has an increase of 2,860. These figures suggest that after tuition tax
credit is in effect, public school student enrollment of Grade 9 has not been adversely affected. So,
public schools may not have faced big threat to increase school productivity in terms of student
enrollment. However, Table 8 shows the changes within public schools in these two years. The
drop is more significant and for two of our high-competitive school district boards, Peel District
School Board and Toronto District School Board, the drop is 1,079 and 1,108 respectively while the
biggest increase is just 389 in York Region District School. For the area with higher competition,
there are more alternative for public school students and with tuition tax credit, more students
can switch to private schools. This may explain part of the switch for these two high competitive
district school boards. Factors other than public school performance, e.g. out-of-province move
are important in affecting such movement which we should look into to isolate the effect related to
public school performance. Also, if this move is concentrated in a few schools of the school district
board, then the sorting effect will be very significant for those schools. To proceed, we have to
36Belfield (2001) summarizes the target efficiencies for other tuition programs and the ratio ranges from 5% to
15%. This means that the tax credit appears to primarily benefit those already enrolled in private schools.
38
Table 7: Board Enrollment, Grade 9 comparison
School Board G9(2000-2001) G9(2001-2002) DifferenceAlgoma District School Board 1332 1163 -169Algonquin and Lakeshore Catholic District School Board 1016 953 -63Avon Maitland District School Board 1704 1610 -94Bluewater District School Board 1884 1732 -152Brant Haldimand Norfolk Catholic District School Board 731 842 111Bruce-Grey Catholic District School Board 301 279 -22Catholic District School Board of Eastern Ontario 918 951 33District School Board of Niagara 3445 3476 31District School Board Ontario North East 1009 982 -27Dufferin-Peel Catholic District School Board 5682 6043 361Durham Catholic District School Board 1805 1863 58Durham District School Board 4977 4851 -126Grand Erie District School Board 2753 2567 -186Greater Essex County District School Board 2764 2731 -33Halton Catholic District School Board 1480 1610 130Halton District School Board 3338 3302 -36Hamilton-Wentworth Catholic District School Board 2145 2152 7Hamilton-Wentworth District School Board 4072 4099 27Hastings & Prince Edward District School Board 1525 1600 75Huron Perth Catholic District School Board 310 342 32Huron-Superior Catholic District School Board 474 446 -28James Bay Lowlands Secondary School Board 68 40 -28Kawartha Pine Ridge District School Board 3102 3026 -76Keewatin-Patricia District School Board 754 744 -10Kenora Catholic District School Board 91 73 -18Lakehead District School Board 1191 1088 -103Lambton Kent District School Board 2516 2445 -71Limestone District School Board 1806 1695 -111Near North District School Board 1103 1103 0Niagara Catholic District School Board 1933 1937 4Nipissing-Parry Sound Catholic District School Board 293 303 10Northeastern Catholic District School Board 106 95 -11Ottawa-Carleton Catholic District School Board 2719 2892 173Ottawa-Carleton District School Board 6438 6224 -214Peel District School Board 10460 10416 -44Peterborough Victoria Northumberland and Clarington Catholic District School 1011 1129 118Rainbow District School Board 1375 1381 6Rainy River District School Board 369 349 -20Renfrew County Catholic District School Board 267 298 31Renfrew County District School Board 1062 1049 -13Simcoe County District School Board 3880 3984 104Simcoe Muskoka Catholic District School Board 1616 1563 -53St Clair Catholic District School Board 901 820 -81Sudbury Catholic District School Board 494 523 29Superior-Greenstone District School Board 418 438 20Thames Valley District School Board 6237 5991 -246Thunder Bay Catholic District School Board 519 558 39Toronto Catholic District School Board 6676 6724 48Toronto District School Board 22153 25013 2860Trillium Lakelands District School Board 1693 1614 -79Upper Canada District School Board 3128 3021 -107Upper Grand District School Board 2754 2797 43Waterloo Catholic District School Board 1521 1575 54Waterloo Region District School Board 4662 4410 -252Wellington Catholic District School Board 555 536 -19Windsor-Essex Catholic District School Board 2176 2284 108York Catholic District School Board 3055 3254 199York Region District School Board 6892 7294 402
39
Table 8: Board enrollment, Grade 9 to Grade 10 comparison
School Board G9(2000-2001) G10(2001-2002) DifferenceAlgoma District School Board 1332 1296 -36Algonquin and Lakeshore Catholic District School Board 1016 949 -67Avon Maitland District School Board 1704 1693 -11Bluewater District School Board 1884 1926 42Brant Haldimand Norfolk Catholic District School Board 731 732 1Bruce-Grey Catholic District School Board 301 298 -3Catholic District School Board of Eastern Ontario 918 899 -19District School Board of Niagara 3445 3525 80District School Board Ontario North East 1009 991 -18Dufferin-Peel Catholic District School Board 5682 5888 206Durham Catholic District School Board 1805 1774 -31Durham District School Board 4977 5181 204Grand Erie District School Board 2753 2753 0Greater Essex County District School Board 2764 2798 34Halton Catholic District School Board 1480 1497 17Halton District School Board 3338 3506 168Hamilton-Wentworth Catholic District School Board 2145 2142 -3Hamilton-Wentworth District School Board 4072 4134 62Hastings & Prince Edward District School Board 1525 1561 36Huron Perth Catholic District School Board 310 330 20Huron-Superior Catholic District School Board 474 478 4James Bay Lowlands Secondary School Board 68 56 -12Kawartha Pine Ridge District School Board 3102 3205 103Keewatin-Patricia District School Board 754 712 -42Kenora Catholic District School Board 91 72 -19Lakehead District School Board 1191 1204 13Lambton Kent District School Board 2516 2567 51Limestone District School Board 1806 1854 48Near North District School Board 1103 1142 39Niagara Catholic District School Board 1933 1883 -50Nipissing-Parry Sound Catholic District School Board 293 285 -8Northeastern Catholic District School Board 106 106 0Ottawa-Carleton Catholic District School Board 2719 2745 26Ottawa-Carleton District School Board 6438 6418 -20Peel District School Board 10460 9381 -1079Peterborough Victoria Northumberland and Clarington Catholic District School 1011 1017 6Rainbow District School Board 1375 1377 2Rainy River District School Board 369 381 12Renfrew County Catholic District School Board 267 267 0Renfrew County District School Board 1062 1043 -19Simcoe County District School Board 3880 4068 188Simcoe Muskoka Catholic District School Board 1616 1565 -51St Clair Catholic District School Board 901 874 -27Sudbury Catholic District School Board 494 517 23Superior-Greenstone District School Board 418 333 -85Thames Valley District School Board 6237 6104 -133Thunder Bay Catholic District School Board 519 515 -4Toronto Catholic District School Board 6676 6655 -21Toronto District School Board 22153 21045 -1108Trillium Lakelands District School Board 1693 1717 24Upper Canada District School Board 3128 3234 106Upper Grand District School Board 2754 3096 342Waterloo Catholic District School Board 1521 1515 -6Waterloo Region District School Board 4662 4732 70Wellington Catholic District School Board 555 560 5Windsor-Essex Catholic District School Board 2176 2257 81York Catholic District School Board 3055 3179 124York Region District School Board 6892 7281 389
40
study the school enrollment figures and see if the move is concentrated or just thinly spread across
different schools. The board aggregate enrollment suggests that the number of students in public
school may not have been adversely affected for Grade 9 but the Grade 9 to Grade 10 move suggests
that sorting impact from switching students may be significant for public school achievement.
7 Future Work
Apart from refining the statistics with data of the student and teacher’s characteristics as discussed
in previous sections. Information on the private schools are important to the understanding of the
competition of public schools in Ontario. Statistics Canada collects information of enrollment and
tuition fees of private schools in Canada in the Survey of Financial Statistics of Private Schools.
This survey is conducted every three year and the last survey was done in 1998-1999. The current
survey undergoing is for year 2002-2003. This information is not so timely for our analysis as we
need information of the changes in number, tuition fee and enrollment of private schools before
and after 2001. This means that we have to assemble the private school information from other
sources which is a big project of its own. Nevertheless, this information would be very useful for
the analysis as it gives us a more complete picture on the area of more competitive area. This could
improve our present use of cross-sectional variations in defining treatment group of the empirical
model. By knowing the changes in the private school entry, enrollment and tuition, we can see
whether tuition tax credit can induce greater competition on public schools or result in rent-seeking
behavior of private schools.
With more extensive controls for the neighborhood characteristics, the competitive index can
be extended to a continuous measure instead of a discrete rule. The continuous measure can be
closer to the different levels of competition existing in different CSD of the same school districts.
41
8 Conclusion
Ontario Equity in Education Tuition Tax Credit provides an opportunity to test whether increas-
ing school choice can improve public school quality. By looking at individual student scores of
EQAO Grade 9 tests, the existing data suggests no significant improvement in 2001-2002. To mea-
sure the effect of competition, we need to control for student, teacher, family and neighborhood
characteristics. As tax credit universally applies to all districts in Ontario, there is no natural
control group in Ontario data. Section 5 discusses the empirical approach to define high- and low-
competitive school districts. Using the average geographical characteristics of the school districts,
a competitive score is created for each school district. Taken such school district average charac-
teristics are exogenous to individual public school performance, this competitive score can avoid
the endogeneity problem but the current measure may not be precise enough which results in un-
derestimation of the competitive effect. Geographical characteristics of the local area surrounding
the public schools are controlled in the regression as they are correlated with local public school
performance. With our empirical estimation, the competition effect for schools in high-competitive
school district is not significant. This may suggest that public schools have not responded with
increased choice. However, with data limitation, there are other important factors contributing to
student achievement not controlled, e.g. student and teacher’s characteristics. So, we still cannot
be certain about the competitive estimate of the Ontario’s EiETC program. Using the outlined
strategy, a more precise estimate can be found when more data become available. As discussed in
Section 6, we may not be able to isolate sorting effect from productive effect in the Ontario data.
But by studying the sorting direction of the Grade 9 students in 2000-2001, more can be learnt
from the competitive estimate on the productive response of public schools and we should be able
to at least sign the direction of productivity change of public schools under a more competitive
environment.
42
References
[1] Joseph G. Altonji, Todd E. Elder, and Christopher R. Taber. Selection on Observed and
Unobserved Variables: Assessing the Effectiveness of Catholic Schools. NBER Working Paper
No. 7831, August 2000.
[2] Clive R. Belfield. Tuition Tax Credits: What do we know so far? Occasional Paper No.33.
National Center for the Study of Privatization in Education, 2001.
[3] John E. Chubb and Terry M. Moe. Politics, Markets and America’s Schools. The Brookings
Institution, 1990.
[4] William N. Evans, Wallace E. Oates, and Robert M. Schwab. Measuring Peer Group Effects:
A Study of Teenage Behavior. Journal of Political Economy, 100(5):966 – 991, 1992.
[5] Eric A. Hanushek. The Economics of Schooling. Journal of Economic Literature, 24:1141–
1177, 1986.
[6] George M. Holmes, Jeff DeSimone, and Nicholas G. Rupp. Does School Choice Increase School
Quality. NBER Working Paper No. 9683, May 2003.
[7] Caroline Hoxby. School Choice and School Productivity. NBER Working Paper No. 8873,
April 2002.
[8] Caroline M. Hoxby. Do Private Schools Provide Competition for Public Schools? NBER
Working Paper No. 4978, December 1994.
[9] Caroline M. Hoxby. Does Competition Among Public Schools Benefit Students and Taxpayers?
American Economic Review, 90(5):1209 – 1238, December 2000.
[10] Chang-Tai Hsieh and Miguel Urquiola. When schools compete, how do they compete? An
assessment of Chile’s nationwide school voucher program. NBER Working Paper No. 10008,
October 2003.
43
[11] Christopher Jepsen. The role of aggregation in estimating the effects of private school com-
petition on student achievement. Journal of Urban Economics, 52:477–500, November 2002.
[12] Robert McMillan. The Identification of Competitive Effects Using Cross-Sectional Data: An
Empirical Analysis of Public School Performance. Working Paper, January 2002.
[13] Robert McMillan. Competition, Incentives, and Public School Productivity. forthcoming in
Journal of Public Economics, January 2003.
[14] Jesse M. Rothstein. Good Principals or Good Peers? Parental Valuation of School Charac-
teristics, Tiebout Equilibrium, and the Incentive Effects of Competition among Jurisdictions.
PhD thesis, University of California, Berkeley, November 2002.
[15] Bruce W. Wilkinson. Educational Choice: Necessary But Not Sufficient, volume 3 of Educa-
tion. The Institute for Research on Public Policy, 1994.
Appendix
Competitive measure of district school board
Section 5 outlines the empirical strategy using the school district geographical variation to measure
competitiveness of school districts. Five variables are chosen to measure the competitiveness of
a district: average earnings, population with college or above education, proportion of recent
immigrants, number of families with children below 18 and unemployment rate. An alternative
control sets are used too to illustrate the sensitivity problem related to the competition index.
Without a first stage projection to a competitive proxy variable, it is not easy to derive the
appropriate instrument sets. The present choice is guided by economic hypothesis and needed to
be refined later when compared with private school entry, enrollment and tuition data. Also, it
will be useful to extend the competitive index to a continuous set instead of discrete level as in
44
the current model. From the Canadian Census 2001, CSD information for 20% sample can be
obtained. After getting statistics at the CSD level, average statistics are obtained at the school
district board level. An overall average across the school boards for each variable is calculated.
Dummy variable is then generated for each variable. The dummy equals one if the board average
statistics are higher than the overall average for the first four variables and smaller than the overall
average for unemployment rate. Adding up the dummies provides us a score how competitive the
district is. A higher score reflects that the district is more likely to accommodate alternative
schooling. School districts are assigned to be high-competitive districts when the score is larger
than or equal to 3. Table 9 summarizes the dummies and score of each school district board. The
highlighted ones are the 18 high-competitive school district according to this rule. Census provides
us with a tighter income measure to the student’s family, median income of couple families with
children below 18. So, this allows us to proxy for the wealth level of student’s family. To abstract
from number effect of population which may bias those big school districts, instead of using just
the number of families with children under 18, proportion of families with children under 18 is
used. This measure is calculated by dividing the number measure by number of total families.
Table 10 summarizes the dummies and score of each school district board. Using this measure,
Toronto DSB and Toronto Catholic DSB fall out of the high-competitive area.
45
Table 9: School District Board Competitive Score using the average earnings criteria set
Boardname education unemployment immigrant earnings family scoreAlgoma DSB 0 0 0 0 0 0Algonquin and Lakeshore Catholic DSB 1 1 0 0 0 2Avon Maitland DSB 0 1 0 0 0 1Bluewater DSB 0 1 0 0 0 1Brant Haldimand Norfolk Catholic DSB 0 1 0 0 0 1Bruce-Grey Catholic DSB 0 1 0 0 0 1Catholic DSB of Eastern Ontario 0 1 0 0 0 1DSB of Niagara 0 0 0 0 0 0DSB Ontario North East 0 1 0 0 0 1Dufferin Peel Catholic DSB 1 1 1 1 1 5Durham Catholic DSB 1 1 0 1 0 3Durham DSB 1 1 0 1 0 3Grand Erie DSB 0 1 0 0 0 1Greater Essex County DSB 0 1 1 1 0 3Halton Catholic DSB 1 1 1 1 0 4Halton DSB 1 1 1 1 0 4Hamilton-Wentworth Catholic DSB 1 1 1 1 1 5Hamilton-Wentworth DSB 1 1 1 1 1 5Hastings and Prince Edward DSB 0 0 0 0 0 0Huron-Perth Catholic DSB 0 1 0 0 0 1Huron-Superior Catholic DSB 0 0 0 0 0 0James Bay Lowlands SSB 1 0 0 0 0 1Kawartha Pine Ridge DSB 0 0 0 0 0 0Keewatin-Patricia DSB 0 0 0 1 0 1Kenora Catholic DSB 0 0 0 1 0 1Lakehead DSB 0 0 0 0 0 0Lambton Kent District School Board 0 0 0 0 0 0Limestone DSB 1 1 0 0 0 2London District Catholic School Board 0 1 0 0 0 1Near North DSB 0 0 0 0 0 0Niagara Catholic DSB 0 1 0 0 0 1Nipissing-Parry Sound Catholic DSB 0 0 0 0 0 0Northeastern Catholic DSB 0 0 0 0 0 0Ottawa-Carleton District Catholic School Board 1 1 1 1 1 5Ottawa-Carleton DSB 1 1 1 1 1 5Peel District School Board 1 1 1 1 1 5Peterborough Victoria Northumberland and Clar 0 0 0 0 0 0Rainbow District School Board 0 0 0 0 0 0Rainy River DSB 0 0 0 0 0 0Renfrew County Catholic DSB 0 0 0 0 0 0Renfrew County DSB 0 0 0 0 0 0Simcoe County DSB 0 1 0 0 0 1Simcoe Muskoka Catholic DSB 0 1 0 0 0 1St. Clair Catholic District School Board 0 1 0 0 0 1Sudbury Catholic DSB 0 0 0 0 0 0Superior-Greenstone DSB 0 0 0 0 0 0Thames Valley District School Board 0 1 0 0 0 1Thunder Bay Catholic DSB 0 0 0 0 0 0Toronto Catholic District School Board 1 0 1 1 1 4Toronto DSB 1 0 1 1 1 4Trillium Lakelands DSB 0 1 0 0 0 1Upper Canada DSB 0 1 0 0 0 1Upper Grand DSB 0 1 0 1 0 2Waterloo Catholic DSB 1 1 1 1 0 4Waterloo Region DSB 1 1 1 1 0 4Wellington Catholic DSB 0 1 0 1 0 2Windsor-Essex Catholic DSB 0 1 1 1 0 3York Catholic DSB 1 1 1 1 0 4York Region DSB 1 1 1 1 0 4
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Table 10: School District Board Competitive Score using the median income criteria set
Boardname education unemployment immigrant income family scoreAlgoma DSB 0 0 0 0 1 1Algonquin and Lakeshore Catholic DSB 1 1 0 0 0 2Avon Maitland DSB 0 1 0 0 0 1Bluewater DSB 0 1 0 0 0 1Brant Haldimand Norfolk Catholic DSB 0 1 0 0 0 1Bruce-Grey Catholic DSB 0 1 0 0 0 1Catholic DSB of Eastern Ontario 0 1 0 0 1 2DSB of Niagara 0 1 0 0 1 2DSB Ontario North East 0 0 0 1 1 2Dufferin Peel Catholic DSB 1 1 1 1 0 4Durham Catholic DSB 1 1 0 1 0 3Durham DSB 1 1 0 1 0 3Grand Erie DSB 0 1 0 0 0 1Greater Essex County DSB 0 1 1 1 1 4Halton Catholic DSB 1 1 1 1 0 4Halton DSB 1 1 1 1 0 4Hamilton-Wentworth Catholic DSB 1 1 1 0 0 3Hamilton-Wentworth DSB 1 1 1 0 0 3Hastings and Prince Edward DSB 0 0 0 0 1 1Huron-Perth Catholic DSB 0 1 0 0 0 1Huron-Superior Catholic DSB 0 0 0 0 1 1James Bay Lowlands SSB 1 0 0 0 1 2Kawartha Pine Ridge DSB 0 1 0 0 0 1Keewatin-Patricia DSB 0 0 0 1 0 1Kenora Catholic DSB 0 0 0 1 0 1Lakehead DSB 0 0 0 0 0 0Lambton Kent District School Board 0 0 0 1 0 1Limestone DSB 1 1 0 0 0 2London District Catholic School Board 0 1 0 0 0 1Near North DSB 0 0 0 0 1 1Niagara Catholic DSB 0 1 0 0 0 1Nipissing-Parry Sound Catholic DSB 0 0 0 0 1 1Northeastern Catholic DSB 0 0 0 0 1 1Ottawa-Carleton District Catholic School Board 1 1 1 1 0 4Ottawa-Carleton DSB 1 1 1 1 0 4Peel District School Board 1 1 1 1 0 4Peterborough Victoria Northumberland and Clar 0 1 0 0 0 1Rainbow District School Board 0 0 0 0 1 1Rainy River DSB 0 0 0 1 1 2Renfrew County Catholic DSB 0 0 0 0 1 1Renfrew County DSB 0 0 0 0 1 1Simcoe County DSB 0 1 0 0 0 1Simcoe Muskoka Catholic DSB 0 1 0 0 0 1St. Clair Catholic District School Board 0 1 0 1 0 2Sudbury Catholic DSB 0 0 0 0 1 1Superior-Greenstone DSB 0 0 0 0 0 0Thames Valley District School Board 0 1 0 0 0 1Thunder Bay Catholic DSB 0 0 0 0 0 0Toronto Catholic District School Board 1 0 1 0 0 2Toronto DSB 1 0 1 0 0 2Trillium Lakelands DSB 0 1 0 0 0 1Upper Canada DSB 0 1 0 0 1 2Upper Grand DSB 0 1 0 1 0 2Waterloo Catholic DSB 1 1 1 1 0 4Waterloo Region DSB 1 1 1 1 0 4Wellington Catholic DSB 0 1 0 1 0 2Windsor-Essex Catholic DSB 0 1 1 1 1 4York Catholic DSB 1 1 1 1 0 4York Region DSB 1 1 1 1 0 4
47