TEACHERS COLLEGE, COLUMBIA UNIVERSITY
TEACHER SELECTION AND EFFECTIVENESS IN TURKEY
M. ALPER DINCER
6/8/2011
[In Turkey, a teacher hiring system based on subjective evaluation was replaced with a teacher
selection model which operates through centralized testing. This study evaluates the impact of
this new teacher selection policy on mathematics and science test scores of 8th graders. The
findings show that a 0.17 standard deviation increase in test scores can be attributed to the policy
change and the estimated impact is much higher for below median achievers and students with
female teachers. The findings also suggest that the new policy assigns more teachers to relatively
poor schools and classrooms.]
1. Introduction: test scores
The primary and secondary education systems in Turkey have been undergoing a
restructuring since late 1990s in response to swift developments in the formation of its
economy and demographics of its young population. One of the main goals of this
restructuring is to increase the quality of education in Turkey (Aksit, 2007). Thus it is
important to investigate empirically whether these reform efforts achieve the intended
outcomes or not.
Trends in International Mathematics and Science Study (TIMSS) and Program for
International Student Assessment (PISA) periodically measure student achievement on an
international scale and assemble information about students, families and schools. With
the help of these projects it is possible to track student achievement in participating
countries and make cross-country comparisons.
A representative set of the student body in 8th grade which is the final grade of mandatory
schooling in Turkey participated in TIMSS 1999 and 2007. The average mathematics and
science scores of students in Turkey in 1999 were 429 and 433 whereas the international
average scores were 487 and 488, respectively. Similarly the average mathematics and
science scores of students in Turkey in 2007 were 433 and 454 whereas the international
average scores were 488 and 500, respectively: The students in Turkey performed lower
than the average international student achievement. On the other hand Table 1 gives the
percentages of students in Turkey reaching the TIMSS international benchmarks and
these figures present the another pattern in mathematics and science: There are more
students in advanced and high international benchmark levels and there are fewer
students in low international benchmark levels in 2007 relative to 19991.
As a cautionary note, it should be stated that these percentages are not directly
comparable for Turkey between 1999 and 2007 (Martin, et al., 2008a, 2008b).
Table 1: The percentages of students reaching the TIMSS international benchmarks
Advanced
High
Intermediate
Low
1999
1
6
20
38
2007
5
10
18
26
1999
1
5
19
37
Science
2007
3
13
24
31
Source: (Martin et al., 2001a), (Martin et al., 2001b), (Martin, et al., 2008a), (Martin, et
al., 2008b)
Mathematics
In order to render the TIMSS data more comparable I kept the countries for which data is
available in TIMSS 1999 and 20072 and standardized mathematics and science test scores
for this set of countries with mean 0 and standard deviation 1. This analysis suggest that
average mathematics achievement in Turkey increased by 0.17 standard deviation and
average science achievement increased by 0.21 standard deviation from 1999 to 2007.
Figure 1 presents kernel density estimations of standardized mathematics and science
scores in Turkey and all of the countries. These estimations suggest two important points:
First, compared to 2007, the density of students who perform below cross-country
average were much higher in 1999. Second, the density of students who perform above
cross-country average were much lower in 1999.
1
For the description of these benchmark proficiency levels please see (Martin, Mullis, Foy, & Olson, 2008b) and
(Martin, Mullis, Foy, & Olson, 2008a).
2
Bulgaria, Taiwan, Cyprus, Czech Republic, Hong Kong, Hungary, Iran, Israel, Italy, Japan, Jordan, Korea,
Malaysia, Romania, Russia, Singapore, Slovenia, Thailand, Tunisia, Turkey and United States. The sample size is
195242 and represents a student population of 25 millions.
Figure 1: Kernel density estimations of TIMSS 1999 and 2007 for Turkey and a selected set of countries
PISA offers more definitive information about the trend of learning outcomes of students
in Turkey. Similar to TIMSS, PISA measures the reading, mathematics and science test
scores of a student body which is representative for the 15-year old student population in
each participating country. Turkey participated PISA in 2003, 2006 and 2009 and the
trend in mathematics score is comparable between 2003 and 2009 and the trend in
science test score is comparable between 2006 and 2009 (OECD, 2010).
According to PISA results average mathematics score of 15-year old students in Turkey
increased by 22 points (more than 0.2 standard deviation) and average science score of
15-year old students in Turkey increased by 30 points (approximately 0.3 standard
deviation) (OECD, 2010).
PISA data also shows that in which segment of the student body these improvements
occurred. The percentage of students that falls below the proficiency level 2 decreased
from 52 to 42 percentages in mathematics and from 47 to 30 in science. On the other
hand the percentages of top performers did not show any increase or decrease between
the respective periods (Figure 2).
Trend of the average student achievement in mathematics and science in Turkey highlight
at least three important facts. First, for a period which follows 1999, average student
achievement in mathematics and science is increasing for the student population which is
either in grade 8 or 15 years old. Second, this increase in average student achievement is
not homogenous. Indeed it is more intensive on the lower part of the student achievement
distribution in these subjects. Third, these improvements are not due to inflation in test
score scales; average performance of students in Turkey is converging to
Source: (OECD, 2010)
-20
+
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
+
o
o
o
o
o
o
o
o
o
o
o
o
+
o
o
-
20
15
10
5
0
-5
-10
Percentage of students below proficiency Level 2
27
-25
35
30
25
o
2009
Indonesia o
Tunisia -
Brazil -
Thailand o
Mexico -
Uruguay o
Turkey -
Serbia o
Greece -
Russian Federation o
Italy -
Luxembourg +
Spain o
Portugal -
United States o
Latvia o
France +
Czech Republic +
Hungary o
Sweden +
Slovak Republic o
Ireland +
2009
Poland o
Belgium +
Germany o
Norway o
Denmark o
Iceland +
Australia o
-15
Switzerland o
-10
New Zealand
-5
Japan o
0
Netherlands o
5
Canada o
10
Macao-China o
15
Liechtenstein o
20
Korea o
25
Hong Kong-China o
30
Percentage of students below proficiency Level 2
35
Finland o
Czech Republic 0
Ireland 0
Sweden 0
France 0
Belgium 0
Netherlands 5
Denmark 1
Australia 1
Iceland 0
Canada 7
Japan 36
New Zealand
Luxembourg 10
Finland 28
Macao-China 58
Spain 67
Slovak Republic 76
Latvia 78
Russian Federation 92
OECD average-28 98
Hungary 97
Liechtenstein 97
Thailand 74
Norway 48
Korea 47
Hong Kong-China 46
United States 37
Uruguay 33
Poland 29
Serbia 29
Switzerland 15
Germany 4
Indonesia 5
Tunisia 0
Italy 0
Portugal 0
Greece 0
Turkey 1
Brazil 0
Mexico 0
Score point change in mathematics performance between 2003 and 2009
40
Finland
Korea
Hong Kong-China
Estonia
Canada
Macao-China
Japan
Chinese Taipei
Liechtenstein
Australia
Poland
Netherlands
New Zealand
Switzerland
Hungary
Latvia
Slovenia
Germany
United Kingdom
Ireland
Norway
Portugal
Denmark
Lithuania
Czech Republic
Iceland
Belgium
United States
Spain
Croatia
Sweden
Slovak Republic
France
Italy
Russian Federation
Luxembourg
Greece
Turkey
Chile
Israel
Serbia
Bulgaria
Romania
Uruguay
Thailand
Jordan
Mexico
Argentina
Montenegro
Tunisia
Colombia
Brazil
Qatar
Indonesia
Azerbaijan
Kyrgyzstan
Qatar 0
Turkey 0
Portugal 0
Korea 0
Tunisia 0
Brazil 0
Colombia 1
Italy 0
Norway 1
United States 3
Poland 2
Romania 10
Argentina 23
Chile 11
Japan 14
Kyrgyzstan 12
Serbia 12
Hong Kong-China 13
Mexico 13
Bulgaria 56
Switzerland 31
Iceland 15
Germany 38
Latvia 38
Thailand 34
Lithuania 47
Denmark 47
France 59
OECD average -33 24
Slovak Republic 70
New Zealand 72
Israel 86
Australia 93
Macao-China 94
Spain 97
Ireland 95
Uruguay 84
United Kingdom 80
Russian Federation 83
Hungary 79
Liechtenstein 70
Luxembourg 43
Netherlands 69
Greece 57
Estonia 43
Belgium 39
Canada 11
Jordan 21
Croatia 13
Slovenia 2
Sweden 6
Azerbaijan 6
Finland 2
Montenegro 0
Indonesia 14
Chinese Taipei 2
Czech Republic 2
Score point change in science performance between 2006 and 2009
Figure 2: PISA indicators
90
2003
80
70
60
50
40
30
20
-20
10
0
100
2006
90
80
70
60
50
40
30
20
-15
10
0
international benchmarks as they are defined either by TIMSS or PISA. This convergence
is pretty quick at least according to the measure PISA provided.
These facts immediately raise several questions: Are these changes in student
achievement related to restructuring in the education system in Turkey? If yes, which
aspects of the reform initiative in Turkey did lead to higher learning outcomes in
mathematics and science? Is it possible to identify the channels through which the policy
intervention leads to increases in student achievement? This study attempts to offer some
candidate answers to these questions.
2. Possible explanations
OECD (2010) stresses the role of the Basic Education Programme (BEP) in increasing
learning outcomes in Turkey. This World Bank supported programme defined the
framework of reform initiative in education according to the Law No. 43063. With this
legislation in August 1997, Ministry of National Education (MONE) aimed to achieve
increasing primary school education, improving the quality of education and overall
student outcomes, closing the performance gap between boys and girls, providing equal
opportunities, matching the performance indicators of the European Union, developing
school libraries, increasing the efficiency of the education system, ensuring that qualified
personnel were employed, integrating information and communication technologies into
the education system and creating local learning centers, based in schools, that are open
to everyone4.
In response to these efforts the attendance rate in the eight-year primary education system
soared from 85 to 100 percent. Similarly, the attendance rate in pre-primary education
3
4
http://mevzuat.meb.gov.tr/html/24.html
http://www.meb.gov.tr/Stats/Apk2002/502.htm
system increased from 10 to 25 percent. These increases led to an expansion of the
education system by 3.5 million pupils. These quantitative expansions were accompanied
by qualitative improvements: During the same period average class size was reduced
from approximately 40 to 30; conditions were improved in all rural schools and computer
laboratories were established in every primary school. Lastly the cost of the BEP exceed
the equivalent of USD 11 billion (OECD, 2010).
OECD (2010) as well as MONE also highlight the importance of recent curriculum
change in mathematics and science (TTKB, 2008): New curriculum were launched in the
2006-2007 school year, starting from the 6th grade. Similarly, mathematics and language
curriculum were also updated and starting from the 9th grade in the 2008-2009 school
year a new curricula of science was in force. According to the Board of Education
(TTKB) the aim of this change was to update the content of school education as well as
to change the teaching philosophy and culture within schools.
Although the new curriculum are the preferred explanation of MONE and some other
research institutions in Turkey5 for the increased learning outcomes, the connection is not
clear and there is a problem with this specific explanation: First, given that TIMSS covers
the period between 1999 and 2007 the new curriculum cannot not explain the
improvement in learning outcomes which is evident in TIMSS data. Second, average
achievement in mathematics in PISA is not comparable between 2006 and 2009. Thus the
timing of the inception of the new curriculum and the increase in average mathematics
achievement do not overlap. Third, the students who were subject to the curricula change
in science are 9th graders which constitute only a portion of the PISA 2009 sample;
moreover they experienced the new curricula only for two semesters. It is not clear
5
http://bit.ly/iVZojN; http://bit.ly/j5tOLv
whether these students may drive a 0.3 standard deviation increase in science between
2006 and 2009.
As mentioned earlier, one of the targets of the BEP was to ensure that qualified personnel
were employed. In line with this goal teacher selection policy was changed in 2002 which
might have affected teacher quality in public primary and secondary institutions. In the
following I will present a brief review on teacher quality and then go on with the nature
of the teacher labor market and testing system in Turkey.
3. Why teacher quality is important?
Learning outcomes are affected by many factors, including: students’ ability, potential,
enthusiasm and behavior; school management, resources and atmosphere; curriculum and
content; and teacher ability, preparation, attitudes and practices. Schools and classrooms
are elaborate and dynamic mediums and identifying the education production function
and underlying technology continues to be a major challenge of educational research.
This problem has many aspects ranging from research design and methodology to data
availability. Usually researchers are forced to use measures which are only partial
indicators of learning and in many cases it is not possible to apply the relevant
methodologies. Therefore the results, interpretations and policy implications of such
studies are regularly questioned.
Keeping this caveat in mind some general inferences can be drawn from the body of
research on the determinants of learning. First, out-of-school factors such as the ability,
motivation, parental characteristics, neighborhood and socioeconomic status are the
strongest predictors of learning and it is not easy to change these factors through policy
intervention in the short run.
Second, among the factors which are open to policy influence teacher quality is the most
important school input affecting learning. Santiago (2002), Schacter and Thum (2004)
and Eide, Goldhaber and Brewer (2004) present extensive and detailed reviews of this
line of research.
The difference in teacher quality may lead to substantial differences in student
achievement. Empirical investigations analyzing the impact of total teacher effect on
learning usually estimate individual teacher effects by making use of student-teacher
matched panel data sets with teacher fixed effects and then construct a teacher
effectiveness distribution based on individual teacher effects. The studies employing this
methodology suggest that teachers differ substantially in terms of effectiveness. Most
recent studies show that one standard deviation increase in teacher quality is associated
with 0.1-0.15 standard deviation increase in mathematics test scores of primary and
secondary school students (Hanushek & Rivkin, 2010).
In order to understand the relative significance of teacher quality Rivkin et al. (2005)
analyze a unique matched panel data from the UTD Texas Schools Project which allows
them to identify teacher quality based on student performance. They conclude that the
contribution of a ten student reduction in class size is less than that of a standard
deviation increase in teacher quality.
In another study, Rockoff (2004) analyzes a 10-year panel data of test scores and teacher
assignments to understand how much teachers affect learning. The panel structure allows
him to focus on differences in the performance of the same student with different teachers
and to decompose the variation in teacher quality from variation in students’
characteristics. His analysis shows that variation in teacher quality explains 23 percent of
the variation in the test scores which is potentially open to policy influence.
In addition to these findings, researchers in this literature also tried to decompose total
teacher effect into subcomponents of measured teacher characteristics such as teacher
experience, teacher education, teacher certification etc. However these studies are in
consensus that measured teacher characteristics are not much associated with estimated
individual teacher effects (Aaronson, Barrow, & Sander, 2007; Hanushek, 1992; Rivkin,
et al., 2005; Rockoff, 2004).
This finding is in line with the meta-analyses conducted on school resources and teacher
characteristics: Hanushek covered a significant number of studies from US in three
reviews. He compiled 147 separately estimated educational production functions from 33
publications (Hanushek, 1986). Later he updated this survey twice. In his 1989 study he
compiled 187 estimations from 38 articles (Hanushek, 1989) and in 1997 he covered a set
of publications available through 1994. This set included 90 publications and 377
separate estimates (Hanushek, 1997). The selection criteria to be included in these
surveys were to be published in a book or refereed journal, relating some objective
measure of student output to family and school characteristics, providing information
about statistical significance of estimated relationships (Table 2).
Hanushek draws three main conclusions from these summaries: First, the estimates of
educational production functions are not promising for teacher education. These studies
fail to establish a strong and consistent relationship between teacher education and higher
student achievement. Second, the majority of the estimated coefficients for teacher
experience point in the correct direction and 30 percent of the estimated coefficients
exceed the conventional statistical significance level of 5 percent. However the results for
teacher experience are hardly overwhelming; the relationship between teacher experience
and student achievement is strong only relative to other school inputs6. Hanushek (1986,
1989, 1997) claims that the positive association between teacher experience and student
achievement may be due to possible selection effects. He stresses that this finding may be
a result of experienced teachers being allowed to select schools and classroom with
higher achieving students. Third, among all of the teacher characteristics (as well as other
explicit measures) higher teacher test scores are most consistently associated with
stronger learning outcomes.
Table 2: Percentage Distribution of Estimated Effect of Teacher Characteristics on
Student Performance
1986
1989
1997
Statistically
Statistically
Statistically
significant & Positive significant & Positive significant & Positive
positive
positive
positive
Teacher education
6
30
7
35
9
Teacher experience
30
60
29
60
29
Teacher test score
28
78
26
58
37
Source: Author’s calculations from the studies of Hanushek (1986, 1989, 1997)
42
59
64
These meta-analyses cover only studies from US and the evidence from other developed
countries are scarce. To my knowledge there is not any meta-analysis for developed
countries other than US. However the study of Hanushek and Luque (2003) gives insight
at this front. They analyze TIMSS data for 18 developed and developing countries7 and
suggest that the findings are very similar to the meta-analyses which only include studies
from US.
6
Hanushek also summarizes estimated coefficients for student teacher ratio, per pupil spending, facilities and
administrative inputs.
7
Canada, Cyprus, Czech Republic, Greece, Hong Kong, Ireland, Japan, Latvia, Netherlands, New Zealand, Norway,
Portugal, Thailand, Scotland, United States and Slovenia
As for developing countries, the first comprehensive meta-analysis dates back to 19878.
Fuller (1987) reviews 60 studies investigating student achievement in developing
countries. Later Fuller and Clarke (1994) expand Fuller’s 1987 study by surveying an
additional 47 studies published between 1987 and 1993. These meta-analyses included
studies that used at least some measure of student’s social class as a control variable. A
year later Hanushek (1995) conducted a larger survey with the same criteria for study
selection based on his previous study (Harbison & Hanushek, 1992). Lastly, very
recently, Glewwe et al. (2011) conducted a meta-analysis on developing countries and
extracted estimates from studies published between 1990 and 2010. They filtered a very
large pool of studies according to their methodological approaches and ended up with 79
studies which use at least simple OLS with at least one family background, school
expenditure, teacher and one additional school variable as controls. 43 of these 79 studies
had a more complicated identification strategy such as randomized controlled trials,
difference-in-differences, regression discontinuity and matching designs.
These meta-analyses which cover the last three decades consistently show that teacher
test score is a stronger indicator of teacher effectiveness when compared with teacher
education and teacher experience and this distinction appears to be more pronounced in
developing countries than developed countries. Therefore, especially in developing
countries, teacher test scores may provide valuable input for policy makers in designing
teacher selection and hiring mechanism (Table 3).
Interestingly, there are some studies from Turkey which point to the same direction:
Several studies which analyze PISA 2006 data for Turkey suggest that students who were
8
Relatively smaller surveys appeared earlier (Heyneman & Loxley, 1983; Schiefelbein & Simmons, 1981; Simmons
& Alexander, 1978).
taught by teachers who passed rigorous testing procedures are associated with higher test
scores (Alacaci & Erbas, 2010; Dincer & Uysal, 2010).
Table 3: Percentage Distribution of Estimated Effect of Teacher Characteristics on
Student Performance in Developing Countries
Glewwe et al. (2011)
Fuller
Fuller and Clarke Hanushek
(1987) (1994)
(1995)
inc. OLS exl. OLS
Teacher education
46
54
56
33
15
Teacher experience
43
40
35
27
18
Teacher test score
100
100
55
65
Source: Authors calculations from the studies of Fuller (1987), Fuller and Clarke (1994),
Hanushek (1995) and Glewwe et al. (2011)
4. Basic characteristics of teacher labor market in Turkey
The main characteristic of teacher labor market in Turkey is excess supply of teachers.
As of 2010, approximately 327 thousand teachers are in line to be employed by public
sector and the number of applicants is three to four times higher than the number of the
opening teaching positions (Figure 3). This army of inactive teachers represents a
significant population given that the number of employed teachers in the public sector is
680 thousand. Whereas MONE predicts that the desired level of employed teachers in
public education system is 717 thousand9 the gap between supply and demand of teachers
widens cumulatively.
As of 2010, MONE demanded 782 mathematics teachers and it received 2798
applications. For science these figures are 861 and 354610, respectively and the gap more
or less is evident in every subject; thus excess supply is not specific to some of the
subjects.
9
http://icden.meb.gov.tr/digeryaziler/MEB_ic_denetim_faaliyet_raporu_2009.pdf
http://personel.meb.gov.tr/ana_sayfa.asp
10
Figure 3: The number of open positions and applicants by subject
4000
3500
3000
2500
2000
1500
1000
500
0
Math
Science
Physics
Biology Chemistry
and Tech
# Open positions
# Applicants
Source: Author’s own calculations from http://personel.meb.gov.tr/ana_sayfa.asp
A reasonable explanation of this situation may be the presence of very attractive teacher
salaries. However teacher salaries in Turkey are not attractive at all. In the public sector
the starting annual salary of a teacher is around 14000$ and it does not improve much
with experience (Figure 4). The salary of a teacher with 15 years of experience is around
16000$ (OECD, 2009).
Dolton and Gutierrez (2011) present a cross-country analysis of teacher pay and
performance by taking the relative earning distribution in each country into account.
Their analysis confirms that the teacher salaries are not especially attractive in Turkey
and the salary-experience profile is flat (Figure 5).
Therefore starting salaries and expectation of higher salaries in the teaching profession
cannot explain the excess supply in the teacher labor in Turkey.
Another important feature of teacher labor market is that all public servants in Turkey are
protected by law and unions and job separation is a very unlikely event. Therefore
teaching profession offers substantial job security and given the presence of very high
chronic unemployment rates, individuals value job security heavily.
Figure 4: Ratio of salary after 15 years of experience to GDP per capita
2.5
2
1.5
1
0.5
Korea
Germany
Portugal
Japan
Scotland
New Zealand
Switzerland
Mexico
Spain
England
Czech Republic
Turkey
Slovenia
Ireland
Belgium (Fl.)
Australia
OECD average
Greece
Netherlands
Belgium (Fr.)
Denmark
Chile
Finland
Austria
Italy
France
United States
Sweden
Luxembourg
Hungary
Iceland
Norway
Israel
Estonia
0
Source: (OECD, 2009)
Figure 5: Average teacher wage-experience profile in Turkey
Source: (Dolton & Marcenaro Gutierrez, 2011)
One study (Caner & Okten, 2010) analyzes the college major choice decision in a risk
and return framework using university entrance exam data from Turkey and shows that
individuals are very sensitive to risk during career choice.
It should be also noted total enrollment in education faculties in Turkey also increased
steadily in time: The annual enrollment increased from 33 thousand in 2007 to 45
thousand in 2008 and 54 thousand in 2009 and MONE expands the teaching force by
approximately 40 thousand each year11.
Thus a combination of an intense demand for job security and increased quotas of
education faculties may provide a more sensible explanation for the excess supply in
teacher labor market in Turkey.
5. Legal framework of teacher selection in Turkey
There are three main legal sources which regulates hiring of teachers in Turkey. First,
teachers working in the public sector are subject to Law No. 657. This law defines the
rights as well as legal obligations of public servants since 1965. Second, the regulation of
the tests concerning the assignments of public servant candidates describes the testing
procedure for public servant posts since 2002. Third, MONE’s regulation of teacher
assignment and replacement explains how the testing procedure and test results apply to
teacher selection process. The current version of this regulation is legislated in 2010 and
it has changed many times in the past according to the needs of MONE.
The regulation of the tests concerning the assignments of public servant candidates
basically forms a turning point in teacher selection; because it causes a radical change in
teacher selection policy in Turkey.
11
http://www.ogretmenportali.net/HaberGoster/228716e4-64bf-4b55-bb17-fc0ee89baf38/atanmayan-ogretmenordusu-buyuyor.aspx
Under the teacher regime system before the legislation of this regulation, i.e. prior 2002,
any eligible teacher candidate was able to apply to any available position announced by
MONE. The applications were processed in provincial offices of MONE and then the
final decision was announced by the headquarters of MONE in the capital, Ankara
(Figure 6).
Figure 6: A presentation of teacher selection system before 2002
This system was a cause of concern of MONE as well as State Planning Organization
(SPO) (SPO, 1989). One of the main issues of the pre-2002 system was a constant
imbalance of teacher population across regions. According to the Research and
Development department of MONE, one preliminary report of the 1993 National
Education Assembly stressed that more than 10 percent of teachers employed by MONE
in urban areas did not teach a single class. Another issue documented in MONE’s records
was that political pressures and interventions damaged the fairness and equality
principles in teacher employment and caused unrest among teachers (EARGED, 1995).
Indeed this was well-known publicly that to have connections in provincial offices as
well as in the capital was essential to get hired. Thus nepotism was a general worry
concerning this selection process.
Following the legislation of the above mentioned testing regulation Center of
Measurement, Selection and Placement (OSYM) launched a central examination process
which is known as Public Servant Selection Examination (KPSS). This exam has two
sessions: For the first session teacher candidates have to answer 120 multiple choice
questions about Turkish, Mathematics, History, Citizenship, General Culture and
Geography in 180 minutes. In the second session teacher candidates have to answer 120
multiple choice questions about educational psychology, educational programs and
teaching and educational guidance in 180 minutes. Then applicants are assigned to
teaching positions centrally by MONE according to their test scores in the central
examination and their ranked list of preferred teaching positions (Figure 7). OSYM
conducts the exam annually and if a teacher candidate fails to be placed to a teaching
position then s/he has to take the exam again in the following year.
Figure 7: A hypothetical presentation of teacher selection after 2002
Under this teacher selection regime it is not possible to game the hiring process and it is
also not possible to leverage nepotism in order to get a teaching position. Thus it is
reasonable to claim that the central examination and allocation of teaching positions
based on test scores address the problem of lack of fairness. However two questions
remain to be answered: Does the new system ensure that the qualified teachers are
employed? Does this system have an impact on the regional imbalance of teacher
population? The first question is critical because it was one of the main goals of BEP.
The second question is critical because the imbalance constitutes a chronic problem of
education system (EARGED, 1995; SPO, 1989).
6. Data
In order to answer these research questions I employ TIMSS 199912 and TIMSS 200713
data sets. These data sets have some important qualities which render them very suitable
to investigate these issues.
First, as mentioned earlier, these projects assess a representative set of 8th graders. 8th
grade is the final grade of primary education in Turkey and hence students in the sample
should have spent at least a couple of years in their current institutions.
Second, it is possible to link teachers to students in the same classroom which makes
these data sets especially attractive for this analysis.
Third, TIMSS project conducts four questionnaires, i.e. student, school, mathematics and
science teacher questionnaires. The student and teacher questionnaires contain extensive
information about demographic and socioeconomic characteristics of students and
teachers. In addition, the school questionnaire contains information on school location,
resources and governance.
Fourth, the information collected in 1999 and 2007 is comparable to a certain extent. The
questionnaires in 1999 and 2007 do not overlap extensively; however most of the
essential information is available in both data sets.
Fifth and most importantly, the policy change which is subject to the evaluation in this
study falls into the middle of 1999 and 2007, the dates Turkey participated to TIMSS.
12
13
http://timss.bc.edu/timss1999.html
http://timss.bc.edu/timss2007/index.html
This allows me to have a reasonable number of observations who are subject to the policy
change which was launched in 2002.
Lastly, the teacher experience is reported in years such as 1, 2, 3 etc. but not in year
categories such as 0-4, 5-8 etc. This distinction is crucial for this analysis because the
data on teacher experience in TIMSS allows me to define the treatment and control
groups with respect to the inception date of the new policy.
7. Methodology and empirical analysis
For the empirical analysis, first, I merged the student, school and teachers data sets for
1999 and 2007 and compiled the 1999 and 2007 TIMSS data sets. Then I defined the
treatment group as the students whose teachers have four or less years of experience. This
assumption is necessary because I do not observe whether the teachers were selected via
central examination or not. Thus I assume that this definition of treatment group
approximates the ideal case.
The justification of this assumption is based on the timing of the TIMSS application and
the central examination. The first central examination in Turkey was conducted in July
2002; OSYM announced the test scores in August 200214 and MONE distributed the
teaching posts based on announced test scores in September, October and November
200215. On the other hand TIMSS 2007 application in Turkey was conducted in April,
May and June 2007 (Olson, Martin, Mullis, & Arora, 2008). Thus a teacher who was
selected with the first central examination should have assigned to a post as early as
September 2002 and the same teacher should have answered TIMSS teacher
questionnaire as late as June 2007. According to this hypothetical example this teacher
14
15
http://www.osym.gov.tr/belge/1-6128/2002-sinavlari.html
http://personel.meb.gov.tr/sayfa_goster.asp?ID=207
could not have five years of experience at the time of TIMSS application. Therefore the
treatment group is assumed to be as defined above.
However this is an imperfect measure of selection via central examination: First, teacher
turnover leads to measurement error; because it is possible to quit and return teaching
which may be an issue especially for female teachers who may substitute teaching with
child raising for a couple of years. Second, OSYM conducted another central
examination which is known as Central Elimination Examination for Institutions (KMS)
in 200116. KMS was different than KPSS and it is not clear how many teaching posts
were distributed based on KMS scores as well as whether KMS scores were the sole
determinant of the teacher assignments. This issue may also lead to measurement error.
Keeping these shortcomings in mind I basically compare the difference of average
student achievement between treatment and control groups in 1999 and 2007 with a basic
differences-in-differences approach. The main assumption of this approach is that the
change in mean test scores that the control group experiences over time reflects the same
change that the treatment group would have experienced had they not been exposed to the
treatment. Another important assumption of differences-in-differences approach is that
unobserved characteristics have the same distribution across time points and across
treatment groups. I will discuss the validity of these assumptions in the subsequent
sections.
For the differences-in-differences analysis I estimated the following regression models:
16
http://www.osym.gov.tr/belge/1-12485/2001-sinavlari.html
Table 4: Difference-in-Differences estimations
In these regression models 𝑦𝑖𝑐𝑠𝑝 represents the dependent variable which is either the
mathematics or science test score. However it should be mentioned that TIMSS does not
provide point estimates of mathematics and science test scores instead gives five
plausible values of mathematics and science ability. For the sake of simplicity I averaged
the five plausible values for each subject and then used the averaged plausible values as
the measure of the subject test score. TIMSS 2007 Technical Report highlights that
averaging plausible values will not yield suitable estimates of individual student scores
(Olson, et al., 2008). However I repeated some of the estimations with plausible values
and then compared the point estimates and the standard errors of the population
parameter in interest, i.e. 𝑏3 . In all cases the point estimates were very close to each other
and the standard errors were slightly larger which did not affect the statistical
significance.
In these regression models 𝑇𝐼𝑀𝑆𝑆𝑝 stands for the TIMSS cycle (1999 and 2007),
𝑇𝑅𝐸𝐴𝑇𝑐𝑠𝑝 defines the treatment variable which equals to 1 if the subject teacher has four
or less years of experience. Observed information regarding teachers, students, classes
and schools enters the regression models as control variables (Table 5).
The list of control variables was basically constructed within the data limitations. The
variables available in TIMSS 1999 and 2007 data sets do not overlap to a significant
degree and although some of the necessary variables are available in both data sets the
scales of measurement are different. All in all I experimented with every variable which
is available in both data sets. The number of missing observations partially had an impact
on the selection of control variables.
Table 5: List of Control Variables
Teacher
Class
characteristics characteristics
Sex
Diversity in
academic ability
Student
characteristics
Sex
Age
Age
Subject degree
Experience
Diversity in
socioeconomic
background
Presence of
disruptive students
Class size
Instructional time
Parental
education
# books at home
Computer at
home
Language
spoken at home
School
resources
An indicator
for school
resources
Location
Following the difference-in-differences analysis with mathematics and science
achievement I utilized another aspect of the data structure: The treatment variable offers
variation by student. This means that same student may have a mathematics teacher who
has four or less years of experience whereas her/his science teacher may have more than
four years of experience (or vice versa). Given that both the mathematics and science test
scores are observed for each student this structure allows me to employ student fixed
effects (Table 6). For that purpose I compiled the mathematics and science data sets and
incorporated student fixed effects into the regression models as defined in Table 2. This
approach allowed me to relax one of the assumptions which are associated with
difference-in-differences approach. After adding student fixed effects into the model I do
not have assume that unobserved student and school characteristics have the same
distribution across time points and across treatment groups. However I still have to
assume that unobserved class characteristics have the same distribution across time points
and across treatment groups. Lastly it should be also mentioned that there are other
examples which employ very similar identification strategies such as the study of Lavy
(2010). Lavy (2010) establishes a causal link between instructional time and student
achievement by making use of the within-student variation in the test scores and withinsubject variation in the instructional time. In its essence the identification strategy I make
use of is identical to the approach Lavy (2010) uses with one exception that I embed it
into a difference-in-differences framework.
Although this identification strategy allows me to relax some of the assumptions of the
differences-in-differences approach it has also its own shortcomings: First, it leads to a
reduction in the sample size and this problem becomes more pronounced in sub-group
analyses. Second, it is not possible to decompose the effect into two parts as learning
gains in mathematics and learning gains science.
Table 6: Fixed effects and difference-in-differences estimations
8. Findings
The following table presents the estimated values for the coefficient of interest under
different specification as described in Table 4 as well as it also gives sub-group estimates
of this coefficient. The analysis has been conducted separately for mathematics and
science test scores (Table 7).
The results in Table 7 draw attention to several important issues: First, standard errors are
very large. Among 50 point estimates of the treatment effect only three of them are
statistically different than zero at least at 10 percent significance level. Second, almost all
of the point estimates have a negative sign. Third, the point estimates are not stable. In
Model 1 without any control variables the point estimates are negative and large;
however the addition of teacher, class, student and school characteristics into the
regression model rasps this negative treatment effect towards zero. All in all, the
difference-in-differences analysis does not provide any information about the possible
impact of treatment on student learning because of the very large standard errors the
Table 7: Estimation results of difference-in-differences
Whole sample
Coef
Female teacher sample
Std Err Adj R²
Coef
Std Err Adj R²
Mathematics
Male teacher sample
Coef
Below median achievers
sample
Std Err Adj R² Coef Std Err Adj R²
Above median achievers
sample
Coef
Std Err Adj R²
Model 1
-17.86
[13.94]
0.04
-23.53
[22.02]
0.07
-15.50 [17.44]
0.03
-2.38
[6.32]
0.03
-11.90
[8.50]
0.05
Model 2
-28.15*
[16.46]
0.08
-25.76
[20.89]
0.19
-20.10 [21.03]
0.06
-4.34
[7.47]
0.04
-18.43*
[10.30]
0.08
Model 3
-27.12
[17.26]
0.10
-10.76
[21.23]
0.26
-28.35 [24.38]
0.09
-2.80
[7.12]
0.05
-16.59
[11.18]
0.10
Model 4
-14.19
[14.02]
0.27
-7.01
[18.09]
0.38
-10.20 [20.20]
0.24
-2.40
[6.82]
0.10
-8.38
[9.29]
0.21
Model 5
-0.61
[13.56]
0.30
6.45
[17.84]
0.40
0.26
0.22
[6.66]
0.11
-0.26
[9.14]
0.23
Obs.
6,750
0.55
2,757
[19.41]
3,993
3,354
3,396
Science
-8.46
[6.25]
0.12
0.03
-5.87
[6.94]
0.14
[6.05]
0.04
-4.79
[7.31]
0.18
-2.70
[5.23]
0.10
-2.21
[6.52]
0.27
-4.15
[5.32]
0.11
-1.38
[6.53]
0.27
Model 1
-15.49
[12.30]
0.07
-42.53**
[16.51]
0.10
4.36
[17.24]
0.05
-3.41
[6.31]
0.01
Model 2
-17.42
[12.34]
0.09
-28.14
[17.63]
0.12
-2.40
[17.70]
0.08
-5.99
[5.76]
Model 3
-17.85
[13.71]
0.14
-15.22
[21.73]
0.18
-12.49 [17.03]
0.17
-4.83
Model 4
-6.89
[10.63]
0.31
-4.98
[17.31]
0.35
-5.00
[13.05]
0.31
Model 5
-6.29
[10.56]
0.31
-11.90
[18.50]
0.36
-2.73
[12.71]
0.31
Obs.
7,085
3,131
3,954
Robust standard errors in brackets clustered at the class level, *** p<0.01, ** p<0.05, * p<0.1
3,536
3,549
treatment effect may be negative, zero or positive. However it also shows that observed
class, student and school characteristics do not have the same distribution across time
points and across treatment groups given that the point estimates are instable and change
signs. Therefore it is very likely that unobserved class, student and school characteristics
do not have the same distribution across time points and across treatment groups which is
a violation of the assumptions underlying difference-in-differences approach. This may
also be a sign of differential assignment of teachers with four or less years of experience
to classrooms between 1999 and 2007. In the following I incorporate the student fixed
effects into the regression models in order to take into account the factors at student and
school levels (Table 8). However teacher and class characteristics vary within student;
thus the regressions contain controls for observed teacher and class characteristics.
The results in Table 8 are in contrast with the results in Table 7. Generally the standard
errors are smaller; more interestingly, with one exception, all of the point estimates of the
treatment effect are positive. The point estimates are not sensitive to the addition of the
teacher characteristics to the regression; however they are very sensitive to the addition
of class characteristics. According to the Model 3, i.e. after controlling for teacher and
class characteristics, the impact of the treatment is estimated precisely for the whole,
female teacher and below median achievers samples.
The standard deviation of the dependent variable in the whole sample is 89. Thus the
impact of the policy change on student achievement is around 0.17 standard deviations.
However the sub-group analysis exhibits that this impact is channeled mostly through
female teachers.
Table 8: Estimation results of student fixed effects and difference-in-differences
Coef
Std Err Adj R²
Mathematics & science scores combined
Female teacher sample
Male teacher sample
Below median achievers
Above median achievers
sample
sample
Coef
Std Err Adj R² Coef Std Err Adj R²
Coef
Std Err Adj R² Coef Std Err Adj R²
Model 1
3.68
[10.62]
0.01
15.10
[12.92]
0.04
7.11
[10.85]
0.03
3.94
[12.59]
0.02
2.94
[8.44]
0.00
Model 2
4.28
[9.36]
0.09
16.07
[12.61]
0.10
8.59
[13.17]
0.14
-0.60
[12.49]
0.20
2.42
[8.13]
0.03
Model 3
14.77**
[6.89]
0.22
41.56**
[18.32]
0.30
17.63 [14.13]
0.23
20.67***
[5.32]
0.52
6.17
[9.91]
0.06
Whole sample
Obs.
4619
612
1166
2959
1675
Robust standard errors in brackets clustered at the class level, *** p<0.01, ** p<0.05, * p<0.1
Table 9: Alternative treatment definitions
Coef
Std Err Adj R²
Math & science scores combined – Model 5
Female sample
Male sample
Below median achievers
Above median achievers
sample
sample
Coef Std Err Adj R²
Coef
Std Err Adj R²
Coef
Std Err Adj R² Coef Std Err Adj R²
-4.63
[8.18]
0.22
6.64
[19.58]
0.30
0.55
[23.93]
0.23
-14.89**
[6.64]
0.51
-0.29
[8.58]
0.06
9-20 years -0.55
[7.01]
0.23
-16.63
[16.99]
0.31
1.53
[10.58]
0.27
-13.69*
[8.04]
0.53
6.92
[5.85]
0.06
20+ years -7.34
[8.32]
0.22
4.23
[17.16]
0.30
-33.28**
[14.63]
0.24
12.04
[9.98]
0.51
-7.36
[6.14]
0.06
Whole sample
5-8 years
Robust standard errors in brackets clustered at the class level, *** p<0.01, ** p<0.05, * p<0.1
The estimated impact of the treatment effect in female teacher sample is 2.8 times higher
than the whole sample whereas in male teacher sample the impact is not precisely
estimated. Another important inference is that the below median achievers benefit more
from the new policy compared to above median achievers. Thus the treatment effect is
concentrated on below median achievers. Lastly, the sensitivity of the point estimates to
the addition of class characteristics are in line with the findings in Table 7. This may be
due to the within-school (between classroom) differential assignment of teachers with 4
or less years of experience to classrooms between 1999 and 2007.
The findings in Table 8 provide evidence in favor of a positive and moderately large
treatment effect. It may be claimed that within the contextual framework of Turkey,
teacher selection with centralized testing leads to higher learning outcomes compared to a
recruitment system based solely on subjective evaluation. However, there may be other
underlying reasons which can potentially explain the findings in Table 8: For example,
there may be a secular increase in the quality of education faculties in Turkey. If this is
the case the estimated impact may be due to the quality increase in education faculties
instead of the new teacher selection policy. In the same line of thought it can be said that
more and more high school students with higher ability opt for education faculties; thus
ability distribution of the pool of teacher candidates may shift in time. If these arguments
are true I should expect to detect positive estimates of treatment effect for different age
brackets of teachers. In order to test these arguments I divided the sample of teachers who
have more than four years experience into three parts such that the sizes of the
subsamples are equal. These segments are 5-8, 9-20 and 20+ years of experience. These
categories defined the alternative treatment variables for each case and I repeated the
individual fixed effects exercise with the full model which includes teacher and class
characteristics as controls. In Table 9 none of the point estimates are statistically
significant and positive; additionally statistically insignificant point estimates are small
when compared with the positive point estimates in Table 8. Thus I failed to detect any
positive impact of the treatment effect with alternative treatment definitions. Therefore it
is more likely that the estimated impact is due to the new selection policy rather than a
secular increase in the quality of education faculties or student body.
9. Conclusion
These findings are suggestive in their nature and they are not suitable to make causal
inferences: Combining individual fixed effects with difference-in-differences allows for a
relatively precise estimation of the treatment effect. The remaining problem with this
approach is the lack of a complete set of classroom characteristics. The point estimates
are sensitive to the classroom characteristics and unobserved classroom characteristics
may cause bias on estimates. Although the analysis shows that a downward bias is much
more probable.
The findings also provide a reasonable explanation for the trend in TIMSS and PISA
results. First, since the analyzed period precedes the curriculum reform in Turkey the
findings cannot be attributed to the curriculum reform. Second, the findings present a
concentrated impact on below median achievers whereas no impact for above median
achievers. This is perfectly in line with what we observe in PISA cycles for students in
Turkey.
The findings are also in accordance with the literature on teacher quality: As mentioned
earlier teacher test score is one of most robust indicators of teacher’s effectiveness
(Hanushek, 2002, 2003; NCTQ, 2004). Basturk (2008) shows that test scores in college
entrance exam are highly predictive for the KPSS test score. Therefore it should be
reasonable to interpret success in KPSS as an indication of higher academic ability.
10.Unanswered questions and further research
Basic tabulations with TIMSS data point to several important issues: The female share in
teaching force is increasing after the introduction of centralized testing. Similarly new
teachers are assigned to classrooms which were much more diverse in terms of
socioeconomic background and have fewer resources for instruction. The students in
these classrooms were more likely to speak Turkish sometimes (but not always), had
fewer books at home and their parents were more likely to have less than lower
secondary education regions (Table 10).
Table 10 provides evidence in favor of the presence of differential assignment of teachers
into schools and classrooms. It may be claimed that MONE attempts to ensure a more
balanced distribution of teacher assignment across resource rich and poor regions. As
mentioned earlier MONE as well as SPO were concerned about the imbalance of
teaching force across regions.
However these basic tabulations are far from presenting a detailed picture about the
change of the distribution of teaching force across resource rich and poor
regions/schools/classrooms in Turkey. Thus I plan to extract more information from
Household Labor and Budget Surveys of Turkey. These surveys are conducted annually
and Household Labor Surveys are available starting from 2000 and Household Budget
Surveys are available starting from 2002.
Table 10: Differential teacher assignment between 1999 and 2007
1999
2007
TREAT=0 TREAT=1 TREAT=0 TREAT=1
Teacher's sex (%)
Female
41
40
35
67
Male
59
60
65
33
Wide range of backgrounds in class (%)
not at all
12
6
16
28
a little
49
48
35
20
quite a lot
31
36
38
23
a great deal
8
10
11
29
Resources for math instruction (%)
low
32
27
19
31
medium
65
66
72
65
high
4
7
9
5
Language at home
Always Turkish
93
84
94
78
Sometimes Turkish
6
14
6
20
Never Turkish
1
2
1
2
# books at home
0-10
20
27
20
37
11-25
36
40
36
41
26-100
29
21
27
15
101-200
9
5
10
5
200+
6
6
7
2
Parental education
University Degree
10
5
10
2
Completed Post-Secondary
21
13
4
2
Completed Secondary
68
80
72
71
Less Than Lower-Secondary
2
2
13
22
Do Not Know
0
0
1
2
These micro data sets are publicly available upon request from TURKSTAT17 and
contain detailed regional information and occupational codes. Therefore they allow me to
provide more insight concerning the change of teaching force across regions in time.
17
http://www.tuik.gov.tr/UstMenu/body/bilgitalep/mvListe.pdf
Another shortcoming of my analysis is that I do not observe whether a teacher is selected
via central examination or not. In its ideal case, this analysis should be conducted with a
larger student-teacher matched panel data set which contains KPSS test scores for
teachers. This data is available in MONE; however it is not publicly available.
On the other hand, MONE as well as OSYM usually share aggregated data. Thus I think I
will be able to get more data on provincial distribution of teacher assignments and quality
of education faculties measured as average test scores of incoming students from MONE
and OSYM.
There are also methodology related problems in my analysis: Especially TIMSS 1999
Turkey data suffers from substantial missing data mostly due to missing school resource
information such as class size and instructional time. As a result I cannot use more than
half of the 1999 data in the difference-in-differences regression samples and this problem
becomes even more severe with student fixed effects regression samples. For example,
the sample size for the female teacher student fixed effect analysis is 612. A basic
comparison of complete cases and excluded cases do not exhibit dramatic differences
between these groups (Table A1, Table A2); however I plan to solve this problem with
multiple imputation techniques.
Lastly, the difference-in-differences analysis can be combined with matching techniques.
The treatment and control groups can be matched on observable characteristics. Then the
difference-in-differences analysis can be repeated on the matched sample. The analysis
on the non-matched sample may be problematic because of the lack of overlap of
observable characteristics of treatment and control groups. The matching techniques may
mitigate this problem.
A. Descriptive Tables
Table A1: Complete and excluded cases – Difference-in-differences analysis, mathematics regression, 1999
Mathematics score
Teachers with 4 or less than 4 years of experience
Teacher experience
Teahcer's age - under 25
Teahcer's age - 25 to 29
Teahcer's age - 30 to 39
Teahcer's age - 40 to 49
Teahcer's age - 50 to 59
Sex of teacher: Female
Sex of teacher: Male
Subject degree: No
Subject degree: Yes
Different academic abilities - not at all
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - not at all
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - not at all
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
Class size
Instructional time
Sex of student: Female
Sex of student: Male
Language at home - Always Turkish
Language at home - Sometimes Turkish
Language at home - Never Turkish
# of books at home - 0 -10
# of books at home - 11 -25
# of books at home - 26-100
# of books at home - 101 -200
# of books at home - more than 200
Computer at home: No
Computer at home: Yes
Age of student
Parents' highest education - University degree
Parents' highest education - Completed secondary
Parents' highest education - Upper secondary
Parents' highest education - Lower secondary
Parents' highest education - Less than lower
secondary
Parents' highest education - Don't know
School location: Urban
School location: Rural
Instruction resources - low
Instruction resources - medium
Instruction resources - high
Obs
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
3332
Complete cases - 1999
Std.
Mean Dev.
Min
429
75 214
18%
0.39
0
13.3
7.5
1
5%
0.21
0
26%
0.44
0
17%
0.38
0
51%
0.50
0
2%
0.13
0
43%
0.50
0
57%
0.50
0
7%
0.25
0
93%
0.25
0
14%
0.34
0
46%
0.50
0
35%
0.48
0
5%
0.22
0
8%
0.27
0
45%
0.50
0
41%
0.49
0
7%
0.25
0
6%
0.23
0
47%
0.50
0
33%
0.47
0
13%
0.34
0
42
14
18
168
33 120
42%
0.49
0
58%
0.49
0
91%
0.28
0
7%
0.26
0
1%
0.11
0
22%
0.41
0
37%
0.48
0
27%
0.44
0
7%
0.26
0
6%
0.24
0
91%
0.29
0
9%
0.29
0
14.2
0.8 10.6
8%
0.27
0
19%
0.40
0
59%
0.49
0
12%
0.32
0
2%
0%
82%
18%
30%
68%
2%
0.13
0.00
0.39
0.39
0.46
0.47
0.13
0
0
0
0
0
0
0
Max
812
1
30
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
90
300
1
1
1
1
1
1
1
1
1
1
1
1
19.3
1
1
1
1
Obs
4509
4369
4369
4476
4476
4476
4476
4476
4476
4476
3638
3638
4229
4229
4229
4229
4412
4412
4412
4412
4364
4364
4364
4364
1826
2960
4502
4502
4250
4250
4250
4396
4396
4396
4396
4396
4416
4416
4504
4202
4202
4202
4202
1
0
1
1
1
1
1
4202
4202
4217
4217
4225
4225
4225
Excluded cases - 1999
Std.
Mean Dev.
Min
428
82 209
16%
0.36
0
16.2
7.9
1
4%
0.19
0
14%
0.35
0
14%
0.35
0
60%
0.49
0
8%
0.27
0
40%
0.49
0
60%
0.49
0
6%
0.24
0
94%
0.24
0
15%
0.36
0
46%
0.50
0
31%
0.46
0
7%
0.26
0
14%
0.34
0
52%
0.50
0
25%
0.44
0
9%
0.29
0
11%
0.32
0
50%
0.50
0
27%
0.44
0
12%
0.32
0
44
13
5
172
45 120
43%
0.49
0
57%
0.49
0
92%
0.27
0
7%
0.25
0
1%
0.10
0
21%
0.41
0
36%
0.48
0
28%
0.45
0
9%
0.28
0
6%
0.23
0
90%
0.30
0
10%
0.30
0
14.2
0.8 10.4
10%
0.30
0
20%
0.40
0
61%
0.49
0
8%
0.27
0
1%
0%
77%
23%
33%
61%
6%
0.12
0.00
0.42
0.42
0.47
0.49
0.24
0
0
0
0
0
0
0
Max
695
1
31
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
90
300
1
1
1
1
1
1
1
1
1
1
1
1
18.9
1
1
1
1
1
0
1
1
1
1
1
Table A2: Complete and excluded cases – Difference-in-differences analysis, mathematics regression, 2007
Mathematics score
Teachers with 4 or less than 4 years of experience
Teacher experience
Teahcer's age - under 25
Teahcer's age - 25 to 29
Teahcer's age - 30 to 39
Teahcer's age - 40 to 49
Teahcer's age - 50 to 59
Sex of teacher: Female
Sex of teacher: Male
Subject degree: No
Subject degree: Yes
Different academic abilities - not at all
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - not at all
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - not at all
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
Class size
Instructional time
Sex of student: Female
Sex of student: Male
Language at home - Always Turkish
Language at home - Sometimes Turkish
Language at home - Never Turkish
# of books at home - 0 -10
# of books at home - 11 -25
# of books at home - 26-100
# of books at home - 101 -200
# of books at home - more than 200
Computer at home: No
Computer at home: Yes
Age of student
Parents' highest education - University degree
Parents' highest education - Completed secondary
Parents' highest education - Upper secondary
Parents' highest education - Lower secondary
Parents' highest education - Less than lower
secondary
Parents' highest education - Don't know
School location: Urban
School location: Rural
Instruction resources - low
Instruction resources - medium
Instruction resources - high
Obs
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
3418
Complete cases - 2007
Std.
Mean Dev.
Min
433
104 179
29%
0.46
0
10.9
9.3
1
14%
0.35
0
35%
0.48
0
17%
0.37
0
20%
0.40
0
14%
0.34
0
40%
0.49
0
60%
0.49
0
3%
0.17
0
97%
0.17
0
13%
0.34
0
20%
0.40
0
28%
0.45
0
38%
0.49
0
19%
0.40
0
29%
0.46
0
35%
0.48
0
16%
0.37
0
5%
0.21
0
29%
0.45
0
31%
0.46
0
35%
0.48
0
34
10
8
161
6 160
46%
0.50
0
54%
0.50
0
89%
0.31
0
10%
0.30
0
1%
0.10
0
25%
0.43
0
38%
0.48
0
23%
0.42
0
9%
0.28
0
5%
0.22
0
57%
0.49
0
43%
0.49
0
14.0
0.7 10.5
7%
0.26
0
3%
0.18
0
20%
0.40
0
51%
0.50
0
17%
1%
74%
26%
21%
74%
5%
0.37
0.11
0.44
0.44
0.41
0.44
0.21
0
0
0
0
0
0
0
Max
802
1
37
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
70
280
1
1
1
1
1
1
1
1
1
1
1
1
18.3
1
1
1
1
Obs
1080
737
737
1080
1080
1080
1080
1080
1080
1080
981
981
1018
1018
1018
1018
809
809
809
809
1004
1004
1004
1004
857
1080
1077
1077
1069
1069
1069
1051
1051
1051
1051
1051
1017
1017
1080
1034
1034
1034
1034
1
1
1
1
1
1
1
1034
1034
998
998
1080
1080
1080
Excluded cases - 2007
Std.
Mean Dev.
Min
429
108 171
39%
0.49
0
11.1
10.1
1
18%
0.38
0
32%
0.46
0
13%
0.34
0
14%
0.34
0
24%
0.43
0
61%
0.49
0
39%
0.49
0
12%
0.33
0
88%
0.33
0
8%
0.27
0
18%
0.38
0
34%
0.47
0
40%
0.49
0
23%
0.42
0
41%
0.49
0
21%
0.41
0
15%
0.36
0
6%
0.23
0
30%
0.46
0
35%
0.48
0
29%
0.45
0
30
9
9
161
9 160
48%
0.50
0
52%
0.50
0
91%
0.29
0
8%
0.28
0
1%
0.10
0
29%
0.46
0
36%
0.48
0
22%
0.41
0
8%
0.27
0
5%
0.22
0
58%
0.49
0
42%
0.49
0
14.1
0.7 11.1
6%
0.25
0
4%
0.19
0
21%
0.41
0
55%
0.50
0
13%
1%
77%
23%
36%
48%
16%
0.33
0.11
0.42
0.42
0.48
0.50
0.37
0
0
0
0
0
0
0
Max
813
1
31
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
70
240
1
1
1
1
1
1
1
1
1
1
1
1
18.6
1
1
1
1
1
1
1
1
1
1
1
B. Estimation outputs
VARIABLES
Mathematics - Difference-in-differences
Model 1
Model 2
Model 3
coef
se
coef
se
coef
se
Model 4
coef
se
Model 5
coef
se
TIMSS 2007 cycle
12.55 [9.05] 22.65** [10.40] 25.90** [11.06] 30.96*** [8.76] 23.86*** [8.42]
Treatment
-30.25*** [8.82] 17.98 [16.06] 11.55 [16.57]
4.82
[13.33] -0.50 [12.82]
TIMSS 2007 cycle * Treatment
-17.86 [13.94] -28.15* [16.46] -27.12 [17.26] -14.19 [14.02] -0.61 [13.56]
Teacher experience
3.70*** [1.41] 3.04** [1.42]
1.36
[1.03]
0.85
[1.00]
Teacher's age 25-29
10.19 [13.21] 6.80 [13.32]
3.34
[10.16]
6.93
[9.72]
Teacher's age 30-39
18.28 [20.70] 19.63 [20.46]
6.64
[14.65] 10.41 [13.80]
Teacher's age 40-49
-1.05 [26.11] 1.32 [24.94] -1.79 [18.72]
6.86
[17.74]
Teacher's age 50-59
-41.92 [34.44] -31.79 [33.35] -21.21 [24.74] -18.37 [24.73]
Male teacher
-9.03 [9.77] -10.75 [9.84]
-3.31
[7.05]
4.70
[6.59]
Subject degree
24.74 [19.01] 9.93 [20.01] -7.62 [19.20] -18.08 [18.68]
Different academic abilities - a little
23.62 [15.28] 15.63 [11.35] 19.82* [10.92]
Different academic abilities - some
20.37 [15.61] 14.02 [11.59] 12.15 [11.43]
Different academic abilities - a lot
21.36 [16.87] 18.14 [12.21] 19.14 [12.22]
Range of background - a little
-9.94 [12.71] -8.06
[9.01]
-1.67
[8.71]
Range of background - some
-1.46 [13.29] -4.32
[9.85]
3.12
[9.39]
Range of background - a lot
-19.42 [14.82] -16.30 [10.43] -10.67 [9.92]
Disruptive students - a little
0.99 [23.55]
6.66
[16.12] 18.57* [10.39]
Disruptive students - some
5.48 [23.05] 13.21 [16.10] 26.02** [10.08]
Disruptive students - a lot
-18.63 [23.96] -14.99 [16.73] -6.02 [10.31]
Class size
-0.29 [0.34]
-0.29
[0.26] -0.50** [0.23]
Instructional time
0.21 [0.15]
0.17
[0.11]
0.11
[0.11]
Male student
10.53*** [2.75] 11.13*** [2.73]
Age of student
-10.12*** [1.70] -9.31*** [1.67]
Parents' highest education - Completed secondary
-53.32*** [7.07] -48.55*** [7.36]
Parents' highest education - Upper secondary
-61.73*** [6.45] -55.98*** [6.94]
Parents' highest education - Lower secondary
-90.93*** [7.62] -80.16*** [8.27]
Parents' highest education - Less than lower secondary
-105.17*** [8.15] -93.36*** [8.68]
Parents' highest education - Don't know
-151.92*** [14.50] -143.59*** [13.98]
Language at home - Sometimes Turkish
-29.64*** [5.87] -27.11*** [5.88]
Language at home - Never Turkish
-47.51*** [13.97] -42.27*** [13.69]
# of books at home - 11 -25
24.22*** [3.41] 22.30*** [3.34]
# of books at home - 26-100
41.24*** [4.07] 38.62*** [3.93]
# of books at home - 101 -200
52.67*** [5.96] 49.17*** [5.86]
# of books at home - more than 200
24.41*** [6.41] 24.09*** [6.13]
Absence of computer at home
-17.21*** [3.84] -13.77*** [3.83]
School location - Rural
-19.94*** [7.10]
Instruction resources - medium
17.59*** [6.44]
Instruction resources - high
92.85*** [14.79]
Observations
6,750
6,750
Adjusted R-squared
0.04
0.08
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
6,750
0.10
6,750
0.27
6,750
0.30
VARIABLES
Mathematics - Female teacher sample - Difference-in-differences
Model 1
Model 2
Model 3
coef
se
coef
se
coef
se
Model 4
coef
se
Model 5
coef
se
TIMSS 2007 cycle
36.34** [16.19] 47.60*** [13.75] 31.73** [14.08] 28.68** [11.17] 22.15* [12.87]
Treatment
-34.70***[11.86] 25.64 [19.36] 9.13 [20.99] 4.12 [17.53] -5.80 [16.35]
TIMSS 2007 cycle * Treatment
-23.53 [22.02] -25.76 [20.89] -10.76 [21.23] -7.01 [18.09] 6.45 [17.84]
Teacher experience
2.71* [1.52] 2.47* [1.40]
0.95
[1.01]
0.30
[1.02]
Teacher's age 25-29
7.90 [19.66] 2.70 [17.59] 4.04 [12.44] 1.45 [11.17]
Teacher's age 30-39
53.88* [31.15] 21.27 [27.64] 12.83 [19.67] 12.54 [17.97]
Teacher's age 40-49
43.78 [30.41] 19.24 [30.25] 11.99 [21.22] 18.32 [19.70]
Teacher's age 50-59
136.87***[42.57]133.18***[46.71] 106.05*** [32.37] 81.51** [32.24]
Subject degree
66.23*** [12.12] 35.91* [18.33] 31.95** [13.46] 14.96 [12.35]
Different academic abilities - a little
-0.82 [13.82] -4.16 [11.49] 2.83 [10.58]
Different academic abilities - some
15.97 [17.04] 8.28 [13.97] 7.05 [12.65]
Different academic abilities - a lot
40.96** [17.65] 24.15* [13.02] 21.95* [11.67]
Range of background - a little
-58.81*** [18.55] -46.33*** [14.39] -35.35** [14.01]
Range of background - some
-13.12 [16.92] -11.84 [13.80] -2.91 [12.92]
Range of background - a lot
-65.11*** [20.63] -39.08** [15.64] -24.46 [15.46]
Disruptive students - a little
35.66* [20.18] 38.03** [15.69] 36.42*** [13.24]
Disruptive students - some
19.17 [22.65] 28.85 [17.76] 32.37** [15.70]
Disruptive students - a lot
-5.88 [19.82] 0.59 [15.10] 0.52 [12.57]
Class size
0.22 [0.47]
-0.05
[0.36]
-0.46 [0.37]
Instructional time
0.01 [0.16]
0.05
[0.13]
0.03
[0.16]
Male student
9.63*** [3.53] 9.19** [3.57]
Age of student
-12.42*** [2.50] -11.82*** [2.43]
Parents' highest education - Completed secondary
-51.94*** [10.26] -50.48*** [10.89]
Parents' highest education - Upper secondary
-56.25*** [9.42] -53.82*** [9.95]
Parents' highest education - Lower secondary
-74.88*** [12.09] -69.66*** [12.41]
Parents' highest education - Less than lower secondary
-83.50*** [12.34] -76.48*** [12.66]
Parents' highest education - Don't know
-134.71***[20.88]-129.39***[20.12]
Language at home - Sometimes Turkish
-37.63*** [8.40] -31.28*** [8.88]
Language at home - Never Turkish
-27.99 [19.99] -20.88 [19.01]
# of books at home - 11 -25
19.74*** [4.80] 19.45*** [4.54]
# of books at home - 26-100
40.28*** [6.25] 39.42*** [6.00]
# of books at home - 101 -200
48.62*** [8.68] 47.47*** [8.30]
# of books at home - more than 200
34.26*** [9.83] 33.35*** [9.30]
Absence of computer at home
-18.34*** [4.60] -16.24*** [4.69]
School location - Rural
-28.12*** [8.89]
Instruction resources - medium
10.67 [9.38]
Instruction resources - high
50.95** [19.93]
Observations
2,757
2,757
Adjusted R-squared
0.07
0.19
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
2,757
0.26
2,757
0.38
2,757
0.40
VARIABLES
Mathematics - Male teacher sample - Difference-in-differences
Model 1
Model 2
Model 3
coef
se coef se
coef
se
Model 4
coef
se
Model 5
coef
se
TIMSS 2007 cycle
-2.03 [11.19] 9.68 [15.28] 21.26 [16.98] 31.62** [13.06] 27.31** [12.56]
Treatment
-32.67***[11.68] 3.97 [21.72] 12.31 [22.61] 6.31 [18.47] 4.07 [17.97]
TIMSS 2007 cycle * Treatment
-15.50 [17.44]-20.10[21.03] -28.35 [24.38] -10.20 [20.20] 0.55 [19.41]
Teacher experience
3.60* [1.89] 3.65** [1.77]
1.79
[1.32]
1.40
[1.29]
Teacher's age 25-29
23.79 [15.91]30.81**[14.78] 22.73 [14.25] 28.11** [13.51]
Teacher's age 30-39
11.82 [25.03] 28.99 [24.16] 20.46 [20.03] 25.62 [19.23]
Teacher's age 40-49
-11.50[35.22] -2.56 [32.06] 3.52 [26.30] 11.39 [24.77]
Teacher's age 50-59
-52.61[42.49] -46.10 [42.26] -23.39 [33.63] -17.50 [33.97]
Subject degree
-0.09 [14.15] -15.70 [19.21] -32.77* [18.70] -39.82** [19.16]
Different academic abilities - a little
33.69 [20.69] 22.96 [15.71] 24.69 [16.14]
Different academic abilities - some
30.62 [20.38] 23.68 [15.54] 18.25 [15.91]
Different academic abilities - a lot
4.96 [22.46] 9.69 [16.89] 9.96 [18.38]
Range of background - a little
13.97 [15.23] 11.76 [11.08] 13.25 [11.01]
Range of background - some
9.90 [17.27] 4.68 [13.38] 9.49 [13.43]
Range of background - a lot
12.47 [17.73] 1.21 [13.61] 1.65 [13.17]
Disruptive students - a little
13.40 [20.35] 10.62 [15.53] 15.93 [11.85]
Disruptive students - some
29.13 [19.52] 24.85 [15.22] 29.88*** [10.89]
Disruptive students - a lot
12.15 [21.68] 1.15 [16.73] 3.49 [12.58]
Class size
-0.86** [0.43] -0.67** [0.32] -0.69** [0.31]
Instructional time
0.47* [0.26] 0.35* [0.20] 0.27* [0.16]
Male student
12.76*** [3.83] 13.50*** [3.79]
Age of student
-6.80*** [2.18] -6.66*** [2.17]
Parents' highest education - Completed secondary
-43.73*** [8.43] -42.23*** [8.13]
Parents' highest education - Upper secondary
-55.84*** [7.95] -53.78*** [7.85]
Parents' highest education - Lower secondary
-87.02*** [9.62] -80.80*** [9.35]
Parents' highest education - Less than lower secondary
-102.58***[10.82] -96.26*** [10.38]
Parents' highest education - Don't know
-149.79***[18.02]-150.02***[17.21]
Language at home - Sometimes Turkish
-26.53*** [7.90] -26.52*** [7.84]
Language at home - Never Turkish
-65.42*** [15.85] -62.51*** [14.02]
# of books at home - 11 -25
24.09*** [4.20] 21.31*** [4.32]
# of books at home - 26-100
35.46*** [4.64] 32.94*** [4.71]
# of books at home - 101 -200
49.36*** [7.21] 45.81*** [7.15]
# of books at home - more than 200
16.79** [7.70] 15.80** [7.79]
Absence of computer at home
-12.86** [5.06] -10.42** [5.05]
School location - Rural
-13.88 [10.75]
Instruction resources - medium
21.73*** [7.91]
Instruction resources - high
91.88*** [16.13]
Observations
3,993
3,993
Adjusted R-squared
0.03
0.06
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
3,993
0.09
3,993
0.24
3,993
0.26
VARIABLES
Mathematics - Below median achiever sample - Difference-in-differences
Model 1
Model 2
Model 3
Model 4
coef
se
coef
se
coef
se
coef
se
Model 5
coef
se
TIMSS 2007 cycle
-16.03***[3.25]-13.76*** [3.88] -12.70*** [4.36] -10.39** [4.25] -11.65*** [4.17]
Treatment
-8.54* [4.40] 0.35 [6.22] -4.46 [6.14] -2.23 [6.03] -3.93 [5.89]
TIMSS 2007 cycle * Treatment
-2.38 [6.32] -4.34 [7.47] -2.80 [7.12] -2.40 [6.82] 0.22 [6.66]
Teacher experience
0.66 [0.41] 0.19 [0.45] 0.02 [0.43] -0.03 [0.44]
Teacher's age 25-29
4.07 [6.74] 2.94 [6.17] 2.85 [5.33] 3.76 [5.20]
Teacher's age 30-39
1.84 [8.69] 3.56 [7.65] 1.59 [6.98] 2.20 [6.69]
Teacher's age 40-49
2.12 [9.71] 7.06 [9.67] 4.64 [9.05] 4.46 [9.18]
Teacher's age 50-59
-6.26 [10.26] 1.30 [11.23] 1.29 [10.78] 1.74 [11.75]
Male teacher
-0.54 [3.76] -1.02 [3.84] -0.23 [3.36] 1.79 [3.25]
Subject degree
12.96 [10.77] 9.02 [9.93] 4.45 [10.73] 2.35 [11.37]
Different academic abilities - a little
7.44 [5.23] 6.39 [4.53] 8.57* [4.80]
Different academic abilities - some
6.57 [5.32] 5.30 [4.61] 5.32 [4.66]
Different academic abilities - a lot
4.90 [5.73] 6.14 [4.99] 6.73 [5.11]
Range of background - a little
-2.41 [4.93] -1.20 [4.07] 0.19 [4.09]
Range of background - some
2.35 [4.89] 1.20 [4.08] 2.06 [4.12]
Range of background - a lot
-4.96 [6.14] -4.06 [5.28] -2.52 [5.08]
Disruptive students - a little
0.42 [8.53] 0.87 [7.34] 0.07 [6.83]
Disruptive students - some
8.69 [7.95] 8.97 [7.03] 8.81 [6.47]
Disruptive students - a lot
-2.17 [8.18] -3.62 [7.18] -3.95 [6.55]
Class size
-0.07 [0.12] -0.08 [0.11] -0.13 [0.11]
Instructional time
-0.04 [0.06] -0.00 [0.05] 0.00 [0.05]
Male student
-1.72 [2.28] -1.57 [2.26]
Age of student
-3.33*** [1.25] -3.21** [1.26]
Parents' highest education - Completed secondary
7.25 [7.04] 7.91 [6.82]
Parents' highest education - Upper secondary
8.39 [6.17] 8.44 [5.90]
Parents' highest education - Lower secondary
1.88 [6.72] 2.59 [6.26]
Parents' highest education - Less than lower secondary
0.83 [6.60] 1.89 [6.14]
Parents' highest education - Don't know
-33.01***[11.40]-32.34***[10.99]
Language at home - Sometimes Turkish
-14.46*** [3.88] -13.72*** [3.84]
Language at home - Never Turkish
-22.80** [9.45] -21.22** [9.45]
# of books at home - 11 -25
8.55*** [3.11] 8.21*** [3.06]
# of books at home - 26-100
11.97*** [3.31] 11.93*** [3.25]
# of books at home - 101 -200
13.39** [5.18] 12.94** [5.15]
# of books at home - more than 200
2.49 [4.48] 2.99 [4.45]
Absence of computer at home
-6.90*** [2.46] -5.92** [2.45]
School location - Rural
-4.67 [3.44]
Instruction resources - medium
8.34*** [2.93]
Instruction resources - high
31.12*** [9.63]
Observations
3,354
3,354
Adjusted R-squared
0.03
0.04
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
3,354
0.05
3,354
0.10
3,354
0.11
VARIABLES
Mathematics - Above median achiever sample - Difference-in-differences
Model 1
Model 2
Model 3
Model 4
coef
se
coef
se
coef
se
coef
se
Model 5
coef
se
TIMSS 2007 cycle
32.48*** [5.98]35.25*** [6.83] 35.10*** [7.20] 34.58*** [6.36] 30.04*** [6.16]
Treatment
-15.16***[3.97] 13.26 [9.65] 10.86 [9.59] 3.82 [7.48] 2.13 [7.27]
TIMSS 2007 cycle * Treatment
-11.90 [8.50] -18.43* [10.30] -16.59 [11.18] -8.38 [9.29] -0.26 [9.14]
Teacher experience
1.68* [0.91] 1.76** [0.89] 0.75 [0.67] 0.52 [0.64]
Teacher's age 25-29
5.97 [8.84] 4.17 [9.72] 0.41 [8.28] 1.26 [7.92]
Teacher's age 30-39
17.90 [13.64] 15.76 [14.58] 6.53 [11.31] 7.07 [10.99]
Teacher's age 40-49
3.48 [17.14] -1.21 [18.00] -1.48 [13.37] 2.83 [12.06]
Teacher's age 50-59
-7.70 [24.74] -13.20 [24.36] -6.65 [18.46] -7.48 [16.75]
Male teacher
-12.08* [6.99] -9.84 [6.45] -5.09 [4.91] -0.01 [4.82]
Subject degree
17.90 [11.74] 10.33 [12.07] 2.35 [10.46] -5.45 [9.78]
Different academic abilities - a little
8.80 [10.92] 6.35 [9.00] 8.41 [8.37]
Different academic abilities - some
2.78 [11.11] 3.39 [8.94] 2.74 [8.57]
Different academic abilities - a lot
10.33 [12.05] 8.86 [9.55] 10.38 [9.19]
Range of background - a little
-16.16* [9.55] -15.09** [7.45] -11.17 [7.14]
Range of background - some
-7.81 [9.45] -8.28 [7.53] -3.49 [7.16]
Range of background - a lot
-18.64* [10.76] -17.83** [7.97] -14.27* [7.42]
Disruptive students - a little
-4.33 [16.85] 0.13 [12.45] 10.88 [9.46]
Disruptive students - some
-9.85 [16.61] -1.91 [12.52] 9.13 [9.21]
Disruptive students - a lot
-12.29 [16.86] -9.38 [12.61] -2.19 [9.13]
Class size
-0.05 [0.22] -0.11 [0.17] -0.25 [0.17]
Instructional time
0.20 [0.13] 0.15 [0.10] 0.12 [0.10]
Male student
7.22** [2.94] 7.69*** [2.94]
Age of student
-2.41 [1.58] -2.24 [1.54]
Parents' highest education - Completed secondary
-30.04*** [6.94] -29.35*** [7.13]
Parents' highest education - Upper secondary
-39.78*** [6.01] -37.93*** [6.33]
Parents' highest education - Lower secondary
-55.28*** [6.99] -50.36*** [7.41]
Parents' highest education - Less than lower secondary
-60.10*** [7.37] -55.16*** [7.75]
Parents' highest education - Don't know
-76.10***[14.77]-78.87***[15.25]
Language at home - Sometimes Turkish
-0.67 [5.13] 0.70 [5.10]
Language at home - Never Turkish
-2.87 [17.08] -1.44 [17.37]
# of books at home - 11 -25
14.97*** [3.36] 14.31*** [3.44]
# of books at home - 26-100
21.77*** [3.78] 20.75*** [3.87]
# of books at home - 101 -200
32.75*** [5.73] 31.51*** [5.61]
# of books at home - more than 200
23.67*** [5.96] 22.83*** [5.73]
Absence of computer at home
-8.69** [3.88] -7.55* [3.86]
School location - Rural
-13.46*** [5.15]
Instruction resources - medium
7.30 [5.09]
Instruction resources - high
42.31*** [10.98]
Observations
3,396
3,396
Adjusted R-squared
0.05
0.08
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
3,396
0.10
3,396
0.21
3,396
0.23
VARIABLES
Science - Difference-in-differences
Model 1
Model 2
Model 3
coef
se
coef
se
coef
se
Model 4
coef
se
Model 5
coef
se
TIMSS 2007 cycle
34.88*** [7.81] 39.36*** [7.13] 52.30*** [9.69] 51.92*** [7.34] 48.20*** [7.26]
Treatment
-31.95*** [8.58] -27.12** [13.02] -16.24 [13.56] -16.95 [10.70] -14.43 [11.35]
TIMSS 2007 cycle * Treatment
-15.49 [12.30] -17.42 [12.34] -17.85 [13.71] -6.89 [10.63] -6.29 [10.56]
Teacher experience
2.12** [0.88] 2.41*** [0.79]
0.78
[0.61]
0.53
[0.62]
Teacher's age 25-29
14.34 [10.86] 16.53 [12.96] 7.84
[8.95]
8.61
[9.33]
Teacher's age 30-39
-16.72 [14.67] -10.46 [15.55] -10.92 [10.56] -9.14 [11.50]
Teacher's age 40-49
-13.06 [17.31] -18.18 [17.87] -8.10 [12.23] -1.93 [12.81]
Teacher's age 50-59
-46.36* [26.02] -38.98 [24.90] -22.26 [17.14] -18.77 [17.54]
Male teacher
-1.13 [7.22] -6.21 [6.82]
-0.95
[5.00]
-0.51 [4.92]
Subject degree
-3.78 [8.86] -0.45 [8.30]
-0.46
[6.08]
-0.68 [6.00]
Different academic abilities - a little
-16.47 [11.49] -15.87* [8.46] -16.24* [8.28]
Different academic abilities - some
-27.87** [11.33] -20.48** [8.86] -19.24** [8.83]
Different academic abilities - a lot
-16.86 [12.27] -12.41 [9.54] -12.70 [9.73]
Range of background - a little
-21.98** [9.82] -11.02* [6.57]
-7.89 [6.81]
Range of background - some
-30.30***[11.15] -16.68** [7.31] -11.78 [7.38]
Range of background - a lot
-37.82***[11.96] -23.51*** [8.89] -19.95** [9.08]
Disruptive students - a little
-14.36 [16.43] -7.00 [13.50] -3.07 [13.97]
Disruptive students - some
-29.67* [16.77] -20.26 [13.78] -17.69 [14.19]
Disruptive students - a lot
-23.05 [17.91] -13.07 [14.66] -11.57 [15.16]
Class size
0.27 [0.30]
-0.05
[0.21]
-0.10 [0.22]
Instructional time
0.33*** [0.11] 0.20*** [0.07] 0.16** [0.08]
Male student
7.11*** [2.53] 7.35*** [2.49]
Age of student
-8.38*** [1.55] -8.27*** [1.54]
Parents' highest education - Completed secondary
-36.60*** [5.30] -35.52*** [5.52]
Parents' highest education - Upper secondary
-49.54*** [5.02] -48.49*** [5.27]
Parents' highest education - Lower secondary
-73.06*** [5.86] -70.74*** [6.04]
Parents' highest education - Less than lower secondary
-83.01*** [6.32] -80.38*** [6.54]
Parents' highest education - Don't know
-121.91***[13.11]-119.68***[12.93]
Language at home - Sometimes Turkish
-37.04*** [4.52] -36.41*** [4.62]
Language at home - Never Turkish
-49.47*** [9.87] -48.72*** [9.70]
# of books at home - 11 -25
19.56*** [2.93] 18.75*** [2.86]
# of books at home - 26-100
36.81*** [3.83] 35.61*** [3.77]
# of books at home - 101 -200
39.09*** [4.73] 37.58*** [4.69]
# of books at home - more than 200
20.24*** [5.74] 19.60*** [5.65]
Absence of computer at home
-15.30*** [3.11] -14.34*** [3.12]
School location - Rural
-7.73 [6.23]
Instruction resources - medium
4.45
[5.79]
Instruction resources - high
27.58*** [9.70]
Observations
7,085
7,085
Adjusted R-squared
0.07
0.09
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
7,085
0.14
7,085
0.31
7,085
0.31
VARIABLES
Science - Female teacher sample - Difference-in-differences
Model 1
Model 2
Model 3
coef
se
coef
se
coef
se
Model 4
coef
se
Model 5
coef
se
TIMSS 2007 cycle
53.75***[11.81]53.07***[10.98] 57.95*** [16.13] 53.81*** [10.56] 49.39*** [10.88]
Treatment
-12.39 [9.55] -13.97 [16.86] -6.43 [21.74] -10.06 [17.72] 7.32 [20.02]
TIMSS 2007 cycle * Treatment
-42.53** [16.51] -28.14 [17.63] -15.22 [21.73] -4.98 [17.31] -11.90 [18.50]
Teacher experience
3.25* [1.68] 4.71*** [1.59] 2.97** [1.29] 2.86** [1.22]
Teacher's age 25-29
33.92** [13.35] 30.54* [17.17] 9.61 [13.55] 9.39 [14.56]
Teacher's age 30-39
-0.06 [21.80] 0.83 [26.24] -15.42 [17.94] -13.10 [19.06]
Teacher's age 40-49
-9.83 [28.92] -38.51 [31.77] -34.71 [22.64] -23.46 [23.80]
Teacher's age 50-59
-34.70 [56.16] -49.34 [60.15] -60.07 [42.76] -52.99 [41.62]
Subject degree
-8.22 [13.21] -13.55 [13.55] -12.88 [10.20] -12.13 [9.67]
Different academic abilities - a little
-22.66 [17.09] -18.31 [13.60] -18.80 [12.46]
Different academic abilities - some
3.55 [19.22] 2.39 [16.48] 1.52 [16.04]
Different academic abilities - a lot
-10.05 [17.54] -7.37 [14.68] -11.26 [15.64]
Range of background - a little
-30.78 [19.52] -11.93 [14.41] -7.60 [12.23]
Range of background - some
-42.22* [23.66] -23.99 [16.32] -14.15 [14.92]
Range of background - a lot
-30.33 [21.75] -16.55 [16.33] -14.07 [14.51]
Disruptive students - a little
-62.26** [24.44] -49.34** [22.37] -50.39** [20.97]
Disruptive students - some
-77.16***[26.60] -59.57** [23.81] -56.17** [22.48]
Disruptive students - a lot
-74.47***[27.01] -52.53** [24.16] -53.92** [23.16]
Class size
-0.11 [0.51]
-0.48
[0.38] -0.90** [0.40]
Instructional time
0.43*** [0.16] 0.26* [0.13]
0.22
[0.16]
Male student
1.14
[3.76]
1.22
[3.63]
Age of student
-8.15*** [2.45] -8.19*** [2.42]
Parents' highest education - Completed secondary
-30.50*** [7.91] -24.84*** [8.04]
Parents' highest education - Upper secondary
-46.67*** [6.93] -41.83*** [7.30]
Parents' highest education - Lower secondary
-67.67*** [8.61] -60.74*** [8.74]
Parents' highest education - Less than lower secondary
-77.59*** [9.28] -70.58*** [9.42]
Parents' highest education - Don't know
-109.12***[19.58]-100.56***[18.48]
Language at home - Sometimes Turkish
-28.78*** [6.13] -27.54*** [6.24]
Language at home - Never Turkish
-38.11*** [13.32] -35.02*** [12.54]
# of books at home - 11 -25
21.77*** [4.29] 20.82*** [4.29]
# of books at home - 26-100
45.44*** [5.39] 43.70*** [5.36]
# of books at home - 101 -200
46.25*** [7.40] 44.59*** [7.35]
# of books at home - more than 200
28.04*** [8.44] 26.20*** [7.86]
Absence of computer at home
-19.08*** [4.54] -16.95*** [4.50]
School location - Rural
-15.72 [10.32]
Instruction resources - medium
12.45 [8.91]
Instruction resources - high
48.47*** [12.47]
Observations
3,131
3,131
Adjusted R-squared
0.10
0.12
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
3,131
0.18
3,131
0.35
3,131
0.36
VARIABLES
Science - Male teacher sample - Difference-in-differences
Model 1
Model 2
Model 3
coef
se
coef
se
coef
se
Model 4
coef
se
Model 5
coef
se
TIMSS 2007 cycle
22.65** [10.11] 29.47*** [10.02] 49.20*** [11.82] 50.95*** [9.33] 48.99*** [9.27]
Treatment
-44.33***[13.10]-51.57***[19.66] -23.62 [18.52] -20.40 [14.59] -25.72* [14.53]
TIMSS 2007 cycle * Treatment
4.36 [17.24] -2.40 [17.70] -12.49 [17.03] -5.00 [13.05] -2.73 [12.71]
Teacher experience
1.26 [0.83] 2.12** [0.83]
0.76
[0.66]
0.75
[0.65]
Teacher's age 25-29
-7.83 [13.29] -5.34 [13.33] -0.08 [10.84] -4.63 [10.21]
Teacher's age 30-39
-47.60***[16.94] -27.85* [16.35] -17.55 [12.70] -24.07* [12.21]
Teacher's age 40-49
-31.34* [17.58] -35.74* [18.72] -18.23 [14.36] -23.62* [13.56]
Teacher's age 50-59
-60.29** [24.96] -51.64** [24.08] -26.10 [17.93] -31.57* [17.47]
Subject degree
-0.34 [12.65] 8.54 [9.37]
8.32
[6.53]
6.49
[6.55]
Different academic abilities - a little
2.11 [12.17] -0.14
[9.11]
0.96
[8.98]
Different academic abilities - some
-36.79***[11.82] -25.09*** [8.72] -22.86** [9.23]
Different academic abilities - a lot
-23.70* [13.96] -14.89 [9.37] -15.61 [10.01]
Range of background - a little
-19.35* [11.66] -12.16 [7.71]
-6.76 [9.24]
Range of background - some
-25.37** [12.77] -14.39 [8.95] -10.69 [9.78]
Range of background - a lot
-52.14***[12.73] -35.55*** [9.84] -29.54*** [10.82]
Disruptive students - a little
4.73 [15.09] 10.22 [9.50] 17.60** [8.23]
Disruptive students - some
-14.37 [16.51] -5.85 [10.44] -0.73 [8.97]
Disruptive students - a lot
5.21 [16.34] 9.13 [11.15] 16.61 [10.14]
Class size
0.46 [0.29]
0.13
[0.22]
0.16
[0.22]
Instructional time
0.30* [0.16] 0.21* [0.11]
0.15
[0.11]
Male student
12.76*** [3.34] 12.93*** [3.36]
Age of student
-8.72*** [2.05] -8.52*** [2.06]
Parents' highest education - Completed secondary
-39.08*** [7.21] -39.08*** [7.08]
Parents' highest education - Upper secondary
-50.71*** [7.27] -51.39*** [7.16]
Parents' highest education - Lower secondary
-74.58*** [7.97] -74.57*** [7.89]
Parents' highest education - Less than lower secondary
-83.97*** [8.83] -84.11*** [8.90]
Parents' highest education - Don't know
-123.96***[15.17]-123.93***[14.72]
Language at home - Sometimes Turkish
-36.89*** [5.87] -36.93*** [5.78]
Language at home - Never Turkish
-51.70*** [11.56] -51.96*** [11.55]
# of books at home - 11 -25
15.19*** [3.64] 15.20*** [3.57]
# of books at home - 26-100
27.02*** [4.44] 27.33*** [4.45]
# of books at home - 101 -200
31.87*** [5.54] 31.63*** [5.33]
# of books at home - more than 200
10.62 [7.52] 11.04 [7.55]
Absence of computer at home
-8.41** [3.95] -8.43** [3.98]
School location - Rural
5.33
[6.65]
Instruction resources - medium
-1.88 [6.42]
Instruction resources - high
24.35* [12.73]
Observations
3,954
3,954
Adjusted R-squared
0.05
0.08
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
3,954
0.17
3,954
0.31
3,954
0.31
VARIABLES
Science - Below median achiever sample - Difference-in-differences
Model 1
Model 2
Model 3
coef
se
coef
se
coef
se
TIMSS 2007 cycle
6.19*
Treatment
-5.49
TIMSS 2007 cycle * Treatment
-3.41
Teacher experience
Teacher's age 25-29
Teacher's age 30-39
Teacher's age 40-49
Teacher's age 50-59
Male teacher
Subject degree
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
Class size
Instructional time
Male student
Age of student
Parents' highest education - Completed secondary
Parents' highest education - Upper secondary
Parents' highest education - Lower secondary
Parents' highest education - Less than lower secondary
Parents' highest education - Don't know
Language at home - Sometimes Turkish
Language at home - Never Turkish
# of books at home - 11 -25
# of books at home - 26-100
# of books at home - 101 -200
# of books at home - more than 200
Absence of computer at home
School location - Rural
Instruction resources - medium
Instruction resources - high
[3.37]
[4.44]
[6.31]
Model 4
coef
se
9.80*** [2.82] 8.13* [4.78] 14.80*** [4.17]
-8.95
[5.95]
-8.46
[5.99]
-7.76
[5.32]
[5.76]
[6.05]
[5.23]
-5.99
-4.83
-2.70
0.52
[0.41]
0.56
[0.39]
0.18
[0.36]
8.08
[5.81]
9.18
[6.43]
5.62
[4.99]
-5.75
[7.98]
-4.03
[7.80]
-5.77
[5.96]
-6.05
[9.59]
-8.18
[9.51]
-7.45
[7.80]
-26.73** [12.82] -24.66* [12.62] -20.08* [10.23]
7.20** [3.06] 7.45** [3.02] 7.74*** [2.64]
1.08
[3.17]
1.34
[3.04]
2.49
[2.62]
-7.53* [4.32]
-8.41**
[3.93]
-7.73* [4.17]
-7.79**
[3.87]
-4.17
[4.70]
-5.52
[4.41]
-0.04
[4.30]
2.84
[3.71]
-9.46** [4.65]
-4.19
[3.92]
-9.33* [4.81]
-5.97
[4.20]
2.12
[5.52]
4.10
[4.82]
2.65
[5.83]
3.32
[5.14]
3.19
[6.08]
6.01
[5.23]
0.15
[0.14]
0.07
[0.11]
-0.02
[0.08]
0.03
[0.06]
-1.81
[2.11]
-3.38*** [1.13]
2.66
[5.53]
-1.84
[4.78]
-11.78** [5.25]
-12.69** [5.60]
-40.91*** [14.04]
-16.12*** [2.76]
-28.30*** [6.14]
6.27**
[2.88]
9.94*** [3.26]
7.11*
[4.25]
-7.46
[5.63]
-7.07*** [2.43]
Observations
3,536
3,536
Adjusted R-squared
0.01
0.03
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
3,536
0.04
3,536
0.10
Model 5
coef
se
16.69*** [3.87]
-3.42
[5.64]
[5.32]
-4.15
0.04
[0.35]
6.70
[4.77]
-3.54
[5.88]
-3.80
[7.42]
-17.97* [10.11]
7.64*** [2.52]
3.31
[2.46]
-9.15**
[4.07]
-8.44**
[3.98]
-7.39
[4.82]
3.89
[3.64]
-0.95
[3.79]
-5.00
[4.11]
4.44
[5.02]
2.04
[5.20]
4.69
[5.60]
-0.01
[0.10]
0.09
[0.06]
-1.82
[2.09]
-3.45*** [1.13]
2.88
[5.46]
-1.49
[4.64]
-10.30** [5.06]
-11.03** [5.43]
-39.27*** [13.70]
-15.92*** [2.72]
-27.27*** [6.12]
5.61*
[2.85]
9.38*** [3.20]
5.78
[4.14]
-7.99
[5.56]
-5.89**
[2.46]
-8.09*** [3.03]
2.33
[2.79]
21.23*** [4.51]
3,536
0.11
VARIABLES
Science - Above median achiever sample - Difference-in-differences
Model 1
Model 2
Model 3
coef
se
coef
se
coef
se
Model 4
coef
se
Model 5
coef
se
TIMSS 2007 cycle
44.34*** [4.63]42.79*** [4.28] 53.17*** [5.26] 51.64*** [5.05] 50.14*** [5.22]
Treatment
-21.54***[4.03] -14.39* [7.57] -7.25 [7.79] -7.27 [6.55] -5.74 [6.70]
TIMSS 2007 cycle * Treatment
-8.46 [6.25] -5.87 [6.94] -4.79 [7.31] -2.21 [6.52] -1.38 [6.53]
Teacher experience
1.48*** [0.52] 1.70*** [0.48] 0.87** [0.41] 0.76* [0.41]
Teacher's age 25-29
9.94* [5.57] 10.70 [6.70] 9.54* [5.47] 9.99* [5.41]
Teacher's age 30-39
-2.84 [8.29] 0.05 [8.16] 1.63 [6.68] 2.85 [7.09]
Teacher's age 40-49
-5.96 [9.82] -8.11 [10.01] -0.33 [8.36] 2.50 [8.48]
Teacher's age 50-59
-16.45 [13.12] -16.84 [12.76] -8.18 [10.62] -6.01 [10.49]
Male teacher
-7.71* [4.11] -10.72*** [3.96] -6.81** [3.24] -6.55** [3.25]
Subject degree
-2.80 [5.57] -3.37 [4.91] -4.84 [3.74] -5.07 [3.76]
Different academic abilities - a little
-6.93 [7.79] -7.40 [6.44] -7.54 [6.30]
Different academic abilities - some
-11.59 [7.46] -8.52 [6.43] -8.28 [6.41]
Different academic abilities - a lot
-12.92 [8.24] -9.50 [6.95] -9.48 [6.96]
Range of background - a little
-10.68* [6.29] -7.65 [4.79] -6.81 [4.78]
Range of background - some
-12.22* [7.12] -10.18* [5.27] -9.06* [5.14]
Range of background - a lot
-17.54** [7.43] -12.87** [6.29] -12.47** [6.30]
Disruptive students - a little
-10.31 [12.42] -9.57 [11.22] -8.04 [11.58]
Disruptive students - some
-15.84 [12.54] -13.30 [11.30] -12.24 [11.54]
Disruptive students - a lot
-10.28 [13.33] -9.25 [12.10] -8.60 [12.35]
Class size
0.15 [0.16] 0.01 [0.14] -0.02 [0.14]
Instructional time
0.26*** [0.06] 0.16*** [0.06] 0.15*** [0.05]
Male student
6.79*** [2.27] 6.89*** [2.27]
Age of student
-1.58 [1.50] -1.49 [1.49]
Parents' highest education - Completed secondary
-22.67*** [5.11] -22.49*** [5.21]
Parents' highest education - Upper secondary
-31.81*** [4.51] -31.51*** [4.64]
Parents' highest education - Lower secondary
-41.49*** [5.11] -40.90*** [5.28]
Parents' highest education - Less than lower secondary
-50.76*** [5.27] -50.17*** [5.42]
Parents' highest education - Don't know
-62.91***[15.56]-62.66***[15.66]
Language at home - Sometimes Turkish
-5.15 [4.83] -4.52 [4.82]
Language at home - Never Turkish
-16.57*** [6.22] -17.17*** [6.21]
# of books at home - 11 -25
11.33*** [2.84] 11.09*** [2.86]
# of books at home - 26-100
14.89*** [2.97] 14.42*** [3.02]
# of books at home - 101 -200
18.06*** [4.39] 17.64*** [4.40]
# of books at home - more than 200
16.63*** [4.88] 16.38*** [4.88]
Absence of computer at home
-5.42* [2.90] -5.26* [2.89]
School location - Rural
-4.73 [4.24]
Instruction resources - medium
1.00 [3.98]
Instruction resources - high
5.76 [6.80]
Observations
3,549
3,549
Adjusted R-squared
0.12
0.14
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
3,549
0.18
3,549
0.27
3,549
0.27
VARIABLES
Difference-in-differences and student fixed effects
Model 1
Model 2
coef
se
coef
se
TIMSS 2007 cycle
TIMSS 2007 cycle * Treatment
Teacher experience
Teacher's age 25-29
Teacher's age 30-39
Teacher's age 40-49
Teacher's age 50-59
Male teacher
Subject degree
Class size
Instructional time
Instruction resources - medium
Instruction resources - high
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
-11.21 [7.80]
3.68 [10.62]
6.88
[7.40]
[9.36]
4.28
1.05** [0.48]
9.16
[9.10]
24.89** [9.72]
12.34 [11.92]
-0.46 [15.01]
-7.26** [3.50]
-8.21** [4.00]
Number of observations
4,916
4,916
Adjusted R-squared
0.01
0.09
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
Model 3
coef
se
-9.33
[6.78]
14.77** [6.89]
0.06
[0.35]
7.09
[5.05]
11.24*
[5.84]
13.83** [6.71]
8.76
[9.43]
-3.73
[2.53]
0.84
[3.06]
-0.08
[0.22]
-0.48*** [0.05]
5.24
[4.56]
44.80*** [10.70]
2.23
[4.98]
-1.21
[4.74]
1.60
[4.66]
4.27
[3.74]
7.57*
[4.33]
3.80
[4.97]
0.67
[7.86]
0.87
[7.51]
-4.89
[7.73]
4,916
0.22
Difference-in-differences and student fixed effects - Female teacher sample
Model 1
Model 2
Model 3
VARIABLES
coef
se
coef
se
coef
se
TIMSS 2007 cycle
TIMSS 2007 cycle * Treatment
Teacher experience
Teacher's age 25-29
Teacher's age 30-39
Teacher's age 40-49
Teacher's age 50-59
Subject degree
Class size
Instructional time
Instruction resources - medium
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
-32.13*** [0.00] -12.17 [9.66] -35.48** [14.43]
[12.92] 16.07 [12.61] 41.56** [18.32]
15.10
1.07
[0.88]
0.24
[0.73]
-7.71 [19.91]
0.30
[11.72]
9.03 [21.81]
-5.86
[15.12]
-0.96 [22.65]
10.72
[18.84]
14.96 [26.05] 55.19** [21.44]
6.35
[8.13] 12.80** [5.97]
0.43*
[0.24]
-0.64*** [0.14]
31.50** [14.79]
-2.11
[9.63]
-3.48
[10.65]
-4.48
[6.60]
-10.47
[9.80]
-6.90
[9.77]
-10.73
[9.34]
15.86
[10.04]
10.79
[11.63]
8.24
[14.13]
Observations
612
612
Adjusted R-squared
0.04
0.10
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
612
0.30
Difference-in-differences and student fixed effects - Male teacher sample
Model 1
Model 2
Model 3
VARIABLES
coef
se
coef
se
coef
se
TIMSS 2007 cycle
TIMSS 2007 cycle * Treatment
Teacher experience
Teacher's age 25-29
Teacher's age 30-39
Teacher's age 40-49
Teacher's age 50-59
Subject degree
Class size
Instructional time
Instruction resources - medium
Instruction resources - high
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
-24.94
[.]
7.11 [10.85]
10.91
8.59
0.93
23.43*
47.15***
31.10*
24.40
-7.71
[9.76]
[13.17]
[0.71]
[13.82]
[13.92]
[17.95]
[21.35]
[8.22]
Observations
1,166
1,166
Adjusted R-squared
0.03
0.14
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
-20.29
17.63
-0.29
6.11
18.31
18.78
15.97
-0.40
0.02
-0.38***
-4.53
32.43*
-4.87
-7.00
-12.29
8.69
10.74
1.65
7.60
9.36
9.31
[17.07]
[14.13]
[0.75]
[12.17]
[12.48]
[12.90]
[17.06]
[6.60]
[0.50]
[0.08]
[7.03]
[17.23]
[10.60]
[11.22]
[8.83]
[6.89]
[10.83]
[13.21]
[14.69]
[13.73]
[16.30]
1,166
0.23
Difference-in-differences and student fixed effects - Below median achiever sample
Model 1
Model 2
Model 3
VARIABLES
coef
se
coef
se
coef
se
TIMSS 2007 cycle
TIMSS 2007 cycle * Treatment
Teacher experience
Teacher's age 25-29
Teacher's age 30-39
Teacher's age 40-49
Teacher's age 50-59
Male teacher
Subject degree
Class size
Instructional time
Instruction resources - medium
Instruction resources - high
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
-15.55** [7.85]
[12.59]
3.94
16.23
-0.60
2.08***
3.25
27.26**
-0.39
-22.03
-7.47
-14.58**
[10.98] -18.57*** [4.78]
[12.49] 20.67*** [5.32]
[0.77]
0.19
[0.31]
[11.13]
2.06
[3.95]
[12.58]
4.75
[5.25]
[16.90]
3.70
[6.51]
[21.57]
-2.07
[8.61]
[5.18]
-0.67
[2.45]
[5.78]
0.87
[2.79]
-0.09
[0.32]
-0.80*** [0.06]
1.06
[4.47]
150.53*** [9.17]
-0.02
[4.47]
-2.92
[3.88]
-0.66
[4.11]
4.75
[3.38]
4.66
[3.47]
0.85
[3.61]
1.29
[6.60]
1.73
[6.30]
-1.90
[6.62]
Observations
2,959
2,959
Adjusted R-squared
0.02
0.20
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
2,959
0.52
Difference-in-differences and student fixed effects - Above median achiever sample
Model 1
Model 2
Model 3
VARIABLES
coef
se
coef
se
coef
se
TIMSS 2007 cycle
TIMSS 2007 cycle * Treatment
Teacher experience
Teacher's age 25-29
Teacher's age 30-39
Teacher's age 40-49
Teacher's age 50-59
Male teacher
Subject degree
Class size
Instructional time
Instruction resources - medium
Instruction resources - high
Different academic abilities - a little
Different academic abilities - some
Different academic abilities - a lot
Range of background - a little
Range of background - some
Range of background - a lot
Disruptive students - a little
Disruptive students - some
Disruptive students - a lot
-4.42
2.94
[6.98]
[8.44]
2.59
2.42
0.02
13.76***
18.82***
20.22***
18.12*
-7.97***
-0.59
[6.71]
[8.13]
[0.31]
[5.18]
[6.05]
[7.12]
[9.74]
[3.02]
[3.30]
Observations
1,675
1,675
Adjusted R-squared
0.00
0.03
Standard errors clustered at the class level; *** p<0.01, ** p<0.05, * p<0.1
-1.77
6.17
-0.17
14.01***
14.46**
19.76***
19.38**
-6.27**
3.43
-0.15
-0.17***
8.63*
7.04
-1.08
-3.50
1.08
0.57
3.12
1.59
-5.50
-5.41
-4.52
1,675
0.06
[9.16]
[9.91]
[0.32]
[4.69]
[5.74]
[5.96]
[8.55]
[2.99]
[3.44]
[0.19]
[0.05]
[4.98]
[11.28]
[4.09]
[4.24]
[4.07]
[3.04]
[3.89]
[5.45]
[7.76]
[7.68]
[7.80]
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