DISCUSSION PAPER SERIESIZA DP No. 15101

Do Teachers’ College Majors AffectStudents’ Academic Achievement in the
Sciences? A Cross-Subfields Analysis with
Student-Teacher Fixed Effects
Atsushi Inoue
Ryuichi Tanaka

FEBRUARY 2022

DISCUSSION PAPER SERIESIZA DP No. 15101

Do Teachers’ College Majors AffectStudents’ Academic Achievement in the
Sciences? A Cross-Subfields Analysis with
Student-Teacher Fixed Effects
Atsushi Inoue

Nippon Institute for Research Advancement

Ryuichi Tanaka

University of Tokyo, RIETI and IZAFEBRUARY 2022

Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may
include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA
Guiding Principles of Research Integrity.
The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics
and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs theworld’s largest network of economists, whose research aims to provide answers to the global labor market challenges of ourtime. Our key objective is to build bridges between academic research, policymakers and society.
IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper
should account for its provisional character. A revised version may be available directly from the author.

IZA – Institute of Labor Economics
Schaumburg-Lippe-Straße 5–953113 Bonn, Germany

Phone: +49-228-3894-0
Email: publications@iza.org

www.iza.org

IZA DP No. 15101

FEBRUARY 2022

ABSTRACTDo Teachers’ College Majors AffectStudents’ Academic Achievement in the
Sciences? A Cross-Subfields Analysis with
Student-Teacher Fixed Effects*
We examine whether and how teachers’ major fields of study affect students’ achievement,
exploiting within-student variation across subfields in natural science (i.e., physics,
chemistry, biology, and Earth science). Using middle-school students’ data from the Trends
in International Mathematics and Science Study and controlling student-teacher fixed
effects, we find that teachers with college majors in natural sciences improve students’achievement of subfields in natural sciences corresponding to their subfields of college
majors. Teaching practices explain about half of the effect of teachers’ major fields. Mostof the effects of teaching practices are accounted for by teachers’ preparation for teachingscience topics. The results are robust to potential endogenous matching between students
and teachers.
JEL Classification:

H75, I21, J24

Keywords:

education, teacher, natural science, college major, middle
school, TIMSS

Corresponding author:
Ryuichi Tanaka
Institute of Social Science
The University of Tokyo
7-3-1 Hongo
Bunkyo-ku
Tokyo 113-0033
Japan
E-mail: ryuichi.tanaka@iss.u-tokyo.ac.jp* This study is conducted as a part of the Project “Microeconometric Analysis of Education Policy with Large
Administrative Data” undertaken at the Research Institute of Economy, Trade and Industry (RIETI). The authors are
grateful to valuable comments from Masayuki Morikawa, Daiji Kawaguchi, and the participants of research meetingof the project and Discussion Paper seminar participants at RIETI. The authors acknowledge financial support for thisproject from the Japan Society for the Promotion of Science (Grant Numbers 20H05629 for Ryuichi Tanaka). The usual
disclaimer applies. Any errors are our own.

1.

Introduction

Science literacy is one of the most critical determinants of success in the labor market. Altonji et al.
(2012) and Kirkebøen et al. (2016) showed that science, technology, engineering, and math (STEM)
majors are positively associated with earnings later in life. Card and Payne (2021) showed that mostof the gender gap in STEM entry is due to differences in the share of college entrants who are STEMready. They also found that the gender gap in the likelihood of graduating with a STEM-related degree
explains up to a fifth of the wage gap between young college-educated men and women in the UnitedStates and Canada. These findings indicate that promoting STEM education can be a policy measure
to improve labor productivity and reduce the gender gap in wages.
Improving science teachers’ quality is one of the key measures for enhancing students’ science
literacy. Researchers argue that teacher quality significantly impacts student learning outcomes(Hanushek and Rivkin, 2010). However, the predictive power of “observable” characteristics ofteachers, such as gender and teaching experience, is generally limited (Hanushek and Rivkin, 2006).
Measurement of teacher quality is not straightforward; hence, researchers and education
administrators need to find observable characteristics of teachers associated with teacher quality toachieve optimal recruitment and assignment of highly qualified teachers.
Many researchers have studied the relationship between teachers’ subject-related degrees,
knowledge, and students’ achievement as another observable predictor of teacher quality. To examine
this relationship, they used various information, such as major fields (Chingos and Peterson, 2011;
Croninger et al., 2007; Goldhaber and Brewer, 1997; 2000), subject-specific certification (e.g.,
Clotfelter et al., 2010; Dee and Cohodes, 2008; Goldhaber and Brewer, 2000; 2001; Sharkey andGoldhaber, 2008), teacher training (Backes et al., 2018; Harris and Sass, 2011), and more direct
measures of a teacher’s knowledge level, including teachers’ test scores and grade point average(Bietenbeck et al., 2018; Boyd et al., 2009; Goldhaber et al., 2017; Hill, Rowan, and Ball, 2005; Kukla-2

Acevedo, 2009; Metzler and Woessmann, 2012; Monk, 1994; Rockoff et al., 2011). A review by
Coenen et al. (2018) argued that teachers’ subject-related degrees and knowledge, particularly formathematics and science, are positively associated with students’ academic performance. 2 The
mechanism of teachers’ subject-related degrees and knowledge on students’ academic performance
are not very well understood, despite some studies finding a positive association between them.
This study examines whether and how teachers’ majors affect students’ achievement. In contrast
to previous studies focusing on the relationship between teachers’ majors and students’ academic
achievement at the subject level, such as language, mathematics, and science (Coenen et al., 2018;
Wayne and Youngs, 2003), we study the relationship between science teachers’ “subfields” in thenatural sciences of college majors and students’ performance in the corresponding subfields. Sciencetaught in middle schools is generally divided into four subfields: physics, chemistry, biology, andEarth science. We exploit within-student variation across subfields in science to identify the effects ofteachers’ majors in natural science. Within-student variation across subfields enables us to estimate
the effects of a teacher’s college major, controlling student fixed effects “specific to natural science.”
Furthermore, we examine the primary determinants of these effects. This examination of themechanism behind the effect of teachers’ majors in natural science is one of our contributions.
In estimating the causal effects of teachers’ major fields, it is necessary to consider potential
biases due to endogenous matching between teachers and students. Ideal to estimate causal effects
would be random student-teacher assignment within and across schools. However, assignment israrely random because of school choice and optimal classroom formation (Clotfelter et al., 2006;
Bonesrønning et al., 2005). For our research question, if unobserved characteristics are associated withboth teachers’ majors and students’ science test scores, the estimates of the effect of teachers’ majors
will be biased. A typical strategy is to control students’ fixed effects by taking within-student cross2
Several studies reported that the effect of subject-related knowledge or degrees is greater in STEM subjects than in
non-STEM subjects (e.g., Clotfelter et al, 2010; Goldhaber and Brewer, 1996; Metzler and Woessmann, 2012).

3

subject differences (Bietenbeck et al., 2018; Clotfelter et al., 2010; Dee, 2007; Hanushek et al., 2019;
Metzler and Woessmann, 2012). This cross-subject differencing works well to eliminate subjectinvariant fixed effects for both students and teachers when the same teacher teaches students different
subjects. However, this may not resolve the issue when other teachers teach each subject and whenteacher assignments are based on students’ unobserved characteristics. We address this issue by
restricting our sample to students taught by a science teacher responsible for all four subfields inscience and controlling student-teacher fixed effects by exploiting the variation within students acrosssubfields in science. Provided that student-teacher fixed effects are invariant across subfields, crosssubfield differencing identifies the impact of teachers’ major fields in natural science on students’
academic achievements by eliminating student-teacher fixed effects.
By estimating a model with data of middle school students, taken from the Trends in International
Mathematics and Science Study (TIMSS) for 2003, 2007, and 2011, we find that students taught byteachers majoring in natural science fields improve their academic achievement in the corresponding
subfields in science by 0.04 standard deviation. In addition, to remove potential bias due toendogenous matching between students and teachers, we estimate the same model with a sample ofstudents who attend a school that is assumed to have only one class in the grade and find that theresults are robust.3 Teachers who majored in a specific field of natural science would be more likely
to improve students’ achievement not only in the field but also in other fields of it. If these spillover
effects exist, the estimates here are considered lower bounds because of the spillover effects of teachers’
major fields.

3

A recent working paper employed a similar identification strategy with the TIMSS data to account for student fixed
effects by exploiting the variation within students across subfields in science (Sancassani, 2021). Since the focus of thispaper is to examine the effect of teacher characteristics (including subfields in science) on students’ scienceachievement, the sample was restricted to students from countries where the subfields in science are taught by “different”
teachers. In contrast, the main focus of our paper is to identify the effects of subfields in natural science of teachers’
major fields and its mechanism “controlling student-teacher fixed effects” by restricting our samples to students taught
by “same” teachers. The first draft of our paper was written in November 2020 without any awareness about thisprevious paper, which, however, is complemented by our paper.

4

To understand the mechanisms behind the effect of teachers’ major fields, we investigate therelationship between teachers’ major fields and their teaching practices. We find that teachers who
majored in a natural science field tend to prepare well to teach science topics, allocate more time, andteach a broader range of subfields corresponding to the major field. In addition, we conduct an analysis
to examine the extent to which these teaching practices explain the effects of teachers’ majors. We
find that the degree of preparation to teach science topics explains about 22% of the effect of teachers’
major fields, which is the largest ratio among other channels, such as time allocation and range ofteaching topics. This result suggests that teachers’ majors in natural science positively affect students’
academic outcomes by improving the quality of their preparation for teaching. Furthermore, about
50% of the effect remains unexplainable, implying that the unobservable quality of teachers associatedwith their major fields plays a vital role in improving students’ academic achievement. We also findheterogeneity in the effects of teachers’ major fields: the positive effect is more substantial for studentstaught by teachers with a greater number of major fields, female students, and students with a highlevel of ability.
The remainder of this paper is organized as follows. Section 2 discusses the empirical strategy.
Section 3 elucidates our data and reports summary statistics. Section 4 presents the main effects ofteachers’ subfield majors. Section 5 discusses the potential mechanisms behind these effects. Section
6 presents heterogeneous effects. Section 7 concludes.

2.

Econometric Framework

To investigate the effect of teachers’ majors in natural science on students’ academic achievement, weestimate the following regression model:!!"# = $%&'()"# + +" + ,!# + -!"#

5(1)

where Yifj is the science test score of student i in subfield f in class j. Majorfj is an indicator that
represents whether the teacher’s major fields in natural science and the subfields of the student’s testscore match, and thus, β is the coefficient of our primary interest. δf is the subfield fixed effect. ηij isthe student-teacher fixed effects, controlling for any subfield-invariant determinants of science testscores attributable to the pair of student i and teacher j. εifj is an error term. To show the importance ofcontrolling student-teacher fixed effects, we present the ordinary least squares estimation results
without student-teacher fixed effects too.
The identifying assumption of the model parameters is that the error term is orthogonal to teachers’
major fields %&'()"# conditional on student-teacher fixed effects. A potential threat to identification
is sorting students into teachers within a school based on students’ subfield-specific unobservables.
For example, if students are more capable of studying physics than other subfield subjects, and scienceteachers who majored in physics are systematically assigned to these students, the estimates are biased.
To confirm the robustness of the results against potential endogenous matching between students andteachers based on students’ subfield-specific unobservables, we restrict our sample to small schools
with only one class in the grade, wherein multiple science teachers in the grade are unlikely. Standard
errors were clustered at the class level, accounting for the correlation in outcomes among students inthe same class.

3.

Data

The data used in this study include information on middle school students taken from the TIMSS
survey for 2003, 2007, and 2011. 4 TIMSS, an international survey conducted every four years,
measures the mathematics and science achievement of fourth- and eighth-grade students and students’,4

We excluded TIMSS 2015 and 2019 survey because the published dataset does not include enrollment information
used for a robustness check in this paper.

6homes’, teachers’, and schools’ characteristics, as well as curricular backgrounds in participating
countries and regions. For example, 608,641 students, 49,429 teachers, and 19,612 school principals
from 63 countries participated in the fifth survey conducted in 2011.
Each student’s test scores in each subfield of science (i.e., physics, chemistry, biology, and Earthscience) have five plausible values (PVs) in the TIMSS dataset.5 PVs were generated by exploiting
the item response theory method based on the results of the tests and questionnaires. The mean of thenational average scores is 500, and the standard deviation is 100 for all countries participating in
TIMSS 1995.6 In all estimations, we use “student senate weight” included in the dataset that rescaled
the student sampling weight to give the same weight to each country because we use student samples
from several countries.7 In addition, we estimate the models using each of the five PVs separately andreport the average of the five estimates.
This study excludes samples of students taught by more than one science teacher from theanalysis because when multiple teachers teach science classes, sorting between students and teachers
can occur across subfields, potentially causing bias in the estimation. We compile student data fromthe four subfields to estimate the student-teacher fixed effects model that compares within-student
cross-subfields in science. The sample for the estimation consists of 219,220 middle school students
and 10,927 science teachers from 10,907 science classes in 9,378 schools from 49 countries worldwide.
The size of the pooled samples from the TIMSS of 2003, 2007, and 2011 is 513,295, excluding
students who could not be linked to their science teachers and who had multiple science teachers. We
exclude samples with missing values for any variable used in the estimation, as listed in Table 1.

5

Samples were extracted by employing the two-stage random sample design where schools are selected as a first stage
and classes are selected as a second stage. TIMSS uses a matrix-sampling booklet design, in which each student isadministered only a subset of the entire TIMSS item pools to cover a wide range of topics. Therefore, not all
participating students took the same test.
6
PVs are multiple imputations of the unobservable latent achievement for each student (Wu, 2005). For more detailed
information about the methods and procedures in TIMSS, see Martin and Mullis (2012).
7
For example, Woessmann (2011) and Hanushek et al. (2015) used international survey data such as PISA and PIAAC,
and conducted weighted analyses with same weights for each country.

7

[Table 1]
Table 1 reports the descriptive statistics of the variables: students’ academic achievement,teachers’ majors, teaching practices, and demographics. As shown in Table 1, the fraction of teachers’
major fields matching a subfield of science test scores (i.e., the mean of %&'()"# ) is 31.6 percent.8
Since this fraction would be 25 percent if all teachers majored in only one of the four fields in naturalscience, and 50 percent if all of them majored in two of the four fields, the result of 31.7 percent
implies that, on average, science teachers majored in more than one subfield but less than two subfields.
In terms of each field, the proportion of teachers majoring in biology is relatively higher, whereas the
proportion of those majoring in Earth science is much lower than the other two subfields.9 Regarding
the number of fields teachers majored in, the percentage of teachers who majored in only one field isthe highest at 41%, whereas the percentage of teachers who majored in all four fields is the lowest at
6.7%.
Regarding student academic outcomes, the mean of the students’ test scores is 471, and thestandard deviation is 116, including all subfield test scores. There is almost no difference in score
distribution across the subfields.10
TIMSS also contains information about teaching practices in each subfield, which we use toinvestigate the relationship between teachers’ major fields and teaching practices. We construct anindicator of teachers’ preparation to teach science topics in each subfield by using the following
question for teachers: “How well-prepared do you feel you are to teach the following science topics?”
This question has several items about science topics in each subfield, and teachers are asked to choose

8Details of science teachers’ major fields are gathered in the questionnaire for teachers. Teachers are asked to choose“yes” or “no” for each major field, including physics, chemistry, biology, and Earth science.
9
While the ratio of science teachers who majored in Earth science is small and may have unique characteristics thatcannot be observed, robust results are obtained when Earth science is excluded from the data set. We can provide theresults upon request.
10
While the TIMSS 2007 and 2011 included test scores for the following four subfields in science: biology, physics,
chemistry, and Earth science, the TIMSS 2003 include following five subfields test score: life science, physics,
chemistry, Earth science, and environmental science. We regard the scores in life science in the TIMSS 2003 as thescores in biology. All results in this paper are robust when the samples of TIMSS 2003 are excluded.

8

from “Not applicable,” “Very well prepared,” “Somewhat prepared,” and “Not well prepared.” Whena topic is not in the eighth-grade curriculum or teachers are not responsible for teaching the subjects,
they are asked to choose “Not applicable.” We score 1, 2, and 3 when teachers chose “Not well
prepared,” “Somewhat prepared,” and “Very well prepared,” respectively, and the average score foreach subfield is used as an indicator of teachers’ preparation to teach science topics.11 As shown in
Table 1, the mean of the teachers’ preparation scale is 2.56 when all subfields are included. This meansthat the readiness of teachers to teach science topics, on average, is somewhere between “Very well
prepared” and “Somewhat prepared.” Focusing on each subfield, while teachers are relatively well
prepared in chemistry, they are not prepared in Earth science. Furthermore, Table A1 shows thecorrelation coefficients between teachers’ major fields and the preparation status for teaching science
topics, indicating that teachers are better prepared for the subfields corresponding to their major fields
than other subfields.
Next, we construct an indicator for teaching time allocation in each subfield using the following
item: “By the end of this school year, approximately what percentage of teaching time will you havespent during this school year on each of the following science content areas for the students in thisclass?” Teachers are asked to respond to the percentage for each subfield, such that the total is 100
percent.12 As shown in Table 1, the total average of the four subfields is approximately 92 percent,indicating that these four are the main areas taught by science teachers. Teachers spend less time on
Earth science than others, focusing on each subfield.13 Furthermore, Table A2 shows the correlation

11

Regarding teachers’ preparation to teach science topics, when more than one-third of the question items in a subfield
are missing or “Not applicable,” we put a missing value in the indicator of teachers’ preparation. The number of items
in each subfield are as follows: in the order of the 2003, 2007, and 2011 TIMSS survey, 5, 7 and 7 in biology; 5, 5, and
4 in chemistry; 5, 6, and 5 in Physics; 3, 5, and 4 in Earth science.
12
While the TIMSS 2007 and 2011 have five science subfields in this question: biology, physics, chemistry, Earthscience, and others, the TIMSS 2003 has six: life science, physics, chemistry, Earth science, environmental science,and others. We regard the answer in the life science area in the TIMSS 2003 as the answer in the biology area.
13
The minimum value of the variable “Time allocation” is 0 and the maximum value is 100, indicating that some of
the teacher sample teach only one specific subfield in a year. In the robustness check section, we only present resultsof teachers who teach all subfields in science in a year and thus, show that our results are robust.

9coefficients between teachers’ major fields and teaching time allocation, indicating that a slightly moretime is spent on the subfields corresponding to the teachers’ major fields than on other subfields.
Finally, we evaluate the range of topics taught in each subfield using the following question:“Choose the response that best describes when the students in your class have been taught each
topic.”14 This question has many items that describe the main topics addressed by the TIMSS Science
Test. If a teacher chooses “Mostly taught before this year” or “Mostly taught this year,” we assume
that they had already taught the topic. As shown in Table 1, the mean percentage of the range of topics
taught is 67.5 percent. Focusing on each subfield, the percentage of Earth science topics is 59.1 percent,lower than approximately 70 percent of the others. Furthermore, Table A3 shows the correlation
coefficients between teachers’ major fields and the range of topics taught, indicating minimalcorrelation between them. This result may be because the national and school curricula regulate theteaching content, and teachers do not have much discretion regarding what to teach.
TIMSS also contains rich information on students, family, teachers, and school characteristics,
such as students’ gender, parents’ education, teachers’ gender, years of teaching, class size, andenrollment in the grade. We use these pieces of information as covariates in the regression analysis,robustness check, and exploration of the heterogeneous effects of teachers’ major fields.

4.

Results

This section presents the results of regression analysis using Eq. (1). First, we explain the main
results. Then, we discuss the robustness of our main findings.

14
Teachers are asked to choose “Mostly taught before this year” if a topic was in the curriculum before the eighth
grade. If a topic was partially taught in this year but not yet completed, they are asked to choose “Mostly taught thisyear.” If a topic is not in the curriculum, they are asked to choose “Not yet taught or just introduced.” When more than
one-third of the question items in a subfield are missing, we put a missing value in the indicator. The number of theitems in each subfield as follows: In the order of the 2003, 2007, and 2011 TIMSS survey, 12, 14 and 7 in biology; 8,8, and 4 in chemistry; 10, 10, and 5 in Physics; 11, 14, and 4 in Earth science.

10

4.1.

The effects of teachers’ major fields on test scores
Table 2 shows the estimated coefficients of the dummy variable indicating the match between

teachers’ major subfields in science and students’ test subjects. We report the results for four
specifications in column order: (1) including year fixed effects, subfield fixed effects, and country
fixed effects; (2) adding student and family controls: students’ gender and parents’ education; (3)adding teacher and school controls: teachers’ gender, years of teaching (quadratic polynomial), classsize (cubic polynomial), enrollment in the grade (cubic polynomial), and (4) controlling studentteacher fixed effects.
[Table 2]
The results show that the relationship between teachers’ majors and students’ achievement is
positive and statistically significant (Column (1)). This positive relationship is stable after controllingfor student and family characteristics (Column (2)) and teacher and school characteristics (Column(3)). The coefficient in Column (3), 5.0081, implies that the subfield score of students taught byteachers who majored in that subfield in college is higher by 0.05 standard deviation than thoseteachers without a college major in that subfield.
Column (4) presents the estimation results with student-teacher fixed effects. The teachers’
majors’ field dummy coefficient remains positive and statistically significant. The estimatedcoefficient, 4.6485, is slightly lower than the estimate in Column (3), suggesting that the estimates
without controlling for student-teacher fixed effects may be biased upward because of a positive
correlation between teachers’ major fields and unobservable determinants of student achievement in
subfields.
Although we obtained a positive and statistically significant effect of teachers’ subfield majors,
the estimated effects may be underestimated due to spillover effects from one subfield to the others.
For example, a teacher who majored in physics may improve students’ achievement in physics and11the achievement of other subfields in science. If these spillover effects exist, our estimates can beconsidered to present a lower bound for the effects of teachers’ major fields.

4.2.

Robustness

The student-teacher fixed effects model identifies the effects of teachers’ majors if the matching
between teachers and students is orthogonal to the comparative advantage of students across subfields
in natural science. The model with student-teacher fixed effects addresses the issue of endogenous
matching between students and teachers based on “absolute ability,” which is common to all subfields
in natural science. However, one may be concerned that the estimate in Column (4) may be derived
from the sorting of students into schools or teachers based on students’ “subfield-specific”
unobservables (“comparative ability”). For example, in cases where a student who is better at physics
than other subfields selects a school with a high proportion of science teachers who majored in physics,
or where a teacher who majored in physics is assigned to a class with students who are better at physics
than the other subfields, the estimates are biased upward. We check whether these sorting mechanisms
between and within schools may be a serious concern in the estimation of Column (4) in Table 2 byrestricting the sample to those in small municipalities—where it is unlikely that there are schools thatspecialize in a particular subfield in science—and to those in schools with only one class and one
science teacher in a grade where within-school matching between students and teachers based oncomparative ability hardly occurs. Additionally, to eliminate schools or teachers that specialize in aparticular subfield, we present results with the sample restricted to those who have teachers teaching
all four subfields, spending an instructional time of at least 10% and 20%.15
First, to confirm whether there exist any between-school sorting effects, we restrict the sample tostudents living in small municipalities with a population of less than 15,000, wherein schools

15

The robustness check here uses an approach similar to the one used by Bietenbeck et al. (2018).

12

specializing in a particular subfield in science are unlikely. The results in Column (1) of Table 3 showthat the estimated effect is almost the same as in Column (4) of Table 2. This result suggests that thepotential sorting of students into schools based on their comparative ability across subfields in naturalscience is not a serious concern in our sample.
Second, we restrict the sample to students in schools with only one class in the grade, where it
seems unlikely that multiple science teachers teach one grade.16 Column (2) of Table 3 reports theestimation results for restricted samples. The coefficient is 3.5778, slightly lower than the estimate in
Column (4) of Table 2, suggesting a positive within-school correlation between teachers’ major fields
and unobservable student achievement determinants of subfield. However, the estimate in Column (2)of Table 3 remains statistically significant, suggesting that the reported positive effect of college
majors in Column (4) of Table 2 is robust for the possible sorting of students to teachers within aschool.
Third, to eliminate schools or teachers specializing in a particular subfield, we restrict the sample
to those in which the teachers’ instructional timeshare is at least 10% and 20% in each subfield.
Columns (3) and (4) of Table 3 report that the coefficients are positive but smaller than the estimate
in Column (4) of Table 2. There are at least two ways to interpret these results. First, we consider
student sorting: A group of students who were originally good at specific subfields was assigned to ateacher who majored in the corresponding fields and had a large proportion of teaching hours in thesubfields, causing an upward bias. The second interpretation considers the loss of the partial effect ofteachers’ major fields. Teachers who majored in an area in natural science tended to increase theirinstructional timeshare in the subfields corresponding to their major fields, resulting in relatively
higher student performance in the other subfields. Although the results of columns (3) and (4) of Table

16
While TIMSS does not include information about the number of classes in the grade, it obtains enrollment in thegrade from school questionnaires and class size from teacher questionnaires. We, therefore, define the student sample
who attend schools where the enrollment in the grade does not exceed the class size as students who attend schools
with only one class in the grade.

13

3 could include either interpretation, the coefficients on major in fields taught remain positive andstatistically significant, suggesting the robustness of our result in Column (4) of Table 2 to the potential
sorting of students to schools and teachers. In the next section, we explore the possible mechanisms
accounting for the positive effect of teachers’ college majors.

5.

Mechanism

Recent studies have shown that some teaching practices substantially impact educational outcomes(Schwerdt and Wuppermann, 2011; Van Klaveren, 2011; Bietenbeck, 2014; Lavy, 2016; Tanaka andIshizaki, 2018; Motegi and Oikawa, 2019). It is possible that differences in teachers’ majors may cause
differences in their knowledge and teaching practices, affecting students’ academic achievement. In
this subsection, we analyze the relationship between science teachers’ major areas and teachingpractices to understand the positive effects of teachers majoring in natural science fields on students’
academic achievement.
Understanding the mechanisms of the effects of teachers’ majors is very helpful in policymaking
and school administration too because it sheds light on practical and accessible ways to improvestudent outcomes. For example, if a teacher does not have majors in the subfields of science for which
they are responsible, the principal would assign another teacher with science majors. In case wheresuch a teacher arrangement is infeasible, the principal could provide teachers with training and othereducational opportunities to encourage them to improve the quality of their teaching, especially interms of preparation for teaching science topics.
To investigate the effect of teachers’ majors on their teaching practices, we use three indicatorsof teaching practices in each subfield described in Section 3: (1) teachers’ preparation to teach science
topics, (2) teaching time allocation, and (3) the range of science topics being taught. We investigate

14whether teaching practices change depending on teachers’ major fields by estimating the following
regression model (2):$./&0ℎ234"#= 5 $ %&'()"# + +" + ,!# + -!"#

(2)

where Teachingkfj is the teaching practice k of teacher j in subfield f.
[Table 4]
Table 4 presents the estimates of the coefficients, αk. These results show that teachers tend toprepare well for teaching and spend more time teaching a broader range of subfields that correspondto their major fields. Overall, there is a tendency for teachers to exert greater effort into subfields that
correspond to their major fields.
To understand the extent to which each channel of teaching practices explains the effect of theteachers’ major fields, we conduct a mediation analysis, following Heckman et al. (2013). Specifically,
we include the variables of teaching practices in Eq. (1) and consider the following model (3).
$!!"# = 6%&'()"# ＋ ∑$ 8 $ ./&0ℎ234"#+ +" + ,!# + -!"# .

(3)

Coefficient ζ represents the effect of teachers' major fields, which is not explained by the observedchange in teaching practices. As a result, the ratio of the effect of teachers’ major fields explained by
the combined differences in the observed change in teaching practices is given by 1 – ζ / β. The ratioof the effect of the teachers’ major fields attributable to the :th teaching practice can then be calculated
by 8kαk / β.
[Table 5]
Table 5 presents the estimation results for Eq. (3), suggesting that these three teaching practices
are positively related to students’ academic achievement (Columns (2) to (5) in Table 5). The
coefficient of majors in fields being taught in Column (5) is 2.3526, smaller than that in Column (1).
This result suggests that the positive effect of teachers’ majors can be interpreted through the15mechanisms of the three teaching practices. Still, most of the positive effect remains unexplained byobservable teaching practices.
Figure 1 plots the estimated results of decomposing the effects of teachers’ major fields on student
achievement into these three teaching practices and unexplained factors. We find that half of thepositive effect of teachers’ college major can be accounted for through the difference of teachingpractices: 21%, 16%, and 12% of the overall effectiveness of teachers’ major fields is through themechanisms of preparation, time allocation, and instructional coverage, respectively. Even after
considering teaching practice, half of the positive effect of teachers’ college major remains
unexplained.17 These results are broadly consistent with the findings in the literature wherein a largepart of teacher effects is unobservable.

6.

Heterogeneous effects

The estimate of Eq. (1) captures the average effects of teachers’ major fields in natural science on
students’ academic achievement in the corresponding subfields. Still, the effects may differ acrossstudents’ and teachers’ characteristics, such as teachers’ qualifications and experiences, and students’gender and ability. We estimate Eq. (1) with interaction terms between teachers’ major fields beingtaught and factors that may cause heterogeneity in effects.
[Table 6]First, to explore whether the deeper expertise of teachers in major fields increases the positiveeffect, we add the interaction term between “Major in fields taught” and the possession of a master’s
degree or higher. The estimated coefficient reported in Column (1) of Table 6 indicates that the effect17
When we restrict the sample to students who are in schools with only one class in the grade, we find that 39% isthrough the mechanisms that cannot be explained. The ratio is smaller than that in Fig.1, which is the result when we
do not consider the within-school sorting of students based on students’ subfields specific unobservables. This suggeststhat part of the 50%, that is, the mechanism through unexplained factors in Fig.1, may include the influence of student
sorting within schools.

16

of teachers’ majors is smaller for those with a master’s degree. Still, the difference is not statisticallysignificant, implying that to improve student science achievement, science teachers need to have adegree in the subfields that are acquired at the bachelor’s level but not to the depth of it at the master’slevel.
To explore whether having a more comprehensive range of major fields increases the effect, we
add the interaction term between “Major in fields taught” and the number of teachers’ major areas.
Column (2) of Table 6 shows that, compared to teachers with only one major field in natural sciences,teachers with two and three major fields in natural sciences have larger positive effects on student
achievements by 0.016 and 0.045 standard deviations, respectively. The results suggest that scienceteachers with broader major areas improve student achievement.18
Furthermore, to explore whether teaching experience complements teachers’ lack of expertise,
we add the interaction term between “Major in fields taught” and years of teaching experience.
Column (3) of Table 6 reports the estimate of the effect of teachers’ majors. The effect seems smaller
for those with longer teaching experience, but the difference is not statistically significant.19 This
result implies that the lack of a major field in natural science is not supplemented by accumulatingteaching experience.
[Table 7]
Next, we examine whether the effect of teachers’ major fields differs depending on the student
characteristics of gender and ability by adding the interaction terms. Using math test scores as a proxymeasure of student ability, we divided students into upper, middle, and lower groups. The results show

18

Teachers who have broader major fields may indicate greater competence. In fact, the average number of major
fields in the natural sciences for teachers who hold a master’s degree or higher is 1.39, which is higher than the averageof 1.24 for teachers who do not. The result is still robust when we estimate the model with the interaction term of“Major in fields taught” and the possession of a master’s degree or higher added to Table 6, Column 2. Therefore, it isplausible to interpret the results in Table 6, Column 2 as indicating that the effects of major fields vary based on
differences in the breadth of teacher expertise, rather than differences in teacher ability.
19
Instead of the variable of years of teaching, we also add the interaction terms with dummy variables for years ofteaching, but no statistically significant results are obtained.

17

that the effect of teachers’ majors is larger for female students (Column (4)) and students with a higher
ability (Column (5)). These results are consistent with previous studies (Metzler and Woessmann,
2012; Sancassani, 2021).20 Teachers’ majors likely differ depending on the students’ ability because
the students who are more capable are taught more specialized content and can understand it.
Finally, we examine whether the effect of teachers’ major fields differs among science subfields.
We add the interaction term between “Major in fields taught” and each subfield dummy variable. The
results reported in Column (6) of Table 7 suggest that the effect of teachers’ majors varies by subfield.
In descending order of the degree of the effect of teachers’ major fields, chemistry is the largest,
followed by physics, biology, and Earth science. While the effect of teachers’ majors in chemistry
improves student achievement by 0.07 standard deviations, the effect of teachers’ majors in Earthscience is positive but statistically insignificant.

7.

Conclusion

This study examined the effect of teachers’ majors on the science test scores of middle-school students
using student-teacher matched data from the TIMSS for middle school students across the world.
Information specific to science subfields enabled us to exploit variation across subfields within
students and thus estimate causal relationships between teachers’ major fields and student achievement.
We found that eighth-grade students taught by teachers with majors in the natural science field
improved their achievement in science subfields by 0.05 standard deviations corresponding to teachers’
major fields, compared to those taught by teachers without it. Exploring the potential mechanisms
through which teachers’ majors play a role reveals that teachers improve their preparation for teaching,increase their time allocation for teaching, and expand the range of teaching topics in the subfields

20
On the heterogeneity in students’ gender, Sancassani (2021) found that while teacher expertise has a positive effecton female students’ test scores, it is positive, but not statistically significant, on male students’ test scores. On theheterogeneity in students’ abilities, Metzler and Woessmann (2012) found that teachers’ rich subject knowledge
influences the academic performance of high-achieving students. Sancassani (2021) found that teachers with expertisehave positive and significant effects only on students with high socioeconomic status.

18corresponding to their major fields. These mechanisms of teaching practices explain roughly half ofthe effects of teachers’ majors. In particular, improving the quality of their preparation for teaching
explained approximately 21 percent of the effects. Observable teaching practices cannot explainroughly 50 percent of the mechanisms of the effect of teachers’ major fields, which may suggest thatthe unobservable quality of teachers associated with their major fields plays a vital role in improving
the academic achievement of students.
Our findings make several contributions to the literature and have policy implications. First,
whereas most of the literature focuses on the effects of teachers’ subject-specific knowledge on
students’ academic performance in subjects such as mathematics and science, our study provides
evidence on the impact on students’ performance in each subfield of science using a cross-subfield
analysis with student-teacher fixed effects. Second, we provide evidence of the mechanism that drives
the effects of teachers’ major fields, namely teaching practices. Third, we provide evidence of theheterogeneity in the effects of teachers’ majors in natural science across the characteristics of teachersand schools. These findings shed light on educational policies designed to improve students’ outcomesin science. For example, our results suggest that assigning science teachers to teach subfields that
correspond to their major fields is beneficial. One way to compensate for teachers without majors innatural science fields could be to provide them with training opportunities to acquire expertise andpedagogy in natural science and apply it to their teaching practices, which may achieve outcomessimilar to those of having natural science majors.

References
Altonji, J. G., Blom, E., & Meghir, C. (2012). Heterogeneity in human capital investments: Highschool curriculum, college major, and careers. Annu. Rev. Econ., 4(1), 185-223.

19

Backes, B., Goldhaber, D., Cade, W., Sullivan, K., & Dodson, M. (2018). Can UTeach? Assessing therelative effectiveness of STEM teachers. Economics of Education Review, 64, 184-198.
Bietenbeck, J. (2014). Teaching practices and cognitive skills. Labour Economics, 30, 143-153.
Bietenbeck, J., Piopiunik, M., & Wiederhold, S. (2018). Africa’s Skill Tragedy Does Teachers’ Lackof Knowledge Lead to Low Student Performance? Journal of human resources, 53(3), 553-578.
Bonesrønning, H., Falch, T., & Strøm, B. (2005). Teacher sorting, teacher quality, and student
composition. European Economic Review, 49(2), 457-483.
Boyd, D. J., Grossman, P. L., Lankford, H., Loeb, S., & Wyckoff, J. (2009). Teacher preparation andstudent achievement. Educational Evaluation and Policy Analysis, 31(4), 416-440.
Card, D., & Payne, A. A. (2021). High school choices and the gender gap in STEM. Economic Inquiry,
59(1), 9-28.
Chingos, M. M., & Peterson, P. E. (2011). It's easier to pick a good teacher than to train one: Familiarand new results on the correlates of teacher effectiveness. Economics of Education Review, 30(3),449-465.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2006). Teacher-student matching and the assessment ofteacher effectiveness. Journal of human Resources, 41(4), 778-820.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2010). Teacher credentials and student achievement inhigh school a cross-subject analysis with student fixed effects. Journal of Human Resources,
45(3), 655-681.
Coenen, J., Cornelisz, I., Groot, W., Maassen van den Brink, H., & Van Klaveren, C. (2018). Teacher
characteristics and their effects on student test scores: A systematic review. Journal of economic
surveys, 32(3), 848-877.

20

Croninger, R. G., Rice, J. K., Rathbun, A., & Nishio, M. (2007). Teacher qualifications and earlylearning: Effects of certification, degree, and experience on first-grade student achievement.
Economics of Education Review, 26(3), 312-324.
Dee, T. S. (2007). Teachers and the gender gaps in student achievement. Journal of Human resources,
42(3), 528-554.
Dee, T. S., & Cohodes, S. R. (2008). Out-of-field teachers and student achievement: Evidence frommatched-pairs comparisons. Public Finance Review, 36(1), 7-32.
Goldhaber, D. D., & Brewer, D. J.

(1997). Why Don't Schools and Teachers Seem to Matter?

Assessing the Impact of Unobservables on Educational Productivity. Journal of Human
Resources, 32(3), 505-523.
Goldhaber, D. D., & Brewer, D. J. (2000). Does teacher certification matter? High school teacher
certification status and student achievement. Educational evaluation and policy analysis, 22(2),129-145.
Goldhaber, D. D., & Brewer, D. J. (2001). Evaluating the evidence on teacher certification: A rejoinder.
Educational Evaluation and Policy Analysis, 23(1), 79-86.
Goldhaber, D., Gratz, T., & Theobald, R. (2017). What's in a teacher test? Assessing the relationshipbetween teacher licensure test scores and student STEM achievement and course-taking.
Economics of Education Review, 61, 112-129.
Hanushek, E. A., Piopiunik, M., & Wiederhold, S. (2019). The value of smarter teachers international
evidence on teacher cognitive skills and student performance. Journal of Human Resources,
54(4), 857-899.
Hanushek, E. A., & Rivkin, S. G. (2006). Teacher quality. Handbook of the Economics of Education, 2,1051-1078.

21

Hanushek, E. A., & Rivkin, S. G. (2010). Generalizations about using value-added measures of teacher
quality. American Economic Review, 100(2), 267-71.
Hanushek, E. A., Schwerdt, G., Wiederhold, S., & Woessmann, L. (2015). Returns to skills aroundthe world: Evidence from PIAAC. European Economic Review, 73, 103-130.
Harris, D. N., & Sass, T. R. (2011). Teacher training, teacher quality and student achievement. Journalof public economics, 95(7-8), 798-812.
Heckman, J., Pinto, R., & Savelyev, P. (2013). Understanding the mechanisms through which an
influential early childhood program boosted adult outcomes. American Economic Review, 103(6),2052-86.
Hill, H. C., Rowan, B., & Ball, D. L. (2005). Effects of teachers’ mathematical knowledge for teaching
on student achievement. American educational research journal, 42(2), 371-406.
Jackson, C. K. (2012). Recruiting, retaining, and creating quality teachers. Nordic Economic Policy
Review, 3(1), 61-104.
Kirkeboen, L. J., Leuven, E., & Mogstad, M. (2016). Field of study, earnings, and self-selection. The
Quarterly Journal of Economics, 131(3), 1057-1111.
Kukla-Acevedo, S. (2009). Do teacher characteristics matter? New results on the effects of teacher
preparation on student achievement. Economics of Education Review, 28(1), 49-57.
Lavy, V. (2016). What makes an effective teacher? Quasi-experimental evidence. CESifo Economic
Studies, 62(1), 88-125.
Martin, M.O. & Mullis, I.V.S. (Eds.). (2012). Methods and procedures in TIMSS and PIRLS 2011.
Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.
Metzler, J., & Woessmann, L. (2012). The impact of teacher subject knowledge on student
achievement: Evidence from within-teacher within-student variation. Journal of Development
Economics, 99(2), 486-496.

22

Monk, D. H. (1994). Subject area preparation of secondary mathematics and science teachers andstudent achievement. Economics of education review, 13(2), 125-145.
Motegi, H. & Oikawa, M. (2019). The effect of instructional quality on student achievement: Evidence
from Japan. Japan and the World Economy, Volume 52, 100961.
Rockoff, J. E., Jacob, B. A., Kane, T. J., & Staiger, D. O. (2011). Can you recognize an effective
teacher when you recruit one? Education finance and Policy, 6(1), 43-74.
Sancassani, P. (2021). The effect of teacher characteristics on students' science achievement, ifo
Working Paper, No. 348, ifo Institute-Leibniz Institute for Economic Research at the Universityof Munich, Munich
Schwerdt, G., & Wuppermann, A. C. (2011). Is traditional teaching really all that bad? A withinstudent between-subject approach. Economics of Education Review, 30(2), 365-379.
Sharkey, N. S., & Goldhaber, D. (2008). Teacher licensure status and student achievement: Lessons
from private schools. Economics of Education Review, 27(5), 504-516.
Tanaka, R., & Ishizaki, K. (2018). Do teaching practices matter for students’ academic achievement?
A case of linguistic activity. Journal of the Japanese and International Economies, 50, 26-36.
Van Klaveren, C. (2011). Lecturing style teaching and student performance. Economics of Education
Review, 30(4), 729-739.
Wayne, A. J., & Youngs, P. (2003). Teacher characteristics and student achievement gains: A
review. Review of Educational research, 73(1), 89-122.
Woessmann, L. (2011). Cross-country evidence on teacher performance pay. Economics of Education
Review, 30(3), 404-418.
Wu, M. (2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation,31(2-3), 114-128.

23

Table 1. Summary statistics
Outcome variables:
Test score
Test score in Physics
Test score in Chemistry
Test score in Biology
Test score in Earth Science
Independent variables:
Teacher
Major in fields taught
Major in Physics
Major in Chemistry
Major in Biology
Major in Earth Science
Number of major fields in natural science
one
two
three
four
Preparation
Preparation in Physics
Preparation in Chemistry
Preparation in Biology
Preparation in Earth Science
Time allocation
Time allocation in Physics
Time allocation in ChemistryTime allocation in Biology
Time allocation in Earth Science
Range of topics taught
Range of topics taught in Physics
Range of topics taught in Chemistry
Range of topics taught in Biology
Range of topics taught in Earth Science
Female teacher
Teaching experience
Graduate degree
Class size
SchoolGrade 8 Enrollment
Student and Family
Girl
Parents' education (college degree orhigher)
Year
2011
2007
2003

Mean

S.D.

Min.

Max.

471
469
468
473
472

116
117
116
115
117

5
5
5
5
5

912
882
912
879
880

0.317
0.300
0.385
0.426
0.154
1.266
0.409
0.144
0.101
0.067
2.56
2.55
2.64
2.59
2.46
0.230
0.257
0.232
0.272
0.159
0.675
0.696
0.722
0.689
0.591
0.524
12.6
0.176
32.0

0.465
0.458
0.487
0.495
0.361
1.166
0.492
0.351
0.301
0.250
0.50
0.49
0.48
0.47
0.55
0.138
0.121
0.117
0.150
0.133
0.291
0.261
0.285
0.261
0.333
0.499
9.3
0.381
9.8

0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1

1
1
1
1
1
4
1
1
1
1
3
3
3
3
3
1
1
1
1
1
1
1
1
1
1
1
50
1
116

180.9

162.5

1

1661

0.506

0.50

0

1

0.283

0.45

0

1

0.256
0.412
0.332

0.44
0.49
0.47

0
0
0

1
1
1

Note: Number of observations is 876,880.

24

Table 2. The effects of teachers’ major fields on academic achievement
Physics + Chemistry + Biology + Earth Science
OLS
FE
(1)
(2)
(3)
(4)
5.2161*** 4.8776*** 5.0354***4.6485***(0.8497)(0.8047)(0.7953)(0.3259)

Major in fields taught
Student and family controls
Teacher and school controls
Observations
R-squared

NoNo
876,880
0.369

Yes
No
876,880
0.393

Yes
Yes
876,880
0.398

abs.
abs.
876,880
0.920

Notes: Students and family controls include gender, parents’ education. Teacher and school
controls include gender, experience (quadratic polynomial), and whether they have a
graduate degree, class size (cubic polynomial) and enrollment in the grade (cubic
polynomial). All regressions include year fixed effects, subfields fixed effects, and country
fixed effects, and give same weight to each country. Standard errors are clustered at classlevel and reported in parentheses. * p < 0.10; ** p < 0.05; *** p < 0.01.

Table 3. Robustness check
Teachers'
Teachers'
instructional instructionaltime share is at time share is atleast 10% in
least 20% ineach subfield each subfield
(2)
(3)
(4)
3.5778***3.5135***2.6817***(1.2473)(0.3957)(0.6484)

Rural area
(15,000 people One class inor fewer in the the grade
area)
Major in fields taught
Observations
R-squared(1)
4.9623***(0.6732)
261,2200.914

99,7320.907

598,8280.920

232,7120.925

Notes: The table shows fixed effects regressions of student scores on teachers' major fields beingtaught with restricted sample shown in each column. All regressions include subfield fixed effects,
and give same weight to each country. Standard errors are clustered at class level and reported inparentheses. * p < 0.10; ** p < 0.05; *** p < 0.01.

25

Table 4. Mechanism: Teaching practices
Preparation(1)
0.3515***(0.0074)

Major in fields taught
Observations
R-squared

876,880
0.646

Range of topics
taught(2)(3)
0.0672***0.0378***(0.0033)(0.0043)

Time allocation

876,880
0.166

876,880
0.547

Notes: The table shows fixed effects regressions of teaching pratices on teachers'
major fields being taught. All regressions include student-teacher fixed effects andsubfield fixed effects, and give same weight to each country. Standard errors are
clustered at class level and reported in parentheses. * p < 0.10; ** p < 0.05; ***p < 0.01.

Table 5. Mediation Analysis

Major in fields taught

Physics + Chemistry + Biology + Earth Science
(1)
(2)
(3)
(4)
(5)
4.6485***2.5321***3.4147***3.9831***2.3526***(0.3259)(0.3555)(0.3248)(0.3173)(0.3437)

Preparation

6.0217***(0.4048)

Time allocation

2.8347***(0.4108)
18.3498***(0.9864)

Range of topics taught
Observations
R-squared

10.9235***(0.9849)
17.6111*** 14.9581***(0.6561)(0.7023)876,8800.920

876,8800.920

876,8800.920

876,880
0.921

876,880
0.921

Notes: The table shows fixed effects regressions of student scores on teachers' major fields being taught.
All regressions include subfield fixed effects, and give same weight to each country. Standard errors are
clustered at class level and reported in parentheses. * p < 0.10; ** p < 0.05; *** p < 0.01.

26

Table 6. Heterogeneous effects

Physics + Chemistry + Biology + Earth Science
(1)
(2)
(3)
(4)
(5)
4.6463*** 3.6094*** 4.8522*** 1.8133*** 3.2709***(0.3703)(0.3884)(0.5199)(0.4084)(0.5640)

Major in fields taught
×Graduate degree(6)1.3705(0.8647)0.0104(0.7594)

×Number of majors in naturalsciences: two

1.6479**(0.8007)

×Number of majors in naturalsciences: three

4.4695***(1.0100)-0.0167(0.0316)

×Teaching experience

5.5927***(0.5478)

×Girl
×Math Score: middle

0.6752(0.5867)

×Math Score: upper

3.0259***(0.7014)[
×Physics

3.5350***(1.0053)

×Chemistry

5.7213***(1.0312)×Biology

2.4731***(0.9133)

Observations
R-squared

876,8800.920

876,8800.920

876,8800.920

876,8800.920

876,8800.920

876,880
0.920

Notes: All regressions include student-teacher fixed effects and subfields fixed effects, and give same weight toeach country. Standard errors are clustered at class level and reported in parentheses. * p < 0.10; ** p < 0.05;*** p < 0.01.

27

Figure 1. Decomposition of mechanism

Table A1. Correlation coefficients between teachers’ major fields and preparation status for teachingscience topics

Major in Physics
Major in Chemistry
Major in Biology
Major in Earth Science

Physics
0.26
0.14
-0.09
-0.02

Preparation
Chemistry
Biology
0.13
-0.09
0.30
-0.01
0.03
0.29
-0.03
0.03

Earth Science
-0.03
-0.03
0.00
0.16

Note: Number of observations is 876,880.

Table A2. Correlation coefficients between teachers’ major fields and the time allocation forteaching

Major in Physics
Major in Chemistry
Major in Biology
Major in Earth Science

Physics
0.12
0.06
-0.13
-0.07

Time allocation
Chemistry
Biology Earth Science
0.13
-0.06
-0.13
0.17
-0.03
-0.14
-0.07
0.16
0.00
-0.03
-0.01
0.11

Note: Number of observations is 876,880.

28

Table A3. Correlation coefficients between teachers’ major fields and the range of topics taught

Major in Physics
Major in Chemistry
Major in Biology
Major in Earth Science

Physics
0.04
0.01
-0.08
-0.01

Range of topics taught
Chemistry
Biology
0.07
0.01
0.03
-0.04
-0.06
-0.03
0.00
0.03

Note: Number of observations is 876,880.

29

Earth Science
-0.07
-0.12
-0.03
0.04