Teacher quality is an important input in student achievement
although identifying quality teachers is difficult without detailed
panel data (Rivkin, Hanushek, and Kain, 2005). Research in economic
education finds that teacher knowledge is one factor affecting student
learning (Allgood and Walstad, 1999). In addition, there is suggestive
evidence that teacher attitudes matter (Marlin, 1999).
This paper addresses the question of what makes a high school
teacher an effective economics instructor. We consider two main
possibilities: subject matter knowledge and attitudes towards teaching
economics. The innovative feature is to analyze both possibilities
simultaneously with good measures of both teacher knowledge and teacher
attitude. We separate the two effects and compare the importance of
Attitudes and knowledge are highly correlated. For example, Schober
(1984) suggests that teacher achievement affects teacher opinions about
economics. Allgood and Walstad (1999), albeit with a small sample of 12
teachers, convincingly demonstrate that a teacher's economic
knowledge or a teacher's economic thinking positively affects
student performance. Marlin (1991) finds that students with teachers
more enthusiastic about teaching economics score higher on measures of
economic knowledge. Although Marlin controls for teacher experience in
teaching economics, he is unable to control for teacher knowledge. Given
the correlation in Schober (1984) between teacher attitudes and
achievement, one could easily interpret Marlin's estimates on
teacher attitudes as an effect of teacher knowledge on student
achievement. Relatedly, Boex (2000) provides evidence that college
instructors' presentation skills, which includes enthusiasm for the
subject, are more important than their intellectual or scholarly nature
in affecting student ratings of teacher effectiveness. Untangling the
two effects requires good information on teacher knowledge, teacher
attitudes, and student learning.
To test these relationships directly, we survey high school
economics teachers and their students. Focusing on high school students
is important in two ways. First, seventeen states include economics as
part of the high school curriculum (NCEE, 2005). Second, the more
economic knowledge students have, the more likely they are to study
economics in college (Ashworth and Evans, 2001).
Teachers were assessed on their knowledge of and attitude towards
economics. Teachers tested their students at the beginning and end of an
economics course to provide a measure of student learning. We then
estimate an empirical model of student learning on teacher knowledge and
teacher attitudes. We find that better teacher knowledge consistently
improves student learning. Teacher attitude, as measured by a survey,
has a statistically insignificant effect on student learning. We explore
an alternate measure of teacher attitude towards economics: whether the
teacher volunteered to teach the class. This revealed preference measure
of teacher attitude has a positive and statistically significant effect
on student learning. The effect size is similar to that of teacher
We measure teacher knowledge and teacher attitudes towards
economics using standard assessments. We then test how teacher attitudes
and teacher knowledge affect student learning. We estimate Ordinary
Least Squares (OLS) regressions of the following for student i in class
[post-test.sub.ij] - [pre-test.sub.ij] = [gamma][knowledge.sub.j] +
[phi][attitude.sub.j] + [error.sub.ij]
Our variables of interest are teacher knowledge and attitude. We
expect both [gamma] and [phi] to be positive and are interested in their
relative magnitudes. To assess relative magnitudes, we standardize the
measures of teacher knowledge and attitude to variables with a zero mean
and a variance of one. The coefficients then reflect the effect of a one
standard deviation change in that teacher attribute.
Much of the discussion on specification of economic learning models
focuses on econometric difficulties in regressing the change in test
scores (post-test minus pre-test) on various attributes versus
regressing the post-test score on the pretest score and various
attributes (see Becker, et al, 1990 for example). Generally, the change
score method is preferred; we follow this preferred approach.
Student gain scores are measured at the student level; teacher
characteristics are measured at the teacher level. The regressions thus
include a large number of observations for each teacher, exaggerating
the amount of variation occurring in the teacher characteristics. To
account for the correlation among students in the teacher
characteristics, we cluster the standard errors by teacher. These
regressions are similar to regressions using teacher-level data,
weighted by the number of student observations.
DATA COLLECTION AND VARIABLE MEASUREMENT
We sent information packets to South Carolina teachers. We
supplemented a list of economics teachers provided by the South Carolina
Council on Economic Education with a search for social studies teachers
at every high school in the state. In total, we mailed out 468 surveys
to South Carolina teachers in April 2006. A second round of information
packets were mailed in late July 2006 to 394 teachers. A total of 52
teachers agreed to participate; 41 of these fully completed their
participation. Some teachers participated in more than one quarter or
semester of classes. Teachers were compensated $100 upon completion of
participation; some teachers were compensated more than once if they
participated in more than one quarter or semester.
Teachers completed the Survey of Economic Attitudes (SEA) and the
Test of Economic Literacy (TEL) at the beginning of their economics
course. The TEL is a nationally-normed high school level assessment that
provides a pre- and a posttest. Teachers administered Form A of the TEL
to their students at or near the beginning of the course; they
administered Form B of the TEL to their students at or near the end of
the course. These two tests provide a measure of student learning during
the course. Teachers also completed a demographic survey.
The first part of the SEA, the Attitudes towards Economics (ATE)
section, measures teacher attitudes towards economics using fourteen
questions with responses on a five-point Likert-type scale. These
questions include such items as "Economics is dull" and
"I would be willing to attend a lecture by an economist".
Respondents note whether they strongly agree, agree, are undecided,
disagree, or strongly disagree. Soper and Walstad (1983) provide
reliability statistics for the ATE. The Cronbach Alpha of 0.88 for the
ATE demonstrates good internal consistency; Soper and Walstad (1983)
report a low standard error of measurement of 3.18.
Both assessments, the ATE and the TEL, are designed by the National
Council on Economic Education (NCEE). They are used frequently in
student and teacher assessment and research into student achievement
(see, for example, Dutkowsky, Evensky and Edmonds, 2006; Walstad and
Rebeck, 2001; or the summary in Becker, Greene, and Rosen, 1990). The
raw scores for the teachers are presented in Table 1.
On average, teachers preformed quite well on the TEL averaging 35
questions right out of forty. There is a large variance in
teachers' performance ranging from 16, below the average for
students who've taken some economics, to a perfect score. The
national average score on the TEL Form A for those without economics is
about 19 out of 40; the average score with economics is about 25 out of
40 (Walstad and Rebeck, 2001).
For positive statements about economics the ATE is scored as
follows: if a teacher strongly agrees with the statement, we assign that
answer a 5; agrees receives a 4; undecided, 3; disagrees, 2; and
strongly disagrees, 1. For negative statements, strongly disagrees
receives a 5 and so on. Possible scores on the ATE range from 14 to 70.
Teachers averaged a score of 60 ranging from a low of 42 to the maximum
We consider an alternate measure of teacher attitudes: how teachers
are assigned to economics classes. (1) About half of the teachers in our
sample (56%) report choosing to teach economics. The principal or
department head assigned the remaining teachers to the economics course.
Teachers' revealed preference in their choice to teach economics
may better reflect their enthusiasm in the classroom, as compared to the
survey-based measure of their attitude towards economics.
The previous research on teacher attitudes focuses on enthusiasm.
Marlin (1991), for example, used a three-choice rating of teacher
enthusiasm for teaching economics in his study of teacher attitudes.
Patrick, Hisley, and Kempler (2000) discuss how more motivated teachers
may be more effective at eliciting student motivation and thus, student
learning. In a psychology experiment, they manipulate teacher enthusiasm
and show that students lectured by more enthusiastic teachers are more
interested in learning more about the topic.
Choosing to teach economics is positively correlated with teacher
scores on the ATE. Teachers choosing to teach economics scored 62 on the
ATE as compared to a score of 57.4 for those assigned to teach
economics. This reflects a little more than one-half of a standard
deviation change in the ATE.
Table 1 also presents summary statistics of the sampled
teachers' characteristics. On average, each teacher submitted
scores for 47 students. Many teachers submitted scores for more than one
class during a semester or for more than one class during the school
year. The average teacher is aged 46 with 18 years of experience
teaching and 10 years of experience teaching economics. Teachers in the
sample are 87.8 percent white and 44 percent male. 78 percent of the
teachers have a Master's degree; one teacher has earned his
The sampled teachers are somewhat comparable to an average social
studies teacher in South Carolina (authors' calculations from the
Schools and Staffing Survey, 2003-2004). On average, South Carolina
social studies teachers have taught in public schools for 13.9 years and
are 41 years old. Among these teachers, 88.8 percent are white and 58.3
percent male. 56.5 percent of South Carolina social studies teachers
have a Master's degree.
Our sampled teachers are somewhat older, more educated, and more
experienced than the relevant average teacher. Also, their high scores
on both the ATE and TEL reflect the higher than average interest these
teachers expressed by choosing to participate. We keep this revealed
preference and sample selection in mind when considering our results.
Among our economics teachers, about 30 percent of them majored or
minored in economics. Fully one-third have attended an NCEE or state
training session. Again, this high rate of participation likely reflects
the sampled teachers' interest in economics and willingness to
participate. More than 40 percent use NCEE materials in their classroom.
For the 41 teachers in our sample, we standardize their scores on
the ATE and TEL to be mean zero and variance one. This allows us to
compare the magnitudes of the effects of these teacher qualities on
student learning. Teachers used Form A of the TEL to assess students at
the beginning of the course and Form B at the end of the course. We
convert the Form A scores to a scale comparable to Form B as suggested
by the TEL Examiner's Manual. The outcome of interest is student
learning: the change in students' scores between the post-test and
the pre-test. On the pre-test, students, on average, correctly answered
17 of the 40 questions; on the post-test, students, on average,
correctly answered 21 of 40 questions. In our sample, both the pre-test
and post-test scores are lower than the national averages. The national
average on Form A of the TEL for students without economics is 19.05;
the national average on Form B of the TEL for students with economics is
25.74 (Walstad and Rebeck, 2001). The average change in test scores for
our sample was 3.89. The difference in the national averages is about
6.7 questions. The gain in test scores is also smaller than the
difference between the national averages.
Other factors may affect student test scores. Becker, Greene, and
Rosen (1990) enumerate the factors that may affect student learning of
economics: student ability, teacher ability, course work, technology,
demographics, and time usage. Females and blacks tend to perform worse
than males and whites in college economics courses (Dynan and Rouse,
1997 and Borg and Stranahan, 2002). Class size may affect student
performance (Arias and Walker, 2004). The evidence on the effects of
instructor gender is mixed: Robb and Robb (1999) and Dynan and Rouse
(1997) find no effect of gender on college student performance while
Ashworth and Evans (2001) find that female secondary teachers increase
the likelihood of studying economics in high school. Klopfenstein (2005)
finds same-race effects for black math achievement.
Our limited sample of teachers precludes including many control
variables including teacher characteristics. In addition, data
limitations prevent us from including student characteristics such as
race or sex. (2) For these exclusions to bias our estimates, the omitted
variables must be correlated with teacher knowledge or teacher attitude.
For example, if better prepared or more positive instructors are matched
with students who learn more quickly, this would bias our estimates
We check for indications of this bias although the limited sample
size makes it difficult to control for those factors. We split the
sample at the median of the teachers' TEL scores. The average
student's pre-test score for the top half of teachers is 18.62
versus 16.03 for the average student of a teacher from the bottom half.
Students with higher pre-test scores tend to be taught by higher scoring
teachers. Students with higher pre-test scores also tend to be taught by
teachers with higher ATE scores. For a teacher scoring in the top half
of teacher's attitudes, the average student pre-test score is
17.80; for a teacher in the bottom half, the average is 16.95.
If students that would have learned more regardless of their
teacher are matched with higher quality teachers, this selection would
bias the effects of teacher quality upwards. In this case, we observe
that students with higher pre-test scores learn less, on average, than
students with lower pre-test scores. The above median student
experienced test scores gains of 2.6 points; the below median student
gained 5.4 points. This difference isn't driven by ceiling effects;
less than one percent of students correctly answered 38 or more of the
40 questions on the post-test.
Since high pre-test students are matched with higher knowledge
teachers and high pre-test students have smaller growth in their test
scores, this suggests that teacher-student matching may bias downwards
the estimated teacher knowledge coefficients. This makes it more
difficult to find effects of teacher knowledge on student learning.
Similarly, teacher-student matching may bias downwards the estimated
teacher attitude coefficients.
Higher pre-test scoring students tend to be matched to teachers who
did not volunteer to teach economics, although this difference is not
statistically significant. The average student pre-test score for a
teacher choosing to teach economics is 17.32; the average student
pre-test score for a teacher assigned to teach economics is 17.67. As
lower pre-test students tend to have higher gain scores and are matched
with assigned teachers, this suggests a potential downward bias in the
coefficient estimate on whether a teacher choose to teach economics.
Figure 1a presents mean changes in student test scores by teacher
scores on the TEL and ATE. Teachers with below median scores on the TEL
taught students with smaller gains in test scores; this difference is
statistically significant. Among the lower knowledge teachers, having a
higher score on the ATE does not correlate with higher student test
score gains. Among the higher knowledge teachers, a higher score on the
ATE correlates with somewhat high student test score gains.
Figure 1b uses whether a teacher volunteered to teach economics as
the measure of teacher attitude. Teachers choosing to teach economics
taught students with significantly larger gains in test scores; this
difference is statistically significant. This difference is particularly
large for the lower knowledge teachers. However, for both the below and
above median knowledge teachers, choosing to teach economics corresponds
to statistically significantly higher test score gains.
We estimate a regression of the change in student test scores on
the teachers' standardized TEL and ATE scores. Regressions include
1,946 students in 41 teachers' classes. Table 2 presents the
The first column presents the estimates of changes in test scores
on the standardize measures of teacher knowledge and teacher attitude
(from the ATE). This column echoes the pattern shown in Figure 1a.
Students taught by a teacher with economic knowledge one standard
deviation above the mean experience an additional increase in their
scores of a little less than one question. This reflects about a 19
percent increase in their test score growth. Controlling for teacher
knowledge, teacher attitude as measured by the ATE has a small and
statistically insignificant effect on student test scores. The effect of
teacher knowledge is about twice as large as that of teacher attitude.
These estimates support previous research showing that teacher knowledge
increases student learning. The existing evidence on teacher attitudes
is thin; our evidence merely corroborates its thinness. Given the
potential upward bias on teacher attitude, there seems to be little
effect of teacher attitude, as measured by the ATE, on student learning.
We focus on the other measure of teacher attitude in the remaining
The second column presents the estimates of changes in test scores
on the teacher's score on the TEL and an indicator variable for
whether the teacher chose to teach economics. The second column echoes
the pattern shown in Figure 1b. Teachers with more knowledge correlate
with increased student learning; teachers choosing to teach economics
also correlates with increased student learning. The effect of a teacher
volunteering is large, amounting to about 38 percent of average student
test score gains. Both teacher characteristics have a statistically
significant and economically relevant effect on student test score
The third column adds two teacher demographic variables to the
specification: an indicator for whether the teacher is male and one for
whether the teacher is white. Male teachers experience significantly
lower gains than female teachers with their students gaining, on
average, one less question from pre-test to post-test. The coefficient
on the white dummy variable is small, positive, and statistically
insignificant. Further, including these demographics has little effect
on the estimated coefficients on teacher knowledge or whether the
teacher chose to teach economics.
We include teacher experience in column (4). Teachers with one more
year of experience teaching economics have students with slightly higher
test score gains; the estimate is small, less than a tenth of a
question, and statistically insignificant.
The last three columns of Table 3 include controls that may reflect
teacher knowledge or teacher interest in economics. In column (5) we
include an indicator variable for whether the teacher was an economics
major or minor in college. Economics majors or minors instruct students
with smaller test score gains, although the difference is not
statistically significant. Column (6) adds an indicator variable for
whether the teacher has attended a NCEE or state training session.
Column (7) includes an indicator for whether the teacher uses NCEE
materials in his or her class. The coefficient on the training session
is statistically insignificant and changes sign when we include the
materials indicator variable. The students of teachers using NCEE
materials experience much greater test score gains.
Including the last set of variables, particularly the use of NCEE
materials, reduces both the magnitude and the significance of the
estimated coefficient on whether the teacher chose to teach economics.
These two variables are highly correlated ([rho] = 0.5342): teachers who
chose to teach economics also typically use NCEE materials in their
classroom. (3) Further, including these variables reflecting economics
training reduces the magnitude and the significance of the male
variable; any effect of teacher gender appears to be driven by their
varying backgrounds in economics.
CONCLUSIONS AND POLICY IMPLICATIONS
We find that teacher knowledge of economics is an important
determinant of student learning. Teacher attitude towards economics, as
measured by the first part of the Survey of Economic Attitudes, has a
small and statistically insignificant effect on student learning.
However, teachers choosing to teach economics experience greater student
test score gains. These volunteering teachers likely exhibit more
enthusiasm for the class. The effect of teachers choosing to teach
economics and teacher knowledge of economics are similarly sized and
These results suggest two things. First, that much emphasis,
correctly, has been placed on improving teacher economic knowledge and
providing teachers with materials for their economics courses. We
encourage the continued development of materials and interventions
focused on improving teacher understanding of economics. In fact, the
use of NCEE's materials appears to improve student learning.
Second, reconsidering how teachers are assigned to economics classes may
alleviate some of their anecdotal distress and improve student learning
in the process. To the extent allowed by resource constraints, our
results suggest that allowing teachers increased control over the
classes taught could improve student outcomes.
This research was funded in part by an Excellence in Economic
Education grant from the National Council on Economic Education. We
thank Jeff Gibisch, Irina Ionescu, Hillary Morgan, Christy Redmon, and
Aileen Sampson for their research assistance. We also thank Andrew I.
Kohen and participants in the NCEE session at the 2008 ASSA meetings in
New Orleans for their comments. All errors are, of course, our own.
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Angela K. Dills, Mercer University
Dennis Placone, Clemson University and Center for Economic
(1) We thank Andrew I. Kohen for this suggestion.
(2) To satisfy the Institutional Review Board's Exemption
category for human subjects research, we opted not to collect
student-specific demographic information. A full IRB review outside the
exempt categorization would require parental permission for each
student, likely further limiting our sample size.
(3) Results are qualitatively similar when using the teachers'
ATE scores instead of the teacher chose variable.
Table 1: Summary Statistics for Teachers and Students
Mean Dev. Min Max
TEL 35.02 6.03 16 40
ATE 59.95 7.54 42 70
Students taking economics 47.46 33.09 2 178
Teacher's Age 46.69 10.27 25 60
Years Teaching 18.14 10.77 2 38
Years Teaching Economics 10.31 6.85 0 26
Percent of teachers that:
chose to teach economics 56.10%
are male 45.00%
are white 87.80%
have their highest
DTEL 3.89 5.54 -16.511 23.729
pretest TEL 17.48 6.65 -0.009 36.711
posttest TEL 21.37 7.28 3 40
Table 2: Regression results of students' change in test scores on
teacher ability and attitude toward economics
(1) (2) (3)
students' change in test scores on TEL
teacher's TEL 0.753 * 0.655 * 0.684 *
(1.79) (1.72) (1.71)
teacher's ATE 0.395
teacher chose 1.477 ** 1.131 *
male teacher -1.165 *
white teacher 0.0681
or state training?
Constant 3.713 *** 2.957 *** 3.613 ***
(9.57) (7.08) (3.83)
R-squared 0.02 0.03 0.04
(4) (5) (6)
students' change in test scores on TEL
teacher's TEL 0.602 0.681 * 0.671 *
(1.58) (1.72) (1.77)
teacher chose 1.110 * 1.202 * 1.169
(1.70) (1.80) (1.68)
male teacher -1.142 * -0.765 -0.645
(-1.78) (-1.17) (-0.85)
white teacher -0.0203 -0.755 -0.851
(-0.019) (-0.67) (-0.76)
years teaching 0.0583 0.0561 0.0548
(1.30) (1.30) (1.28)
economics major -0.906 -0.933
Attended NCEE 0.39
or state training?
Constant 3.039 ** 3.717 *** 3.667 ***
(2.67) (3.28) (3.42)
R-squared 0.05 0.05 0.05
in test scores on TEL
teacher's TEL 0.645 *
teacher chose 0.223
male teacher -0.392
white teacher -1.013
years teaching 0.0734 **
economics major -1.264
Attended NCEE -0.518
or state training?
Use NCEE 2.266 ***
Constant 3.530 ***
Robust t statistics in parentheses. Standard errors clustered
by teacher. * significant at 10%; ** significant at 5%;
*** significant at 1%. There are 1,946 students in the sample
and 41 teachers.
Figure 1a: Mean changes in test score by teacher characteristics
above median ATE above median TEL
below median TEL 3.16 3.19
below median ATE 4.14 4.64
Note: Table made from bar graph.
Figure 1b: Mean changes in test score by teacher characteristics
above median ATE above median TEL
below median TEL 2.07 4.57
below median ATE 4.03 4.69
Note: Table made from bar graph.