The Impact of the Teaching High School Economics Workshop for Teachers on Student
Achievement
(RRH: Impact of High School Economics Workshop)
John R. Swinton * ,
Georgia College and State University Center for Economic Education
CBX 14
Milledgeville, Georgia 31061
Benjamin Scafidi, Georgia College and State University
Howard C. Woodard, Georgia College and State University
Abstract:
Forty-five states have Councils on Economic Education and 275 college and universities house
Centers for Economic Education that have as their primary goal assisting k-12 teachers who
teach economics. Much of this is done through in-service teacher training. While there is
empirical evidence that in-service workshops for high school economics teachers increase
student achievement, it would be useful to determine which workshops have the biggest impact.
In this paper we analyze the effect of one of most popular workshops, Teaching High School
Economics. Using new data on all Georgia high school economics students and their scores on a
state-mandated end of course test we find that the students of teachers who have attended this
workshop achieve significantly higher scores on these tests than other students.
JEL code: A21
Keywords: Education production function, In-service training,
*
Corresponding author: john.swinton@gcsu.edu. This research was funded by grants from the United States
Department of Education through the National Council on Economic Education Excellence in Economic Education
Grant and Georgia College and State University. The authors thank the Georgia Department of Education for
sharing its student administrative data and expertise, which made this study possible. Finally, the authors thank
participants at the 2009 NCEE/NAEE Conference for helpful comments.
1
INTRODUCTION
Education policy analysts and professional educators have long called for more and better
professional learning opportunities for in-service teachers [NCTAF, 1996]. This issue appears
to be an acute problem in high school economics. As early as 1977, researchers called for
more content training for high school economics teachers [Mackey et al., 1977]. More
recently, Walstad [2001] found that the typical teacher who is teaching economics has
completed no more than one college course in economics. Mackey et al. suggested that
teachers at least minor in economics to be qualified to teach at the high school level.
However, few high school economics teachers do so.
State Councils on Economic Education in 45 states and the 275 colleges and university
based Centers for Economic Education, provide a variety of in-service workshops for teachers
that offer both economic content and lesson material. The various workshops address material
that ranges across all grades and virtually all economic subject matter. Some of the more popular
offerings include the Stock Market Game workshops (where teachers learn how to administer the
Stock Market Game in their classroom and pick up strategies as to how to incorporate economic
lessons into the game as their students play), Virtual Economics (where teachers receive the
Council for Economic Education (CEE) CDROM that includes both an explanation of economic
concepts and access to all of the Council’s print teaching material), and Teaching High School
Economics (where teachers get lesson plans that correlate with the state economics standards). 1
In this study we examine the impact of the Teaching High School Economics workshop for
teachers as measured by their students’ scores on Georgia’s state mandated and high stakes endof-course test in economics.
One of the motivations behind examining individual workshops such as the Teaching
High School Economics workshop is to help state Councils and college and university based
Centers for Economic Education determine which workshops have the most influence on
teachers’ ability to instruct their students. An understanding of the relative impact of different
offerings will help such organizations better allocate their scarce resources.
In this study we provide estimates from an empirical model of student test scores, where
performance on Georgia’s standardized end-of-course test (EOCT) in economics is a function of
1
The National Council on Economic Education (NCEE) recently changed its name to the Council for Economic
Education (CEE).
2
student characteristics, teacher fixed effects, and teacher in-service training from this specific
workshop. Our intent is to isolate the impact of the Teaching High School Economics workshop
while accounting for other factors in student performance. Our estimates suggest that the
Teaching High School Economics workshop leads to a significant increase in student
performance on Georgia’s economics end-of-course test. Further, this increase is above the
average increase that previous research has found to come from other workshops offered by
Georgia Council on Economic Education (GCEE). Since the Teaching High School Economics
workshop represents only two days of training, these gains in student achievement come at a
very low cost when compared to other treatments typically used in k-12 education. Furthermore,
the high-stakes testing environment in Georgia is uniquely suited for studying the impact of inservice workshops.
In the following section we review the past studies concerning the effectiveness of inservice teacher training. In the third section we offer some background information about the inservice workshops offered through GCEE and other state Councils and why the Georgia testing
environment is uniquely suited for addressing this issue. We describe the data in the fourth
section and present the model in the fifth. We discuss our results in the sixth section and provide
concluding remarks in the seventh section.
PAST ANALYSIS OF IN-SERVICE TEACHER TRAINING
Education policy makers, analysts, and groups that represent professional educators
routinely call for more in-service professional learning opportunities for k-12 teachers [NCTAF,
1996]. Interestingly, almost all of the empirical literature evaluating in-service professional
learning concerns the evaluation of workshops for high school economics teachers. Below we
discuss the research from economic education and the two notable exceptions that study other
types of in-service professional learning for k-12 teachers.
Literature from Economic Education
Much of the early literature in economic education investigated individual workshops
with relatively small groups of teachers. The number of student observations in the studies
varies. The earlier studies typically contain 300 to 600 student observations. The more recent
studies tend to have larger sample sizes with 1,500 students being about average. The benefit of
the earlier studies is that the researchers were often able to pre- and post-test students (and in
3
some cases teachers) to directly measure changes in understanding and attitude. While the
studies differ in approaches, most find that workshops for teachers are associated with gains in
student learning.
Highsmith [1974] offered one of the earliest empirical studies of in-service economic
education for teachers. His research compared the student results on the Test of Economic
Understanding between students of teachers who had attended training workshops to
students of teachers who did not attend workshops. Thorton and Vredeveld [1977] used a
similar experimental design. Schober [1984] refined the previous approaches by adding a
pre-test to his experimental design. This allowed him to control for pre-existing knowledge
which other studies had not been able to do. All of the above mentioned studies found
positive impacts of in-service teacher training.
Walstad [1979] introduced the notion that workshops and other in-service training
programs may do more than provide information for teachers. They may change teachers’
attitudes toward economics. Walstad endeavored to address a simultaneity issue of both
workshops and teacher attitudes affecting student achievement. Specifically, teachers may
self-select into workshops because they have more interest in teaching economics. None of
the research that examines the impact of in-service training offers a truly random selection
of the treatment group. Consequently, controlling for selection issues has now become an
important focus in this literature. Following Walstad’s work, researchers have attempted to
separate the effect of workshops on student achievement from the effect of having a more
interested teacher. The selection may be in the other direction as well. Perhaps less
qualified teachers select into workshops (or are compelled to do so). Bosshardt and Watts
[1990; 1994] and Watts and Bosshardt [1991] introduced the use of fixed-effects models to
address the problem of unobserved time-invariant characteristics of either schools or
teachers. This allows them to control for some of the characteristics that may compel
teachers to participate in in-service training. Bosshardt and Watt’s research also ushered in
the use of larger data sets as they took advantage of standardized tests that were
administered to a national sample of students.
Another strand of current research that shines a light on the ability of teachers to teach
economics examines the impact that specific workshops that use material published by the
Council for Economic Education have on various measures of student performance. Grimes
[1995] found that use of the Choices & Changes material led to statistically significant increases
4
in student test performance when compared to a control group of students. Notable in the study
was the large sample of teachers and students from nine cities nationwide. Harter and Harter
[2009] and Swinton et al. [2007] look at the Financial Fitness for Life workshop and
accompanying material. They find that students benefit from the specific material as well.
Swinton et al. [forthcoming] uses information on over 160,000 Georgia students to
examine the impact of Georgia Council’s full complement of in-service training workshops.
None of the workshops examined lasts more than two days, and the economic content of the
different workshops varies tremendously. They found the impact of taking one or two
workshops was not statistically different from zero. But, the impact was positive and
significant for teachers who attended three or more workshops. Swinton, et al. use teacher
fixed effects to help control for the selection of teachers into workshops. In this paper, we
expand upon Swinton et al. [forthcoming] by analyzing the impact of one specific GCEE
workshop, Teaching High School Economics, on student achievement.
In an examination of the most recent National Assessment of Educational Progress
(NAEP) data, Walstad and Buckles [2008] find that students who had a teacher with
certification, college course work, or even a degree in economics do not have significantly higher
scores than other students as measured on the economics portion of the NAEP test. Similarly,
having teachers who have participated in in-service economics workshops seems to have little
discernable impact on student test scores. The authors believe this result may be because there
might not have been an accurate linkage between individual teachers to their students’ test
scores.
Two Studies that Analyze Other Forms of In-service Teacher Training
As mentioned previously, there are two noteworthy studies of the effectiveness of inservice teacher training outside of economic education. Angrist and Lavy [2001] show that inservice training coupled with other school-wide reforms can aid teachers in their efforts to
teach both math and language skills in Jerusalem public schools. Some students in their
study experienced as much as a 0.25 standard deviation increase in test scores after their
teacher participated in the training programs. Their treatment is not, however, limited just
to the impact of in-service training for teachers. The program in the Jerusalem schools that
they study was a comprehensive attempt to improve educational outcomes.
Jacob and Lefgren [2004] also studied programs that cut across academic disciplines
and target general measures of learning such as reading and math scores. All of the schools
5
in their study were initially low performing schools in Chicago. The program costs up to
$90,000 per school which is paid by the school district’s central office, and some individual
schools provided additional funds to supplement the program. In contrast to Angrist and
Lavy, they find no evidence that in-service programs helped Chicago teachers improve the
performance of their students on standardized tests.
IN-SERVICE WORKSHOPS FOR ECONOMICS TEACHERS AND THE TESTING
ENVIRONMENT IN GEORGIA
The Georgia Council on Economic Education (GCEE) offers in-service workshops for
teachers of k-12 students in Georgia. The Teaching High School Economics workshop is but one
of almost 40 different workshops available through GCEE. 2 In 2008, GCEE budgeted $243,500
to provide 122 in-service workshops to teachers throughout Georgia. A few workshops are meant
as an introduction for teachers to GCEE and its offerings. 3 Many other workshops offer
substantial training and materials that are designed to directly assist teachers in the classroom.
Of all the workshops, the Teaching High School Economics offers more economic
pedagogy and teacher materials to attendees than just about any other workshop (the one
exception is the AP Economics workshop). The Teaching High School Economics workshop is a
two-day workshop that introduces teachers to Council material appropriate for their students and
provides them with lesson plans they can correlated to Georgia’s performance standards.
Teachers are responsible for addressing the standards that are the basis for the state-mandated
end-of-course test in economics. The workshop introduces teachers to the resources available to
them through GCEE including lesson plans, other workshops, and message boards that allow
them to consult other high school teachers. While the Teaching High School Economics
workshop is not intended to be a stand-alone course in economics, it does provide instruction on
some important economics concepts and lessons. The lesson plans correlate directly with the
Georgia Department of Education’s Georgia Performance Standards so that the teachers are
assured that any lesson plan they use from the material will fit into the overall curriculum. The
workshop facilitators stress the link between the material and the performance standards
throughout the workshop. Over the past seven years (2002-2009) GCEE has offered 52 Teaching
2
For a list of workshops offered see Swinton et al. [forthcoming].
This information about specific GCEE workshops and their content comes from personal communications with
GCEE associate director and chief program officer Glen Blankenship.
3
6
High School Economics workshops to 968 teachers. 4 About eight percent of Georgia’s highs
school students who have taken economics during this time period had a teacher who had
attended one of these workshops.
There is no registration fee for teachers to take the workshop and GCEE reimburses
school systems for substitute teachers. Furthermore, teachers can earn one professional learning
unit upon completion of the two-day workshop. Georgia teachers need to earn professional
learning units to maintain their certification.
Testing Environment in Georgia
Since 2004 Georgia has required every student who is enrolled in or receives credit for a
course coved by an EOCT to take the EOCT once the student completes the course. Currently
there are standardized EOCTs in eight different corses. 5 These tests count for 15 percent of their
final grade and students must achieve at least a 70 in each class to graduate. While it is possible
to pass the courses without passing the EOCT, the testing environment is high-stakes.
Consequently, students have an incentive to perform well on the test. This high stakes testing in
an economics course required for high school graduation makes Georgia uniquely suited for
studying the impact of in-service workshops. The EOCTs provide a standardized measure of
student performance across the entire population of students.
The economics EOCT covers five domain areas: (1) Fundamentals of Economics, (2)
Microeconomic Concepts, (3) Macroeconomic Concepts, (4) International Economics, and (5)
Personal Finance Economics. The test is a standardized, 90 question test with normed scores
ranging from 200 to 750. 6 A score of 400 represents the minimum score required for passing the
test.
DATA
Data for this study come from two sources. The Georgia Department of Education
(GaDOE) maintains administrative data for all of its EOCTs. The administrative data include
4
During the course of the workshop teachers also receive the Capstone, Focus: High School, and Focus:
International publications from CEE. Teachers also receive the Economics Classroom video series developed by the
Corporation for Public Broadcasting. In addition, teachers received the Virtual Economics 2.0 disk (currently the
Virtual Economics 3.0 disk is distributed in a separate dedicated workshop).
5
Algebra, Geometry, United States History, Economics/Business/Free Enterprise, Biology, Physical Science, Ninth
Grade Literature and Composition, and American Literature and Composition [GaDOE, 2008].
6
Of the 90 questions, 75 count toward the student’s score. Each test also field tests 15 questions which do not count
toward the student’s score.
7
student information such as gender, ethnicity, whether the student receives a free or reduced
price lunch, and whether or not the student is categorized as having a special need or disability
by the DOE. Each student EOCT score is matched to a teacher of record. The teacher of record is
the teacher listed as having taught the course associated with that EOCT. The tests are
administered twice a year. 7 We have three years of these data (fall 2004 through spring 2007) on
all students who took the economic EOCT.
The second source of information comes from GCEE. The Georgia Coucil keeps a record
of every teacher who attends a GCEE in-service workshop. We merge the GCEE database with
the GaDOE data in order to create records with both student and teacher level data. To ensure the
confidentiality of the data, the data are merged at the GaDOE then stripped of all identifying
information. While it would be nice to have additional information about teacher preparation
before entering the economics classroom, such information is not available.
The annual trend of scores on the economics EOCT has been upward since its first
implementation in 2004. This general trend indicates, in part, that teachers and students may be
getting more familiar with the testing environment over time. Therefore, we norm the test results
so that they are comparable from year to year. We create Z-scores for each test score equal to:
(
/
, were
test scores in year t, and
is the individual student’s EOCT score at time t,
is the mean of the
is the standard deviation of the test score for year t.
The data allow us to include demographic information for each student as control
variables – including gender, race, economic status, and disability status, as defined by the
GaDOE. Gender is represented by an indicator variable that equals one for “male” and zero for
“female”. Ethnicity is a vector of four indicator variables that represent “Black,” “Asian,”
“Hispanic,” and “Other,” respectively. “White, non-Hispanic” is the omitted comparison group.
Economic status is an indicator variable that equals one if the student is categorized by the
GaDOE as being “Economically Disadvantaged,” (defined as eligible for a free or reduced price
lunch). Similarly, Disability status is an indicator variable that equals one if the GaDOE defines
the student as one who is a “Student with Disabilities” – which covers a broad range of
disabilities.
7
The EOCTs are officially administered in the spring and the fall. Some students take remedial EOCTs in the
summer. These tests are not included in the data.
8
Our control group is comprised of students whose teachers have not attended any GCEE
workshop in the past five years. To avoid picking up the impact of other workshops, our
treatment group is comprised of students whose teachers have attended the Teaching High
School Economics workshop but no other GCEE workshops. This eliminates some teachers who
have attended multiple workshops, and may thereby remove some of the more motivated
economics teachers who have taken this workshop from the sample. We believe this approach is
cautious. The Georgia Council has offered over three dozen different types of workshops over
the past five years. (The average number of workshops teachers attend once they have attended
at least one workshop is 1.8 workshops, see Swinton et al. forthcoming.)
Even with this preliminary restriction, we have at our disposal almost 220,000 student
test scores. Second, most students take geometry before they take economics. It is common for
the geometry course to occur in the freshman or sophomore year while the economics course is
typically scheduled during the junior or senior year. We use the geometry EOCT score as an
explanatory variable for the economics EOCT score. Therefore, we eliminate student
observations where no geometry EOCT score exists. This eliminates many of the observations
early in our sample period because many of the early takers of the economics EOCT had taken
geometry before the EOCTs were required. This second restriction leaves us with 132,256
observations. 8 As shown below, our results on the variable of interest do not vary significantly
between the full and restricted samples. We also analyze only students enrolled in the basic
Economics course and do not use information on students taking AP Economics. 9 Tables 1 and 2
present summary statistics for the restricted and full samples.
MODEL
Our empirical model relates student performance on the economics EOCT score to
student characteristics and prior performance in geometry, teacher fixed effects, and teacher
attendance at a Teaching High School Economics workshop:
EOCTScoreit = α + ∑1 β j Studenti + β 8 GEOTCi + β 9 HSEWt + λ t + ε it .
7
(1)
8
This restriction disproportionately eliminates observations early in our sample period. If any bias from this
sampling procedure comes from teachers learning more about the test over time, our norming of the EOCT scores
ought to address this problem.
9
In future work we hope to specifically examine AP Economics and GCEE’s AP Economics workshop.
9
In Equation (1), we model students’ economics EOCT results, EOCTScoreit, as a function of
student characteristics (gender, race, economic status, and disability status – Studentit), the
students’ previous EOTC in geometry (GEOTCi), and a dummy variable that equals one if
the teacher has attended a Teaching High School Economics workshops within the previous
five years of the student taking the exam (HSEWt). The subscript i indexes students, while
the subscript t indexes teachers. The inclusion of the students’ geometry test scores allow
us to control for preexisting knowledge and analytical acumen that is likely to be correlated
with success in a high school economics course. Ballard and Johnson [2004] demonstrate
that such math skills are an important determinant of student success in economics.
Finally, like Bosshardt and Watts [1990; 1994] and Watts and Bosshardt [1991], we
utilize teacher fixed effects (λt) to account for time-invariant and unobservable teacher
characteristics that may influence student achievement. When one examines a treatment such as
attendance at an in-service workshop where the choice to attend is in the hands of the teacher, it
is reasonable to ask if characteristics of the teachers determine the probability of attendance at
the workshop. If these same characteristics also determine what makes for a good (or not so
good) teacher, then the problem of selection bias arises. Because we have insufficient
information about teachers to model the choice of attending a workshop explicitly, we rely on the
fixed-effects to capture these underlying differences.
By including the students’ previous EOCT score in geometry, teacher fixed-effects, and
several characteristics of the students, we endeavor to isolate the impact the Teaching High
School Economics workshops have on student test scores rather than other extraneous effects.
FINDINGS
In previous research Swinton et al. [forthcoming] results suggest that, all else equal,
GCEE in-service workshops for high school economics teachers improve student achievement
by almost two percent of a standard deviation in test scores if the teacher attended three
workshops. 10 These results below show the impact of a specific workshop, Teaching High
10
Other studies of the impact of in-service workshops for economics teachers suggest larger effects. However, as
discussed in our literature review, these studies tend to be small scale analyses that do not attempt to control for any
selection bias.
10
School Economics, on economics EOCT scores. Of special interest is how this particular
workshop compared to the average effect found in Swinton et al. [forthcoming].
We estimate Equation 1 utilizing a teacher fixed-effects approach. 11 In the estimation we
utilize the 132,256 student observations where we observe both an EOCT score for economics
and an EOCT score for geometry. The results for the restricted sample appear in Table 3. We
easily reject the null hypothesis of a zero value for each of the estimated parameters. We also
easily reject the null hypothesis that the teacher fixed effects parameters are jointly zero. The
estimates of the coefficients on the student characteristics have signs similar to the wider
literature on the determinants of student achievement. Since the dependent variable contains Zscores, a coefficient estimate of 0.5 on an indicator variable, for example, is interpreted as the
value of indicator variable equal to “1” leading to a 0.5 of a standard deviation increase in
economics EOCT scores, relative to the indicator variable equal to “0”.
Before turning our attention to the effect of the Teaching High School Economics
workshop, it is worth noting the strength of the impact the geometry EOCT plays in predicting
student performance on the economics EOCT. When one cannot pretest student, the next best
way to control for the level of understanding they bring to a course is to find a good proxy for
their preexisting knowledge and skills. Because teachers can demonstrate much of the logic of
economic concepts using geometry (e.g. consumer surplus as a triangle, profit as a rectangle and
so forth), it stands to reason that a student who has a strong background in geometry is likely to
have an advantage in an economics class. Furthermore, research by Ballard and Johnson [2004]
finds a strong link between a student’s math skills and the student’s performance in an
economics class. Both subjects require significant analytical skills and abstract reasoning. The
results presented here suggest that a one standard deviation increase on the geometry EOCT
score increases the expected economics EOCT score by half a standard deviation all other things
being equal. The geometry EOCT score represents the single strongest explanatory variable we
have available to predict economics EOCT scores.
The results from the restricted sample indicate that the Teaching High School Economics
workshop improves student performance on the EOCT in economics. The students of teachers
who have participated in this two-day workshop score on average 5.7 percent of a standard
11
Teacher shift parameters are available upon request. They are omitted in the reported results to economize on
space.
11
deviation higher on the economics EOCT than other students. To put this in a meaningful
context, the impact of coming from a poor household (as defined by receiving a free or reducedprice lunch) decreases the expected economics EOCT score by 8.9 percent of a standard
deviation. Two days of targeted in-service education has about two-thirds of the same magnitude
statistical impact as this measure of poverty.
We show in Table 4 the results of the full sample. The full sample contains a much larger
number of students, but does not include geometry EOCT performance as a control variable. The
point estimate on the Teaching High School Economics workshop using the full sample suggests
that students of teachers who have participated in this two-day workshop score on average 3.1
percent of a standard deviation higher on the economics EOCT than other students. This estimate
is within the 95 percent confidence interval of the 5.7 percent estimate from the restricted
sample. Thus, these two results are not statistically different from each other at a conventional
level.
An important way to compare the results is to consider the findings of Swinton et al.
[forthcoming]. They found when examining all GCEE workshops, teachers who took three
workshops led to their students scoring 1.9 percent of a standard deviation higher on the
economics EOCT. Thus, the Teaching High School Economics workshop has a much larger
impact on student achievement than the average GCEE workshop.
CONCLUSIONS
In this study we have focused on just one of the almost 40 in-service workshops the
Georgia Council on Economic Education offers teachers of economics. While previous work has
shown that the overall impact of GCEE workshops has positive on student performance in
economics, we demonstrate that the Teaching High School Economics workshop has a much
bigger effect than the average GCEE workshop. Specifically, we estimate using our restricted
sample that the Teaching High School Economics workshop leads to a 5.7 percent of a standard
deviation increase in student performance on Georgia’s high stakes economics end-of-course test
(EOCT). Using the full sample, the analogous estimate is 3.1 percent, and the two results are not
significantly different. Compared to other treatments usually considered in education policy,
such as across the board class size reductions, this two-day seminar appears to be extremely cost
effective.
12
In the attempt to control for preexisting student knowledge and skills that may account
for success on the economics EOCT, we evidence similar to Ballard and Johnson’s [2004]
regarding a strong link between student math skills as measured by their performance on the
geometry EOCT and student performance on the economic EOCT. More importantly, the results
of this paper will be helpful to public school administrators and teachers, the GCEE and other
state Councils, college and university based Centers for Economic Education, and the Council
for Economic Education as they make resource decisions about professional learning
opportunities for high school economics teachers.
13
Table 1: Summary Statistics
(sample restricted by availability of geometry EOCT score, N=132,256)
Variable
EOCTScore
HSEW
Poor
Male
Disabled
Black
Asian
Hispanic
Other
GEOTC
Mean
0.0079
0.0602
0.3415
0.4671
0.0474
0.3960
0.0283
0.0517
0.0178
-0.0627
Std. Dev.
0.9004
0.2379
0.4742
0.4989
0.2125
0.4891
0.1659
0.2214
0.1323
0.9498
Min
-5.0433
0
0
0
0
0
0
0
0
-7.1159
Max
5.3304
1
1
1
1
1
1
1
1
6.1942
Min
-5.0433
0
0
0
0
0
0
0
0
Max
5.4748
1
1
1
1
1
1
1
1
Table 2: Summary Statistics
(full sample, N= 218,128)
Variable
EOCTScore
HSEW
Poor
Male
Disabled
Black
Asian
Hispanic
Other
Mean
-0.0851
0.0832
0.3648
0.4813
0.0747
0.3979
0.0272
0.0536
0.0173
Std. Dev.
0.9501
0.2762
0.4814
0.4997
0.2630
0.4895
0.1627
0.2253
0.1304
14
Table 3: Impact of High School Workshops controlling for Geometry EOCT score
(sample restricted by availability of geometry EOCT score)
Variable
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
HSEW
0.0571
0.0095
6.00
0.000
[0.0384, 0.0757]
Poor
-0.0888
0.0044
-20.19
0.000
[-0.0974, -0.0801]
Male
0.1478
0.0035
41.99
0.000
[0.1409, 0.1547]
Disabled
-0.1918
0.0094
-20.35
0.000
[-0.2103, -0.1733]
Black
-0.2152
0.0051
-42.06
0.000
[-0.2252, -0.2051]
Asian
-0.2005
0.0109
-18.34
0.000
[-0.2220, -0.1791]
Hispanic
-0.1978
0.0088
-22.53
0.000
[-0.2150, -0.1806]
Other
-0.0699
0.0134
-5.23
0.000
[-0.0961, -0.0437]
GEOTC
0.5063
0.0023
224.12
0.000
[0.5018, 0.5107]
Constant
0.1089
0.0034
31.66
0.000
[0.1026, 0.1156]
TEACHER_SE~R F(1860, 130386) = 11.396 0.000
(1,861 categories)
Number of obs = 132,256
F( 9,130386) = 7781.08
Prob > F
= 0.0000
R-squared = 0.5233
Adj R-squared = 0.5165
Root MSE
= .62615
Table 4: STATA output: Impact of High School Workshops
(full sample)
Variable
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
HSEW
0.0308
0.0082
3.74
0.000
[0.0146, 0.0469]
Poor
-0.1944
0.0041
-47.55
0.000
[-0.2024, -0.1864]
Male
0.1869
0.0033
56.15
0.000
[0.1804, 0.1934]
Disabled
-0.5943
0.0076
-78.66
0.000
[-0.6091, -0.5795]
Black
-0.4915
0.0046
-105.87 0.000
[-0.5006, -0.4824]
Asian
-0.1153
0.0106
-10.89
0.000
[-0.1360, -0.0945]
Hispanic
-0.4052
0.0081
-49.84
0.000
[-0.4212, -0.3893]
Other
-0.1692
0.0129
-13.16
0.000
[-0.1944, -0.1440]
Constant
0.1611
0.0033
48.77
0.000
[0.1547, 0.1676]
TEACHER_SE~R F(2242, 215877) = 22.196 0.000
(2,243 categories)
Number of obs = 218,128
F( 8,215877) = 3588.91
Prob > F
= 0.0000
R-squared = 0.3588
Adj R-squared = 0.3521
Root MSE
= .76478
15
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