No. 54 July 2008
Building on the Basics:
The Impact of High-Stakes Testing on Student Proficiency in Low- Stakes Subjects
Marcus A. Winters, Ph.D., Senior Fellow, Manhattan Institute
Jay P. Greene, Ph.D., Senior Fellow, Manhattan Institute
Julie R. Trivitt, Ph.D., Assistant Professor, Arkansas Tech University
School systems across the nation have adopted
policies that reward or sanction particular
schools on the basis of their students
performance on standardized math and reading
tests. One of the most frequently raised concerns
regarding such high-stakes testing
policies is that they oblige schools to focus
on subjects for which they are held accountable
but to neglect the rest. Many have worried that
the limited focus of these policies could have
an unintended negative effect on student proficiency
in other subjects, such as science, that are
important to the development of human capital
and thus to future economic growth.
This paper uses a regression discontinuity
design utilizing student-level data to evaluate
the impact of sanctions under Floridas
high-stakes testing policy on student proficiency
in science. Under that states A+ program,
every public school receives a letter grade
from A to F that is based primarily upon its
students performance on the states
standardized math and reading exams. Students
in Florida were also administered a standardized
exam in science, but this test was low-stakes
because its results held no consequences under
the A+ program or any other formal accountability
Previous research has found that the rewards
and sanctions of receiving an F grade in the
prior year led to improved gains in student
proficiency in the high-stakes subjects of math
and reading. This current paper is the first
to evaluate the impact of the incentives under
this high-stakes testing system on student proficiency
in science. This paper adds to a sparse previous
literature quantitatively evaluating whether
high-stakes testing policies have crowded
out learning in a low-stakes subject.
The primary findings of the study are:
F-grade sanction produced after one year a
gain in student science proficiency of about
a 0.08 standard deviation. These gains are
similar to those in reading and appear smaller
than the gains in math that were due to the
is some evidence to suggest that student science
proficiency increased primarily because student
learning in math and reading enabled that
increase. That is, learning in math and reading
appear to contribute to learning in science.
About the Authors
MARCUS A. WINTERS is a senior fellow
at the Manhattan Institute. He has conducted
studies of a variety of education policy issues
including high-stakes testing, performance pay
for teachers, and the effects of vouchers on
the public school system. His research has been
published in the journals Education Finance
and Policy, Teachers College Record,
and Education Next. His op-ed articles
have appeared in numerous newspapers, including
the Wall Street Journal, Washington
Post, and USA Today. He received
a B.A. in political science from Ohio University
in 2002 and a Ph.D. in economics from the University
of Arkansas in 2008.
JAY P. GREENE is endowed chair and head
of the Department of Education Reform at the
University of Arkansas and a senior fellow at
the Manhattan Institute. He conducts research
and writes about topics such as school choice,
high school graduation rates, accountability,
and special education.
Dr. Greenes research was cited four times
in the U.S. Supreme Courts opinions in
the landmark Zelman v. Simmons-Harris
case on school vouchers. His articles have appeared
in policy journals such as The Public Interest,
City Journal, and Education Next;
in academic journals such as The Georgetown
Public Policy Review, Education and Urban
Society, and The British Journal of Political
Science; as well as in newspapers such as
the Wall Street Journal and the Washington Post.
He is the author of Education Myths (Rowman
& Littlefield, 2005). Dr. Greene has been
a professor of government at the University
of Texas at Austin and the University of Houston.
He received a B.A. in history from Tufts University
in 1988 and a Ph.D. from the Department of Government
at Harvard University in 1995.
JULIE R. TRIVITT is an assistant professor
of economics at Arkansas Tech University. She
earned her Ph.D. in economics from the University
of Arkansas in December 2006. Her focus was
health economics and applied econometrics. Since
completing her Ph.D., she has worked as a senior
research associate in the Department of Education
Reform at the University of Arkansas and as
a consultant on education projects to the World
Trade Center in Brescia, Italy. Her research
agenda focuses on human capital, health, labor,
and education economics.
School systems across the nation have adopted
policies that reward or sanction particular
schools on the basis of their students
performance on standardized tests. Such testing
has been a dominant force in education policy
since at least the 1990s. More than half the
states had already implemented some form of
high-stakes test before the No Child Left Behind
Act (NCLB) made it universal in 2002. We call
a test a high-stakes test when there are meaningful
consequences for schools or students that are
based on how students perform on the test.
One of the most frequently raised concerns
regarding high-stakes testing policies is that
they oblige schools to focus on subjects for
which they are held accountable but to neglect
the rest (Nichols and Berliner 2007; Gunzenhauser
2003; Groves 2002; Patterson 2002; Murillo and
Flores 2002; McNeil 2000; Jones et al. 1999).
The vast majority of these policies base their
rewards or sanctions exclusively on the results
of reading and math tests. Though some policies
are more expansive than others, few threaten
meaningful consequences when students fail to
meet standards in subjects such as science,
history, or the arts. Failure to assure student
mastery of subjects other than basic math and
reading could have important implications for
the future of human capital in the United States.
If schools reallocate time and resources away
from important but low-stakes subjects and toward
the high-stakes subjects, with the result that
students achieved in the high-stakes subjects
at the expense of proficiency in the low-stakes
subjects, we would say that the policy crowded
out learning in the low-stakes subjects.
It is important to note that this definition
of crowding out focuses on learning output,
not teaching inputs. In other words, if schools
increased time spent on math or reading by decreasing
time spent on science, we would consider high-stakes
testing of math or reading to have crowded out
science teaching only if students actually learned
less science as a result.
A substantial amount of anecdotal and qualitative
evidence suggests that schools and teachers
have responded to high-stakes testing by adjusting
their teaching styles (McNeil 2000; New York
State Education Department 2004) and by shifting
focus away from low-stakes subjects (Center
on Education Policy 2006; Jones et al. 1999;
King and Mathers 1997; Gordon 2002; Groves 2002;
Murillo and Flores 2002). However, there is
currently very little empirical evidence of
the impact of high-stakes testing policies on
measured student proficiency in subjects that
are not part of the accountability system.
In the only quantitative evaluation of this
topic of which we are aware, Jacob (2005) found
that Chicagos high-stakes testing system
led to significant learning gains in the low-stakes
subjects of science and social studies. However,
he found that these gains were smaller than
those in the high-stakes subjects of math and
In this paper, we add to the limited previous
research by evaluating the effects on student
proficiency in the low-stakes subject of science
and the high-stakes subjects of math and reading
of a high-stakes testing system in Florida that
employs sanctions. There are two important reasons
to research this question in a system other
than Chicagos. First, by evaluating the
impact of sanctions under high-stakes testing
on student proficiency in low-stakes subjects
in another school system, we can help determine
whether the results in Chicago are limited to
that area or hold more generally. Second, it
is important to investigate outcomes in another
system, since some research has found systematic
manipulations of Chicagos high-stakes
exams that could skew results (Jacob 2005; Jacob
and Levitt 2003). Previous research in Florida
found that the results of that states
high-stakes exams have not been systematically
manipulated and are generally reliable indicators
of student proficiency (Greene, Winters, and
Forster 2004; West and Peterson 2006).
Floridas high-stakes testing program
is also worth studying because its accountability
system, unlike that of many other accountability
systems, makes it possible to use a rigorous
regression discontinuity design,
which allows for a causal test of the impact
of the programs sanctions. Beginning in
the 200102 school year, schools received
letter grades reflecting points earned under
an elaborate system for capturing several aspects
of a schools performance. As described
below, Florida imposes meaningful sanctions
only when a school receives a failing grade.
We follow the strategy of a previous paper by
Rouse et al. (2007) that uses the change in
the policy to control for the heterogeneity
of schools that receive a failing or passing
We find that students attending schools designated
as failing in the prior year made greater gains
on the states science exam than they would
have done if their school had not received the
F sanction. The gains that students made in
science were similar to those that previous
research (which we replicate here) has found
that students made in the high-stakes subjects
of math and reading. These findings suggest
that the incentives of Floridas high-stakes
testing program have not led to significant
crowding out of student knowledge in the low-stakes
subject of science.
At first, our results may seem counterintuitive,
in that high-stakes testing in only certain
subjects would be expected to lead schools to
focus on those areas. In fact, encouraging schools
to shift their priorities toward subjects commonly
recognized as academically important (i.e.,
math and reading) is arguably one of the purposes
of the policy.
There are two reasons that high-stakes testing
might instead have a positive effect on student
achievement in low-stakes subjects. First, the
pressure of accountability testing could lead
schools to adopt reforms that improve their
overall quality. For example, a school could
more effectively motivate its students, or it
could improve relations with its teachers. Though
schools purpose may be to improve student
scores in math and reading to avoid the sanctions
of the high-stakes testing policy, general improvements
of this kind might produce across-the-board
increases in student achievement. Second, sanctions
under high-stakes testing could improve student
achievement in low-stakes subjects if the resulting
mastery of high-stakes subjects facilitates
mastery of other subjects.
Though a true test of the prevalence of either
of these kinds of explanations is not available
to us, we have discovered evidence suggesting
that student proficiency in science has increased
under the high-stakes sanctions primarily because
the improvements that students have made in
math and reading have enhanced their ability
to learn science material as well. However,
we stress that future research using stronger
strategies than are available here to explain
a positive relationship between high-stakes
testing and student improvement in low-stakes
subjects is necessary.
Floridas A+ Accountability
Florida is among the nations leaders
in high-stakes testing. Most agree that the
states A+ Accountability Program (A+)
is one of the most aggressive programs of its
kind. It was clearly a template for the federal
Each year, the state administers a standardized
test, the Florida Comprehensive Assessment Test
(FCAT), in math and reading to all public school
students in the state who are enrolled in grades
310. Schools receive letter grades, from
A to F, based on the percentage of their students
meeting particular achievement levels and the
academic progress of students in certain subgroups.
There are two important reasons that we might
expect schools deemed to be failing to respond
positively. Those that have received an F grade
for the first time may be shamed into improving
their performance (Figlio and Rouse 2005; Ladd
2001; Carnoy 2001; Harris 2001). Those that
have received at least one failing grade may
decide to raise their performance because they
fear attrition of their student body. This may
occur as the result of a policy of issuing Opportunity
Scholarships (vouchers) to students in schools
that have received two failing grades within
a four-year period that they can use to attend
another public school or a private school willing
to accept the voucher as a full tuition payment.
In this paper, we are not particularly concerned
with whether these or any other phenomena drive
increases in student performance in either high-
or low-stakes subjects.
A change in the administration of the program
provided an interesting avenue for researching
Floridas policy. In the programs
initial years, school grades were based on the
percentage of students earning level 2 (the
second-lowest of five levels) or above on the
reading, math, and writing portions of the FCAT
and the percentage of eligible students tested.
A school could avoid earning an F if at least
50% of tested students scored at achievement
level 3 in writing, or if 60% of tested students
scored at level 2 in reading or math and 90%
of eligible students were tested. If a school
met one or two of these criteria, it earned
a D. If it met all three of these criteria,
it earned a C. Schools with particular subpopulations
meeting all three received a B. To earn an A,
schools had to meet more stringent requirements
for the overall student population and each
subpopulation. The opinion was widespread that
schools had determined that satisfactory scores
in writing were the easiest to achieve under
the original school-grading format and that
the teaching of writing in struggling schools
therefore stressed techniques geared to the
writing portion of the exam.
Starting in the 200102 school year, Florida
adopted an accumulating point system to evaluate
schools. Schools earn one point for each percent
of students who score in achievement levels
3, 4, or 5 (the three highest of five levels)
in reading and one point for each percent of
students who score in levels 3, 4, or 5 in math.
Schools earn one point for each percent of students
scoring 3.5 or above in writing, which is graded
from 1 to 6. Schools earn one point for each
percent of students who make learning gains
in reading and one point for each percent of
students who reach a higher achievement level
or maintain a 3, 4, or 5 in math. Schools also
earn one point for each percent of the lowest-performing
readers who make test-score improvements in
the year in question. A school that earns fewer
than 280 points receives a failing grade. The
multifarious nature of the grading process has
probably made direct manipulation of the system
Beginning in the 200203 school year,
Florida public schools also were required to
test for proficiency in science when they administered
the math and reading exams. The science part
of the FCAT is currently administered to all
public school students in grades 5, 8, and 11.
The results of the science exam have now been
incorporated directly into the accountability
program; but during the years of our analysis,
they had no effect on the schools grade,
nor did they represent any other form of official
Several researchers have evaluated the impact
of the A+ program on the academic gains of public
school students in math and reading (Rouse et
al. 2007; Greene and Winters 2004; Chakrabarti
2005; Figlio and Rouse 2005; West and Peterson
2006; Greene 2001). Though there is some disagreement
about which aspect of the accountability policy
was effective (the threat of vouchers or the
shame of an F grade), each of these analyses
found that the policy improved the math and
reading proficiency of students in public schools
designated as failing. We are aware of no previous
research analyzing the impact of the A+ program
on science test scores.
Data and Method
We utilize a data set provided by the Florida
Department of Education that contains test scores
in math, reading, and science as well as demographic
characteristics of the universe of students
enrolled in grades 310 in Florida public
schools. We supplement the individual-level
data set with school-level informationspecifically,
the schools point total and letter grade
under A+ at the end of the 200102 school
year. To simplify the comparison of scores in
different subjects, we convert the FCAT scores
of students who were in our sample into a scale
score with a mean of 0 and standard deviation
In order to align our findings with those in
the previous literature, we utilize the comparison
strategy implemented in a 2007 study conducted
by Rouse et al. that evaluated the impact of
Floridas A+ policy on student achievement
in math and reading. Our sample consists of
the universe of Florida public school students
who were enrolled in the fifth grade in 200203
and were promoted at the end of the prior year.
This was the first class of fifth-grade students
attending a school that had received a letter
grade under the revised point system of the
A+ policy. We focus on only those students with
both a math and reading test score reported
in 200102 and 200203.
We supplement the individual-level data with
administrative information on the schools
grade and points earned under the A+ system
during the summer of 2002. In the analyses that
follow, along with observable characteristics
of the student and school we control for both
the schools letter grade at the end of
the 200102 year and the total points earned
under the grading system. The idea here is that
controlling for the points earned by the school
accounts for differences in school performance,
and thus the remaining differences in the performance
of students at schools receiving an F grade
must reflect responses to the incentives that
exist under the accountability policy.
We use this general comparison strategy to
perform a series of cross-sectional regressions.
We are first concerned with discovering whether
students made academic gains in science due
to the F sanction, and we also confirm the finding
of an impact of the sanction on student proficiency
in math and reading. We then evaluate the extent
to which any gains made by students in science
due to the F sanction were driven by improvements
in the overall performance of the school or
a symbiotic relationship between learning in
the high- and low-stakes subjects.
The Impact of the F-Grade
Sanction on Student Proficiency in High- and
We adopt the strategy of Rouse et al. to measure
the impact of the F-grade sanction on student
proficiency in science. As a check on our procedure,
we also attempt to replicate in math and reading
the results of this previous paper.
To evaluate the impact of the F-grade sanction
on student proficiency, we estimated cross-sectional
regression models using the students test
score on the fifth-grade test in 200203
in the subject being evaluated as the dependent
variable. The regression controls for a variety
of observable characteristics about the student
and school, including the letter grade and cubic
function of the number
of points earned by the school at the end of
the 200102 school year. The variable of
interest is that which indicates whether the
childs school received an F grade in the
In the math and reading analyses, we control
for a cubic function of the students test
score in that subject at the end of the previous
year (when the student was in the fourth grade),
which allows us to measure improvements in the
students math and reading proficiency
and account for unobserved differences among
students. Unfortunately, students did not take
a science exam in the fourth grade, so a similar
control is not available for the science evaluation.
Instead, we use the cubic functions of the students
fourth-grade scores in math and reading to substitute
for their scores in science. This procedure
assumes that student proficiency in these subjects
is highly correlated and that there was no differential
relationship in student knowledge among these
subjects in the five categories of schools before
the F-grade sanction was introduced.
The results of our estimations of student proficiency
in math, reading, and science are reported in
Table 1. Our findings in math and reading are
very similar to those reported by Rouse et al.
(2007). Our estimation suggests that the scores
of students enrolled in an F-graded school exceeded
by 0.09 standard deviations in reading and 0.17
standard deviations in math the scores of students
in D-graded schools. At the same time, there
was no statistically significant difference
in the performance of A-, B-, C-, and D-graded
schools, strengthening the view that sanctions
have an effect on performance. The similarity
of our results to those reported by Rouse et
al. suggests that we have reproduced their procedure
relatively well, which should provide additional
confidence in our findings for science.
Column 3 of Table 1 reports the results in
science. Here we find that the F-grade sanction
produced after one year a gain in student proficiency
of about a 0.08 standard deviation relative
to students in schools that earned a D grade.
The result is significant at the 5% confidence
level, meaning that we can be highly confident
that the F sanction had a positive impact on
students science test scores.
These findings suggest that the F-grade sanction
not only improved student learning in the high-stakes
subjects; it also had a positive effect on student
proficiency in the low-stakes subject of science.
It appears that the positive impact of the F
sanction on science proficiency was similar
to that found in reading but somewhat lower
than that found in math.
Understanding the Effects
of the F-Grade Sanction on Science Proficiency
Our finding that the F-grade sanction led to
substantial improvements in science proficiency
seems odd at first glance. It is clear that
under Floridas policy, point-maximizing
public schools have an incentive to focus on
the high-stakes subjects, even if doing so is
to the detriment of the low-stakes subjects.
Qualitative research and anecdotal evidence
suggest, moreover, that the reallocation of
resources precipitated by high-stakes testing
has curtailed general student knowledge.
There are two possible ways of explaining how
high-stakes testing could increase performance
in low-stakes subjects. One is that gains in
one subject may facilitate mastery of another.
We call this the correlation effect.
Another is that implementation of high-stakes
testing could lead to the adoption of policies
and attitudes that improve performance generally.
For example, high-stakes testing could lead
schools to expect improved student achievement
across the board, to be shamed into improving
their overall performance, to recognize excellence
in other subjects, and so on. Rouse et al. find
that schools responded to receiving the F-grade
sanction in a variety of ways, including lengthening
school periods (block scheduling) and increasing
time for collaborative planning and class preparation.
Such changes in the overall school environment
could affect the teaching of science as much
as they do the teaching of math or reading.
We refer to this possibility as the systemic
Unfortunately, we cannot produce a true causal
estimation of the prevalence of systemic and
correlation effects. We can, however, produce
some evidence suggesting the relative importance
of each by analyzing a regression of student
proficiency in science, controlling for observable
characteristics used in the previous regression,
and including a control for the test-score gain
that the student made in math and reading from
the 200102 to the 200203 school
year. Here the estimate of the variables for
the students gains in math and reading
measures their contribution to gains in science;
as explained earlier, this is the correlation
effect. The variable for the grade earned by
the students school in 200102 measures
the grades impact on science proficiency
independently of the correlation effect; this
is what we call the systemic effect.
results of this estimation are found in Table
2. We find a strongly positive relationship
between science scores and gains in math and
reading, indicating the likely existence of
a correlation effect. The variable representing
the independent effect of the F-grade sanction
on gains in science is quite small and statistically
insignificant, indicating the lack of a systemic
effect. These results suggest that the entire
gain found in science due to the F-grade sanction
is likely due to the correlation effect.
Summary and Discussion
In this paper, we have evaluated whether the
F-grade sanction in Floridas A+ program
has led schools to increase student learning
in the high-stakes subjects of math and reading
to the detriment of learning in the important
but low-stakes subject of science. Our results
indicate that the F-grade sanction led to substantial
student gains in the learning of math, reading,
and science. Finally, we produced a simple model
to explain the impact of high-stakes testing
on student learning in low-stakes subjects.
We provide some evidence suggesting that virtually
all the positive findings in science are attributable
to complementarities in the learning of math
It could be said that student performance in
science is not the most authoritative test of
the proposition that high-stakes testing crowds
out instruction in other subjects, since science
proficiency may be more dependent on reading
and math proficiency than other subjects are.
In effect, such criticism would be assuming
the validity of the correlation effect.
Because students in Florida do not take standardized
tests in other low-stakes subjects, we are unable
to test this hypothesis. It should not be forgotten,
however, that much of the discussion of the
crowding-out effect focuses on its impact on
science learning. Nevertheless, we look forward
to future work evaluating the impact of high-stakes
testing on student learning in low-stakes subjects
other than science.
- The voucher provision of this policy was
recently overturned by the states supreme
court, though it was in effect during all the
years in which this study takes place.
- A more technical treatment of the methods
utilized in this paper is available online at
This simply means that we included variables
for the points, the points squared, and the
points cubed. Use of the cubic function allows
for a more flexible model because it relaxes
the assumption of linearity in measuring the
impact of school points on student proficiency.
That is, only controlling for the number of
points earned by the school makes the strong
assumption that every point has the same impact
on science proficiency. That is, it would
assume that the impact on a students
science proficiency of a schools raising
its score from 100 points to 110 points was
identical to the impact of a schools
raising its score from 200 to 210 points.
The cubic function allows us to account for
nonlinearities in this relationship. The same
basic argument also holds for the control
for a cubic function of the childs prior
test scores, which are also discussed and
used in this analysis.
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