1
HOPE Scholarship Eligibilitiy and Retention Rates as a Function of High School Characteristics
Daniel Waldroup
I. Introduction
A. HOPE Program Details1
Since its introduction in 1993, the HOPE (Helping Outstanding Pupils Educationally)
Scholarship has become the largest and most prominent college merit-aid program ever. It was not
always so well-liked, however. In fact, the 1992 referendum to create the Georgia Lottery—which
funds HOPE and other educational programs—barely squeaked out a victory. Since that time, the
HOPE Scholarship has helped to pay for the in-state college expenses of thousands of students. This
project’s pertinent question is: what high school factors are most strongly associated with students who
successfully qualify for HOPE? What explains retention of the award in college? These questions are
based on the hypothesis that success at earning and maintaining HOPE is highly correlated with
educational success and learning in general. If we discover policy changes that help students better
earn and maintain the HOPE Scholarship, then these policy changes will also increase overall learning
and educational attainment.
The requirements of the HOPE Scholarship are clear-cut. To qualify, Georgia high school
students must earn a grade point average (GPA) of at least 3.0 on a 4.0 scale in their core classes
(classes like physical education and art do not count towards the HOPE GPA). If they graduate with a
GPA that meets this minimum requirement, the scholarship program will pay their in-state tuition and
fees to any public Georgia college or university. If they attend an in-state private college or university,
students are given $3000 per year to help defray tuition expenses. In its initial 1993 conception, the
program had a family income cap of $66,000, which was quickly increased to $100,000. In 1995 the
1
For a more detailed history of the HOPE Scholarship Program, see the HOPE Scholarship Joint Study Commission Report
at http://www.cviog.uga.edu/hope/report.pdf.
2
income cap was lifted entirely, and since that time any student who meets the minimum GPA
requirement has been eligible to receive HOPE.
Stipulations for maintaining HOPE eligibility are similar to the requirements for qualification.
HOPE recipients must earn at least a 3.0 GPA in college to continue receiving the lottery funds.
Student GPAs are checked after thirty, sixty, and ninety hours of semester credit to make sure that
recipients continue to qualify. About two-thirds of students lose the scholarship at the thirty hour
check-up. Because many students who knew they were going to lose HOPE took less than thirty hours
in their freshman year so as to qualify for an additional semester of HOPE eligibility, the program
recently made some changes. Specifically, the GPAs of HOPE recipients are now checked at the end
of every academic year, regardless of earned credit hours. This new rule prevents students who
initially qualify for HOPE but do very poorly in college from maintaining the award for an entire extra
semester simply by not taking thirty credit hours.
B. Observed Effects of HOPE
The HOPE Scholarship Program was designed with many intentions. The program’s architects
desired to increase the in-state college enrollment of Georgia high school students, especially of the
state’s top performing students. In this way, they hoped to minimize the state’s “brain drain” and help
the state’s future economic and educational attainments. In this regard, the program appears to have
succeeded. Many students attend in-state schools because they are an economic bargain compared to
out-of-state alternatives (Cornwell, Mustard, and Sridhar 2004). Average SAT scores at Georgia’s
colleges and universities—particularly at the largest and most prestigious state universities—have
increased since HOPE’s institution, as illustrated in Figure 1 in the appendix (Cornwell and Mustard
2005). Thus HOPE is generally credited with increasing the caliber of students that attend state
colleges and universities. As a side effect, the prestige of state higher educational institutions has risen
since the program’s beginning. State planners of the program also hoped to increase educational
effort, and therefore overall educational levels, of students. Although indicators are less persuasive at
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documenting this effect, some evidence suggests that this intention has been at least partially
successful as well, especially amongst students whose high school GPAs are near the 3.0 level since
these students have the greatest marginal benefit for improved performance (Henry and Rubenstein
2001). In short, HOPE has accomplished much of what it was intended to do.
HOPE, however, has had other, unintended consequences on students as well. Research at the
University of Georgia has indicated that HOPE alters the way students take college courses (Cornwell,
Lee, and Mustard 2004). Specifically, students have been found to take lighter course loads,
presumably 1) so that they can focus more attention on each class to earn grades that will maintain
their HOPE eligibility, and 2) so that, until recently, they could postpone HOPE GPA checkups and
thus circumvent the system’s accountability measures. Students’ taking of lighter course loads leads to
longer than anticipated college careers, which ends up costing the scholarship program more money
than anticipated. In addition to increasing the length of college for many students, HOPE has failed to
attract sizable numbers of new students to college. Cornwell, Mustard, and Sridhar (2004) found that
most students who receive HOPE would have gone to college somewhere even if the program were not
available. Thus HOPE has largely failed in its goal of attracting students to college who would
formerly not have considered it. On the contrary, HOPE recipients are generally the children of
middle- to upper-middle-class families who would value college education with or without the
program. In practice, the HOPE Scholarship is generally a regressive form of educational funding—
lottery players are disproportionately the poor and minorities, whereas scholarship recipients are
overwhelmingly white and well-off. (Rubenstein and Scafidi 2002).
C. HOPE’s Future
Most recently, HOPE has been in the news because of its looming budget shortfall. The growth
of scholarship expenditures continues to outpace revenue growth, and unless changes are made to the
program, the current trend will lead to deficits in a short number of years. The most obvious ways to
combat the budget shortfalls are to either cut benefits or increase qualification requirements. Neither
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measure is popular. It remains to be seen how this issue will play out. With the budget crisis looming,
it may seem ironic that this paper seeks to find factors most associated with HOPE success in order to
increase scholarship qualification and retention. Nonetheless, I maintain that, ceteris paribus, greater
HOPE achievement equates to better education for Georgia students. Thus even if continued
scholarship growth causes shortfalls, we should search for ways to help more students become eligible,
and help those students to maintain their scholarships. Barring the effects of grade inflation, rapid
growth of HOPE is a good problem to have.
D. Georgia High School Basics
While the HOPE Scholarship generally receives favorable press coverage and is held in high
regard by many Georgians, the state’s public high schools are often derided by both the media and
public opinion. Perhaps the most publicized statistic is that Georgia’s average SAT score consistently
ranks as one of the worst five scores among the nation’s states. This statistic is somewhat misleading,
since Georgia’s students take the SAT at a much higher rate than students in most other states. Even
so, the low average score and the slowness of its improvement contribute to the public perception that
Georgia’s educational system is lackluster and sub par. Other statistics also seem to reinforce this
conclusion. Georgia’s dropout rate (of those who make it to ninth grade) is over forty percent, well
above the national average of twenty-eight percent. Additionally, the state ranks 48th in college
enrollment for those in the 18 to 24 age range (AP 2005).
In reality, Georgia’s public education system is an eclectic collection of schools and students
who often share very little in common with one another. Forty-six percent of public school students
were eligible for free or reduced lunch in the 2003-04 school year. Thirty-eight percent were AfricanAmerican (Question!!! Of those who were eligible for free/reduced lunch, or of all public school
students?). Thirty-two percent of 11th graders failed their first attempt at passing the science portion of
the Georgia High School Graduation Test (GHSGT). But you would not guess any of these statistics if
you only considered suburban Atlanta schools. Suburban schools are mostly white, middle- to upper-
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middle-class, and high-achieving. Schools within the Atlanta city limits, on the other hand, have
students who are overwhelmingly black, poor, and low-achieving. This suburban/urban contrast
repeats itself within and/or between districts around almost every major city in the state. Rural schools
have a variety of racial and poverty-level compositions, but almost all of them have test scores below
the state average. Schools in rural South Georgia, which are more likely to have student populations
characterized by poverty, score especially poorly on standardized tests. In sum, any serious discussion
of the “conditions of Georgia high schools” needs to take the major variation between the state’s
different types of schools into account. To say that the state of Georgia’s high schools is “good” or
“bad” is superficial. Some schools do an excellent job academically, while others have educational
statistics and test scores that make us shudder. Thus it is important to understand what factors cause
such disparate educational effects among high school students. Specifically, what high school factors
explain post-secondary educational success?
II. Methodology
A. Data and Equation Basics
To answer this question, I use ordinary least squares regression analysis to explain observed
HOPE eligibility and retention rates amongst Georgia public high schools between the 1995-96 and
2001-02 school years. Data from individual students would be most accurate in determining causation,
but because of privacy issues and the overwhelming size of the data set, that information was neither
available nor feasible for this project. Nonetheless, the data set of approximately 325 high schools
tracked over a seven year period was sufficiently large to allow me to draw conclusions. Using
information provided by the Georgia Department of Education, I account for a number of high school
variables including attendance, racial composition, poverty rate as proxied by reduced/free student
lunch rates, and various standardized test scores. Most variables are tracked through all seven years.
The longitudinal nature of the study allowed me to adjust my equations for unobserved variables
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associated with individual years and schools. Unfortunately, HOPE retention rates, which were
provided by the Georgia Student Finance Commission, were only available for the class of 2002.
Therefore, because retention rate data are only available for one year, I was unable to take individual
year and school effects into account. This misfortune is reflected by the depressed strength of the
explanatory variable in the HOPE retention regression—the adjusted R squared variable equals
0.3937—which, while still significant, is not nearly as impressive as the 0.7993 value observed in the
HOPE eligibility regression.
B. Explanatory Variables
One variable that might be related to student performance is high school size. The statistic
recorded to measure size was attendance, the number of students legally enrolled in a school. There
was the possibility of bias in this measurement because of the different structures of individual schools.
Most Georgia high schools simply contain grades 9-12, but not all schools are structured in this way.
Specifically, some DeKalb County high schools contain grades 812. Many school districts in small,
rural counties educate grades 6-12, or sometimes even grades K-12, within the same school building.
Thus while a rural school may have attendance of six hundred, only a fraction of that number are
enrolled in the 9th through 12th grades. Alternative, evening, and other non-traditional types of schools
further complicated the variable. These schools almost always contained a low number of students
who were low-achieving, self-selected individuals. All of these issues complicated the explanatory
power of the attendance variable.
Another variable was racial composition. The three categories I recorded with regard to this
factor were the percentages of the student body that were white, black, or Hispanic. Because the sum
of these percentages equaled one, I omitted the white variable from my regressions.
School dropout rates were also taken into account. The dropout rate is equal to the total
number of dropouts divided by school attendance. Because school attendance for purposes of the
dropout rate were calculated differently in the 1995-96 school year, the data for that year’s dropout rate
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are not exactly comparable to the data for other years, although I assume that they are closely related.
Also, because some schools include more grade levels than others, the dropout rate is not necessarily
comparable across all schools either. However, this effect is minimized because the dropout rate is
only reported for grades 7-12, even, for example, if a school’s enrollment encompasses grades K-12.
I used the free/reduced lunch rate statistic to proxy the characteristic of student poverty. The
free/reduced lunch rate reports what percentage of students at a particular school fell below a specified
income line. The exact dollar amount of that line changes over time, is set by the federal government,
and is based upon family size. Free/reduced lunch rate is a blunt measure of poverty because it does
not tell us how far above or below the line students at any particular school are, but it is nonetheless a
useful way of comparing relative poverty rates between schools.
HOPE eligibility rates for each school were recorded as a percentage of each year’s graduating
class. The rate tells what percentage of a class graduated with at least a 3.0 GPA in core classes.
HOPE retention rates for each school were based upon what percentage of graduates from each high
school who accepted the HOPE Scholarship were eligible for renewal after completing their first thirty
semester hours in college.
Besides HOPE retention rates, the only other post-high school variable I included was
percentage of graduates eligible for learning support at Georgia public colleges. Students attending
Georgia’s public colleges who do not meet minimum academic requirements set by the Board of
Regents or individual colleges are required to enroll in learning support (LS). I recorded what
percentage of the previous year’s graduates who entered Georgia public colleges were required to
enroll in LS.
Finally, I recorded a number of standardized test results from each high school. Average SAT
scores from each school are documented—both average overall score and average score for collegeprep students, when schools reported that breakdown. I also recorded overall school average ACT
scores. I noted the total number of Advanced Placement (AP) tests taken by students at each school, as
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well as the percentage of AP tests in which passing scores of 3 or higher were earned. Finally, I
recorded passage rates related to the Georgia High School Graduation Test. The GHSGT consists of
five sections—math, science, social studies, English, and writing. In addition to noting the passage
rates of students at each school on each of these individual tests upon the test’s first administration, I
also documented what percentage of students passed all the tests, excluding writing, on their first try.2
All GHSGT component test pass rates are reported for all years with the exception of 1995-96, when
the science test pass rates are omitted. The science test, which has proven every year to have the
lowest passage rates, was not implemented until the 1996-97 school year. Because of the omission of
the science test from the 1995-96 data, the “all passed” statistic for that year is not comparable to the
same statistic from other years.
III. Results
A. HOPE Eligibility
Because of the collinearity between a number of explanatory variables, not all of them were
used in my final regression equation regarding HOPE eligibility. For example, all but one of the
GHSGT component pass rates—the “all” variable—were dropped, and only school average SAT score,
not the SAT score of college prep students or a school’s average ACT score, was considered. For the
GHSGT, I report the results that use the pass rate on all the exams. However, when I included the test
scores for each individual exam, the results on the basic variables changed relatively little. After
eliminating some of the collinearity problems, very few variables are shown to have statistically
significant effects on HOPE eligibility rates. For example, school size, dropout rate, free/reduced
lunch rate, and GHSGT passage rates do not have a statistically significant effect on HOPE eligibility
outcome.
2
I have no idea why the state excludes the writing test from the “all” data, but it does.
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However, some factors were statistically significant. For example, both SAT scores and the
racial composition of a school were found to affect HOPE eligibility rates. The regression allows me
to conclude at the 1 percent level that average school SAT score was positively associated with
eligibility rates. My results predict that for every 100 point increase in average SAT score, HOPE
eligibility increases by 2.9 percent. At the 5 percent level, I can conclude that the “percentage black”
variable is negatively associated with HOPE eligibility rates. For every 1 percent increase in the
percentage of the student body composed by African-Americans, the equation predicts that eligibility
rate falls by 0.127. The “percentage Hispanic” variable has an even stronger negative association with
HOPE eligibility rates. At the 1 percent level, my regression equations predict that for every 1percent
increase in the percentage of the student body composed of Hispanics, HOPE eligibility rates will fall
by 0.549.
Even these significant variables, however, leave much unexplained about HOPE eligibility
rates. The mean HOPE eligibility rate across my data was 52.123 percent. Approximately three-fifths
of this amount was explained by the constant term in my regression equation. The coefficient value of
β1, which was statistically significant at the 1 percent level, was 32.363. Such a high coefficient value
for the constant indicates to me that high school explanatory variables only account for a portion of
any school’s HOPE eligibility rate. In other words, no matter how bad a school is, it is still improbable
that it will fall below a certain percentage of graduates who are eligible for HOPE (say, 25% or so),
especially if that school is a traditional high school. Depending upon interpretation, this is either a
reason to rejoice or motive to despair. Viewed in a positive light, the statistic indicates that even at the
worst high schools, a sizable minority continues to work hard and earn decent grades. In the more
cynical view, no matter how bad a high school is, teachers and school districts will continue to
undeservingly award inferior students good grades. My suspicion is that a combination of these two
sources best explains the value of β1.
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I am curious as to whether high school size really has no effect on HOPE eligibility as my
regression indicates, or whether other variables are masking the true effect. For example, perhaps
smaller size causes better educational results in normal high schools, but the inclusion of so many
alternative and non-traditional high schools in the data set (which generally have very small student
populations) has hidden this effect. I plan to run future regressions that exclude non-traditional high
schools to try to answer this question.
Theoretically the effect of the dropout rate is ambiguous. High dropout rates generally indicate
bad schools, and thus the dropout rate should reduce HOPE eligibility rates. However, a high dropout
rate also means that many of a school’s worst students are leaving—many of the students who would
not have been eligible for the scholarship are self-excluded from the calculation of eligibility rates. The
net effect is that the dropout rate does not have an effect statistically different from zero.
After controlling for other variables, especially the average SAT score, the GHSGT pass rate
has little explanatory power. Similarly, the poverty rate does not have an independent, statistically
significant effect. This is likely because poverty rates are correlated with other variables like race,
ethnicity, and standardized test scores that have do statistically significant effects.
B. HOPE Retention
The HOPE retention regression shares some characteristics with the HOPE eligibility
regression. Specifically, in both equations, attendance, number of AP tests, and percentage of passing
AP tests variables do not show statistical significance. Also, SAT scores showed a statistically
significant positive association with both HOPE eligibility and retention rates. In this regression, for
every 100 point increase in SAT score, retention rates are predicted to rise by 5.38 percent.
However, the HOPE retention regression showed unique results as well. One of the most
interesting is the negative association of HOPE eligibility rates with HOPE retention rates, which is
statistically significant at the 1 percent level. Specifically, my calculations predict that for every 1
percent increase in HOPE eligibility rates, HOPE retention rates fall by 0.131. The implication is that
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high eligibility rates are artificially inflated – that the higher the eligibility rate, the greater the
percentage of non-retaining students. This is expected because as the fraction of HOPE Scholars
increases at a given institution, the average quality of those qualifiers likely decreases controlling for
other variables. While the “percent black” explanatory variable is not statistically significant in this
regression, the “percent Hispanic” variable is significant at the 5 percent level. Interestingly, it is
positively associated with HOPE retention, whereas it was negatively associated with eligibility.
Regression results predict that for every 1 percent increase in the amount of the student body that is
Hispanic, HOPE retention will increase by 0.215. Perhaps because highly Hispanic schools have
lower initial eligibility rates, the average quality of HOPE recipient is higher, which increases the
retention rate among students who receive HOPE.
The dropout rate, on the other hand, is negatively associated with retention, which is
statistically significant at the 5 percent level. For every 1 percent increase in the dropout rate, retention
rates are predicted to fall by 0.328. Apparently, schools with high dropout rates do not do as good a
job preparing their students for college as similar schools with lower dropout rates.
The last explanatory variable that was significant at the 5 percent level was the “all-pass
GHSGT” category. For every 1 percent increase in pass rates in this category, HOPE retention rates
were predicted to increase by 0.119. As expected, the higher the percentage of graduates who meet
minimum graduation requirements in high school, the higher the percentage who will meet minimum
HOPE requirements in college.
As in the HOPE eligibility regression, the value of the constant β1 is fairly large and statistically
significant. In this case, the estimated coefficient is -22.520. In other words, before we even consider
other explanatory factors such as HOPE eligibility rates, we can expect almost 23 percent of students
to lose HOPE in the first year. This large coefficient testifies to two truths: 1) it is harder to keep a “B”
average in college than in high school, and 2) no matter where they go to school, many “B” high
school students will be unprepared or unwilling to maintain a “B” average in college.
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IV. Conclusion
My regression analysis of Georgia high school data indicates that statistically significant factors
in explaining HOPE eligibility rates include a high school’s racial composition and average SAT
scores. Why racial composition plays a role in eligibility rates is an important question that deserves
research attention. However, this study cannot identify the exact answer. In explaining HOPE
retention rates, dropout rates, HOPE eligibility rates, the percentage of a high school’s students that are
Hispanic, average SAT scores, and average GHSGT pass rates are all statistically significant variables.
It is especially noteworthy that average SAT scores are an important variable in explaining both HOPE
eligibility and retention rates. The SAT is regularly criticized for its supposed bias and limited
usefulness. But biased or not, these regression equations indicate that SAT scores are positively
associated with initial HOPE qualification and subsequent college success, consistent with an
expansive literature that links high SAT scores with college success. Therefore, Georgia should
continue to take steps that emphasize improving student SAT performance because the unobserved
factors that affect doing well on the SAT are also linked with scholarship receipt and doing well in
college. Other standardized tests, such as the GHSGT, also seem to be of at least limited usefulness in
predicting college success.
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Table 1 – Summary Statistics
Variable
# of
Observations
336
2460
2460
Mean
Value
27.795
1113.067
38.960
Standard
Deviation
11.169
579.622
30.009
Minimum Maximum
HOPE Retention Rate
0
60
Attendance
5
3796
Percent of Student Population that
0
100
is Black
Percent of Student Population that
2460
56.493
29.762
0
100
is White
Percent of Student Population that
2460
2.235
3.822
0
45.5
is Hispanic
2457
33.901
22.185
0
100
Percent of Student Population
Eligible for Free or Reduced
Lunch
Dropout Rate
2459
7.692
8.196
0
88.7
Pass Rate of English GHSGT
2365
93.215
5.970
33
100
Pass Rate of Math GHSGT
2365
86.822
10.105
23
100
Pass Rate of Social Studies
2363
77.247
13.041
9
100
GHSGT
Pass Rate of Science GHSGT
2030
68.567
15.122
10
100
Pass Rate of all Components of
2356
64.989
16.159
5
100
GHSGT (except Writing)
Pass Rate of Writing GHSGT
2363
89.198
8.575
0
100
Percentage of Graduates Eligible
2388
52.123
18.749
0
100
for HOPE
Average SAT Score
2316
947.105
80.847
560
1220
Average SAT Score of College
1961
979.287
77.168
520
1225
Prep Students
Average ACT Score
1886
19.235
2.144
13.3
28
Number of AP Tests
2456
43.259
57.705
0
459
Administered
Percentage of AP Tests with
2456
32.439
29.092
0
100
Scores of 3 or Higher
2240
26.153
16.163
0
100
Percentage of Graduates
Requiring Learning Support in
College
Number of AP Tests/Attendance
2454
.0308269 .0331837
0
.2066946
2454
1.528547 2.233041
0
16.33364
Number of AP
Tests/Attendance*Percentage of
AP Tests with a Score ≥ 3
Data Sources: All high school data from the Georgia Department of Education Annual Report Cards:
http://techservices.doe.k12.ga.us/reportcard/; HOPE retention rates from the Georgia
Student Finance Commission Spreadsheet “HOPE Scholarship Renewal Rate for High
School Graduates”
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Table 2 – HOPE Eligibility Regression Results
Variable
Coefficient Estimate Standard Error Significance
Attendance
-.001
.001
n/a
% Black
-.127
.059
.05
% Hispanic
-.549
.136
.01
% Free/Reduced Lunch
-.009
.032
n/a
Dropout Rate
.045
.048
n/a
Average SAT Score
.029
.006
.01
% Passing All GHSGT Components
-.016
.026
n/a
(except Writing)
Number of AP Tests
-.001
.008
n/a
% of AP Tests scoring at least a 3
.016
.012
n/a
Time Fixed Effects
Yes
.639
.10
County Fixed Effects
Yes
.637
.01
Constant: β1
32.363
6.773
.01
Data Sources: All high school data from the Georgia Department of Education Annual Report Cards:
http://techservices.doe.k12.ga.us/reportcard/
Notes: Number of Observations = 2282
Adjusted R-squared = 0.7993
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Table 3 – HOPE Retention Regression Results
Variable
Coefficient Estimate Standard Error Significance
Attendance
.000
.001
n/a
% Black
-.036
.023
n/a
% Hispanic
.215
.093
.05
Dropout Rate
-.328
.133
.05
Percent eligible for HOPE
-.131
.046
.01
Average SAT Score
.054
.011
.01
% Passing All GHSGT Components
.119
.060
.05
(except Writing)
Number of AP Tests
.007
.010
n/a
% of AP Tests scoring at least a 3
.017
.025
n/a
% Requiring Learning Support
.002
.041
n/a
Constant: β1
-22.520
10.674
.05
Data Sources: All high school data from the Georgia Department of Education Annual Report Cards:
http://techservices.doe.k12.ga.us/reportcard/; HOPE retention rates from the Georgia
Student Finance Commission Spreadsheet “HOPE Scholarship Renewal Rate for High
School Graduates.”
Notes: Number of Observations = 324
Adjusted R-squared = 0.3937
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Figure 1 – Comparing SAT Scores of HighSchool Seniors and College Freshmen,
United States and Georgia, 1989-90 to 1998-99
Re-centered SAT Score
1060
1040
1020
1000
980
960
0
1
2
3
4
5
6
7
8
9
0
1
2
3
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
2 00
2 00
2 00
2 00
940
Year
US HS Seniors
GA HS Seniors
Georgia College Freshmen
Source: The data for Georgia college freshmen are from the University System of Georgia. The
Southern Regional Education Board provided the data on high-school seniors for both
the United States and Georgia. Figure, title, and explanation found in: Cornwell,
Christopher and David B. Mustard. 2005. “Merit Aid and Sorting: The Effects of
HOPE-Style Scholarships on College Stratification by Ability.” University of Georgia
Working Paper.
Explanation: “Figure 1, which contrasts the SAT series for freshmen enrolled in Georgia public
colleges with those of high-school seniors in Georgia and the rest of the US, supports the proposition
that Georgia’s scholarship program has affected student quality. Between 1992 and 2003 the SAT
scores of Georgia freshmen increased by 67 points compared to increases of 36 points for Georgia
high-school seniors and 25 points for high-school seniors throughout the US. During the four preHOPE years, the average score of Georgia college freshmen exceeded the score of Georgia high-school
seniors by 16 points and trailed the average score of high-school seniors in the US by 35 points. By the
end of the period, Georgia college freshmen outscored the Georgia high-school senior group by 52
points and the US comparison group by 10 points. Furthermore, between 1993 and 2000, Georgia’s
rate of retaining students with SAT scores greater than 1500 had climbed from 23 to 76 percent.”
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References
Cornwell, Christopher M., Kyung Hee Lee and David B. Mustard. 2004. “The Effects of Merit Based
Financial Aid on Course Enrollment, Withdrawal, and Completion in College.” University of
Georgia Department of Economics Working Paper. Revise and Resubmit at Journal of Human
Resources.
Cornwell, Christopher and David B. Mustard. 2005. “Merit Aid and Sorting: The Effects of HOPEStyle Scholarships on College Stratification by Ability.” University of Georgia Working Paper.
Cornwell, Christopher M., David B. Mustard and Deepa Sridhar. 2004. “The Enrollment Effects of
Merit-Based Financial Aid: Evidence from Georgia’s HOPE Scholarship”. Revise and
Resubmit at the Journal of Labor Economics.
Georgia Department of Education. 2005. Georgia Department of Education Annual Report Cards
(1996-2002): http://techservices.doe.k12.ga.us/reportcard/. Atlanta, GA.
Georgia Student Finance Commission. 2004a. “HOPE Scholarship and Grant Program Highlights: A
Summary of Changes and Requirements.” (June 1). Retrieved Sep. 30, 2004
http://www.gsfc.org/main/publishing/pdf/2004/hope_highlights.pdf.
Georgia Student Finance Commission. 2004b. “HOPE Scholarship Renewal Rate for High School
Graduates.” January 20, 2004. Tucker, GA.
Henry, Gary T. and Rubenstein, Ross. 2002. “Paying for Grades: Impacts of Merit-Based Financial
Aid on Educational Quality.” Journal of Policy Analysis and Management21(1), 93-109.
Rubenstein, Ross, and Benjamin Scafidi. 2002. “Who Pays and Who Benefits? Examining the
Distributional Consequences of the Georgia Lottery for Education.” National Tax Journal
(June): 223-238.
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Copyright by Associated Press, 2005.