A Quasi-Experimental Investigation Of How The Gates Millennium Scholars Program Is Related To College Students Time Use And Activities

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Policy Analysis
Educational Evaluation and
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The online version of this article can be found at:
DOI: 10.3102/0162373710380739
2010 32: 456
EDUCATIONAL EVALUATION AND POLICY ANALYSIS
Stephen L. DesJardins, Brian P. McCall, Molly Ott and Jiyun Kim
Related to College Students' Time Use and Activities
A Quasi-Experimental Investigation of How the Gates Millennium Scholars Program Is
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Fall XXXX, Vol. XX, No. X, pp. 215–229
A Quasi-Experimental Investigation of How the
Gates Millennium Scholars Program Is Related to
College Students’ Time Use and Activities
Stephen L. DesJardins
Brian P. McCall
Molly Ott
Jiyun Kim
University of Michigan, Ann Arbor
A national scholarship program provided by the Bill & Melinda Gates Foundation is designed to
improve access to and success in higher education for low-income high-achieving minority students
by providing them with full tuition scholarships and non-monetary support. We use a regression
discontinuity approach to investigate whether the receipt of the scholarship changes the use of time
and participation in different activities among college students during their freshmen and junior
years. We find that receiving a Gates scholarship reduces hours worked per week, as well as influ-
encing high participation in volunteering activities and cultural events. The sub-group analyses
reveal racial/ethnic differences in the allocation of time to and the levels of participation in various
activities in response to the Gates scholarship.
Keywords: time allocation, financial aid, regression discontinuity, student time use, minority student
HOW STUDENTS allocate their time to studying,
work, and nonacademic activities while enrolled
in college and how these choices affect educa-
tional outcomes is an interesting but not well
understood phenomenon. Students choose to do
a variety of things with their time while pursu-
ing a postsecondary education, such as attend-
ing class, studying, working for pay, doing vol-
unteer work, attending to family responsibili-
ties, or engaging in extracurricular and leisure
activities. The effective management of their
scarce time, especially if students use any freed
up time for academic endeavors, may reduce feel-
ings of stress and academic anxiety (Campbell,
Svenson, & Jarvis, 1992; Macan, Shahani,
Dipboye, & Phillips, 1990; Misra & McKean,
2000) as well as positively affect students’ aca-
demic performance while in college (Britton &
Tesser, 1991; Lahmers & Zulauf, 2000; Nonis,
Philhours, & Hudson, 2006; Young, Klemz, &
Murphy, 2003), their satisfaction with college
(Macan et al., 1990), their graduation chances
(Pascarella, Pierson, Wolniak, & Terenzini, 2004),
and their career-related competencies (Davis &
Murrell, 1993).
Education policies are often designed to change
the relative tradeoffs among students’ alternative
uses of time. For instance, the provision of
The views contained herein are not necessarily those of the Bill & Melinda Gates Foundation. Please address correspondence
to Stephen L. DesJardins, Center for the Study of Higher and Postsecondary Education, University of Michigan, 610 E.
University Avenue, 2117-C SOE Building, Ann Arbor, MI 48109-1259; e-mail: sdesj@umich.edu.
December 2010, Vol. 32, No. 4, pp. 456–475
DOI: 10.3102/0162373710380739
© 2010 AERA. http://eepa.aera.net
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457
The Gates Millennium Scholars Program
457
student financial aid acts as an income increase to
students by reducing financial constraints. Such an
income shock may allow students to reduce or
avoid working while in college, thereby freeing up
time to allocate to other academic and nonaca-
demic activities. Increasingly, institutional, gov-
ernmental, and privately provided financial aid
programs are, at least partially, designed to pro-
vide students with sufficient financial support so
that they can concentrate on academics (e.g.,
Georgia’s HOPE scholarship program) and pre-
sumably reduce self-help including loans (e.g.,
Princeton’s “Aspire” and Michigan’s M-PACT
loan elimination programs) and/or the work com-
ponent for students (Henry & Rubenstein, 2002;
Tilghman, 2007).
One national scholarship designed to reduce
the financial constraints of students is the Gates
Millennium Scholars program (henceforth GMS).
Established by the Bill & Melinda Gates Foun-
dation in 1999, the stated goal of this program is
to establish a cadre of future leaders by improv-
ing access to and success in higher education for
low-income, high-achieving minority students in
the United States by providing them with schol-
arships and nonpecuniary support. As noted by
Melinda Gates when announcing the program’s
establishment, “If we can ease the financial
strains many students encounter, hopefully they
can focus their full efforts on academic pursuits”
(“Bill and Melinda Gates Announce,” 1999).
To determine whether the GMS program is
successful in achieving this goal, we assess
whether GMS participation materially changes
the work, academic, and nonacademic choices of
recipients while enrolled in college for each of
the minority groups covered by the scholarship
(e.g., African Americans, Asian Americans, Native
Americans, and Latinos/as). Specifically, we
focus on the extent to which GMS program
participation affects students’ allocation of time
among competing uses including studying, work-
ing, and various types of other extracurricular
activities, and we employ regression discontinu-
ity techniques to estimate these effects. Given the
selection mechanism used to choose the Gates
scholars, we believe this technique is optimal for
making inferences about the effects of the GMS
on students’ time allocation choices.
This article is organized as follows: In the next
section, we discuss the literature related to how
students allocate their time while enrolled in
college and then discuss the theoretical foundation
of our research. To establish the context, we pro-
vide details about the GMS program, followed by
a presentation of the estimation strategies used, a
presentation of the empirical results, and a discus-
sion of the results. In the final section, we discuss
the limitations and implications of the study and
provide some concluding thoughts.
The Role of Student Time
Allocation in College
Allocation of time while enrolled in college
is crucial to the academic and nonacademic
development of students. As noted by Kuh and
associates (2005), “What students do during col-
lege counts more in terms of desired outcomes
than who they are or even where they go to col-
lege” (p. 8). Kuh cited as evidence “the volumi-
nous research on college student development”
(in particular, reviews of the research by Astin,
1993; Pace, 1980; Pascarella & Terenzini, 1991,
2005) that indicates that “the time and energy
students devote to educationally purposeful acti-
vities is the single best predictor of their learn-
ing and personal development” (p. 8). How much
time students are willing or able to devote to
their academic activities is, however, also related
to the activities that compete for their time. There-
fore, our discussion of the literature is organized
around the main ways students allocate their
time while enrolled in college, namely, studying,
working, and being involved in extracurricular
activities. We are especially attentive to the con-
nections between each form of time allocation
and postsecondary outcomes such as grades and
completion. These relationships underscore the
importance of this analysis in understanding the
overall degree attainment process and offer insight
into opportunities for policymakers to directly
and indirectly affect attainment through programs
such as the GMS.
Time Allocated to Studying
The relationship between studying and aca-
demic performance has been examined by a num-
berofresearchers(Terenzini,Springer,Pascarella,
& Nora, 1995) and the amount of time that stu-
dents spend studying is often used as a proxy for
the degree of engagement, effort, or commitment
students allocate to academics.
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DesJardins et al.
Where grades are the outcome, the findings are
mixed: Some evidence suggests that the amount
of time spent studying is not directly related to
individual course grades (Schuman, Walsh, Olson,
& Etheridge, 1985) or one’s overall grade point
average (GPA; Mouw & Khanna, 1993; Nonis
& Hudson, 2006; Plant, Ericsson, Hill, & Asberg,
2005), whereas others find a positive relation-
ship (George, Dixon, Stansal, Gelb, & Pheri,
2008; Lahmers & Zulauf, 2000; Michaels &
Miethe, 1989; Stinebrickner & Stinebrickner,
2004, 2008; Young et al., 2003). Analyses of data
from Berea College address several methodologi-
cal and data limitations of other studies by using
longitudinal samples, adjusting for participants’
biased estimates of time spent studying, control-
ling for selection problems by adding instrumen-
tal variables, and examining both daily and weekly
study time. The authors conclude that studying has
a positive influence on 1st-year student grades;
specifically, they estimate that a 1-hour increase
per day in time spent studying is associated with
the same increase in first semester GPA as a
5.2-point increase in ACT Composite score
(Stinebrickner & Stinebrickner, 2008). However,
the relationship may be nonlinear and the effects
maydeclineasstudytimeincreases(Stinebrickner
& Stinebrickner, 2004).
Typical of the research on undergraduate study
time are single-institution designs (Lahmers &
Zulauf, 2000; Lammers, Onwuegbuzie, & Slate,
2001; Plant et al., 2005; Rau & Durand, 2000;
Schuman et al., 1985; Stinebrickner &
Stinebrickner, 2004) with small sample sizes
(Beer & Beer, 1992; Michaels & Miethe, 1989;
Plant et al., 2005). However, variation across
institutions likely explains differences in results
about the effects of student time allocation
(Michaels & Miethe, 1989). For example, Kuh
(1999) reported that although full-time undergra-
duates commit less time to class and studying
today than they did in the 1980s, students at small
liberal arts colleges spend disproportionately more
time on these academic activities than their peers
at other types of institutions. Individual student
characteristics and behaviors likely mediate the
relationship between studying and academic
achievement as well. Incoming academic ability,
represented by students’ ACT Composite scores
(Lahmers & Zulauf, 2000; Nonis & Hudson,
2006) or high school class rank (Michaels &
Miethe, 1989), mediates the results of studying
on college GPA, as those with higher prior achi-
evement appear to benefit more from time spent
studying. Michaels and Miethe (1989) found that
increased levels of study time while in college
are associated with higher GPAs for freshman
and sophomore students, although no significant
differencesareobservedintherelationshipbetween
weekly study time and junior or senior year GPA.
Time Allocated to
Employment-Related Activities
Full-time undergraduate students today spend
more time working in paid employment than they
did in the past (Riggert, Boyle, Petrosko, Ash, &
Rude-Parkins, 2006; Stern & Nakata, 1991).
According to the 2003–2004 National Postsec-
ondary Student Aid Survey (NPSAS), more than
two thirds of students at 4-year institutions are
employedinon-oroff-campusjobswhileenrolled,
with 23% working full-time and 47% working
part-time. The main reasons often cited for work-
ing are the need for spending money, to finance
basic living expenses, and to assist in paying
their tuition (Dundes & Marx, 2006). Students
also report that working offers a chance to iden-
tify future career options, enhance their interper-
sonal and time management skills, create network-
ingopportunities,andconnecttothesociety(Cheng
& Alcantara, 2007).
Whether committing to paid employment
comesatacosttoacademicsisnotclear(Pascarella
& Terenzini, 1991; Riggert et al., 2006). Some
evidence suggests that working students do not
cut back on their study time and instead reduce
time dedicated to sleeping, socializing, or leisure
activities (Cheng & Alcantara, 2007; Fjortoft,
1995; Miller, Danner, & Staten, 2008). Yet,
Lammers et al. (2001) found a negative relation-
ship between time spent working and time spent
studying as well as a positive relationship
between good study skills and time spent study-
ing. Miller et al. (2008) reported that binge drink-
ing and lower academic performance are associ-
ated with working 20 hours per week or more
but not with working fewer than 20 hours per
week. Other researchers, however, do not find a
significant relationship between the amount of
time that students are employed and their aca-
demicperformance(Dolton,Marcenaro,&Navarro,
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459
2003; Ehrenberg & Sherman, 1987; Furr & Elling,
2000; Leppel, 2002; Nonis & Hudson, 2006; Rau
& Durand, 2000; Svanum & Bigatti, 2006).
With regard to student learning, Pascarella,
Edison, Nora, Hagedorn, and Terenzini (1998)
found little evidence that on- or off-campus emp-
loyment detrimentally affects students’ learning
or cognitive development, even when the amount
of time spent working exceeds 20 hours per week.
Nor do these authors find differences in the inf-
luence of working on cognitive development
based on student age, gender, ethnicity, pre-college
cognitive ability, or socioeconomic status. Simi-
larly, Lundberg (2004) found that whereas stu-
dents who are employed more than 20 hours per
week off-campus spend less time engaged with
faculty and their peers in academic activities, no
differences in their learning are observed. How-
ever, time spent working may inhibit the devel-
opment of critical thinking skills, preference for
higher order cognitive tasks, and internal locus
of attribution for academic success among first-
generation students (Pascarella et al., 2004).
However, as the number of hours worked
per week increases, students face restrictions on
their academic study such as the inability to carry
a full credit load, limited class choices, and red-
uced access to academic libraries, as well as less
time to spend studying (Horn, 1998; Lammers
et al., 2001). Horn’s (1998) analyses of the 1996
NPSAS data suggest that students who work at
least 15 hours per week perceive that their aca-
demic performance is negatively affected by their
employment.Also,StinebricknerandStinebrickner
(2003) reported that working during the first seme-
ster has a negative influence on one’s GPA, albeit
at a small college in Kentucky. After controlling
for study skills, hours spent studying per week,
age, gender, and social class, Lammers et al.
(2001) found a small but significant and negative
relationship between hours spent working per
week and GPA for a sample of 366 undergradu-
ate Education students.
Beyond its effect on grades, learning, or persis-
tence, working while in college may also influence
students’ career opportunities. Adjusting for stu-
dent self-selection by employing several different
instrumental variable techniques, Light (2001)
estimated that a male student who accumulates the
equivalence of 2 years of work experience while
completing 16 years of schooling will earn at least
10% more in his first job after college than a peer
who does not work while in high school or college.
Using the same National Longitudinal Survey of
Youth 1979 (NLSY79) data collected by U.S.
Bureau of Labor Statistics as Light, Hotz, Xu,
Tienda, and Ahituv (2002) employed econometric
techniquestocontrolforselectionbiasandobserved
higher post-college earnings associated with col-
lege employment for all race/ethnic groups.
In combination, the relationship between hours
worked and college success appears to be more
complex relative to the case of hours spent stu-
dying. The mixed findings concerning the effects
of student employment are due in part to sample
differences, as many of the studies focus on a
single institution. Differences in the level of
aggregation of work-related variables (e.g., dis-
tinctions between on- and off-campus jobs, dis-
tinctions between types of work) as well as dif-
ferences in statistical techniques and model spe-
cification may also contribute to variations in the
results (Riggert et al., 2006). The limited evi-
dence appears to suggest that students who are
employed off-campus or spend more than 20
hours per week working have lower chances of
degree completion, whereas those who limit their
employment to on-campus jobs and work fewer
hours have an enhanced probability of persis-
tence (Pascarella & Terenzini, 1991, 2005).
Allocating Time to Other Activities
In addition to academics and employment,
college students spend their time engaged in a
wide variety of extracurricular and co-curricular
activities, including participation in community
service opportunities, student clubs, and inter-
collegiate athletics. Despite limited evidence,
co-curricular and extracurricular activities appear
to promote both academic and nonacademic out-
comes. Community service and service learning
increase classroom learning and course grades
(Markus, Howard, & King, 1993) and are asso-
ciated with longer term influences on students’
values, attitudes, and educational outcomes such
as graduate school attendance and degree attain-
ment(Astin,Sax,&Avalos,1999).Co-curricular
involvement, including participation in campus-
wide and departmental activities, student clubs,
and leadership positions, is associated with self-
reported gains in cognitive skills, communication
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460
DesJardins et al.
skills, interpersonal interactions, critical thinking,
andself-confidence(Gellin,2003;Huang&Chang,
2004). Lohfink and Paulsen (2005) found that
participation in student clubs has a significant
positive effect on 1st- to 2nd-year persistence for
continuing, but not first-generation students.
Contrary to general club activities, researchers
have found little to no significant relationship
between Greek membership and critical think-
ing, and a negative influence on students’ open-
ness to diversity (Pascarella & Terenzini, 2005).
In contrast, other studies reported that involve-
ment in Greek life activities has several positive
outcomes, including higher levels of academic
effort and increased retention rates (Moore,
Lovell, McGann, & Wyrick, 1998; Tripp, 1997).
With regard to intercollegiate athletics, several
studies found no effect on grades associated with
athletic participation, even after controlling for
many confounding variables (Aries, McCarthy,
Salovev,&Banaji,2004;Hood,Craig,&Ferguson,
1992). Participation in intercollegiate athletics
does, however, appear to positively influence per-
sistence and is related to gains in interpersonal
skillsandself-confidence(Pascarella&Terenzini,
2005; Schulman & Bowen, 2001).
Research and Policy
Implications of the Literature
The review of the research on the relationship
between student time use and a range of educa-
tional outcomes provides insights into how stu-
dents’ time devoted to studying, working, and
other extracurricular and co-curricular activities
directly and indirectly affects their academic and
nonacademic success. Although there is ample
attention to how students’ time allocation choices
affect educational outcomes, there is a dearth of
evidence about antecedents of time allocation,
for instance, whether and how aid policies and
programs such as the GMS affect students’ time
use during college. This study fills this gap in the
literature by examining the process by which
financial aid, in particular the GMS scholarship,
influences students’ allocation of time to study-
ing, working, and other extracurricular activities
while in college. A better understanding of this
relationship will be especially informative for
policymakers interested in promoting college
student success. If the GMS improves college
outcomes by giving students additional time to
focus on academic and extracurricular activities,
then there may be opportunities to intervene with
similar programs, thus structuring students’ exp-
eriences to maximize their chances of engaging
more fully in the learning process and ultimately
completing their degrees.
The Theoretical Framework
We use the human capital-based theory of time
allocation proposed by Becker (1965) as a concep-
tual framework for our study. Developed by
Becker (1993) and Schultz (1961), human capital
theory applies microeconomic concepts and mod-
els to the study of the choices and behavior of
individuals (or households) and establishes the
conceptual relationship between schooling, indi-
vidual productivity, and returns in the labor market
(Becker, 1965; Cohn & Geske, 1990; Mincer,
1958). Becker’s theory of time allocation can be
viewed as the application of the microeconomic
model of household choice, where households are
both consumers and producers of goods and ser-
vices and attempt to maximize their utility func-
tion, which comprises the consumption and pro-
duction of these commodities (DesJardins &
Toutkoushian, 2005; Paulsen, 2001). Novel in his
approach is the explicit inclusion of time as a fac-
tor when making schooling decisions. Particularly
important is the opportunity cost of time (e.g.,
earnings that a student forgoes while enrolled),
which is often the largest indirect individual cost
of attendance.
Students allocate their time and effort among
several competing activities such as studying,
work, and leisure, subject to time and effort con-
straints (Levin & Tsang, 1987). Ben-Porath (1967)
and Becker (1967) suggested that if a college stu-
dent undertakes both leisure and work in the given
period, schooling activities will substitute only for
work hours (as the foregone-earnings approach
predicts) and that the efficiency of schooling
activities decreases as hours of work increase.
In the presence of liquidity (or borrowing)
constraints, however, individuals face difficulty
in financing a college education and instead are
likely to allocate part of their time to working in
order to reduce this constraint. Parental transfers
of income and resources to their children, as well
as financial aid such as the GMS scholarship, can
also substantially affect student time allocation
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461
decisions, such as reducing work during college
and allocating more time to studying and extra-
curricular activities. If students are able to spend
more time engaged in nonemployment activities
over the entire span of their college career, as the
GMS scholarship permits, our review of the lit-
erature suggests that this will positively affect the
students’ academic outcomes, such as grades,
retention, and completion.
The theoretical framework discussed above is
the basis for our hypothesis that the GMS schol-
arship influences students’ allocation of time to
studying, working, and other extracurricular acti-
vities and that GMS scholars are more engaged
in studying and extracurricular activities and
work fewer hours than nonscholars. The next
section provides details about the GMS program
and whether it affects the way students allocate
their time to various activities while in college.
The Empirical Approach
The GMS program is a $1 billion, 20-year-
long project designed to promote academic exce-
llence by providing higher education opportuni-
ties for low-income, high-achieving minority
students. To be accepted, high school students
who apply for the program must meet a number
of eligibility criteria. Cognitive assessment mea-
sures are used to judge the academic potential of
applicants (e.g., the academic rigor of their high
school course work, and they must have 3.33 or
higher high school GPA), and noncognitive
measures are also used in the selection process.
With regard to the noncognitive selection com-
ponent, students must answer a series of ques-
tions developed to measure an applicant’s non-
cognitive abilities.1
The answer to each of these
questions is scored by trained raters and a total
noncognitive test score is assigned to each app-
licant. Thresholds on these noncognitive tests
are established that vary by racial/ethnic group
and by matriculating cohort2
and are used as
another program selection mechanism. Table 1A
(see the appendix) provides detailed descriptive
information about both the cognitive and non-
cognitive tests for the entire sample and the sam-
ple around the cutoff point. In keeping with the
goal of the program to fund needy students, app-
licants must also demonstrate financial need by
documenting that they are Pell Grant3
eligible.
Finally, only U.S. citizens/legal residents are eli-
gible for the program.
Of the 3,000 to 4,500 students who apply for
the program in a given year, about 1,000 of them
are eventually selected for the program. Table 1
provides information about the number of appli-
cants, students surveyed, and participants for
each of three entering cohorts used as the sam-
ple for this study. These three cohorts include
individuals entering college in fall 2001 (known
as Cohort II), fall 2002 (Cohort III), and fall
TABLE 1
Numbers and Percentages of Sample Participants by Cohort
Cohort II Cohort III Cohort V All cohorts
All applicants
Nonscholar 3069 1997 3464 8570
Scholar 1000 1000 1000 3000
Total 4069 2997 4464 5570
Survey sample
Nonscholar 1340 1333 1333 4006
Scholar 1000 1000 1000 3000
Total 2340 2333 2333 7006
Survey participants (Wave I)
Nonscholar 778 996 967 2741
Scholar 831 897 890 2618
Total 1609 1893 1857 5359
Response rate (Wave I)
Nonscholar 58.1% 74.7% 72.5% 68.4%
Scholar 83.1% 89.7% 89.0% 87.3%
Total 68.8% 81.1% 79.6% 76.5%
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DesJardins et al.
2004 (Cohort V). Noteworthy is that the vast
majority of students are disqualified because their
noncognitive test is lower than the established
threshold (cut score) for acceptance.
The scholarship is a “last dollar” award, mean-
ing that it covers the unmet need remaining after
the Pell and any other scholarships or grants are
awarded to the student. The scholarship is portable
to any institution of higher education in the United
States and can be used to pay tuition and fees,
books, and living expenses. The average award to
freshmen is about $8,000 and the upper division
student average (juniors and seniors) is about
$10,000 to $11,000. Average awards differ by
institution type, with students attending public
institutions receiving about $8,000 and private col-
lege attendees receiving slightly more than
$11,000 in support. As undergraduates, students
are eligible for the scholarship for up to 5 years
and can apply for graduate school support if they
study engineering, mathematics, science, educa-
tion, or library science.4
In the spring of their freshman year, all GMS
recipients and a random sample of nonrecipients
are surveyed by the National Opinion Research
Center (NORC) at the University of Chicago. In
this Wave I or “baseline” survey, students are
asked questions about their backgrounds, enroll-
ment status, academic and community engage-
ment, college finances and work, self-concept
and attitudes, and future plans. The overall
response rate was 69% in Cohort II, 81% in
Cohort III, and 80% in Cohort V. The response
rates were higher for GMS recipients in all
cohorts (83% vs. 58% in Cohort II, 90% vs.
75% in Cohort III, and 89% vs. 73% in Cohort
V). These students are also resurveyed in the
late spring of their junior year, constituting the
first follow-up or Wave II of the survey.
The sample used in the analyses described
below was constructed by matching data from a
number of sources including the baseline and
follow-up surveys, a file containing the noncog-
nitive scores of applicants, and a data set con-
taining the reasons that students were eligible or
not. Based on our understanding of the GMS
program and the selection mechanisms used, the
following section outlines our empirical
approach using a regression discontinuity
design.
The Estimation Strategy:
Regression Discontinuity
Thistlewaite and Campbell (1960) used the
regression discontinuity (RD) technique to study
the effects of the National Merit Scholarship pro-
gram on career choice. Subsequently, the method
has been used to examine the effects of compen-
satory education programs (Trochim, 1984), school
district and housing prices (Black, 1999), the
effectofclasssizeonstudentachievement(Angrist
& Lavy, 1999), the effect of school funding on
pupil performance (Guryan, 2000), financial aid
effects on student enrollment behavior (Kane,
2003; Van der Klaauw, 2002), how teacher trai-
ning affects student achievement (Jacob &
Lefgren, 2002), the incentive effects of social
assistance programs (Lemieux & Milligan, 2004),
and the relationship between failing a high school
exit exam and high school graduation and/or
subsequent postsecondary education outcomes
(Martorell, 2004).
The RD design (see Cook & Campbell, 1979)
is one where participants are assigned to the
treatment (e.g., GMS participation) and control
groups (e.g., GMS nonparticipants) based on a
score on some prespecified criterion (or criteria),
such as the noncognitive test score described
above.5
Given the selection mechanism, we exp-
ect that students just above and below the cut
point are distributed in an approximately random
fashion. If true, then the observed and unob-
served characteristics of students around the cut
point are very similar, akin to a randomized exp-
eriment. Under these circumstances, an evalua-
tion of the effect of the program at or near this
point may have causal implications. The analytic
strategy is to use curve fitting techniques to esti-
mate the average effect for students who recei-
ved the scholarship (i.e., the “treated”) and those
who did not, or more accurately the “counter-
factual,” which is the expected outcome if GMS
participants did not receive the treatment (see
Holland, 1986; Rubin, 1978; or Shadish, Cook,
& Campbell, 2002, for an explanation of coun-
terfactual analysis). The causal effect, known as
the Local Average Treatment Effect (LATE), is
the difference between these two means. We use
standard regression techniques to estimate this
effect.
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Using the RD approach, we believe it makes
sense to analyze a number of important correlates
of college completion. Formally, suppose that the
mean value of an outcome variable (y) depends
on whether or not a treatment is received, as rep-
resented by an indicator variable (D). Thus,
y = β0 + Dα + ε (1)
where α measures the effect of the treatment (D)
on the E(y) and ε is a zero mean random error.
In a “sharp” RD design, there is a variable, x, such
that D = 1 if x ≥ x
~
, where the value x
~
is the
thresholdorcutpoint,andDequalszerootherwise.
Taking expectations of both sides of (1) with
respect to x yields
E (y|x) = β0 + α + E (ε|x) (2)
when x ≥ x
~
and
E (y|x) = β0 + E (ε|x) (3)
when x < x
~
.
Under a sharp design, using a parametric app-
roach assumes that E (ε|x) is some function of x
(usually a polynomial of some known order, r)
and estimates
y = β0 + αI (x ≥ x
~
) + β1x + β2 x2
+...+ βr xr
+ v (4)
with E(ε|v) = 0. The selection of scholars for the
GMS program, however, has a “fuzzy” rather
than a sharp design because not all students with
scores above the cut point receive scholarships
because they do not meet other eligibility criteria
(i.e., Pell eligibility, high school GPA requi-
rement, and in rare cases, some do not complete
the application). The sharp and fuzzy RD desi-
gns differ in that in the sharp design, assignment
to the treatment is solely determined by a single
index variable (e.g., noncognitive test score),
whereas fuzzy design assignment to the treatment
may also depend on additional factors (i.e., Pell
eligibility, high school GPA requirement). In the
sharp RD design, the probability of treatment
jumps from 0 to 1 at the threshold point, whereas
the fuzzy RD design allows for a smaller jump
(by less than 1) in the probability of assignment
to the treatment at the eligibility threshold (Lee
& Lemieux, 2009).
In a fuzzy design, the discontinuity at the cut
point is in the probability of receiving the treat-
ment. In this situation, D ≠ I(x ≥ x
~
), so (4) no
longer yields consistent estimates of the treatment
effect. However, since I(x ≥ x
~
) is positively cor-
related with D, instrumental variable estimation of
y = β0 + αD + β1x + β2 x2
+...+ βr xr
+ v (5)
using I(x ≥ x
~
) as an instrument yields a consistent
estimate of α. The fuzzy RD design uses the non-
cognitive index test score that partly determines
the selection of scholars for the GMS program
as an instrumental variable for the receipt of the
GMS scholarship. An instrumental variable app-
roach is used to overcome omitted variable pro-
blems in estimating causal relationships. A valid
instrumental variable is highly correlated with
the treatment (or endogenous explanatory) vari-
ables but has no association with the outcome
variable. An instrumental variable approach
can be estimated using two-stage least squares
regression (2SLS). Therefore, fuzzy RD leads
naturally to a simple 2SLS estimation strategy
(Angrist & Pischke, 2009). In the first stage, the
instrumental variable, as well as any covariates
thought to be related to treatment, are regressed
on the endogenous treatment variable (e.g., the
receipt of the GMS scholarship). In the second
stage, the dependent variable is regressed on
fitted values from the first stage regression model
in addition to any covariates thought to be related
to the outcome (Schneider, Carnoy, Kilpatrick,
Schmidt,&Shavelson,2008).Severalresearchers
have employed this instrumental variable app-
roach when a cutoff score is available (e.g., Hahn,
Todd, & Klaauw, 2001; Lee & Lemieux, 2009),
and Cook (2008), a pioneer in the use of quasi-
experimental methods such as RD, noted that
“the cutoff value (of an index score) functions as
an instrumental variable and engenders unbiased
causal conclusions” (p. 651).
Using (5), we estimate the effect of the GMS
on the amount of time spent per week in different
activities including working, studying, extra-
curricular activities, and relaxing as well as on the
number of credits that students enrolled for in each
term/semester. These events are estimated at the
end of the freshman and junior years of college,
and for each of the race/ethnic groups affected by
the program. Below, we report our findings.
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DesJardins et al.
Findings
Summary Statistics
We first explored the differences between
Gates scholars and nonscholars in terms of the
noncognitive test score. The average noncogni-
tive test score is 73.1 for the pooled sample. The
average noncognitive test score for Gates schol-
ars, however, is 8.5 points higher than for non-
scholars (77.7 vs. 69.2, p < .001). For the pooled
sample of three entering cohorts, the average
amount of time students spend studying is 22.6
hours per week. Gates scholars spend 2.1 more
hours per week studying than nonscholars (23.6
vs. 21.5, p < .001). The average amount of time
students spend pursuing extracurricular activi-
ties is 6.7 hours per week, with Gates scholars
averaging 0.5 more hours per week engaged in
extracurricular activities than nonscholars (p =
.007). The average time students spend relaxing
is 16.8 hours per week, with no statistically sig-
nificant difference between Gates scholars and
nonscholars. There are also differences in the
hours per week spent studying,6
hours per week
engaged in extracurricular activities,7
and hours
per week spent relaxing8
by racial/ethnic group.
To further investigate how students spend their
time on extracurricular activities, we examined
students’ participation in six different types of
activities/events: (a) community service or volun-
teer activity, (b) cultural events sponsored by
groups reflecting one’s own cultural heritage,
(c) tutoring sessions, (d) events sponsored by a
fraternity or sorority, (e) residence hall activities,
and (f) religious activities. Survey participants were
asked about how frequently they participated in
each of these activities in both their freshman and
junior years. Gates scholars participate more fre-
quently in community services or volunteering,
cultural group events, tutoring sessions, residence
hall activities, and religious activities in both their
freshman and junior years.9
In the regression ana-
lysis reported below, we group the categorical res-
ponses to these questions into two categories, the
first containing the “often/very often” responses
(which we refer to as high participation) and the
second we label not, which contains the “never/
seldom/sometimes” responses.
Sample means for the predictor variables
used in the regression are presented in Table 2.
Column 1 of Table 2 presents sample means for
the full sample across all cohorts. The average
total SAT score for the sample is 1117,10
and
there are statistically significant differences (p <
.05) in these scores by race/ethnic group, with
Asian Americans having the highest average at
1200 and Native Americans the lowest average
at 1054. Most GMS applicants graduated from a
public high school (95%) and are female (71%).
The average number of years of high school
math among applicants is 3.86 and the average
number of years of high school science is 3.66.
Table 2 also presents sample means broken
down by whether or not the applicant received a
scholarship. Gates scholars have significantly
higher average total SAT scores than nonschol-
ars. Gates scholars have .043 more years of high
school math than nonscholars (p < .001) and
have more years of high school science than
nonscholars (.02), although the difference is not
statistically significant (p = .086).
One assumption necessary for the RD app-
roach to provide consistent estimates of the LATE
at the cut point is that individuals are randomly
distributed around the cut point. To check this
assumption, Table 2 compares sample means for
the predictor variables for individuals who lie
within two points of the cutoff score. Column 6
of Table 2 presents the p values associated with
these tests, and we find no evidence of signifi-
cant differences (at the 5% level) between the
scholars and nonscholars on these variables within
the two point range.
Of concern, however, is that there may be
nonrandom differences in recipients and nonre-
cipients around the cut point that are related to
the outcome of interest. To check this, we reg-
ressed several of the outcome variables on the
predictor variables (excluding the noncognitive
score). Using the results from these regressions,
we then computed average predicted values for
the outcome variables for each noncognitive
score. Little or no difference between the aver-
age predicted values for those around the cut
point would provide additional evidence of ran-
domization. We observe no large discontinuous
changes in predicted values at the cut point,
adding further support to our assumption of ran-
dom assignment near the selection threshold.11
Finally, it is important that response rates are
similar between GMS recipients and nonreci-
pients around the cut point. As shown in
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DesJardins and McCall (2010), although there
aresignificantdifferencesinresponseratesbetween
GMS recipients and nonrecipients, there are gen-
erally no statistically significant differences around
the cut point.12
Regression Discontinuity Results
In Table 3, we report the RD estimates for the
outcomes related to the amount of time spent per
week in different activities and for the number
of credits taken (operationalized as the fraction
of total credits required for graduation) during
the freshman year.13
In these estimations as well
as those reported below, we combine all the
cohorts but allow for cohort fixed effects, inter-
actions of the noncognitive score variable and
its higher orders with the cohort dummies, and
interactions of the cohort dummies with the race
dummies and race–noncognitive score interac-
tions. This would be equivalent, when no other
controls are included in the model, to estimating
separate models by cohort/race group but res-
tricting the estimated effect of the scholarship to
be the same across these groups. In the results
reported in the tables, we adjust for the noncog-
nitive score using a quadratic function.14
Overall,
the point estimate indicates that GMS recipients
TABLE 2
Sample Means and Means Above and Below the Cut Points for Demographic and High School Background
Variables
All applicants with
total noncognitive scores
equal to the
Variable name
Full
sample
GMS
scholars
Nonscholars
Cut score or
cut score +1
Cut score –1 or
cut score –2
p Value
SAT verbal + math
score
1117.17 1129.60 1105.58 1107.96 1127.92 0.08
Attended religious
high school
0.06 0.06 0.06 0.07 0.05 0.32
Attended private
high school
0.05 0.07 0.05 0.06 0.03 0.06
Years of high school
math
3.86 3.87 3.84 3.86 3.84 0.40
Years of high school
science
3.66 3.67 3.65 3.63 3.66 0.29
Male 0.29 0.30 0.28 0.30 0.27 0.45
Father’s education 0.19
Less than high school 0.21 0.24 0.18 0.21 0.24
High school 0.28 0.28 0.27 0.28 0.26
Some college 0.22 0.21 0.24 0.22 0.23
BA/BS degree 0.14 0.12 0.15 0.14 0.09
Post BA/BS degree 0.10 0.09 0.11 0.09 0.12
Mother’s education 1.00
Less than high school 0.20 0.24 0.17 0.22 0.21
High school 0.25 0.25 0.24 0.25 0.26
Some college 0.28 0.27 0.29 0.26 0.27
BA/BS degree 0.18 0.16 0.19 0.18 0.17
Post BA/BS degree 0.07 0.07 0.08 0.08 0.08
Sample size 5033 2421 2612 602 337
Note. Cohorts II, III, and V combined. Cut scores for total noncognitive score were 71, 72, and 68 for African Americans, Asian
Americans, and Latinos/as, respectively, in Cohort II; 72, 75, and 69 for African Americans, Asian Americans, and Latinos/as,
respectively, for Cohort III; and 75, 72, 76, and 76 for African Americans, Native Americans, Asian Americans, and Latinos/
as, respectively, for Cohort V. All tests of differences were Fisher exact tests for equality based on categorical data except for
family size, SAT scores, which were independent samples t tests for differences in means. For individuals who took the ACT
but not the SAT, the ACT score was converted into the SAT equivalent. GMS = Gates Millennium Scholars program.
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DesJardins et al.
work 4.3 fewer hours per week during the fresh-
man year of college (p < .01) than their nonre-
cipient counterparts.
When disaggregated by racial/ethnic group,
receiving a Gates scholarship is significantly and
negatively associated with hours worked for
African Americans (by 4.1 hours, p < .05) and
Asian Americans (by 8.8 hours, p < .001) during
their freshman year of college. For the outcomes
related to time spent on studying, in extracurricular
TABLE 3
Estimated Effect of Gates Millennium Scholars Program on Outcome Variables at End of Freshman Year in
College: Added Controls for Gender, Parents’ Education, Family Size, SAT Score, Parents’ Income, and High
School Type
Hours per week
worked
Credits enrolled
in term (%)
Time
studying
Time
extracurricular
Time
relaxing
Combined -4.295** -0.022 -1.353 -0.390 0.013
(1.378) (0.033) (1.199) (0.557) (0.125)
African Americans -4.103* -0.067 0.069 -1.516 0.056
(2.014) (0.082) (1.621) (0.998) (0.215)
Native Americans 0.423 0.009 8.322 0.536 0.739
(8.152) (0.044) (7.070) (2.725) (0.745)
Asian Americans -8.834*** 0.001 -2.484 -0.331 0.005
(2.535) (0.029) (2.356) (0.951) (0.434)
Hispanics -2.647 0.011 -2.906 0.527 -0.260
(2.062) (0.023) (2.052) (0.902) (0.277)
Source. Cohorts II, III, and V of Gates Millennium Scholarship Follow-Up Surveys. See text for details.
Note. Standard errors are reported in parentheses. Credits enrolled in for the term are measured as a percentage of total credits
required for graduation. In the Credits Enrolled in Term estimates, only students in schools in the semester or quarter system
were included and a control for whether the students in schools in the semester or quarter system were included. Estimates are
based on two-stage least squares with standard errors adjusted for heteroskedasticity and for intracorrelation among individuals
with equal total noncognitive scores. Controls for cohort, total noncognitive score and its square, and their interaction with
cohort are included in the race specific estimates; the combined model also includes controls for race and interactions of total
noncognitive score and its square with race.
*p < .05. **p < .01. ***p < .001.
TABLE 4
Estimated Effect of Gates Millennium Scholars Program on Hours Worked per Week at End of Junior Year in
School Type
Hours per week worked
Combined -4.233**
(1.445)
African Americans -5.445**
(2.104)
Native Americans -13.992*
(6.832)
Asian Americans -6.950*
(3.196)
Hispanics -0.268
(2.609)
Note. Standard errors are reported in parentheses. Estimates are based on two-stage least squares with standard errors adjusted
for heteroskedasticity and for intracorrelation among individuals with equal total noncognitive scores. Controls for cohort, total
noncognitive score and its square, and their interaction with cohort are included in the race specific estimates; the combined
model also includes controls for race and interactions of total noncognitive score and its square with race.
*p < .05. **p < .01. ***p < .001.
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activities, and on relaxing and to the number of
credits a student takes, however, we did not find
any significant GMS effect.
In Table 4, we report estimates of the effect
of a Gates scholarship on hours of work for
individuals during their junior year. For the com-
bined sample, receiving a Gates scholarship low-
ers hours worked per week by 4.23, holding other
variables constant, and the estimate is significant
at the 1% level. When disaggregated by race/
ethnicity, scholarship receipt reduces hours wor-
ked for African Americans by 5 hours, Native
Americans by 14 hours, and Asian Americans
by 7 hours (all ps < .05).
The estimated effects of receiving a Gates
scholarship on high participation in extracurricu-
lar activities during the freshman year are repor-
ted in Table 5. Overall, receiving a Gates schol-
arship increases the probability that students will
report that they often or very often participate in
community or volunteer activities. Receiving a
Gates scholarship increases the probability of high
participation in community or volunteer activities
by .076 when holding other variables constant
(p = .06).
The results presented in Table 5 also indicate
that for all groups receiving a Gates scholarship,
the probability of high participation in cultural
events related to their own heritage increases
during the freshman year of college (by .092; p <
.05). There is no evidence that receipt of a Gates
scholarship has statistically significant effects on
the probability of high participation in tutoring
sessions, resident hall activities, events sponsored
by a fraternity or sorority, or religious or spiritual
activities.
Estimates for racial/ethnic subgroups indicate
that receiving a Gates scholarship increases the
probability of high participation in community
or volunteer activities for African Americans and
Native Americans (but the estimates are signifi-
cant only at the 10% level). Receiving a Gates
scholarship increases the probability of high par-
ticipation in cultural activities for Latinos/as by .18
(p < .01).
For the pooled sample, receiving a Gates
scholarship during the junior year increases the
probability of high participation in community
or volunteer activities (see Table 6) by .11 (p <
.01). Receiving a Gates scholarship is also asso-
ciated with increases in the probability of high
participation in cultural events related to their own
heritage for juniors by .102 (p < .01).
The Gates scholarship is associated with incre-
ases in the probability of high participation in
tutoring sessions for juniors by .051 (p = .07),
even though no such effect was found for fresh-
men. We also find evidence that receiving a Gates
TABLE 5
Estimated Effect of Gates Millennium Scholars Program on Outcome Variables at End of Freshman Year in
School Type
Community
service
Cultural
events Tutoring Greeks
Residence hall
activities
Religious
activities
Combined 0.076 0.092* -0.013 -0.007 0.074 0.045
(0.040) (0.042) (0.039) (0.033) (0.040) (0.041)
African Americans 0.108 0.001 -0.075 0.036 0.065 0.085
(0.066) (0.068) (0.063) (0.060) (0.067) (0.068)
Native Americans 0.310 0.236 -0.088 0.205 0.117 0.152
(0.169) (0.193) (0.187) (0.141) (0.190) (0.201)
Asian Americans -0.002 0.056 0.096 -0.071 -0.005 -0.005
(0.088) (0.088) (0.086) (0.056) (0.081) (0.084)
Hispanics 0.053 0.179** -0.008 -0.036 0.102 0.014
(0.066) (0.063) (0.062) (0.049) (0.062) (0.063)
Note. Standard errors are reported in parentheses. Estimates are based on IV probit model. Controls for cohort, total noncognitive
score and its square, and their interaction with cohort are included in the race specific estimates; the combined model also
includes controls for race and interactions of total noncognitive score and its square with race.
*p < .05. **p < .01. ***p < .001.
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DesJardins et al.
scholarship increases the probability of high par-
ticipation in religious or spiritual activities for
juniors (by .071), but this relationship is quite
weak (p = .063).
Disaggregating by racial/ethnic group, we find
that receiving a Gates scholarship increases the
probability of high participation in tutoring by
.072 to .082 for Latinos/as only (p < .05). Also,
the receipt of a Gates scholarship is associated
with increases in the probability of high partici-
pation in community services/volunteer activities
for Latinos/as (by .096), although the estimated
effects are significant only at the 10% significance
level.
Heterogeneous Treatment Effects
We also tested for heterogeneous treatment
effects by estimating separate models by gen-
der, whether or not either parent went to college,
and whether the college that the student attends
is public or private.15
To conserve space, we did
not provide these results (they are available on
request). For the most part, there are no statisti-
cally significant differences in the effect of the
Gates scholarship for these subgroups. We did
find, however, that GMS receipt has a larger
negative effect on hours worked in the freshman
year for students with at least one parent who
attended college than for individuals whose
parents had no college experience (p < .001). Our
results also reveal that receiving a Gates scholar-
ship has a significantly larger effect on the proba-
bility of high participation in tutoring activities for
students attending private colleges than for stu-
dents attending public colleges, but only during
the freshman year (p = .05).16
When control vari-
ables are added in the model, however, the effect
is no longer statistically significant.
Limitations
This study has a number of limitations. First,
the research examines the effect of receiving
financial aid on time allocation behavior only for
low-income, high-achieving minority students
who make up a very small proportion of all under-
graduate students in institutions in the United
States. Thus, the scholarship effects estimated may
not fully account for the patterns of time use and
activities engaged in by the general population of
college students. Second, because the GMS is a
last dollar award covering the unmet need remain-
ing after any other scholarships or grants are
awarded, each Gates scholar has different levels of
unmet need and may therefore receive a different
scholarship amount (i.e., a differential “dose”).
However, we believe the RD framework, the con-
trols added to the regressions, and the heteroge-
neous treatment checks conducted mitigate any
TABLE 6
Estimated Effect of Gates Millennium Scholars Program on Outcome Variables at End of Junior Year in
School Type
Community
service
Cultural
events Tutoring Greeks
Residence hall
activities
Religious
activities
Combined 0.110** 0.102** 0.051 0.003 0.014 0.071
(0.039) (0.038) (0.028) (0.030) (0.029) (0.038)
African Americans 0.134* 0.147* 0.053 0.048 –0.002 0.153*
(0.065) (0.064) (0.048) (0.057) (0.051) (0.065)
Native Americans 0.150 0.105 0.044 0.109 0.061 –0.025
(0.078) (0.075) (0.057) (0.061) (0.054) (0.082)
Asian Americans 0.091 0.024 0.001 –0.003 0.077 0.030
(0.086) (0.076) (0.055) (0.054) (0.061) (0.075)
Hispanics 0.096 0.121* 0.072 –0.050 –0.011 0.008
(0.064) (0.058) (0.045) (0.046) (0.046) (0.058)
Source. Cohorts II and III of Gates Millennium Scholarship Follow-Up Surveys. See text for details.
Note. Standard errors are reported in parentheses. Reported estimates are marginal effects based on V probit model. Controls
for cohort, total noncognitive score and its square, and their interaction with cohort are included in the race specific estimates;
the combined model also includes controls for race and interactions of total noncognitive score and its square with race.
*p < .05. **p < .01. ***p < .001.
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bias that might be induced by lack of information
on the size of the scholarship.
Discussion and Conclusions
We explored the effect of receiving a Gates
Millennium Scholarship on time use and participa-
tion in various activities among low-income high-
achieving minority students. We believe our
results support the existing literature suggesting
that low-income minority students are generally
very responsive to financial aid in their college-
related decisions, such as enrollment and persis-
tence (e.g., Heller, 1997; Paulsen & St. John,
2002; Perna & Titus, 2004). However, our find-
ings concerning time allocation behavior provide
new information with regard to the effect of finan-
cial aid on low-income minority students’ time use
and participation in various activities, which are
closely related to their ultimate success in college.
Although we find that receiving a Gates scho-
larship significantly reduces hours of work, we
find no significant effects of receiving a Gates
scholarship on hours spent studying, relaxing,
or in extracurricular activities, as well as on the
number of credits a student takes. The signifi-
cant effect of the GMS scholarship on students’
working hours while in college suggests that the
scholarship may alleviate borrowing constraints
faced by these low-income, high-ability minor-
ity students and thus reduces the need for work-
ing to finance their college expenses. Therefore,
the GMS scholarship appears to enhance these
students’ opportunities to participate in various
nonemployment-related activities.
Furthermore, the differences in the effect of
the GMS scholarship on student time use and
participation in different activities imply a het-
erogeneous effect of the scholarship based on
one’s racial/ethnic group. Estimates by racial/
ethnic group suggest that receiving the scholar-
ship significantly lowers hours worked by African
Americans and Asian Americans in both the fresh-
man and junior years. The significant effect of
the GMS scholarship among these groups sug-
geststhatAfricanAmericansandAsianAmericans
are more likely to substitute grant money for work
incomes than other race/ethnic groups.
Considering the negative influence of working
more than 20 hours on academic performance
andpersistenceincollege(Ehrenberg&Sherman,
1987; Lammers et al., 2001; Leppel, 2002;
Stinebrickner & Stinebrickner, 2003), it is pos-
sible that these Gates scholars who reduced hours
of work may experience a greater level of aca-
demic engagement that positively affects their
college persistence and degree attainment. How-
ever, the linkage between allocating less time to
working to academic performance and persistence
was not explored in this article, but additional
research into the associations between financial
aid, changes in students’ time allocation, and imp-
ortant educational outcomes seems warranted.
In contrast with its significant effect on work
hours, the findings that the GMS scholarship did
not have a significant effect on time allocated to
other activities suggest that reduction in work-
ing hours does not automatically correspond to
an increase in the quantity of time spent study-
ing and course-taking as well as on leisure and
extracurricular activities. This observed student
behavior concerning time use suggests that fin-
ancial aid such as the GMS scholarship may
have behavioral effects on how students manage
their time strategically rather than inducing mea-
surable quantitative changes in time allocation.
Stated differently, receiving the GMS may qua-
litatively affect students’ use of time and partici-
pation in different activities in a way that enriches
their academic and nonacademic experiences
in college. A change in how students deal with
time management induced by the GMS scholar-
ship is, in part, addressed in this study by inves-
tigating the extent to which students participate
in six different activities. Among these activities,
we found evidence that GMS receipt increases
participation in community services/volunteer
activities and cultural events in both the fresh-
man and junior years. Although no direct com-
parisons can be made, the findings suggest that
the GMS scholarship may incentivize the recip-
ients to be highly engaged in community services
or volunteer activities as well as cultural events,
with their enhanced availability of time enabled
by a reduction in the time spent working. The
hypothesized incentive effects of the GMS are
weakly supported by the results, indicating that
African American students, who are more likely
to experience reduced hours of work, exhibit
higher participation in community services and
volunteering than other racial/ethnic groups dur-
ing their junior years.
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DesJardins et al.
Although the GMS scholarship did not sig-
nificantly lower the hours worked by Latino/a
students, these students report significantly
higher levels of participation in cultural events
relative to other racial/ethnic groups. Thus, the
GMS appears to promote positive cultural/
ethnic identity for Latino/a students, who are
generally highly underrepresented in higher
education institutions. The evidence that the
GMS scholarship influences high participation
in volunteering and cultural events indicates
that the scholarship may nurture nonacademic
outcomes in college, such as promoting good
citizenship, cultural identity, and diversity
among low-income minority students, all
specific goals of the GMS program and worthy
goals more generally.
We hope that our efforts encourage others to
investigate the role of financial support and how it
may affect students’ time allocation behavior over
the academic career so that we can provide better
information to decision makers responsible for
instituting policies that will help improve student
educational outcomes. One interesting avenue for
further research is to determine whether this
observed substitution affects the types of jobs that
individuals choose to pursue once they leave col-
lege. It is unfortunate that we will have to wait
until future waves of the survey have been com-
pleted to explore this line of inquiry.
Appendix Table 1A
Average Subscores of Cognitive and Noncognitive Tests (n = 6999)
Subscore
Subscore
All
Subscores as a fraction
of total score
t test of
difference
Subscore
Subscores as a
fraction of total score
t test of
difference
Total score
at or above
cut point
Total score
below cut
point
Total score
at or 1 below
cut point
Total
score =
cut point
Total
score = cut
point –1
1 Positive self-concept 6.92 0.096 0.096 –2.03 6.91 0.095 0.095 -0.10
2 Realistic self-
appraisal
6.74 0.094 0.092 5.65 6.75 0.093 0.093 0.05
3 Understand and
navigate social
system
6.39 0.090 0.085 19.41 6.42 0.089 0.089 -0.20
4 Prefer long-range
goals over short-
term needs
6.75 0.094 0.092 6.37 6.76 0.092 0.094 1.80
5 Strong support person 5.57 0.075 0.083 -39.76 5.61 0.079 0.077 -3.30
6 Leadership 6.62 0.093 0.090 12.10 6.69 0.092 0.092 0.63
7 Community service/
involvement
6.42 0.090 0.087 8.17 6.40 0.088 0.089 -0.21
8 Ability to acquire
knowledge in
nontraditional ways
6.50 0.090 0.089 7.11 6.48 0.089 0.089 1.14
9 Rigor of course work 7.06 0.097 0.101 -14.69 7.15 0.099 0.099 -0.36
10 Math/science/
language courses
6.97 0.096 0.099 -9.62 7.05 0.097 0.098 0.48
11 Scholarly essay score 6.26 0.087 0.085 6.82 6.27 0.087 0.086 -0.82
13 Overall F test for
mean differences
p = .000 p = .080
14 Total noncognitive
component:
Subscores (1)-(8)
51.92 0.721 0.715 10.99 52.01 0.718 0.717 0.28
15 Total cognitive
component:
Subscores (9)-(11)
20.29 0.279 0.285 — 20.47 0.282 0.283 —
Source. Cohorts II, III, and V, Gates Millennium Scholarship Program.
Note. All subscores on 8-point scale.
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471
Declaration of Conflicting Interests
The authors declared no potential conflicts of inter-
ests with respect to the authorship and/or publication of
this article.
Financial Disclosure/Funding
The authors disclosed receipt of the following finan-
cial support for the research and/or authorship of this
article: Financial support for the research conducted
herein was provided by the Bill & Melinda Gates
Foundation, but the authors did not receive any funding
to write this paper. The views contained herein are not
necessarilythoseoftheBill&MelindaGatesFoundation.
Notes
1. The eight areas measured by these noncogni-
tive variables are positive self-concept, realistic self-
appraisal, successfully handling the system, preference
for long-term goals, availability of a strong support
person, leadership experience, community involvement,
and knowledge acquired in a field. For additional infor-
mation on the development and use of the noncognitive
measures, see Sedlacek (1998, 2003, 2004).
2. The GMS scholarship program designates that a
certain fraction of scholarships goes to each ethnic
group. The thresholds are set by moving down the dis-
tribution of total scores on noncognitive tests until all
scholarships for that racial group are allocated. Thus, the
threshold depends on the number of applicants within a
racial group for that year. For our purposes, this limits
whether an applicant can “game” the system since the
threshold is not known in advance.
3. The Pell Grant program is a federal grant program
sponsored by the U.S. Department of Education, covered
by legislation titled the Higher Education Act of 1965.
The maximum award for the 2009–2010 award year is
$5,350; the maximum grant is to increase to $5,400 by
2012. It is awarded based on a “financial need” formula.
In the 2005–2006 school year, students with family
incomes of less than $20,000 accounted for 57% of Pell
Grant recipients. Thirty-five percent of these recipients
attended public 2-year colleges, and 42% attended public
4-year colleges. The National Postsecondary Student
Aid Study found that during the 1999–2000 school year,
students from families making less than $41,000
accounted for 90% of Pell Grant recipients.
4. In this article, we examine the GMS effects on
undergraduates only.
5. As mentioned above, students must also meet
the criteria for the federal Pell financial aid program
and GPA requirements, which are stated on the appli-
cation (and, so, are known beforehand), to receive the
scholarship granted to GMS participants.
6. There are statistically significant differences
in the average amount of time spent studying by race/
ethnic group. On average, African Americans report
studying 22.2 hours per week, Native Americans report
studying 18.7 hours per week, Asian Americans
report studying 24.5 hours per week, and Latinos/as
report studying 22.2 hours per week.
7. There are statistically significant differences
in the average amount of time spent in extracurricu-
lar activities by race/ethnic group. On average,
African Americans, Native Americans, Asian
Americans, and Latinos/as report 7.9, 5.7, 6.0, and
5.8 hours per week engaged in extracurricular activ-
ities, respectively.
8. There are also statistically significant differences
in the average amount of time spent relaxing by race/
ethnic group. On average, African Americans, Native
Americans, Asian Americans, and Latinos/as report
18.3, 18.7, 15.9, and 15.6 hours per week engaged in
extracurricular activities, respectively.
9. Chi-square tests reject the null hypothesis of
equal proportions for Gates scholars and nonscholars
at the 1% significance level for all categories except
fraternity/sorority activities for both the freshman and
junior years.
10. For individuals who took the ACT but not the
SAT, the ACT score was converted into the SAT
equivalent.
11. Another issue that is important is the power to
detect statistically significant differences around the
cut point. To investigate this, we calculated the prob-
ability identifying a .10 difference between GMS
recipients and nonrecipients in the probability of par-
ticipating often or very often in the various activities
for those within two points of the cut point. For all
outcome variables, the power exceeded .80.
12. For more details on checking the assumptions
of the RD design, see DesJardins and McCall (2010).
13. The estimates reported in the tables do not
incorporate sampling weights. Weighted estimates
using sampling weights, however, produced similar
results and thus are not reported in this article.
14. We also estimated models with a noncognitive
score cubed variable and its interactions with cohort,
race, and cohort–race interactions, although in most
cases, these additional variables were not jointly sig-
nificant. In other specifications, we estimated models
that limited the sample to individuals whose noncogni-
tive test score was within 10, 6, and 4 points of the cutoff
score. For these specifications, the estimates were for
the most part similar to those reported in the text.
15. In the estimates broken down by whether the
college was public or private, we restricted the esti-
mates to individuals attending 4-year colleges.
16. Since 54 different comparisons are made, we
would expect two to three statistically significant
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472
DesJardins et al.
results purely by chance. So, some caution is war-
ranted when interpreting these findings.
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Authors
STEPHEN L. DESJARDINS is a professor and the
director of Center for the Study of Higher and
Postsecondary Education in the School of Education
at the University of Michigan, Ann Arbor; sdesj@
umich.edu. His research interests include strategic
enrollment management issues, the study of student
departure from college, the economics of higher edu-
cation, and applying new statistical tools to the study
of these issues.
BRIAN P. MCCALL is a professor of education,
economics, and public policy in the School of
Education, Department of Economics, and Gerald R.
Ford School of Public Policy at the University of
Michigan, Ann Arbor; bpmccall@umich.edu. His
research focuses on the study of student enrollment
and departure behavior from college, the economics
of higher education, labor economics, applied econo-
metrics, econometric methods in duration data, quasi-
experimental methods, health economics, and the
incentive effects of social insurance programs.
MOLLY OTT is a doctoral student in the School of
Education at the University of Michigan, Ann Arbor;
mollyott@umich.edu. Her research interests relate to
the sociology of higher education, including issues of
stratification and inequality.
JIYUN KIM is a doctoral student in the School of
Education at the University of Michigan, Ann Arbor;
jiyunkim@umich.edu. Her research focuses on the
impact of financial aid policy on college access and
choice.
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