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Making Ends Meet: Student Grant Aid, Cost Saving Strategies,
College Completion, and Labor Supply
A Working Paper
Michael U. Villarreal
University of Texas, Austin
March 12, 2017
Abstract
Faced with rising college prices, today's college students use several strategies for overcoming
unmet financial need. These include enrolling in fewer courses, working while enrolled, and coenrolling in lower-cost community college courses. In spite of these efforts, students with unmet
financial need are less likely to complete college and more likely to take longer to graduate.
Using a regression discontinuity research design, this study uncovers the consequences of unmet
financial need by estimating the causal relationship between grant aid and a series of educational
and workforce outcomes that define a student's college and early-career journey. This study also
examines heterogeneous effects of grant aid on both educational and labor market outcomes to
better understand how grant aid programs can be optimized.
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Introduction
Each year, nearly 1.8 million undergraduate students enroll in a four-year college for the
first-time. Based on historical trends, forty percent of these students will drop out of school
before earning their degree (Dunlop Velez, 2014). In a survey of students who left college
without a degree, the number one reason they gave for dropping out was having an unmet
financial need. Sixty percent of students who did not graduate reported that combining work and
school in their first year in college was "too stressful" (J. Johnson & Rochkind, 2016). These
students want to earn a college education. They put forth great effort in juggling school and
work. Yet they fall short in financing a human capital investment that can increase their lifetime
earnings by approximately $870,000 (in 2016 dollars) if they go from having some college to a
bachelor’s degree (Day & Newburger, 2002). This represents a market failure that calls for a
public policy solution.
One policy solution to address this problem is grant aid that balances eligibility criteria
based on financial need and past academic achievement (often referred to as merit). Starting in
1989, states in the south and southwestern regions of the United States began experimenting with
grant programs that struck different balances between need and academic criteria. Louisiana, the
first of these states, created a grant aid program in 1989 that was mostly based on need but would
later be changed to merit-based (Russell, 2016). By 2014, 11 state grant programs combined
need and merit criteria, another 32 state grant programs were based solely on need; while another
20 state grant programs were solely based on a student’s prior academic achievement (“45th
Annual Survey Report on State-Sponsored Student Financial Aid 2013-2014 Academic Year,”
2014 ; “50-State Policy Database,” 2015).
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This study contributes to our understanding of programs that lower the cost of college by
evaluating a hybrid need and merit grant program from a state that has yet to be studied and by
examining a broad set of educational and workforce outcomes. Previous studies of grant aid have
focused on the early years of the college-going process. These studies found that grant aid has a
positive correlation, if not cause, with increases in college application, enrollment, and
persistence. Existing research has also found that grant aid has positive effects on student
outcomes that lead to graduation. These antecedent outcomes include credit accumulation, grade
point average (GPA), and less time spent working while enrolled (Bettinger, 2004; Curs &
Harper, 2012; Chen & DesJardins, 2008, 2010; DesJardins, McCall, Ott, & Kim, 2010;
DesJardins & McCall, 2010; Dynarski, 2002; Deming & Dynarski, 2009).
The present study adds to our understanding of the mechanisms that lead to college
graduation by investigating grant aid effects on co-enrollment at community colleges which
provides college credits at lower costs, likelihood of working while enrolled, and earnings of
working college students, in addition to examining grant aid effects on well-studied outcomes of
persistence rates, and university course enrollment.
Early investigations of the effects of grant aid on college graduation produced mixed
results due to methodological differences in controlling for selection bias (Riegg Cellini, 2008;
Alon, 2007; Alon, 2011). However, today there exist a growing number of studies that use quasiexperimental and experimental methods for estimating effect sizes. These studies found that
grant aid causes an increase college completion or decreases the time to completion (Alon, 2005,
2007; Castleman & Long, 2013; S. Dynarski, 2008; Scott-Clayton, 2011; Goldrick-Rab et al,
2016; Scott-Clayton & Zafar, 2016; Bettinger et al, 2016). Overall, $1,000 of grant aid increased
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six-year graduation rates by 1.9 percentage points with annual grant aid programs that offer
$1,000 to $3,500 per year. This estimate is a handy benchmark derived from a random-effects
meta-analysis summarized in Table 1. But, it should be used with caution given that its source
studies are few and that they vary in both grant aid program design and evaluation methodology.
As described earlier, state-funded grant programs can be categorized by their eligibility
criteria: academic record, financial need, or a combination of both. Past research has found that
Hispanic, Black, and low-income students realize the greatest benefits from receiving grant aid
(Alon, 2007, 2011; Chen & DesJardins, 2008, 2010; Chen & St. John, 2011; S. Dynarski, 2008;
Paulsen & St. John, 2002; Singell Jr., 2004; Schwartz, 1985). While this finding suggests that
grant aid that includes need-based criteria may produce larger effect sizes, no study has found the
optimal balance between academic record and financial need criteria that leads to produce the
greatest impact on college completion. This study will attempt to provide more direction in this
regard by investigating heterogeneous effects across different measures of college readiness in
addition to more commonly investigated student groups defined by gender, race and ethnicity,
and first-generation college students.
Finally, a few recent studies have found that grant aid affects longer-term educational and
workforce outcomes. Scott-Clayton and Zafar (2016) found that the merit-based grant program
in West Virginia caused a 3 to 4 percentage point increase in the probability of earning a
graduate degree after 10 years from first enrolling in college. The grant program also produced
positive effects on earnings that were considered meaningful though not statistically significant.
Bettinger, Gurantz, Kawano, and Sacerdote (2016) found similar results in their study of a statefunded merit-based grant program in California. They found that grant aid increased graduate
TEXAS Grants Impact Analysis
degree attainment after 15 years from first enrolling in college and increased earnings by
approximately 5 percent. The present study will add more evidence to the study of long-term
effects of grant programs by examining effects on annual earnings six to eight years after
enrolling in college; a period referred to in this report as early career.
In summary, this study will extend out understanding of need-based grant aid that
includes merit-based criteria by answering the following research questions. (1) What impact
does receiving grant aid have on the student outcomes that lead to college graduation:
persistence, course enrollment at the university, and co-enrollment at a community college? (2)
What affect does grant aid have on decisions to work while enrolled? (3) How much does grant
aid improve graduation rates? (4) What impact does grant aid have on financial consequences
such as student debt and early career earnings? (5) Finally, do grant aid effects on graduation,
and early career earnings vary by gender, race/ethnicity, first-generation college students, and
measures of college readiness, respectively?
This paper proceeds as follows. I present the methodology and data used in this study in
section two. I present the results in section three. In section four, I conclude with a discussion of
policy implications.
Data, Research Design, & Methodology
Data
I analyzed data sourced from a state longitudinal data system at the University of Texas
at Austin. This system includes student-level administrative data collected by the Texas
Education Agency, Texas Higher Education Coordinating Board, and the Texas Workforce
5
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Commission. Data describe the academic and workforce experience of subjects in addition to
their demographic and family background.
I studied students who enrolled in a Texas public university for the first time in the fall
semesters of 2004 to 2011. I limited the data by only including students who enrolled in
institutions that existed throughout the period studied, a pool of 31 universities. I further
restricted the data by only including students who meet all the eligibility requirements for
receiving a TEXAS Grant other than the financial need criterion, which is based on Estimated
Family Contribution (EFC). As a result, the study population only includes Texas residents who
graduated from high school with a college prep curriculum, and who were admitted to college
within 16 months of graduating from high school.
EFC is a score defined by the US Department of Education that determines a student’s
eligibility for federal financial aid and other financial aid programs such as TEXAS Grants. It is
intended to represent a relative measure of what a family can contribute to pay for a family
member’s college education. EFC a is function of a number of factors including the annual
household income reported to the IRS, family net worth, the number of household dependents,
the number of household dependents enrolled in college, and the costs of college enrollment of
all family members enrolled in college. TEXAS Grant eligibility criteria during the study period
included a cap of $4,000 EFC. An EFC of $4,000 can represent a family of four with a
household income of approximately $60,000 with one in college. The $4,000 EFC cutoff point
provides this study an opportunity to pursue a regression discontinuity research design.
Table 2 summarizes pretreatment covariates for the data initially examined and a more
restricted study sample of students who have an EFC between $3,000 and $5,000, or $1,000
6
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distance from the $4,000 EFC eligibility cap. The larger sample and the study sample consist of
264,210 and 21,460 unique students, respectively. The average value of each pretreatment
covariate is nearly identical across the two groups except for EFC.
Research Design
The student-level data of this study allow for a Fuzzy Regression Discontinuity (Fuzzy
RD) research design. As illustrated in Panel subtitled “% Treated” in Figure 1, the probability of
treatment drops from 57 percent to 12 percent at the EFC cutoff point of $4,000. Sharp
discontinuity is not present because the program is not fully funded and because decentralized
administration of the program allows universities to exercise some administrative discretion in
determining eligibility. This study estimates average treatment effects on the treated (TOT).
Because the program is not fully funded, policymakers are first concerned with the level of
funding provided the program. TOT effects help policymakers understand the benefits produced
by previous investments and what could be if the program were more fully funded.
Throughout this paper, I refer to subjects using terms from the Rubin Causal Model
(Holland, 1988). Never-takers are eligible students who do not receive treatment; while Alwaystakers are ineligible students who receive treatment. Estimating TOT within Fuzzy RD allows for
the control of Never-takers and Always-takers.
The treatment variable (D) is a dummy variable indicating if a student received an initial
TEXAS Grant award, rather than a measure of dosage, such as the total value of TEXAS Grant
received in dollars or years. A measure of dosage is not used because the variance in dosage is
not random. Dosage is partially a function of a student’s ongoing academic achievement.
Students can lose their TEXAS Grant eligibility if their college GPA drops below a 2.5, if they
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earn less than 24 semester credit hours (SCH) per year, if they fail to earn credit in 70 percent of
their enrolled classes, or if their EFC rises above $4,000. As a result, students with large dosages
are more likely to have higher levels of academic achievement relative to those with lower
dosages. Moreover, the financial value of a TEXAS Grant is equal the cost of tuition and fees at
a student’s college. Therefore, a student’s dosage is based on where they are enrolled, not a
random process.
Methodology
I estimate the TOT effect sizes by using an instrumental variable regression analysis. My
model is represented below by a treatment equation and an outcome equation as shown below by
Equations 1 and 2, respectively.
𝐷" = 𝛼& + 𝛼( ∙ 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒" + 𝛼0 ∙ 𝐸𝐹𝐶" + 𝛼3 ∙ 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒" ∙ 𝐸𝐹𝐶" + 𝛼 ∙ 𝑋" + 𝜖" 𝑒𝑞. 1
𝑂𝑢𝑡𝑐𝑜𝑚𝑒"? = 𝛽& + 𝛽( ∙ 𝐷A + 𝛽0 ∙ 𝐸𝐹𝐶" + 𝛽3 ∙ 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒" ∙ 𝐸𝐹𝐶" + 𝛽 ∙ 𝑋" + 𝜖" 𝑒𝑞. 2
In equation 1, Di is a dummy variable indicating that student i received a TEXAS grant at
the start of their college career in year 1. In equations 1 and 2, Eligiblei is a dummy variable that
indicates if student i meets the financial need criteria, an EFC at or below $4,000. EFCi
represents student i’s Estimated Family Contribution centered at the cutoff point of $4,000. The
interaction of Eligiblei and EFCi allows the slope of the relationship between EFC and each
outcome to vary on either side of the EFC cutoff. Xi represents a vector of pretreatment
covariates that control for student demographics, their academic performance in high school, and
dummy variables controlling for their entering cohort and college fixed effects. Xi is augmented
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with a vector of dummy variables that indicate the fiscal year when estimating workforce
outcomes to control for changes in the economy. The variable 𝜖" represent an error term.
In equation 2, Outcomei is one of nine types of student outcomes studied in this paper. In
order of a student’s journey through college and beyond, Outcomei includes: (1) the probability
of persisting year to year, conditioned on prior year enrollment; (2) annual number of university
semester credit hours enrolled (SCH) pooled over years one to four; (3) the annual number of
community college SCH pooled over years one to four; (4) the probability of working while
enrolled in college (outside of federal work-study); (5), if working, the earnings while enrolled in
college; (7) the probability of graduating within set times (four, five, six, seven, and eight years,
respectively); (8) student debt acquired; and, (9) earnings in year 6, 7, and 8, respectively. 𝐷A
represents a student’s predicted probability of treatment. The estimated effect of treatment is
represented by 𝛽( .
I tested hypotheses using a two-sided t-test. I defined estimates to be statistically
significant if their standard errors produce a p-value of 0.1 or less. In the tables that detail effect
sizes, I identify statistical significance at levels below 0.1, 0.05, and 0.01.
Heterogeneous Treatment Effects
I also investigate the existence of heterogeneous effects of grant aid on 6-year graduation
rates and early career earnings in years 6 through 11. Equations 3 and 4 below summarize the
two-stage IV model used to estimate heterogeneous effects across various student subgroups.
𝐷" = 𝛼& + 𝛼( 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒" + 𝛼0 𝐸𝐹𝐶" + 𝛼3 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒" ∙ 𝐸𝐹𝐶" +𝛼C 𝐺𝑟𝑜𝑢𝑝" + 𝛼G 𝐺𝑟𝑜𝑢𝑝" ∙ 𝐸𝑙𝑖𝑔𝑖𝑙𝑏𝑙𝑒" + 𝛼𝑋" + 𝜖" 𝑒𝑞. 3
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𝑂𝑢𝑡𝑐𝑜𝑚𝑒"? = 𝛽& + 𝛽( ∙ 𝐷A + 𝛽0 ∙ 𝐸𝐹𝐶" + 𝛽3 ∙ 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒" ∙ 𝐸𝐹𝐶" +𝛽C 𝐺𝑟𝑜𝑢𝑝" + 𝛽G 𝐺𝑟𝑜𝑢𝑝" ∙ 𝐷" + 𝛽 ∙ 𝑋" + 𝜖" 𝑒𝑞. 4
In equations three and four, the variable Groupki represents one of k student groups under
investigation. The sum of the coefficients 𝛽( and 𝛽G represents the unique effect TEXAS Grants
has on each outcome for a given group of students, Groupki. I investigated heterogeneous effects
for groups defined by race and ethnicity; gender; an indicator of students who are the first in
their family to enroll in college; and measures of college readiness. Proxies for college readiness
include indicator variables identifying students with above average SAT scores (approximately
1150; students who graduated on the more rigorous high school curriculum; and students who
graduated in the top 10 percent of their high school graduating class, and the top 25 percent of
their high school graduating class.
Validating RD Assumptions
The internal validity of Fuzzy RD rests on the requirement that students or their agents
lack precise control over the value of their score variable – estimated family contribution (EFC)
in this case. To verify this, I interviewed financial aid administrators about the process of
calculating EFC. They confirmed that students and their parents are unable to influence this
federally defined metric with precision.
I also performed two statistical tests for local randomization. I first conducted the
McCrary test, a test for manipulation of the score variable around the cutoff point. It found no
evidence of manipulation. A visual inspection of density function of EFC as shown in the panel
subtitled “EFC Density” in Figure 1 also reveals no bunching to the left of the EFC cutoff point.
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I then ran a second statistical test for discontinuity in pretreatment covariates. As
recommended by Lee and Lemieux (2010), I estimated a system of Seemingly Unrelated
Regression equations that regressed the pretreatment covariates on treatment eligibility (ELIGi)
and the score variable (EFCi). Equation 5 below represents this system of equations, where k
represents each of the 14 pretreatment covariates.
𝑍K" = 𝛽K& + 𝛽K( 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒" + 𝛽K0 𝐸𝐹𝐶" + 𝜖K" 𝑒𝑞. 5
I then ran a chi-square test of the joint hypothesis: ß11 = ß21… = ßk1 = 0. It produced a pvalue of 0.47, allowing me to not reject the hypothesis of covariate continuity at the cutoff point.
A final verification of the assumption of local randomization was conducted by visually
inspecting the probability distributions of the pretreatment covariates conditioned on EFC. Four
of the pretreatment covariates are displayed in Figure 1 under the subtitles: % Female, % White,
Avg SAT, and % HS Top 10. These last two represent average SAT scores and percent of studets
who graduated in the top 10 percent of their high school graduating class. Once again, no
discontinuity at the cutoff point was found.
Bandwidth Selection
The most important task in regression discontinuity designs involves defining the
neighborhood of observations to the left and right of the cutoff point to include in the estimation
process. I used the widely accepted Mean Squared Error-optimal bandwidth method first
developed by Imbens and Kalyanaraman (2009) that has been extended to account for model
specification (Cattaneo & Escanciano 2016). An optimal bandwidth was for each outcome
evaluated. However, in investigating heterogeneous effects, I used the optimal bandwidth for
estimating the average effect on six-year graduation rates and early career earnings, respectively.
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Robustness Checks
I conducted two sets of robustness checks. First, I re-estimated effect sizes after replacing
the linear function form of the score variable (EFCi) with a quadratic functional form. Second, I
tested the robustness of estimated treatment effects to changes in the optimized bandwidth. I reestimated effect sizes after varying the optimized bandwidth by increments of 10 percent starting
at 50 percent and increasing to 150 percent.
Results
TOT Effects
Financial Aid. During their senior year, high school students in the study sample
received a letter of admissions and an offer of financial aid from the universities they would
eventually enroll in. The average student that received a TEXAS Grant also received other grant
aid, work-study aid, and loans. While the average value of a TEXAS Grant amounted to $3,920,
receiving a TEXAS Grant caused a decrease in other grant aid, work-study aid, and loans by
$411, $31, and $1,295, respectively. Effect sizes of each were statistically significant, as shown
in Table 3. The net effect is that TEXAS Grant students received $2,214 more in financial aid
than their counterparts in the quasi-control group in their first year of college and a promise by
the university to continue receiving a TEXAS Grant if the student made adequate academic
progress and their parents EFC remained at $4,000 or lower. With each passing year in college,
the effects of receiving the initial TEXAS Grant award on other grant aid dissipated.
Within a $1,000 EFC bandwidth, the share of TEXAS Grant recipients that maintained
their grant aid declined. By their second year in college, 74 percent of initial TEXAS Grant
recipients still received TEXAS Grant funding. This figure declined to 47 percent by their third
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year and 40 percent by their fourth year. In summary, the average recipient of a TEXAS Grant
maintained their TEXAS Grant benefit for 2.7 years. It is important to keep in mind the average
amount and longevity of a TEXAS Grant when reviewing the effect sizes that follow.
Persistence. Though a declining share of TEXAS Grant students maintained their award,
the initial TEXAS grant award produced ongoing positive effects on helping students persist year
to year. The point estimate for the effect on annual persistence conditioned on enrollment in the
prior year was 4 percentage points at the transition to year two, three, and four, as shown in
Table 3. Effects on persistence were statistically significant at the transition to year three and
four. In summary, 51 freshmen out of 100 from the quasi-control group that did not receive a
TEXAS Grant reached their fourth year of college. Of the initial TEXAS Grant awardees, 65 out
of 100 made it to their fourth year of college.
Course Enrollment. Receiving an initial TEXAS Grant also caused students to enroll in
more classes at their enrolled university and to reduce their co-enrollment at a community
college. As displayed in Table 2, an initial TEXAS Grant caused an increase in annual university
course enrollment by 0.64 semester credit hours (SCH) and a decrease in annual community
college course enrollment by 0.46 SCH for each of the first four years of college. On net,
TEXAS Grants caused students to enroll in more courses and have more of their course work
completed at their enrolled university.
Texas has a decentralized high education system. Reducing a student’s reliance on the
community college system has the likely added benefit of reducing issues related to the transfer
and application of course credit to their degree program. Observing the effect on community
college course enrollment, however, reveals a strategy used by students with a greater unmet
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financial need to make progress to a college degree. These students with less grant aid substitute
university coursework for lower-cost community college coursework at levels statistically
equivalent to each other.
Working While Enrolled. This study relied on earnings data collected by the Texas
Workforce Commission through the state unemployment insurance program. This data allowed
for the investigation of student employment outside of campus by subtracting earnings associated
with work-study aid. Work-study aid funded by the federal government is the only on-campus
employment that universities are required to report for students in their employ.
Work-study is unique from other types of employment. It can be related to academics and
takes place on campus. Existing research suggests it has a positive effect on persistence and
eventual graduation (DesJardins et al., 2002); while working in general has been found to have a
negative effect on persistence and graduation (Johnson & Rochkind, 2016).
Students that received an initial TEXAS Grant decreased their probability of working by
6 percentage points in their first year in college and 3 percentage points in their second year and
fourth year. As summarized in Table 3, point estimates for the first and second year were the
only effects on student employment that were statistically significant. In their third year, TEXAS
Grant students experienced a 1 percentage point increase in their third year, though this estimate
was not statistically significant. Given that 46 and 49 percent of the quasi-control group worked
in year one and two, respectively, TEXAS Grants caused 12 percent fewer students to work in a
job outside of school in their first year in college, and 7 percent fewer in their second year. This
pattern of impact in years one and two corresponds to the average longevity of TEXAS Grants,
which ends before year three.
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College Earnings. TEXAS Grants caused earnings of students that worked during their
first year to decline by $641 and $600 in year two, as displayed in Table 3. These declines
represent a 15% and 10% decline in earnings and were the only statistically significant changes
caused by TEXAS Grants. Once again, this corresponds to the average longevity of TEXAS
Grants, which ends before year three.
Based on the assumption of local randomization, we can assume that students in the
quasi-control group on average earn the same hourly wage rate as students in the quasitreatment. Consequently, the 15 and 10 percent decline in earnings in year one and two are
equivalent to a 15 and 10 percent decline in time worked off campus in year one and two.
In summary, an initial TEXAS Grant award has a meaningful impact on student
employment in the first two years of college. An initial TEXAS Grant award causes fewer
students to work and of those that do work, they work less during the first two years of college.
Effects on employment vanish in years three and four, when the share of initial TEXAS Grant
awardees still receiving TEXAS Grant funds drops below fifty and fourth percent, respectively.
College Completion. Students that received an initial TEXAS Grant were more likely to
complete college within five years. As shown in Table 3, TEXAS Grant aid produced positive
effect sizes on all graduation rates. However, effects on 5-year rates were the only statistically
significant ones. When compared to the graduation rates of the quasi-control group, TEXAS
Grants increased the number of degrees produced by 20 percent by year five.
The lack of statistical significance associated with effects on 6-year, 7-year, and 8-year
rates suggests that the average student in the quasi-control group who did not receive grant aid
eventually completes college after five years of enrollment. Note, this finding is for the average
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student. In the section below on heterogeneous effects, it appears that for students with lower
levels of prior academic achievement in high school, receiving a TEXAS Grant has an effect on
college completion not just time to completion.
Student Debt. TEXAS Grant dollars are a substitute for loan dollars in helping finance
college. As displayed in Table 3, an initial TEXAS Grant award causes recipients to reduce their
student debt by $4,044. This point estimate is based on all students including some that drop out
of college and have no need for student loans. When the analysis is restricted to students who
graduated within eight years from first entering college, the magnitude of the effect of TEXAS
Grants on student debt increases. Of students that graduated within eight years, TEXAS Grants
reduced student debt by $6,832. Both effect sizes are statistically significant and meaningful.
The effect on student debt among all students represents a 23 percent decline in student debt. The
effect on student debt among college graduates represents a 29 percent decline in student debt.
Early Career Earnings. After four years from first entering college, students begin
entering the workforce and starting their careers as indicated by a 52% increase in earnings of the
quasi-control group from year four to year five, as shown in Table 3. Students that received a
TEXAS Grant experienced a greater rise in earnings. TEXAS Grant recipients experienced an
additional increase in earnings of $589, $1,103, and $617 in years five, six, and seven. TEXAS
Grant awardees experience a decline in average earnings in year eight by $799. These effect
sizes were meaningful but not statistically significant.
There are data limitations associated with unemployment insurance earnings data worth
noting. The State of Texas collects wage data only for workers who are employed by Texas
employers. As a result, the study sample excludes students who find employment outside of
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Texas, who are self-employed, or who are employed by the US federal government, including
the US military. The Texas Coordinating Higher Education Coordinating Board estimates the
size of this excluded group to be 27 percent of Texas college graduates (Education Pays for
Texas, 2016). This data limitation may bias effect estimates if students in the quasi-treatment
group experience a probability of finding employment outside the state of Texas, with the federal
government, or as self-employed different than their counterparts in the quasi-control group. I
found no evidence to suggest this is the case.
Heterogeneous Effects
TEXAS Grant increased six-year graduation rates for college students that had lower
levels academic achievement in high school. Students who graduated from high school with the
less rigorous college prep curriculum called the Recommended Program experienced an 8percentage point increase in their six-year graduation rate. Students who graduated from high
school on the more rigorous college-prep curriculum experienced a 3-percentage point gain in
their six-year graduation rates. The unique effects of TEXAS Grants for each of these groups
were statistically significant as shown in Table 4.
A similar pattern of heterogeneous effects on six-year graduation rates is also present
based on class ranking. TEXAS Grants improved six-year graduation rates of students who
graduated in the top 25 percent of their high school graduating class by 4 percentage points;
while their counterparts in the bottom 75 percent experienced a 9-percentage point increase.
Though results are not statistically significant, TEXAS Grants appears to also produce
higher effects for the less academically accomplished based on SAT scores and position in the
bottom 90 percent of one’s high school graduating class.
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No statistically significant differences in effect sizes were found based on gender,
ethnicity and race, and status as first-generation college students, as displayed in Table 4.
Heterogeneous effects on early career earnings were explored by pooling earnings data
across years six through 11. Across these years, TEXAS Grants produced statistically significant
heterogeneous effects on early career earnings for a few student groups. As shown in Table 5,
TEXAS Grants caused the early career earnings of Hispanic, White, Black, and Asian students to
increase by 2 percent, 5 percent, 11 percent and 26 percent, respectively. The point estimates of
effects for Hispanics and Asians were the only statistically significant effect sizes.
TEXAS Grants produced larger effect sizes for groups of students with less academic
accomplishment prior to entering high school. TEXAS Grants caused a negative 2 percent effect
on the early career earnings of students who graduated in the top 10 percent of their graduating
class; while their counterparts in the bottom 90 percent experienced an 11 percent increase in
earnings. This was the only heterogeneous effect size that was statistically significant.
However, the general pattern of larger effect sizes on earnings for the less academically
accomplished repeated itself across other variables such as SAT scores and type of high school
curriculum. This pattern may represent the difference between the value of a college degree as a
minimum credential for certain jobs versus the value of the enhanced productivity derived from a
college education. As shown in Table 5, students who are less academically accomplished
experience lower earnings on average relative to their more academically accomplished
counterparts. When these students receive a TEXAS Grant, they are more likely to complete
college, thereby giving them a requisite credential to be considered for higher paying jobs, which
TEXAS Grants Impact Analysis
19
they could not have access to based on human capital alone. In summary, earning a college
degree is more important to students with lower levels of human capital.
Robustness
I performed two checks for robustness to determine the stability of point estimates. The
first tested the sensitivity to changes in the bandwidth. The results of this test can be found in
Figures 3 to 9. Overall, the sign of each effect size remained constant. As changes in the optimal
bandwidth increased the re-estimated point estimate moved further from the original point
estimate; however, the basic relationship between treatment and outcomes did not change.
The second robustness check tested a quadratic form of the score variable, EFC, as an
alternative to the original linear form of EFC. Table 6 presents the results of this test. Once
again, changes in the functional form of the score variable resulted in different point estimates,
however the sign of the point estimates remained constant. The magnitude of re-estimated effect
sizes did not significantly change with one exception. The quadratic model produced
significantly larger effects on graduation rates at six, seven and eight year rates. The quadratic
model also caused these new effect sizes to become statistically significant.
At the time of this writing, the second robustness check had not been run on earnings.
However, an earlier analysis that used a log transformation of earnings produced larger effect
sizes but did not change the sign of effect sizes nor their statistical significance.
Discussion
This paper finds that lowering the cost of college decreases student employment and
increases the time to college completion. It also finds that students with unmet financial need use
the community college system as a source for less costly college credit. Finally, grant aid causes
TEXAS Grants Impact Analysis
20
students to complete college in less time. And, for students considered less academically
prepared, grant aid causes an increase in college completion rates. Conversely, this study finds
that higher college costs produce greater delays in college completion for students overall and
may cause students categorized as less academically prepared to drop out of college.
Several policy lessons emerge from these findings. First, policymakers should consider
the delay on college completion when voting on policies that increase tuition costs or decrease
financial aid. Second, policymakers should improve the relationship between universities and
community colleges so that students with unmet financial need can reliably turn to community
colleges as a source for lower-cost quality college credit to help them earn their college degree.
Finally, the heterogeneous effects on college graduation uncovered suggest that state legislatures
should consider not deprioritizing students admitted to college but with lower measures of
college readiness, as Texas did in 2011, if their goal is to produce more degrees and more
degrees in less time.
TEXAS Grants Impact Analysis
21
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TEXAS Grants Impact Analysis
25
Appendix
Table 1. Grant Aid Effects on Graduation Rates of Existing Experimental and Quasi-Experimental Studies
Meta-Analysis
Study
Effect Size [95% Conf. Interval]
Grant Program Design
% Weight
Avg
Annual
Grant
Amount
Graduati
on Rate
Cattleman 2013
Scott-Clayton 2011
Scott-Clayton 2011
Alon 2005
Alon 2007
Dynarski 2008 Adj
Goldrick-Rab 2016
Scott-Clayton Zafar
Bettinger et al 2016
Bettinger et al 2016
5.2
4.5
3.7
6.3
1.5
2.52
4.7
4
7.1
10.7
1.08
-0.99
1.35
-10.56
-3.20
1.66
4.28
-1.49
2.20
4.43
9.32
9.99
6.05
23.16
6.20
3.38
5.12
9.49
12.00
16.97
8.46
5.36
16.99
0.66
6.89
29.41
32.22
4.79
5.75
3.82
$1,300
$2,500
$2,500
$1,000
$1,000
$1,000
$3,500
$2,500
$2,500
$2,500
7
5
5
6
6
10
4
6
6
6
Weighted average
Weighted average per $1000
4.19
1.93
2.381
5.947
100
$2,166
6.4
Type of
Average
Treatment
Effect
ITT
TOT
TOT
TOT
TOT
ITT
ITT
TOT
TOT
TOT
Jurisdictio
n
Latest
Cohort
Studied
Florida
W. Virgina
W. Virgina
USA
USA
Georgia, Ark.
Wisconson
California
California
California
2000
2003
2003
1989
1989
1996
2008
2003
1990s
1990s
Note: Overall effect estimate was based on a random effects model. The overall annual grant amount and graduation rate
represented weighted averages of the original studies.
TEXAS Grants Impact Analysis
26
Table 2. Descriptive Statistics of Pretreatment Covariates of Students
who Meet All Eligibility Criteria Other than EFC Criteria, Study Sample
and Larger Sample
Study Sample:
Larger Sample:
$3,000 < EFC <
$0 < EFC < $900,000
$5,000
Mean
SD
Mean
SD
Estimated Family Contributon
Received a TEXAS Grant
EFC Eligible
3,942
0.36
0.54
576
0.48
0.50
10,319
0.35
0.51
16,195
0.48
0.51
Age in Year-1
Female
Black
Hispanic
Asian
Other ethnic/racial group
Immigrant
First-Generation college
18
0.56
0.18
0.34
0.07
0.02
0.00
0.53
0.35
0.50
0.39
0.47
0.25
0.15
0.03
0.50
18
0.56
0.17
0.33
0.07
0.02
0.00
0.50
0.36
0.50
0.38
0.47
0.26
0.15
0.04
0.50
Dual-credit earned
Top 10 percent HS ranking
Top 11-25 percent HS ranking
SAT score
HS Distinguished Diploma
0.00
0.28
0.25
1032
0.23
0.15
0.45
0.43
164
0.42
0.00
0.27
0.26
1043
0.24
0.24
0.45
0.44
173
0.43
Exempt from tuition & fees
Other grant aid received, Yr-1
Work-study aid received, Yr-1
0.05
3,075
126
0.21
2,862
498
0.04
3,387
79
0.21
3,267
399
0.11
0.11
0.12
0.13
0.14
0.13
0.14
0.13
0.31
0.32
0.33
0.33
0.35
0.33
0.34
0.34
0.08
0.09
0.11
0.12
0.13
0.15
0.16
0.16
0.28
0.28
0.31
0.32
0.34
0.36
0.37
0.37
Fall Cohort 2004
Fall Cohort 2005
Fall Cohort 2006
Fall Cohort 2007
Fall Cohort 2008
Fall Cohort 2009
Fall Cohort 2010
Fall Cohort 2011
N
21,460
264,210
TEXAS Grants Impact Analysis
27
Table 3. Impact of TEXAS Grants on Student Outcomes, Linear Model
Control Group
Effect Size of TX Grant
BW
from
SE
Cutoff
N
190 ** 976 14,537
408 ** 1029 14,766
34
981 15,672
Mean
Outcome
Outcome
Other grant aid Yr-1 $2,973
Loan aid Yr-1
$4,239
Work study aid Yr-1 $137
SE
***
89
174
8
Mean
TOT
-$411
-$1,295
-$31
Yr-1 to Yr-2
Yr-2 to Yr-3
Yr-3 to Yr-4
0.73
0.81
0.85
0.01
0.01
0.01
0.05
0.09
0.07
0.04
0.04 **
0.04 *
978
664
749
15,833
8,147
8,343
0.07
0.03
0.01
University
Comm College
27.5
2.42
0.134
0.103
0.64
-0.46
0.303 **
0.25 *
1,563
791
30,780
39,566
0.05
0.03
Probability of
Employment
Yr-1
Yr-2
Yr-3
Yr-4
0.49
0.46
0.43
0.42
0.01
0.02
0.02
0.02
-0.06
-0.03
0.01
-0.03
0.03 *
0.03 **
0.03
0.03
961
1,158
1,603
1,232
15,548
18,066
24,955
19,646
0.07
0.01
0.02
0.02
Graduation Rates
4-yr
5-yr
6-yr
7-yr
8-yr
0.25
0.39
0.48
0.51
0.53
0.01
0.01
0.01
0.02
0.02
0.02
0.08
0.06
0.05
0.05
0.02
0.04 **
0.04
0.04
0.05
1,558
880
1,161
1,174
1,091
25,680
14,254
13,643
11,448
8,342
0.14
0.14
0.11
0.08
0.08
Student Debt
All
$17,542
$23,702
319
-$4,044
771 *** 1,565
25,803
0.10
547
-$6,832
1240 *** 1,010
8,071
0.18
$4,264
$6,049
$7,398
$8,808
$13,357
$17,825
$21,361
$24,725
110
133
190
186
331
376
441
554
-$641
-$600
-$208
$180
$589
$1,103
$617
-$799
16,658
19,719
15,848
24,735
15,250
20,036
16,139
11,165
0.004
0.005
0.002
0.000
#####
#####
#####
#####
Outcome Family
Financial Aid
Prob of Persistence
SCH Enrolled
2
1
Graduates
Earnings
3
Yr-1
Yr-2
Yr-3
Yr-4
Yr-5
Yr-6
Yr-7
Yr-8
* p < 0.1, ** p < 0.05, *** p < 0.01, two-tailed test.
1. Conditioned on prior year enrollment
2. Conditioned on enrollment
285.00
350.00
490.00
463.00
863
963
1,142
1,544
**
*
1,036
1,159
933
1,320
999
1,534
1,506
1,355
R2
0.25
0.29
0.01
TEXAS Grants Impact Analysis
28
Table 4. Heterogeneous Impacts of TEXAS Grants on 6-Yr
Graduation Rates
Control Group
Outcome
All
Female
Male
White
Hispanic
Black
Asian
First-Gen
Not First-Gen
Top 10% of HS Graduating Class
Bottom 90%
Top 25% of HS Graduating Class
Bottom 75%
Distinguished HS Diploma
Recommended HS Diploma
SAT Sore, Above Avg
SAT Score, Below Avg
Mean
Outcome
0.48
0.54
0.42
0.48
0.49
0.50
0.53
0.46
0.51
0.61
0.42
0.55
0.40
0.55
0.46
0.53
0.47
N = 13,643
* p < 0.1, ** p < 0.05, *** p < 0.01, two-tailed test.
SE***
0.02
0.02
0.02
0.02
0.02
0.02
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
Effect Size
Change
in pp
0.06
0.04
0.08
0.05
0.05
0.04
0.09
0.05
0.06
0.04
0.07
0.04 *
0.09
0.03 **
0.08 **
0.03
0.07
TEXAS Grants Impact Analysis
29
Table 5. Heterogeneous Impacts on Earnings Yr 6 to Yr 11
Outcome
All
Female
Male
White
Hispanic
Black
Asian
First-Gen
Not First-Gen
Top 10% of HS Graduating Class
Bottom 90%
Top 25% of HS Graduating Class
Bottom 75%
Distinguished HS Diploma
Recommended HS Diploma
SAT Sore, Above Avg
SAT Score, Below Avg
Control Group
Mean
Outcome
SE***
$22,276
463
$20,907
492
$24,092
584
$23,433
568
$22,206
697
$19,915
586
$20,597
859
$22,380
522
$22,120
529
$25,492
715
$20,962
497
$23,850
674
$21,835
461
$23,966
875
$21,869
496
$23,091
828
$22,168
506
N = 46,504
* p < 0.1, ** p < 0.05, *** p < 0.01, two-tailed test.
Effect Size
% Change
0.07
0.09
0.05
0.05
0.02 *
0.11
0.26 **
0.07
0.07
-0.02 **
0.11
0.04
0.07
0.01
0.08
0.03
0.06
TEXAS Grants Impact Analysis
30
Table 6. Impact of TEXAS Grants on Student Outcomes, Quadratic Model
Effect Size of TX Grant
Outcome Family
Financial Aid
Prob of Persistence
SCH Enrolled
2
Probability of
Employment
Graduation Rates
Student Debt
Outcome
Other grant aid Yr-1
Loan aid Yr-1
Work study aid Yr-1
1
BW from
SE
Cutoff
327 **
1,632
410 **
1,687
44
1,168
Yr-1 to Yr-2
Yr-2 to Yr-3
Yr-3 to Yr-4
0.04
0.12
0.05
0.047
0.051
0.045
University
Comm College
0.5
-0.48
-0.348
0.25
Yr-1
Yr-2
Yr-3
Yr-4
-0.08
-0.06
0.00
-0.08
0.04
0.05
0.05
0.04
4-yr
5-yr
6-yr
7-yr
8-yr
0.05
0.10
0.09
0.09
0.17
0.04
0.05
0.05
0.05
0.07
All
Graduates
Earnings
Mean
TOT
-503.265
-$1,585
$3
-$4,044
3
Yr-1
Yr-2
Yr-3
Yr-4
Yr-5
Yr-6
Yr-7
Yr-8
* p < 0.1, ** p < 0.05, *** p < 0.01, two-tailed test.
1. Conditioned on prior year enrollment
2. Conditioned on enrollment
-$6,610
N
24,304
24,202
18,658
R2
0.26
0.29
0.01
$1,647
$1,170
$1,400
27,239
14,559
15,828
0.07
0.03
0.01
$1,563
1,523
77,848
76,160
0.10
0.06
*
$1,679
$1,711
$1,621
$1,676
20,430
16,621
14,197
14,006
0.03
0.03
0.02
0.03
**
**
**
**
$1,690
$1,134
$1,167
$1,500
$1,377
27,981
15,892
13,698
14,699
10,548
0.12
0.11
0.09
0.08
0.08
1,773
28,821
0.12
2,219
62,312
0.18
**
*
*
908 ***
1484
***
TEXAS Grants Impact Analysis
31
Figure 2. Primary Outcomes of Interest
Distributions per $200 EFC Bins
60%
% Grad by Yr4
60%
% Grad by Yr5
60%
50
50
50
40
40
40
30
30
30
20
20
20
% Grad by Yr6
$30k Avg Earnings Yr6
$30k Avg Earnings Yr7
$30k Avg Earnings Yr8
$25k
$25k
$25k
$20k
$20k
$20k
$15k
$15k
2000
3000
4000
5000
6000
$15k
2000
3000
4000
5000
6000
Estimated Family Contribution
2000
3000
4000
5000
6000
TEXAS Grants Impact Analysis
32
Figure 1. Requirements of Regresson Discontinuity
Distributions per $200 EFC Bins
% Treated
80%
3500
70
EFC Density
70
3000
60
% Female
80%
60
50
40
30
2500
50
2000
40
30
1500
20
10
20
1000
% White
80%
1200
10
Avg SAT
% HS Top 10
80%
70
70
1100
60
50
60
50
1000
40
30
40
30
900
20
20
10
800
2000
3000
4000
5000
6000
10
2000
3000
4000
5000
6000
2000
3000
4000
5000
6000
Estimated Family Contribution
Figure 2. Primary Outcomes of Interest
Distributions per $200 EFC Bins
60%
% Grad by Yr4
60%
% Grad by Yr5
60%
50
50
50
40
40
40
30
30
30
20
20
20
% Grad by Yr6
$30k Avg Earnings Yr6
$30k Avg Earnings Yr7
$30k Avg Earnings Yr8
$25k
$25k
$25k
$20k
$20k
$20k
$15k
$15k
2000
3000
4000
5000
6000
$15k
2000
3000
4000
5000
6000
Estimated Family Contribution
2000
3000
4000
5000
6000
TEXAS Grants Impact Analysis
33
Figure 3.
TOT Effects on Persistence Rates
at Varying Factors of Optimal Bandwidth, CI 90
Year 1 to Year 2
Change in Percentage Points
30
Year 2 to Year 3
30
20
20
10
10
0
0
-10
-10
.5
.7
.9
1.1
1.3
1.5
Year 3 to Year 4
30
20
10
0
-10
.5
.7
.9
1.1
1.3
1.5
Bandwidths
.5
.7
.9
1.1
1.3
1.5
TEXAS Grants Impact Analysis
34
Figure 4.
TOT Effects on Enrollment in Semester Credit Hours
at Varying Factors of Optimal Bandwidth, CI 90
University
2
Community College
2
1
1
0
0
-1
-1
-2
-2
.5
.7
.9
1.1
1.3
1.5
.5
.7
.9
1.1
1.3
1.5
1.3
1.5
1.3
1.5
Bandwidths
Figure 5.
TOT Effects on Probability of Working While Enrolled
at Varying Factors of Optimal Bandwidth, CI 90
Year 1
Change in Percentage Points
10
Year 2
10
0
0
-10
-10
-20
-20
.5
.7
.9
1.1
1.3
1.5
Year 3
10
.5
.7
.9
Year 4
10
0
0
-10
-10
-20
1.1
-20
.5
.7
.9
1.1
1.3
1.5
Bandwidths
.5
.7
.9
1.1
TEXAS Grants Impact Analysis
35
Figure 6.
TOT Effects on Graduation Rates
at Varying Factors of Optimal Bandwidth, CI 90
Change in Percentage Points
4-Year
5-Year
6-Year
20
20
20
10
10
10
0
0
0
-5
-5
-5
.5
.7
.9
1.1
1.3
1.5
.5
.7
7-Year
20
10
10
0
0
-5
-5
.7
.9
1.1
1.1
1.3
1.5
1.3
1.5
8-Year
20
.5
.9
1.3
1.5
.5
.7
.9
1.1
Bandwidths
.5
.7
.9
1.1
1.3
1.5
TEXAS Grants Impact Analysis
36
Figure 7.
TOT Effects on Student Debt
at Varying Factors of Optimal Bandwidth, CI 90
All Students
$10,000
Graduate by Yr-8
$10,000
$5,000
$5,000
0
0
-$5,000
-$5,000
-$10,000
-$10,000
.5
.7
.9
1.1
1.3
1.5
.5
.7
.9
1.1
1.3
1.5
Bandwidths
Figure 8.
TOT Effects on Earnings in thousands
at Varying Factors of Optimal Bandwidth, CI 90
Year 1
$1
0
0
-$1
-$1
.5
.7
.9
1.1
1.3
1.5
Year 3
$1
.5
0
-$1
-$1
.7
.9
.7
.9
1.1
1.3
1.5
Bandwidths
1.1
1.3
1.5
1.1
1.3
1.5
Year 4
$1
0
.5
Year 2
$1
.5
.7
.9
TEXAS Grants Impact Analysis
37
Figure 9.
TOT Effects on Earnings in thousands
at Varying Factors of Optimal Bandwidth, CI 90
Year 5
$6
Year 6
$6
0
0
-$6
-$6
.5
.7
.9
1.1
1.3
1.5
Year 7
$6
.5
.7
.9
1.3
1.5
1.1
1.3
1.5
Year 8
$6
0
1.1
0
-$6
-$6
.5
.7
.9
1.1
1.3
1.5
Bandwidths
.5
.7
.9