Honors Thesis - Economics

Federal Work-Study versus Off-Campus Jobs: Does the type of job
matter for post-graduate outcomes?
Larissa Muramoto§
Honors Thesis
Advisor: Professor Caroline Hoxby
June 1, 2013
Abstract
The Federal Work-Study (FWS) Program was established as part of the
Economic Opportunity Act of 1964 to help students find part-time employment to
cover the cost of their tuition. Today, the U.S. government allocates nearly $1
billion in FWS funds for students in 3,000 institutions around the country. Even
though the FWS program is large, it has not been studied comprehensively
because of difficulties finding exogenous variation that explains endogenous work
decisions. To overcome this issue, I exploit differences in FWS availability and
FWS availability relative to other job opportunities at comparable institutions to
instrument for FWS participation and hours worked in school. I find some
evidence that FWS participation improves academic and post-graduation
outcomes.
Keywords: Work-study, employment, post-graduate outcomes, job quality
§
Department of Economics, Stanford University, Stanford, CA 94305 (email:
[email protected]). I would like to thank Caroline Hoxby for her guidance and support. I
would also like to thank Michael Dinerstein for his regular feedback throughout this project.
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Introduction
The Federal Work-Study Program was established as part of the Economic Opportunity
Act of 1964 to help students find part-time employment to cover the cost of their tuition. Today,
the FWS Program is a significant source of student aid. In 2012, the U.S. government allocated
nearly $1 billion in Federal Work-Study funds for students in 3,000 institutions around the
country (Federal Campus-Based Programs Data Book 2012). According to the Beginning
Postsecondary Students Longitudinal Study, approximately 40% of college students hold a FWS
job. In fact, the FWS accounts for 45% of all the jobs college students hold.
Even though FWS is such a large program, relatively little has been done to analyze the
impact it has on students' achievement during and after college. The potential effects are not
obvious: on one hand, the FWS Program might harm students by encouraging them to take up
part-time employment that could compete with classes and homework for time. Theoretically, it
could be optimal for students to take out larger loans instead of working part-time: with less time
to focus on schoolwork, students with part-time jobs might suffer from lower GPAs, take longer
to graduate, or even drop out of school. On the other hand, the FWS Program might help students
by providing access to convenient jobs on-campus. One could argue that students will work
during college regardless of whether FWS jobs are available or not: majority (~70%) of college
students hold part-time jobs and more than half of these are not affiliated with the FWS Program
(Beginning Postsecondary Students 2012). By providing jobs that are intended to compliment a
student’s education, the FWS Program might benefit these students who choose to work.
Compared to alternate jobs that require students to commute off campus, FWS jobs can benefit
students by providing access to jobs that are more compatible with students' unique college
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schedules. Additionally, having a FWS job might increase a student’s persistence via attachment
to the institution (Tinto 1987). A student who is integrated into the environment at a college is
less likely to drop out when he/she regularly interacts with peers on campus.
In the past, it has been difficult to establish a causal relationship between FWS Program
participation and outcomes because it is difficult to find exogenous variation that explains
endogenous work decisions. A student’s decision to work in college is rarely random: he or she
may need the extra finances to fund his or her education. Alternately, he or she might possess
higher levels of motivation that cause him or her to seek early work experience to improve job
market candidacy. Because it is difficult to identify exogenous variation in work decisions, most
existing research on the effects of student employment has simply examined correlations
between student employment and academic outcomes. A comprehensive literature review in
2009 by Hossler et al. found the effects of the FWS Program to be inconclusive. Some articles
argued that FWS might have positive effects on student outcomes via Tinto’s Theory of Student
Departure: participating in the program helps students integrate into the campus community,
thereby lowering their chances of dropping out. However, this conclusion was highly subject to
question because the authors found it difficult to quantify “integration” into a college
environment. Other articles found that work-study had no effect, perhaps because students opt
for off-campus jobs that often pay better than work-study jobs and have no cap on hours worked.
Hossler et al. concluded that more research needed to be done on the FWS program to assess its
true effects on student performance.
Economists have also attempted to derive a causal relationship between student
employment and outcomes by utilizing instruments such as parental schooling, parental religion,
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and net schooling costs (DeSimone 2008; Kalenkoski and Pabilonia 2010). However, these
instruments are questionable because it is not obvious that they affect student outcomes solely
through their impact on part-time employment in college. For example, it could be argued that
parental schooling could impact student outcomes because more educated parents are more likely
to educate their children at home (Davis-Kean 2005).
The strongest instrument for student employment devised so far is the variation in FWS
funds allocated to colleges by Scott-Clayton (2011). This instrument is promising because the
attending a college with a higher FWS allocation is likely to encourage students to participate in
the FWS Program, given that they are eligible. If more FWS jobs are available on campus, one
can expect that more students will participate in the program. Additionally, FWS allocation is
also a promising instrument because it is not inherently obvious that the availability of FWS
funds is correlated with other student outcomes. Utilizing FWS allocation as an instrument for
participation in the FWS Program, Scott-Clayton found that work-study does not have any
positive effects on college GPA, credits earned by semester, probability of dropout, or
probability of completing a degree in college.
Stinebrickner and Stinebrickner (2003) employed an alternate approach to understand the
effects of student employment. They exploited data from students at a private college in
Kentucky that randomly assigned students to on-campus jobs. Variation in the hours worked
stemmed from variation in job assignment: some jobs gave students the choice to work more,
while others were limited to 10 hours per week. The result of this study showed that those
students who had the opportunity to work more and chose to do so suffered from a decline of
0.162 GPA points per additional work hour. While this result is interesting, it is difficult to draw
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larger conclusions because the students who choose to attend such a unique college might not be
comparable to college students around the nation.
More research is needed to truly understand the causal effects of the FWS Program.
While Scott-Clayton and Stinebrickner et al. have provided insight into these effects, both
researchers use small datasets that might not be generalizable to the entire US student
population. Additionally, there are still no papers examining the difference between FWS jobs
and off-campus jobs because of the past difficulties finding an exogenous source of variation in
student employment. One still needs to determine whether FWS might have positive benefits
after a student graduates because work-study jobs differ from alternate jobs a college student
might take such as waitressing or being a cashier. Since work-study jobs are meant to
compliment a student’s education, students who participate in work-study are might have
advantages over their non-work-study peers in job prospects and preparation for entering the
workforce.
The Federal Work-Study Program
Student Eligibility and Receipt of Funds
A student’s eligibility is determined by financial need and enrollment in a Title IV-Eligible
postsecondary institution. Financial need is determined by the Free Application for Student Aid
(FAFSA), which computes a student's “expected family contribution” (EFC) based on family
income, assets, and other variables. The amount of financial aid a student receives is determined by
the difference between a student's EFC and the cost of attending an institution. The opportunity to
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participate in the FWS Program is often awarded to students as a part of a larger financial aid
package (Federal Student Aid Handbook 2003).
Unlike Pell Grants or student loans, FWS funds are not directly allocated to students.
Rather, FWS funds are allocated to post-secondary institutions that subsequently distribute the funds
to their students via government-subsidized part-time employment opportunities on-campus (and
occasionally off-campus in conjunction with community service organizations). In return,
participating institutions must offer students jobs that are complimentary to college education. Some
examples of these jobs are secretarial work for an academic department on campus or tutoring jobs.
Since schools determine the distribution of FWS funds, variation in the student receipt of
funds arises. For example, some schools may choose to use the funds to provide fewer, but betterpaying jobs for the most needy students while other schools may choose to use the funds to provide
more, but lower-paying jobs for students in a wider range of need. Alternately, some schools may
give preference to students with higher financial need, better academic scores, year in school, or
indicated interest in the FWS on their FAFSA when distributing funds (Scott-Clayton 2011). Since
only 16% of schools award FWS to every eligible student, the variation in FWS fund distribution
can be significant: it is easy to see why the only reason a student might not receive FWS funding is
simply because of the college he or she attends.
Institution Eligibility and Receipt of Funds
FWS allocation is currently based on a combination of an institution’s original, or “base
guarantee” allocation and an institution’s current need. State review boards established the
original “base guarantee” allocations in 1965 according to institutions’ grant submissions.
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Initially, an institution’s yearly allocation was based on its prior allocation. However, in the
1970s, these review boards were criticized for being inefficient, inconsistent, and easily
manipulated by grant writers (Smole 2005). Critics argued that the “base guarantee” allocation
was unfair because it gave richer schools an advantage since they had the resources to employ
experienced proposal writers that could manipulate the review board. Ironically, the FWS funds
were allocated away from smaller, poorer institutions in favor of larger institutions with more
resources.
As a result, the FWS allocation rule was adjusted to include an “allocation adjustment”
that addresses institutional need. Policymakers planned to slowly transition from the “base
guarantee” allocation method, but institutional resistance halted the phase-out. Today,
approximately 2/3 of FWS funds are distributed based on the “base guarantee” allocation while
1/3 is distributed based on the allocation adjustment. While this is unfortunate for smaller,
poorer, and newer schools, this variation in FWS allocation provides promising data to
instrument for variation in student employment.
Data
Integrated Postsecondary Education Data System (IPEDS) Data:
The IPEDS contains yearly updated data on all institutions that participate in any federal
student financial aid program authorized by Title IV of the Higher Education Act of 1965,
amounting to over 7,500 schools (Integrated Postsecondary Education Data System 2013).
IPEDS includes data on institutional characteristics, institutional prices, enrollment, student
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financial aid, degrees and certificates conferred, student persistence and success, and institutional
human and fiscal resources. This dataset is useful to examine the underlying relationship
between postsecondary institutions and the FWS program. Since FWS funds are mostly based on
the original allocations in 1965, institutions that were richer in the past are more likely to have
larger FWS allocations. IPEDS variables such as admissions rate, percentage of students eligible
for a Pell Grant, and average tuition are essential to determine how FWS accessibility varies
across institutions. Since the IPEDS is extremely comprehensive, the limitations posed by this
dataset are minor. The biggest drawback of IPEDS is that it does not contain comprehensive data
on the FWS program in particular. This analysis utilizes the 2011 IPEDS release because it has
the most financial data out of all the releases.
Beginning Postsecondary Students Longitudinal Study (BPS) Data:
The BPS contains data on 16,700 students who were surveyed at the end of their first
(2003-04), third (2005-06), and sixth (2008-09) years after entering postsecondary education
(Beginning Postsecondary Students 2012). This dataset is ideal to examine the causal effects of
the FWS program because it includes variables that can be used to examine both post-college
outcomes such as unemployment and job benefits and academic outcomes such as degree
attainment and time to degree completion. Additionally, the BPS contains rich variables to
control for student ability (GPA, SAT/ACT score), demographics (race, gender, EFC), school
and work experiences, and degree attainment.
The biggest limitation of the BPS is that it is a relatively new dataset. Thus, it does not
include more current variables such as income after graduation and outcomes later in these
students’ lives. However, the BPS does include some data on reported post-graduation outcomes
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such as “consider first job as the start of career” and “job provides good benefits” that can
replace income in a regression. While the BPS does not have employment data 3+ years after
graduation, these later income statistics might not be interesting because a person’s collegiate
experience has the largest impact in the first few years after entering the work force.
Pell Grant Data:
Every year, the U.S. Department of Education publishes basic data on Pell Grants,
including the number of recipients per institution and the monetary amount of awards per
institution (Federal Pell Grant Program End-of-Year Report 2006). The Pell Grant dataset
includes variables such as institution name, institution state, institution type, number of
recipients/institution, and monetary amount of awards/institution. This analysis utilizes Pell
Grant data from 2006-2007 because the BPS tracks students from 2004-2009 and 2006-2007 is
in the middle of this date range. The Pell Grant data is used construct instruments for FWS
participation.
Federal Work-Study Data:
Similar to the Federal Aid Data, the U.S. Department of Education publishes basic data
on the allocation and distribution of work-study funds (Federal Work-Study Program 2011). The
FWS dataset includes variables such as institution name, institution state, institution type,
number of recipients/institution, federal allocation of Work-Study funds, and number of
recipients. The Federal Work-Study data is used to construct instruments for FWS participation.
US Census Data:
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The US Census contains employment data on work sectors at the country, state, and
county level. Variables in the utilized dataset include county, employment, and size of sector.
This US Census data is used to construct an instrument for FWS participation that accounts for
student employment opportunities around the colleges they attend.
Bureau of Labor Statistics (BLS) Employment Data:
The BLS provides statistics on the national, state, and county level on occupational
employment and wages by age and NAICS super-sector. This data is used to determine the
sectors that are most likely to employ college students. This information is essential to determine
the sectors to examine in US Census data.
Methodology
To determine the effect of a FWS job on academic and post-graduate outcomes, one
would need to determine the effect of working in college, specifically comparing students with a
FWS jobs to those with a non-FWS job. This will isolate the impact of a FWS job over a nonFWS job. I will examine 6 major outcomes of interest: degree completion, time to degree
completion, 3 proxies for post-graduate job quality, and unemployment 3+ months after
graduation. The proxies for post-graduate job quality are as follows: Received BA/BS, Months to
BA/BS, “Offers freedom for other interests,” “Pays the bills”, “Provides benefits”, and
Unemployed 3mo After Graduation. Since the BPS does not currently contain wage or earnings
data for the students in 2009, these proxies can be used to determine whether FWS helps students
secure “quality” jobs. I included unemployment data as well to determine whether participation in
the FWS Program improves employment prospects after graduation. Some of the control variables
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I included are: high school GPA, SAT/ACT score, gender, race/ethnicity, and EFC. A complete
list of control variables and their reason for inclusion is in Appendix A.
2SLS Model
First stage equations:
ℎ𝑜𝑢𝑟𝑠𝐹𝑊𝑆𝑤𝑜𝑟𝑘!
= 𝛼! + 𝛼! 𝑐𝑜𝑙𝑙𝑒𝑔𝑒𝐹𝑊𝑆𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦! + 𝛼! 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒𝐹𝑊𝑆𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦!
+ 𝒄𝒐𝒏𝒕𝒓𝒐𝒍𝒔𝒊 𝛼! + 𝜐!
ℎ𝑜𝑢𝑟𝑠𝑤𝑜𝑟𝑘!
= 𝛿! + 𝛿! 𝑐𝑜𝑙𝑙𝑒𝑔𝑒𝐹𝑊𝑆𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦! + 𝛿! 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒𝐹𝑊𝑆𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦!
+ 𝒄𝒐𝒏𝒕𝒓𝒐𝒍𝒔𝒊 𝛿! + 𝜉!
Second stage equation:
𝑦! = 𝛽! + 𝛽! ℎ𝑜𝑢𝑟𝑠𝐹𝑊𝑆𝑤𝑜𝑟𝑘! + 𝛽! ℎ𝑜𝑢𝑟𝑠𝑤𝑜𝑟𝑘! + 𝒄𝒐𝒏𝒕𝒓𝒐𝒍𝒔𝒊 𝛽! + 𝜀!
yi represents one of the 6 outcomes of interest: Received BA/BS, Months to BA/BS, “Offers
freedom for other interests,” “Pays the bills”, “Provides benefits”, and Unemployed 3mo After
Graduation.
hoursFWSworki is the number of hours a student worked in a FWS job weekly.
hoursworki is the number of hours a student worked weekly.
This regression requires 2 instruments because there are 2 endogenous variables, “weekly
FWS hours worked” and “weekly hours worked”. Weekly FWS hours worked might be
endogenous because selection into the FWS Program is not random. Only individuals who meet
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the financial need requirements are eligible to participate in the FWS, and even then, not all
FWS-eligible students decide to participate. Additionally, FWS jobs might have restrictions on
the number of hours a student can work. Similarly, weekly hours worked might be endogenous
because people do not choose the number of hours they work completely randomly. For
example, weekly hours worked could correspond with the field a student’s job is in, data I did
not have for this analysis.
To address these issues, I created 2 instruments: “College FWS Availability” and
“Relative FWS Job Availability”. College FWS Availability is defined as: (Allocated FWS
Funding)/(Number of Pell Grant Eligible students). I used the FWS and Pell Grant data to
construct this instrument. Similar to the instrument that Scott-Clayton employed to examine the
effect of the FWS Program in West Virginia, this instrument exploits the variation in FWS
allocations that arises because of the way the US government allots FWS funds to students.
“College FWS Availability” fulfills condition 1 because college FWS availability is
strongly correlated with a person’s participation in the FWS Program. If a college has more
federal FWS funding relative to the number of students who are eligible for the funding, a
student is more likely to participate in FWS. College FWS Availability also fulfills condition 2
because college FWS availability should not be correlated with a student’s EFC or the number of
hours a student works in a college job. While one might argue that students of different income
levels might choose schools with different FWS availability, it is unlikely because students often
do not choose schools based on their propensity to get a FWS job. In fact, many students are
unaware of the FWS availability on campus simply because they do not have access to a school’s
job postings prior to enrollment. Additionally, it is unlikely that FWS availability is correlated
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with the number of hours a student works in a college job because FWS jobs are relatively
standardized across schools.
The second instrument, Relative FWS Job Availability is defined as: (Number of FWS
jobs)/(Number of jobs around the school). I used the FWS, Census, and BLS employment data to
construct this instrument. The FWS data provided the exact number of FWS jobs that were
available at each institution. The Census data provided the number of jobs in the county around
each institution by NAICS sector.
To exclude jobs that would be inaccessible to college
students, I used BLS data to determine the supersectors that attract the most college-age workers:
wholesale and retail trade, educational and health services, and leisure and hospitality (“Labor
Force Statistics” 2013). These supersectors contain the following sectors: wholesale trade
(NAICS 42), retail trade (NAICS 44-45), educational services (NAICS 61), health care and
social assistance (NAICS 62), arts, entertainment, and recreation (NAICS 71), and
accommodation and food services (NAICS 72). Together, these sectors employ 80% of all
college-age workers. While employment data was available for zip codes, I decided to examine
employment at the county level to account for students’ ability to commute to work.
“Relative FWS Job Availability” fulfills condition 1 because relative FWS job
availability is strongly correlated a person’s participation in the FWS Program. If there are more
FWS jobs relative to the number of comparable, entry-level jobs around the school, a student is
more likely to participate in FWS. Additionally, Relative FWS Job Availability fulfills condition
2 because relative FWS job availability should not be correlated with a student’s family income
or the number of hours a student works in a college job. It is unlikely that students of different
income levels choose schools based on the types of college jobs available around it. Relative
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FWS job availability also should not be correlated with the number of hours a student works in a
college job.
Descriptive Statistics
College Students and Employment:
In Table 1a, we can see that around 80% of students hold jobs in college, with over half
of these students holding a FWS job. Table 1b shows the number of STEM units, a proxy for
major, that students take given their employment status. This is important because students with
STEM majors often have higher rates of employment and incomes post-graduation. We can see
that working in a FWS job corresponds to taking fewer STEM units.
Table 2 compares the average hours worked/week and average income/year for students with
FWS and alternate jobs. We can see that students with only FWS jobs work fewer hours than those
with alternate jobs. However, those students with both a FWS and alternate job work about the
same number of hours as those who only work an alternate job. This result is not surprising,
given caps on the number of hours a FWS student can work. By comparing students who work
multiple jobs, we can see that those who have a FWS job do earn about $1000 less per year than
their peers.
Postsecondary Institutions, College FWS Availability, and Relative FWS Job Availability:
Tables 3 and 4 shows the distribution in my IVs, “College Federal Work-Study
Availability” and “Relative Federal Work-Study Job Availability”. The distribution statistics of
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College FWS Availability suggest that the distribution is skewed to the right, indicating that
there are many schools that have little FWS availability: the median is 13.31, while the mean is
273.53. However, we can also see that College FWS Availability still has variation amongst the
remaining schools: the standard deviation is 5,063.57 and the range is 673,974. Relative FWS
Job Availability shows a similar right-skewed distribution. While the mean is 0.00225, the
median is 0.00003. At the 25th percentile, Relative FWS Job Availability is 0, similar to the
College FWS Availability distribution. At the same time, Relative FWS Job Availability has a
standard deviation of 0.04630, which is large compared to its mean of 0.00225. Although there
are many schools with little FWS availability, there is still variation in FWS availability amongst
the remaining schools.
To analyze the distribution of College FWS Availability and Relative FWS Job
Availability in more detail, I analyzed how these two IVs vary across schools with different
admissions rates, percentage of Pell Grant eligible students, and average net price. Tables 4a-c
show the average College FWS Availability and Relative FWS Job Availability for a given
decile of admissions rates, percentage of Pell Grant eligible students, and net price. We can see
from these tables that College FWS Availability is significantly higher in the schools with the
lowest admissions rates and lowest percentage of Pell Grant eligible students, perhaps reflecting
the result of a legacy-based allocation system where richer, and subsequently more selective
schools secure more funds than their poorer counterparts. Additionally, College FWS
Availability is higher in the schools that have higher average net prices, reflecting a similar
trend. While these trends might not be optimal because schools should be equivalent in all
aspects except FWS Availability, one must note that there are still many deciles that look similar
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in terms of College FWS Availability. A more detailed analysis of the distribution of College
FWS Availability across colleges with different characteristics can be seen in Tables 5a-c.
On the other hand, Relative FWS Job Availability does not vary much across schools
with different admissions rates, percentage of Pell Grant eligible students, and average net price.
This is not surprising because there are many factors besides institution age or affluence that
determine the number of jobs available to college students around a particular college. A more
detailed analysis of the distribution of Relative FWS Job Availability across colleges with
different characteristics can be seen in Tables 6a-c.
Results
Table 7a shows the result of the six regressions used to examine the impact of a FWS job
compared to a non-FWS job, not controlling for a student’s major. Table 7b shows the result of
the same regressions used to examine the impact of a FWS job compared to a non-FWS job,
controlling for a student’s major. Standard errors are provided in parentheses. Coefficients that
are statistically significant at the 5% level are indicated with one asterisk. Coefficients that are
statistically significant at the 1% level are indicated with two asterisks. Coefficients that are
statistically significant at the 0.1% level are indicated with three asterisks.
From the results in Table 7a, we can see that working in an FWS job is correlated with a
5.54% increase in the probability of receiving a bachelor’s degree, a statistically significant
result at the 0.1% level. On the other hand, simply working is correlated with a 0.18% decrease
in the probability of receiving a bachelor’s degree. While this is a small decrease, the result is
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significant at the 1% level. These findings suggest that FWS has a positive impact on academic
outcomes while working in general has a slightly negative impact. An explanation for this
outcome is that the benefits of FWS jobs, such as helping students feel more integrated on
campus, outweigh the costs of having a job such as having less time for studying. This might
explain the observed slight decrease in the probability of receiving a bachelor’s degree for nonFWS jobs but a positive effect for FWS jobs.
Similarly, working in a FWS job has no effect on the time to graduate, but each hour
working non-FWS job is correlated with a 1.87-month increase in the time to graduate, a
significant result at the 0.1% level. Since FWS jobs are designed to compliment a student’s
education, it is possible that they have a smaller negative effect on a student’s academics than
comparable non-FWS jobs. Perhaps FWS jobs give students more flexible working hours and/or
require less commute time than non-FWS jobs, making it easier for them to work and study
concurrently. Given that majority of students choose to work in college according to the BPS,
these results suggest that a student who wants to work in college should take a FWS job if he/she
has the opportunity.
Additionally, we can see that working in a FWS job has a significant effect on postgraduation outcomes. For example, working an additional hour in a FWS job corresponds to a
12.8% increase in the probability of reporting that one’s first post-graduate job “offers freedom
for other interests,” an 8.63% increase in the probability of reporting that it “pays the bills”, and
a 10.6% increase in the probability of reporting that it “provides benefits”. These are all
indicators of a “better quality” or “more competitive” job. On the other hand, working an
additional hour in general only corresponds to a 0.98% increase in the probability of reporting
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that one’s first post-graduate job “offers freedom for other interests,” a 3.04% increase in the
probability of reporting that it “pays the bills”, and a 0.75% increase in the probability of
reporting that it “provides benefits”. These results suggest that working in college is intrinsically
beneficial, given that one can find a job post-graduation. One can expect that working during
college could help a student obtain workforce experience that he/she could leverage in the job
search. However, working in a FWS job seems to provide additional benefits over simply
working, suggesting that the skills learned in FWS jobs might translate better to future
employment.
When controlling for a student’s major, the benefit of working in a FWS job is smaller,
yet still larger than the effect of working in a non-FWS job, as can be seen in Table 7b. The
probability of reporting that one’s first post-graduate job “offers freedom for other interests”
drops to 10.8%, the probability of reporting that it “pays the bills” drops to 5.08%, and the
probability of reporting that it “provides benefits” drops to 8.65%. While the estimate on “pays
the bills” is loses its significance, the other two estimates are significant at the 5% level. The
estimates for the benefit of working in a non-FWS job do not change. These differences might
stem from the fact that students of different majors are not equally likely to be employed in a
FWS job. Using the number of STEM credits completed as a proxy for major, we can see that
working in a FWS job negatively correlates with STEM credits completed. Perhaps because
post-graduation unemployment rates are higher for non-STEM majors, those students who do
find jobs are more likely to report being satisfied with their employment outcomes than STEM
majors. If true, these facts would bias the effect of FWS on job quality outcomes upward if one
does not control for major.
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While we see positive effects of FWS and working on post-graduation job quality, we
paradoxically see negative effects for working on post-graduate unemployment. Working in a
FWS job corresponds to a 1.57% increase in the probability of unemployment 3 months after
graduation while working in general corresponds to a 0.81% increase in the probability of
unemployment. Controlling for a student’s major, we can see that students who work in FWS
jobs are 4.51% less likely to report being unemployed 3 months after graduation while students
who work in general are 0.91% more likely to report being unemployed. These results seem
counterintuitive because prior employment experience should help students find post-graduate
jobs more easily. Additionally, graduates who worked in college might also be able to continue
as full-time employees of their companies, an advantage they possess over their non-working
peers. However, it is also possible that working in college can make students appear to be weaker
job candidates upon graduation. From the results discussed earlier, we can see that working in
general corresponds to a decrease in the probability of receiving a bachelor’s degree and an
increase in the time to degree completion. The effect reversal of working in an FWS job when
controlling for major could be explained by the fact that STEM majors are less likely to be
employed in a FWS job. Since working in a non-STEM job is correlated with higher rates of
unemployment and FWS jobs employ higher percentages of non-STEM majors, not controlling
for major in a regression will underestimate the benefits of a FWS job.
Overall, these results suggest that working in a FWS job has additional benefits to simply
working in college. It was hypothesized that FWS jobs could help students in their job searches
by providing them with more opportunities to build workforce-relevant skills while in college
than non-FWS jobs. This is plausible because many FWS jobs focus transferrable office skills
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such as computer skills and communicating, while non-FWS jobs build less-transferrable skills
such as waitressing and manual labor. Perhaps students employed in FWS jobs fare better
immediately post-graduation because FWS jobs emphasize skills that are more applicable to jobs
that one would consider to be a part of a “permanent career”.
Conclusion
In conclusion, FWS jobs do indeed have different effects from non-FWS jobs. There is an
additional benefit from working more hours in a FWS job: the likelihood of graduating with a
bachelor’s degree increases, while it decreases from working in general. Additionally, having a
FWS job has no impact on time to graduation, while a non-FWS job does. These results might
stem from the FWS Program’s original intention to provide jobs that compliment a student’s
education. Alternate jobs commonly taken by college students, such as waitering, might not
compliment a student’s education as well.
This data also suggests that holding a FWS job in college impacts a student’s postgraduate outcomes. Working in a FWS job is correlated with a decline in the probability of postgraduation unemployment. Additionally, working in a FWS job is correlated with higher
likelihoods of reporting that one’s first post-graduate job “offers freedom for other interests,”
“pays the bills”, and “provides benefits”: all indicators of “better quality” jobs. While working in
general is also correlated with the same indicators, the effect of a FWS job is stronger. Even
when controlling for a student’s major, students who were employed in FWS jobs seem to fare
better.
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However, it must be noted that the students in the analyzed cohort graduated in 2009, an
extremely poor job market. The aforementioned effects might disappear in a stronger job
market. Given that the unemployment rate in June 2009 was 9.5%, this analysis should be
repeated for cohorts graduating in different economic conditions.
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Appendix A: Tables and Figures
Table 1a: Students and Work
Table 1b: FWS Participation and Number of STEM Credits (Proxy for Major)
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Table 2: Average Hours Worked/Week and Average Monthly Earnings
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Table 3: College Federal Work-Study Availability and Relative FWS Job Availability
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Tables 4a-c: College Federal Work-Study Availability by Admissions Rate, % of Pell-Grant
Eligible Matriculated Students, and Net Cost
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Table 5a-c: Pairwise Comparisons of Means for College Federal Work-Study Availability,
across Admissions Rate, % of Pell-Grant Eligible Matriculated Students, and Net Cost
27
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Tables 6a-c: Pairwise Comparisons of Means for Relative Federal Work-Study Job Availability,
across Admissions Rate, % of Pell-Grant Eligible Matriculated Students, and Net Cost
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Table 7a: Hours Worked Weekly and Outcomes, not Controlling for Major (Received BA/BS,
Months to BA/BS, “Offers freedom for other interests,” “Pays the bills”, “Provides benefits”,
Unemployed 3mo After Graduation)
BA/BS
Mo. to BA/BS
Job Quality Indicators
Unemployed
"Time for other interests" "Pays bills" "Benefits"
0.174
0.128*
0.0863*
0.106*
0.0157***
(0.42)
(2.37)
(1.99)
(2.3)
(5.54)
Hours FWS Work
0.0554***
(20.87)
Hours Work
-0.00180**
(-3.09)
1.872***
(33.7)
0.00975**
(3.24)
0.0304***
(12.57)
0.00745**
(2.91)
0.00814***
(10.93)
10657
5045
1587
1587
1587
5763
N
T-statistics are in parentheses
* p<0.05, ** p<0.01, *** p<0.001
Table 7b: Hours Worked Weekly and Outcomes, Controlling for Major (Received BA/BS, Months
to BA/BS, “Offers freedom for other interests,” “Pays the bills”, “Provides benefits”,
Unemployed 3mo After Graduation)
BA/BS
Hours FWS Work
Hours Work
N
Mo. to BA/BS
0.0582***
(21.83)
-0.00347***
(-5.81)
1.828***
(32.59)
0.00995***
(3.67)
10657
5045
1587
T-statistics are in parentheses
* p<0.05, ** p<0.01, *** p<0.001
Job Quality Indicators
Unemployed
"Time for other interests" "Pays bills" "Benefits"
0.140
0.108*
0.0508
0.0865*
-0.0451*
(0.35)
(2.08)
(1.35)
(1.97)
(-1.98)
0.0304*** 0.00757***
(15.46)
(3.31)
1587
1587
0.00913***
(11.75)
5763
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Appendix B: Controls and reasons for their inclusion
Control for ability/motivation:
•
High school GPA
•
High school GPA squared
•
ACT test score (or SAT equivalent)
•
ACT test score (or SAT equivalent) squared
•
Number of STEM credits completed
Control for demographics:
•
Indicator for female students
•
Indicator for different races
Control for socioeconomic status:
•
EFC
Control for type of school attended:
•
Type of institution
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Appendix C: Reference List
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accessed May 15, 2013.
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performance (NBER Working Paper No. 14006). Cambridge, MA: National Bureau of
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Federal Campus-Based Programs Data Book 2012. 2012. Last updated 7/2012.
http://www2.ed.gov/finaid/prof/resources/data/databook2012/databook2012.html,
accessed May 15, 2013.
Federal Student Aid Handbook. 2003. Last updated 5/2003.
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2013.
Federal Pell Grant Program End-of-Year Report. 2006. Last updated 5/2008.
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Federal Work-Study (FWS) Program. 2011. Last updated 12/2011.
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Hossler, D., Ziskin, M., Gross, J. P., Kim, S., & Cekic, O. (2009). Student aid and its role in
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Integrated Postsecondary Education Data System. 2013. Last updated 5/2013.
http://nces.ed.gov/ipeds/, accessed May 15, 2013.
Kalenkoski C. M., Pabilonia S. W.(2010). Parental transfers, student achievement, and the labor
supply of college students. Journal of Population Economics, 23, 469–496.
Labor Force Statistics from the Current Population Survey. 2013. “Employed persons in
nonagricultural industries by age, sex, race, and Hispanic or Latino ethnicity.” Last
updated 2/2013. http://www.bls.gov/cps/cpsaat14.htm, accessed May 15, 2013.
Scott-Clayton, J. (2011). The Causal Effect of Federal Work-Study Participation QuasiExperimental Evidence From West Virginia. Educational Evaluation and Policy
Analysis, 33(4), 506-527.
Smole D. P. (2005). The campus-based financial aid programs: A review and analysis of the
allocation of funds to institutions and the distribution of aid to students (Congressional
Research Service Report RL 32775). Washington, DC:Congressional Research Service.
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performance. Journal of Labor Economics 21, 473–491.
Tinto, V. (1987). Leaving college: Rethinking the causes and cures of student attrition.
University of Chicago Press, 5801 S. Ellis Avenue, Chicago, IL 60637.