Debt’s Burden after College

The Effects of Student Loan Debt on Graduates’ Employment and Graduate School Attendance

Erin Dunlop Velez1,2

Melissa Cominole1

Alexander Bentz1

March 2016

Abstract

Some two thirds of students receiving their bachelor’s degree take out loans to fund their postsecondary education and by the time their degree is completed, the average student owes $24,700. This paper measures the effects of student loan debt on students’ post-college employment and graduate school attendance. The analysis uses data from the 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12), which includes a nationally representative sample of 2007-08 bachelor’s degree recipients. Because a student’s debt burden is likely endogenous to their post-college outcomes, we employ an instrumental variables strategy to minimize selection bias. We test four different sets of instruments, each with strengths and weaknesses. While the instruments vary – exploiting both geographic location and the financial aid policies at different institutions – the results are broadly consistent across all models. We find that students who borrow large amounts for their undergraduate education are more likely to be employed, have higher salaries, and are more satisfied with the compensation and job security of their employment. These findings are suggestive evidence that students with more debt spend less time searching for jobs, and prioritize monetary compensation over other job characteristics. Additionally, we find students with large debt burdens are less likely to enroll in additional schooling after completing their bachelor’s degree.

1 RTI International2 Corresponding author: evelez@rti.org

1. IntroductionWith tuition prices rising faster than the rate of inflation, and student loan debt

nationwide topping $1.2 trillion, it is no wonder families, administrators, and policy makers are

so concerned about the effects of student loan debt. And as President Obama, the Lumina

Foundation, and other organizations call for increased college attainment nationally, it is critical

that researchers understand not only how debt affects students’ postsecondary decisions, but how

debt continues to affect students after they have exited postsecondary education.

Some two thirds of students receiving their bachelor’s degree take out loans to fund their

postsecondary education and by the time their degree is completed, the average student owes

$24,700 (Woo 2013). This paper measures the effects of student loan debt on students’ post-

college employment and graduate school attendance. The analysis uses data from the 2008/12

Baccalaureate and Beyond Longitudinal Study (B&B:08/12), which is the second follow-up

study of a nationally representative sample of 2007-08 bachelor’s degree recipients. This cohort

is particularly valuable because it can shed light on the experiences of students who graduated at

the beginning of the Great Recession.

The amount of debt a graduate accumulates is related to certain student characteristics,

such as dependency status, family income, and pre-college academic achievement (Horn and

Paslov 2014). Debt burden is also correlated to the type of institution a student attends (Horn and

Paslov 2014). Because a student’s debt burden is likely endogenous to their post-college

outcomes, we employ an instrumental variables strategy to minimize selection bias, exploring

four different sets of instrumental variables.

The first set of instruments follows Zhang (2013), using institution-level measures of

financial aid as instruments for debt burden. Following Zhang, to instrument for debt burden we

use the percentage of undergraduates that receive any financial aid and the ratio of average gift

aid to total financial aid at the institution from which the student graduated. The idea is that, all

else equal, students who attend an institution with a high percentage of students receiving

financial aid are likely to need a larger amount of loans whereas students who attend institutions

that award a higher percentage of grant aid in their aid packages are likely to borrow less. The

second set of instruments is similar to those used by Zhang, but more directly measures loan

usage, instrumenting for loan burden with the percent of students taking loans at the student’s

bachelor’s degree-granting institution. A shortcoming of these two sets of instruments, however,

is that institutions are not randomly assigned and that these instruments may be correlated with

the selectivity or quality of an institution, which could in turn be related to post-baccalaureate

outcomes. To address this concern, we, as well as Zhang (2013), control for a set of college

characteristics that capture college resources and learning environment.

Because of the endogeneity concerns of using instruments related to students’ chosen

institutions, we develop two additional sets of instruments, both of which exploit geographic

variation. The third set of instruments measures the distance between each students’ home and

the closest postsecondary institution of varying controls and levels (closest public 2-year

institution, closest public 4-year institution, closest private nonprofit 4-year institution, and

closest private nonprofit less than 4-year institution). The final set of instruments is similar, but

measures the distance between each student’s home and the closest high- and low-loan

institution. A high-loan institution is one that is in the top third of all schools in terms of the

percent of students who take out loans. A low-loan institution is in the bottom third. Using

distance to predict college attendance, degree level and quality has been extensively used in the

literature (Card 1995, Kane and Rouse 1995, Currie and Moretti 2003).

This paper makes several important contributions to the current literature. Most

importantly, we are able to estimate the effects of debt on a nationally representative cohort that

completed college in 2007-08. Much of the previous research has examined cohorts of students

completing degrees in the late 1980s and early 1990s and the few papers using more recent data

do not have nationally representative samples. Given the rate at which postsecondary education

in the United States is evolving and the changing economic environment into which students are

graduating, using a recent cohort of college graduates is paramount.

Another strength of this work is the use of an instrumental variables estimation strategy

to address the endogeneity of debt burden. In addition to the observable characteristics related to

both debt burden and post-college outcomes, such as family income and academic ability, it is

likely that debt burden is also influenced by unobservable factors such as whether individuals

view student loan debt as a rational investment. As such, previous papers that have just

controlled for observable student characteristics have likely produced biased estimates of the

effect of debt on students’ post-college outcomes. By testing four sets of instrumental variables,

our paper provides a comprehensive and robust analysis of the effects of undergraduate loan debt

on a recent cohort of students’ post-college employment and graduate school attendance.

Understanding the relationship between debt burden and post-bachelor’s degree outcomes will

help inform policy makers and administrators as they design and amend financial aid policy, and

help students and their families as they decide how much debt they are willing to take.

2. Literature Review

There is an extensive literature examining the effects of student loan debt on students’

post-college outcomes. Descriptive evidence suggests that students with high debt burdens work

more, are less satisfied with their employment, are more likely to be working outside their field,

and may delay home buying and family formation (Huelsman 2015, Kantrowitz 2014).

A number of more rigorous studies have also documented the extent to which student

loan debt shapes individuals decision-making after degree completion. Researchers have found

negative effects of student loan debt on overall financial health after graduation. Elliott and Nam

(2013) used data from the 2007-09 Survey of Consumer Finances and found that post-college net

worth is significantly less for those with considerable student loan debt. Similarly, Thompson

and Bricker (2014) used the same data and found that families with student loans have higher

levels of financial distress (e.g. 60 days late on paying bills, inability to obtain credit).

Minicozzi (2003), Rothstein and Rouse (2011), and Field (2009) found that student loan

debt affects employment and occupation choice. Minicozzi used data from a nationally

representative sample of male postsecondary students in 1987 to measure the effects of debt on

employment four years later. She found that higher education debt was associated with higher

initial wages but lower wage growth. Rothstein and Rouse used data from a “highly selective”

university that introduced a no-loan policy in the early 2000s. The authors found that graduates

with debt tended to choose substantially higher paying jobs and were less likely to take low-

paying public interest jobs. It has also been shown that graduating without debt was associated

with a greater likelihood of taking a public-service occupation. Field found that students

randomly assigned to a “no debt” financial aid package at NYU law school were more likely to

take a job in public interest law.

Researchers have also examined the effects of undergraduate debt on graduate school

attendance. Millett (2003) used data from the Baccalaureate and Beyond Longitudinal Study of

1992-93 (B&B:93/94), which includes a nationally representative sample of 1993 bachelor’s

degree recipients. She found that among students who expected to earn a doctoral degree, those

with debt were less likely to apply to graduate school within the first year of completing their

undergraduate education. Zhang (2013) also used B&B:93 data, but used the 4 year follow-up to

the same sample (B&B:93/97). Using an instrumental variables strategy, she found that among

public college graduates, debt had a negative effect on graduate school attendance, but there was

no effect for students who earned a bachelor’s degree from a private school. Contrary to other

researchers however, Zhang found no effect on other early career choices, such as salary,

occupation, marital status and homeownership. Using data on students who graduated college in

1998, Monks (2001) also found that debt had no effect on the likelihood of pursuing a graduate

degree for students who attended private colleges.

Malcom and Dowd (2011) analyzed the 2003 National Survey of Recent College

Graduates using a propensity score matching estimation technique to explore the effect of debt

on graduate school attendance for STEM bachelor’s degree holders. The authors found that

borrowing had a negative effect on graduate attendance for these students, but the effect varied

by race. Kim and Eyermann (2006) analyzed longitudinal data from the College Senior Survey

(CSS), conducted by the Higher Education Research Institute (HERI). The CSS surveys students

at participating institutions in their senior year. They compared student plans for graduate

enrollment before and after the higher Education Amendments of 1992, which increased

borrowing limits and expanded eligibility for loan aid. The authors found no change in graduate

school plans for students of low- and high-income families as a result of the changes to student

loan policies, but contrary to other findings, Kim and Eyermann found a positive effect of debt

on graduate school attendance for students from middle-income families.

Finally, researchers have found negative effects of undergraduate debt on the timing of

marriage. Bozick and Estacion (2014) used a nationally representative sample of college

graduates in 1993 (B&B:93/97), and found student loan debt delayed marriage for women, but

not men. Gicheva (2011) used Survey of Consumer Finance data from 1995-2007 and found debt

negatively affected the decision to marry, but the strength of the relationship diminished with

age.

While there is an extensive literature on the effects of student loan debt on students’ post-

college outcomes, nearly all of the previous research uses data from students who graduated

college in the 1990s or early 2000s. Given how fast the financial aid landscape in the United

States is changing, it is important to update these studies with a more recent cohort of college

graduates. Additionally, some of these studies draw conflicting conclusions, and many rely on

controlling for observables to address the endogeneity of debt burden. Our paper expands this

current literature by using a much more recent cohort of college graduates, those who completed

a bachelor’s degree in 2007-08. Additionally, because the instrumental variables strategy

simulates random assignment to estimate the effect of a students’ loan burden on post-college

experiences, there is less need to rely on observable data to serve as controls.

3. Empirical Strategy

3.1 Overview of Instrumental Variables Method

The purpose of this analysis is to explore the relationship between the amount of

education-related debt a baccalaureate recipient accumulates and various post-graduation

outcomes. The amount of debt a student has upon graduating from college can influence an array

of decisions and experiences. A student with a large amount of debt may decide to take a job

rather than to enroll in graduate school in order to avoid additional debt and to pay off existing

debt. Large debt amounts may also lead students to work more than desired, affect job

satisfaction, and influence the choice of occupation.

The relationship between undergraduate debt and post-college outcomes can be expressed

as:

Post bachelo r ' soutcome=β0+Debt amounti β1+X i β2+μ i (1)

where post_bachelor’s_outcome represents the dependent variable of interest (employment

status, earnings, job satisfaction, or post-bachelor’s enrollment), Debt_amount represents the

amount of undergraduate debt incurred for student i, and X captures student characteristics that

are related to post-bachelor’s outcomes. These characteristics include gender, race/ethnicity,

dependency status, age at degree completion, parent education level, family income, college

admission test scores, attendance intensity, undergraduate field of study, institution sector, and

institution selectivity.

However, the amount of debt borrowed is likely related to multiple student

characteristics, including family socioeconomic status, academic ability, and choice of

institution, none of which are random. For example, a wealthier student is likely to have less

financial need and thus take on less debt, while a lower ability student is less likely to receive

merit-based financial aid and may take on more debt. Additionally, a student who attends a more

expensive school is likely to take on more debt, and there are likely systematic and unobservable

differences driving institution choice which could also be related to post-college outcomes. To

the extent that these systematic differences are not observable, they will be in the error term, μ,

or “unmeasured factors” term (Murray, 2006; Porter, 2012). The correlation between these

unmeasured factors and institution choice constitutes a violation of a key regression assumption

(endogeneity) and will result in biased estimates of the effect of debt on outcomes.

To correct for endogeneity, we use a two-stage least squares (2SLS) instrumental

variables approach. This approach involves the identification of variables that are predictive of

the undergraduate debt amount but are otherwise uncorrelated with post-bachelor’s outcomes

and also uncorrelated with any unobserved factors affecting the outcomes (Dunning, 2012). To

the extent that our instruments are predictive of the debt amount, but otherwise unrelated to the

outcomes, the instrument can serve as an alternative to random assignment for non-experimental

studies.

We explore four sets of instrumental variables, two that follow previous work by Zhang

(2013) and two that are original instruments. The two instruments that follow Zhang use

institution-level financial aid characteristics at the degree-granting institution to instrument for

undergraduate debt. The two original instruments exploit geographic variation in how far

students live from various types of institutions.

If the assumptions for the instrumental variables approach (described below) are met,

debt amount can be modeled with the instruments in a first-stage model. Then the predicted

values for the amount of debt from the first-stage can be used in the second-stage model to

produce an unbiased estimate of the effect of debt on the post-bachelor’s outcomes. In the first

stage, we will estimate the endogenous predictor variable with four sets of instruments using the

general equation:

Debtamount i = Instrumental variables i α1 + X i α 2 + μi (2)

where Debtamount i is the amount of undergraduate debt accrued and the Instrumental variables

will be one of the following four sets of variables:

1. Following Zhang (2013), institution-level financial aid characteristics

a. Institution-level percentage of students receiving any financial aid

b. Institution-level ratio of grant to total aid

2. Similar to Zhang (2013), institution-level loan usage

a. Institution-level percentage of students receiving federal loan aid

3. Distance (in miles) to nearest institution in the following sectors:

a. Public 4-year institutions

b. Public 2-year institutions

c. Private nonprofit 4-year institutions

d. Private nonprofit less than 4-year institutions

4. Distance (in miles) to nearest institution with the following characteristics:

a. Institution in the lowest third of all institutions in terms of the percentage

of students receiving federal loan aid

b. Institution in the highest third of all institutions in terms of the percentage

of students receiving federal loan aid

In the second stage, the predicted debt value will be used in place of the actual debt

amount as shown in equation (3):

Post bachelo r ' soutcomei = D̂ebtamounti β1 + X i β2 + ε i (3)

where Post bachelo r ' soutcomei is the outcome of interest for student i, D̂ebtamounti is the value predicted

by one of the sets of instruments, X i represents student-level covariates, and ε i captures

unmeasured factors for student i.

The instrumental variables estimation strategy requires that certain assumptions be met in

order to produce unbiased estimates of the causal effect of the treatment on the outcomes

(Angrist, Imbens, & Rubin, 1996). Each of these must be carefully considered in light of the

relationship between the instruments and debt amount:

1. Stable unit treatment value assumption (SUTVA): Is there any reason to think that the

instruments for one student will affect the debt amount of another student?

2. Random (ignorable) assignment: Is it plausible that the instruments are randomly

distributed? If not, can they be considered “ignorably random, conditional on covariates”

with the addition of variables to control for any factors that might affect the relationship

between the instruments and debt amount?

3. Exclusion restriction: The instruments can only be related to the outcome through the

treatment. Invalid instruments are those that are correlated with any unobserved factors

that might also affect the outcome. Is there any connection between the instruments and

outcomes?

4. Nonzero average causal effect of instrument on treatment: Are the instruments highly

correlated with the treatment? Weak instruments (those not highly correlated with the

treatment) can lead to even more biased estimates than an OLS model that does not meet

regression assumptions (Khandker, 2010). Diagnostics can help assess this; the F-statistic

from first-stage should be >10, (Murray, 2006; Stock & Yogo, 2001).

5. Monotonicity: The treatment effect can only be generalized to compliers, that is, the

group for whom the instruments predict treatment assignment (Angrist, et al., 1996;

Porter, 2012).

3.2 Instruments that follow Zhang (2013)

For the first two sets of instruments, we follow Zhang (2013) and use measures of

institution-level financial aid policy. The first set of instruments follows Zhang closely and

includes the percent of students at the degree-granting institution receiving financial aid and the

ratio of average grant aid to total aid.3 The idea is that, all else equal, students who attend an

institution with a higher percentage of students receiving financial aid are likely to take on a

larger amount of debt whereas students who attend institutions that award a higher proportion of

grant aid in their aid packages will tend to borrow less. The second set of instruments is similar

to Zhang’s analysis, but provides a more direct measure of loan usage at the degree granting

institution: the percent of students who borrowed federal loans.

While these instruments pertain to an institution’s student body in general, and are not

directly related to the specific circumstances of individual students, a shortcoming of these

instruments is that they are based on the institution the student chose to attend. While the

financial aid policy of an institution affects all students who enroll there, the fact that the student

chose the institution limits our ability to correct for selection bias inherent in the college choice.

If there are other institution-level characteristics associated with the aid policies that are related

to the student’s college choice, such as peer and faculty quality, as noted by Zhang (2013), then

our instruments are not fully exogenous. To address this concern, following Zhang (2013), we

control for a set of institution-level characteristics that capture factors related to the college

selection process: institution sector and selectivity.

3.3 Instruments Based on Distance

The third and fourth sets of instruments measure the distance between each student’s

home and the closest postsecondary institution of varying types. Using distance to predict college

attendance, institution level, and quality has been extensively used in the literature (Card, 1993;

Currie & Moretti, 2003; Kane & Rouse, 1993). For the first of the two sets of distance

3 Zhang (2013) uses the ratio of average grant aid to total need-based aid, but there is no measure of need-based aid in IPEDS, which is the data source for our institution-level financial aid measures.

instruments, distance was calculated between the nearest institution of various sectors (public 4-

year, public 2-year, private nonprofit 4-year, and private nonprofit less than 4-year) and the zip

code of the student’s permanent residence in 2007-08, the year in which students completed

requirements for their bachelor’s degrees.4

The second of the distance instruments combines elements of the previous instruments,

incorporating both distance and an institutions’ loan policy. This set of instruments explores the

distance to the nearest institution, categorizing institutions based on financial aid characteristics

rather than sector. Specifically, we measure the distance to the nearest high- and low-loan

institution. A high-loan institution is an institution in the highest third of all institutions in terms

of the percent of students receiving federal loan aid. A low-loan institution is in the lowest third.

We hypothesize that the closer students are to an institution that has a low percentage of students

receiving loan aid, the lower their debt accumulation will be, and the closer students are to an

institution with a high percentage of students receiving loan aid, the higher their debt

accumulation will be.

These distance instruments exogenously model the choices available to a student in their

college selection decision and this choice ultimately affects the amount of debt students are

likely to accumulate. It is reasonable to assume that, conditional on a rich set of student

observables, proximity to various types of colleges does not have a direct effect on students’

post-college employment. Historical happenstance has placed colleges throughout the country

and when families choose where to locate, there are many major factors to consider, such as

4 The 2007-08 permanent address is not technically the place of residence at the time of deciding where to attend college and requires the assumption that permanent residence in 2007-08 is the same as the pre-college residence for most students. While it is likely that this is not always the same as the pre-college residence, analysis of ELS:2002/12 Indicates that 73% of students reported the same permanent address (as opposed to a local address which was also collected) in high school and again when in college. Among those with a different permanent zip code in college, the 25th percentile of distance between the two zip codes was 7.7 miles and the median difference in distance was 52.6 miles.

employment opportunities, housing prices, and proximity to family. Proximity to postsecondary

institutions is likely a very minimal factor, if considered at all.

However, using distance to various institution types to instrument for the amount of

education-related debt students accumulate does have limitations. First, it is possible that

students and families may choose to live in certain areas because of their proximity to

institutions and other related factors such as employment opportunities and culture (Currie &

Moretti, 2003). If these location choices are also related to the amount of debt a student

accumulates, then these instruments may still be somewhat endogenous and may not fully correct

for the selection bias involved. Additionally, there are likely several unmeasured factors that

mediate the relationship between the measure of college proximity used in this analysis (straight-

line distance) and student debt burden, such as population density and transportation

infrastructure.

3.4 Summary of Instrumental Variables Strategy

Overall, there are several reasons to believe undergraduate debt burden is endogenously

related to students’ post-college outcomes. As such, previous papers that have just controlled for

observable student characteristics likely produced biased estimates of the effect of debt on

students’ post-college outcomes. By using four sets of instrumental variables and a rich set of

covariates, our paper provides a comprehensive analysis of the effects of undergraduate loan debt

on students’ post-college employment and enrollment.

It is important to consider, however, that with any instrumental variables analysis, there

are several cautions when interpreting the results. First, the quality of the instruments chosen –

both in terms of the strength of their relationship to the endogenous variable and their exogeneity

to the outcome – are both critical in understanding the reliability of the results. Second, it is

important to remember that the instruments are not equally predictive for all students, and the

final analysis results will speak to the effects for those students whom the instruments are most

predictive.

4. Data

We use two primary data sources in our analysis. Our analytical sample of bachelor’s

degree recipients is from the 2008/12 Baccalaureate and Beyond Longitudinal Study

(B&B:08/12), which is administered by the National Center for Education Statistics (NCES). It

is a nationally representative longitudinal sample of approximately 17,000 students who

completed the requirements for a bachelor's degree during the 2007–08 academic year. B&B

study members were interviewed three times: near the end of their senior year in college (2007–

08), approximately 1 year later in 2009–10, and approximately 4 years later in 2012–13. As part

of the study, information on respondents was also collected from postsecondary institutional

records (including enrollment, financial aid, and transcript data) and the National Student Loan

Data System (NSLDS).

The base-year interview asked questions about enrollment and financial aid during the

students’ undergraduate years. These data provide many of the control variables used in the

analysis. The follow-up interviews asked questions about students’ post-graduate enrollment,

employment, debt repayment, and post-bachelor’s experiences. We limit our analytical sample to

the 94% of students in B&B:08/12 who were first-time bachelor’s degree recipients.

Approximately 16,000 graduates met this criteria.

Our analysis also incorporates institutional characteristics from the Integrated

Postsecondary Education Data System (IPEDS), which is also administered by NCES. IPEDS

collects data annually from all postsecondary institutions participating in Title IV of the federal

student loan program. We used the IPEDS 2007-08 data for our analysis – the year in which

students in our analytical sample graduated from college. The IPEDS data were used to create

the instrumental variables, which are described in more detail later in this section.

This analysis focuses on the effect of debt on students’ post-college employment and

enrollment outcomes. The employment outcomes we examine are employment status in 2012,

employment intensity (full- or part-time), earnings from all jobs in 2012, and occupation sector

(public service or non-public service).5 We also investigate whether graduates reported that debt

had influenced their decisions to work more than desired and whether they were satisfied with

compensation and job security in their primary post-college job.6 Lastly, we examined any

additional postsecondary enrollment.

Table 1 shows descriptive statistics for the analytical sample. About a third of the sample

reported working more than desired due to the cost of their undergraduate education. Some 80

percent of the sample was employed in 2012 (about 4 years after they completed their bachelor’s

degree), and 88 percent of those who worked were employed full-time. The 2012 average

earnings for the entire sample were about $39,000 and the average earnings among those

employed were about $48,000. When asked about their primary job, about half the sample

reported being satisfied with compensation, about two-thirds were satisfied with their job

security, and about one-third of the sample were employed in a public service occupation. In the

5 Public service occupations include those that are eligible for loan forgiveness under the federal Public Service Loan Forgiveness Program: healthcare occupations (including nurses), military service members, protective service members, PK-12 and postsecondary educators, and social service professionals. For more information on the program, visit https://studentaid.ed.gov/sa/repay-loans/forgiveness-cancellation/public-service.6 The primary job is defined as the respondent’s current or most recent job that had lasted more than 3 months as of the 2012 interview.

four years between their 2008 bachelor’s degree and 2012, about 43 percent of the sample had

enrolled in additional postsecondary education (at the undergraduate or graduate level).

The treatment variable of interest is the cumulative amount of debt borrowed for the

2007-08 bachelor’s degree. Student borrowing includes all loans (public and private) taken for

undergraduate education through 2007-08, with the exception of Parent PLUS loans. We test

both a continuous and quartile specification of debt in our analyses. About one-third of the

sample did not take loans and the average amount borrowed among all students was $16,400.

Among borrowers, the bottom third borrowed less than $16,000, the middle third borrowed

between $16,000 and $28,000, and the top third borrowed between $28,000 and $150,000 in

loans.

There are four different sets of instruments we test for undergraduate loan debt. The first

and second sets of instruments follow Zhang (2013) and capture the financial aid system at the

student’s degree-granting institution.7 On average, the B&B:08 cohort attended institutions

where about 80 percent of students received financial aid, just under half of students took federal

loans, and where grant aid comprised about 57 percent of the total aid package.

The third and fourth sets of instruments assume that the amount of debt is related to the

type of institution attended, and the type of institution attended is related to the types of

institutions near the student’s home. The third set of instruments reflects the number of miles to

the nearest institution in the following sectors: public 4-year, public 2-year, private nonprofit 4-

year, and private nonprofit less than 4-year. To calculate the distance, we measured the straight-

line distance between the centroid latitude and longitude of the permanent residence zip code and

7 A difference between our specification and Zhang (2013) is that Zhang used institution-level data from Peterson’s College Money Handbook and we use data from IPEDS.

the coordinates of all institutions included in the IPEDS 2007-08 dataset. On average, students in

the B&B:08 cohort lived within 11-14 miles of the closest public 4-year, public 2-year and

private non-profit 4-year institutions and about 75 miles from the closest private nonprofit less

than 4-year institution.

The fourth set of instruments explores the distance to nearest institution, categorizing

institutions based on financial aid characteristics rather than sectors. Specifically, this instrument

captures the distance to the closest high- or low-loan institution. Low-loan institutions are those

in the bottom third in terms of the percentage of students taking out federal loans, whereas high-

loan institutions are in the top third. The average distance to the nearest low-loan institution was

about 10 miles and the average distance to the nearest high-loan institution was about 12 miles.

About 30 percent of the B&B:08 cohort attended an institution in the lowest third of all

institutions in terms of the percentage of students who took federal loans. These are institutions

where less than 36 percent of the student body took out federal loans. Some 58 percent of 2007-

08 graduates attended an institution in the middle third, that is, institutions where between 37 and

71 percent of the student body took out federal loans; and 14 percent of graduates attended an

institution in the top third, which includes institutions where 72 percent or more of the students

borrowed federal loans.

In addition to the amount borrowed, all regression specifications include a rich set of

observable characteristics as controls, including student demographics, pre-college academic

achievement, postsecondary education experiences, and institutional characteristics. The

demographic controls include gender, race, dependency status for financial aid purposes in 2007-

08, age at bachelor’s degree completion, parent’s highest education level (don’t know, high

school or less, some college, and bachelor’s degree or above), and percentile rank of family

income. For dependent students, the percentile rank variable ranks parent’s 2006 income relative

to other dependent students, and for independent students, the variable ranks their own 2006

personal income relative to other independent students. Academic preparation is controlled for

by including college admissions scores (either ACT or SAT scores).8 The postsecondary

academic experience measures included are attendance intensity during the student’s

undergraduate years (exclusively full time, exclusively part time, and mixed full and part time)

and bachelor’s degree major. The institutional characteristics included are sector (public, private

non-profit, for-profit, and other or attended more than one institution), and selectivity (very

selective, moderately selective, and minimally selective or open admissions).

5. Results

5.1 First-Stage Results

As described in Section 3 above, we tested four different instruments, each with different

strengths and weaknesses. Table 2 shows the first stage regressions for each set of instruments.

For each specification, we estimate debt first as a continuous value and then we estimate the

amount borrowed in quartiles.

The first set of instruments, shown in models 1 and 5 (debt as a continuous measure and

then in quartiles, respectively), follow Zhang (2013), and describe the percent of students at the

institution that the respondent attended that take financial aid, and the ratio of average grant aid

to total aid.9 The second instrument, in models 2 and 6, follows the logic of Zhang’s instruments,

but instead more directly measures the percent of students at the respondent’s institution who 8 Scores are available for those who took the SAT or ACT and were under the age of 30 in 2007-08. Those who did not have SAT or ACT scores were included in the analysis by setting their test score to zero and including a binary variable to identify students with missing scores.9 Zhang (2013) used the ratio of grant aid to total need-based aid, but a measure of total need-based aid was not available in IPEDS.

take out loans. The coefficients of both the first and second sets of instruments are statistically

significant and in the expected direction. Students who attend schools with a larger percentage of

students taking financial aid borrow more, while students who attend schools with a higher grant

to total aid ratio borrow less. Additionally, students who attend schools with a larger percentage

of students taking loans also borrow more. For these two sets of instruments, the F statistics of

the regressions are large (28-42), as are the F statistics on the instrumental variables (33-235).

The third set of instruments, shown in models 3 and 7, is the distance to the closest public

4-year, public 2-year, private nonprofit 4-year, and private nonprofit less than 4-year institution.

Because of the likely non-linear effects of distance, the square of each distance measure is

included as well. While these coefficients have the expected sign – being close to a public

institution is associated with less debt while being close to a private institution is associated with

more debt – only a few of the coefficients are statistically significant. And while the F statistics

for the regressions are large (24 and 28), the F statistics for the instrumental variables are much

lower than that of the first two sets of instrument (2.6 and 5.1). These results indicate that

distance to institutions based on sector is likely not strong enough to support credible estimation.

The final instrument we tested was the distance from the student’s permanent zip code to

the closest low-loan and high-loan institution. Again, squared distance terms for each institution

type were included as well. Models 4 and 8 show that these distance measures have a statistically

significant effect on debt. Being close to a low-loan institution is associated with lower debt

amounts, while being close to a high-loan institution is associated with higher debt amounts.

These regressions have a high F statistic (26 and 31), but a moderate F statistic on the instrument

(5 and 6.5).

The various instrumental variables differ both in strength and validity. The first two sets

of instruments – those based on institution-level financial aid characteristics – are much stronger,

but it is less plausible that these instruments are exogenous, since students select their institution.

The second two sets of instruments – those based on distance to institutions by sector and by

low-loan or high-loan status – are very to moderately weak; however the case for exogeneity is

more plausible. Because the final instrument set – distance to nearest low-loan and high-loan

institution – is moderately strong, and arguably more exogenous to students’ post-college

outcomes, this is our preferred specification. However, all models were tested on each instrument

set to assess the level of consistency across the various specifications.

5.2 Second Stage Results – Main Specification

Tables 3a and 3b show the estimated effect of the amount borrowed on student’s post-

college employment and enrollment outcomes. In the second stage models, the amount

borrowed is the fitted value from the first stage equation that used distance to the closest high-

and low-loan institution as an instrument. In both the continuous and quartile specifications, the

amount borrowed has a statistically significant effect on students’ post-college outcomes. Table

3a shows that for each additional $1,000 borrowed, graduates are about 13 percent more likely to

report working more than desired due to the cost of their education. For each additional quartile

of debt, graduates are about 3.7 times more likely to report working more than desired as a result

of education cost. Not only do students report working more than desired, but they also were

more likely to be employed and have higher earnings. Individuals were about 13 percent more

likely to be employed in 2012 for each $1,000 in debt, and about 5.5 times more likely for each

additional quartile of debt. There is almost a 1:1 ratio in terms of debt burden and additional

salary. For each additional $1,000 in debt, individuals earned about $941 more, and they earned

about $11,400 more for each quartile of debt. Among those employed, there was no statistically

significant relationship between the probability of working full-time and debt burden.

Table 3b shows a similar pattern. Not only were individuals with more debt working and

earning more, but they were more likely to report being satisfied with the compensation at their

job, and also with its job security. For each additional $1,000 in debt, graduates report being

about 6 percent more satisfied with both their compensation and job security, and with each

quartile of debt graduates report being about 3 times more satisfied. This is suggestive evidence

that when individuals with high debt are choosing jobs, they prioritize compensation and job

security over other factors, such as enjoyment of the work, intellectual challenge, location, or

benefits.

There was no statistically significant effect of amount borrowed on whether an individual

entered a public service occupation, although both point estimates – especially the estimate on

quartiles of debt – indicate a negative effect.10 This suggests that individuals who borrowed large

sums for their undergraduate education may be less likely to take a public service job, which are

often low paying. Finally, students who have larger amounts of undergraduate debt are less likely

to enroll in additional schooling. For each $1,000 in debt, individuals are about 5 percent less

likely to have additional postsecondary enrollment (either graduate or additional undergraduate

enrollment); and for each quartile of debt, they are about 57 percent less likely to have post-

bachelor’s enrollment.

5.3 Second Stage Results – Additional Specifications

10 The p values for these specifications were 0.121 for the continuous specification and 0.171 for the quartile specification.

Overall, these findings are broadly consistent across the other instrumental variables, as

shown in Appendix tables A1a-A1d. Across all the instruments, individuals with higher debt are

more likely to report working more than desired due to their education’s cost. They are also more

likely to be employed in 2012. The results in regards to job satisfaction are also consistent, with

the additional instruments suggesting that those with more debt are more satisfied with their

compensation and job security. The results on post-BA enrollment in the additional

specifications are also mostly consistent, and generally indicate a negative relationship between

debt and additional postsecondary enrollment, although the results are not statistically

significant. The one outcome with somewhat incongruent results between the main specification

and the additional models is total earnings in 2012. Whereas in the main specification, the effect

of debt on earnings was positive and significant, the results are mixed in the additional

specifications, with some negative and positive effects.

5.4 Second Stage Results – Sample Limited to those Not Enrolled

Because being enrolled in 2012 could affect employment outcomes, we also estimated

the main specifications on just a sample of students who were not enrolled full-time in 2012. The

results are shown in Appendix tables A2a and A2b. The results are very similar in magnitude and

significance, indicating that conditioning on no post-bachelor’s enrollment in 2012 did not alter

the effect of debt on employment outcomes.

6. Discussion and Conclusions

Understanding the multitude of the effects that debt burden has on students after

completion of their baccalaureate degree will help inform policy makers and administrators as

they design and amend financial aid policy, as well as students as they decide how much debt

they are willing to take. Previous studies have sought to identify the effects of undergraduate

debt on post-graduate employment and graduate school attendance. However, estimating the

effect of debt is empirically challenging because the amount of debt taken is likely endogenous

due to unobservable factors affecting both the debt amount and post-graduate outcomes.

We employ four sets of instrumental variables that contribute to the empirical literature.

The first two sets of instrumental variables identify a set of institution-level financial aid

characteristics that strongly predict student loan debt but are likely still endogenous, given that

students select which college to attend. The next two sets of instruments exploit variation in the

location of student’ pre-college residences relative to various postsecondary institution options.

These distance-based instruments are weaker predictors of student loan debt, however, they

better control for the endogeneity present in the college selection decision. Our preferred

specification uses distance to the closest high- and low-loan institution, which is only moderately

strong, but is likely the most exogenous instrument. Given that all our instruments have some

weaknesses (either in exogeneity to the outcomes or in the strength of their relationship to the

endogenous variable), we estimate all models on every set of instruments, and look for consistent

findings across models.

We find that for each additional $1,000 borrowed, graduates are approximately 13

percent more likely to report working more than desired due to the cost of their education. In

addition to reporting working more than desired, graduates with more debt are more likely to be

employed, have higher earnings (consistent with Minicozzi 2004), report being more satisfied

with the compensation and job security at their jobs, and are less likely to enroll in further

postsecondary education (consistent with Zhang 2013). These result were qualitatively consistent

across different specifications and the other instruments tested. These results provide suggestive

evidence that students with higher debt choose jobs with more hours and higher pay in the short-

run, likely sacrificing other job characteristics, such as fit, location, interest in the work, benefits,

and potential for salary growth.

While our paper measured results that were fairly stable across models, the results should

still be interpreted with some caution. First, none of our instruments are ideal. Two sets of

instruments are strong predictors of debt, but might not be completely exogenous. Two other sets

of instruments may be more exogenous, but are not nearly as strong. The fact that fairly

consistent relationships have been measured across instruments with different shortcomings is

somewhat reassuring, but the lack of an ideal instrument indicates that these results should be

interpreted carefully. While the limitations of this analysis are acknowledged, this study builds

upon previous research by offering a new measure for predicting debt burden, and contributes to

the collective understanding of the relationship between debt and student’s post-college

outcomes. There is little other research examining this issue that uses a robust estimation strategy

on a nationally representative and recent sample of graduates. Much of the previous research on

the topic is either descriptive and therefore cannot justify claims of causal relationships, or is

reliant on controlling for observable characteristics. The more rigorous studies that better address

the assumptions required for establishing causality are generally not based on current data or are

based on non-representative samples.

In the first four years after graduating from college, graduates make decisions about

employment and additional education that are based on many factors including their job

prospects, personal interests, family considerations, and financial situation (including debt).

While student loan debt is only one among many factors in post-graduate outcomes, it is of

particular to interest to policy makers, institutions, students, and families. Our research has

shown that, because of student loan debt, students may make employment decisions that

prioritize short-term benefits (such as taking higher paying jobs, working more than desired) and

may delay or decide against further enrollment. These decisions have the potential to negatively

affect related outcomes such as lifetime earnings and job satisfaction, as well as other outcomes

not examined here (such as marriage and family formation, buying a home, saving for

retirement). Many of the decisions made in the early years after completing college can have

significant and lasting implications, and it will be increasingly important to understand the role

that the amount of student loan debt plays in those decisions.

References

Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables: Rejoinder. Journal of the American Statistical Association, 91(434), 468-472.

Bozick, R. and Estacion, A. (2014). Do student loans delay marriage? Debt repayment and family formation in young adulthood. Demographic Research, 30(69), pp. 1865-1891.

Card, D. (1993). Using Geographic Variation in College Proximity to Estimate the Return to Schooling. National Bureau of Economic Research Working Paper Series, No. 4483. Retrieved from http://www.nber.org/papers/w4483

Currie, J., & Moretti, E. (2003). Mother's Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings. The Quarterly Journal of Economics, 118(4), 1495-1532. Retrieved from http://www.jstor.org/stable/25053945

Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge: Cambridge University Press.

Elliott, W. and Nam, I (2013). Is student debt jeopardizing the short-term financial health of U.S. households? Federal Reserve Bank of St. Louis Review, 95(5), pp.405-24.

Field, E. (2009). Educational debt burden and career choice: Evidence from a financial aid experiment at NYU law school. American Economic Journal: Applied Economics, 1(1), pp 1-21.

Gicheva, D. (2011). Does the student-loan burden weigh into the decision to start a family? Working paper.

Haulsman, M. (2015). The debt divide – The racial and class bias behind the “new normal” of student borrowing. Report by Demos. Downloaded on 1/28/16 from: http://www.demos.org/publication/debt-divide-racial-and-class-bias-behind-new-normal-student-borrowing

Horn, L. and Paslov, J. (2014). Trends in Student Financing of Undergraduate Education: Selected Years, 1995-96 to 2011-12 (NCES 2014-013REV). National Center for Education Statistics, U.S. Department of Education, Washington, DC.

Kane, T., & Rouse, C. (1993). Labor market returns to two- and four-year colleges: Is a credit a credit and do degrees matter? Retrieved from https://ideas.repec.org/p/pri/indrel/dsp0102870v868.html

Kantrowitz, M. (2015). Who graduates with excessive student loan debt? Report by Student Aid Policy. Downloaded on 1/28/16 from http://www.studentaidpolicy.com/excessive-debt/

Khandker, S. R. K., G.B.; Samad, H.A. (2010). Handbook on Impact Evaluation: Quantitative methods and practices. Washington, D.C.: The World Bank.

Kim, D. and Eyermann, T. (2006). Undergraduate borrowing and its effects of plans to attend graduate school prior to and after the 1992 Higher Education Act Amendments. Journal of Student Financial Aid, 36(2).

Malcom, L. and Dowd, A. (2012). The impact of undergraduate debt on the graduate school enrollment of STEM baccalaureates. The Review of Higher Education, 35(2), pp. 265-305.

Millet, C. (2003). How undergraduate loan debt affects application and enrollment in graduate or first professional school. The Journal of Higher Education, 74(4), pp. 386-427.

Minicozzi, A. (2004). The short term effect of educational debt of job decisions. Economics of Education Review, 24, pp. 417-430.

Monks, J. (2001). Loan burdens and educational outcomes. Economics of Education Review, 20, pp. 545-550.

Murray, M. P. (2006). Avoiding Invalid Instruments and Coping with Weak Instruments. The Journal of Economic Perspectives, 20(4), 111-132. Retrieved from http://www.jstor.org/stable/30033686

Porter, S. R. (2012). Using instrumental variables properly to account for selection effects. Unpublished manuscript. Retrieved November 15, 2015 from http://www.stephenporter.org/papers/Pike_IV.pdf.

Rothstein, J. and Rouse, C. E. (2011). Constrained after college: Student loans and early-career occupational choices. Journal of Public Economics, 95, pp. 149-163.

Stock, J. H., & Yogo, M. (2001). Testing for Weak Instruments in Linear IV Regression. Unpublished manuscript, Harvard University. Retrieved November 15, 2015 from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1734933.

Thompson, J. and Bricker, J. (2014). Does education loan debt influence household financial distress? An assessment using the 2007-09 SCF panel. Finance and Economics Discussion Series, Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, D.C.

Woo, J. (2013). Degrees of Debt: Student Loan Repayment of Bachelor’s Degree Recipients 1 Year After Graduating: 1994, 2001, and 2009 (NCES 2014-011). National Center for Education Statistics, U.S. Department of Education, Washington, DC.

Zhang, L. (2013). Effects of college educational debt of graduate school attendance and early career and lifestyle choices. Education Economics, 21(2), 154-175.

Tables

Table 1. Descriptive Statistics

  Variable Description Mean Std. Error

Outcomes Employment

Worked more than desired due to cost of education 0.34 0.01Employed in 2012 0.80 0.01Among those employed, worked full-time in 2012 0.88 0.00Annualized total salary for all jobs in 2012 38,658 495.20

Among those employed, annualized total salary for all jobs in 201248,376 532.10

Satisfied with compensation at primary job in 2012 0.55 0.01Satisfied with job security at primary job in 2012 0.62 0.01Primary job in 2012 was in a public-service occupation 0.32 0.01

Post-bachelor's enrollment Enrolled in postsecondary education after earning bachelor's degree 0.43 0.01

Key Predictor Variables Cumulative loan amount borrowed for undergraduate through 2007-08, in $1,000 16.41 0.24

Cumulative loan amount borrowed for undergraduate through 2007-08, QuartilesDid not borrow 0.34 0.01$100 - $16,000 0.22 0.01$16,001 -$28,012 0.22 0.01$28,013 - $150,000 0.22 0.01

Instruments Institution-level financial aid measures

Institution-level percentage of students receiving any financial aid 79.43 0.31Institution-level percentage of students receiving federal loan aid 48.17 0.34Institution-level ratio of grant to total aid 0.57 0.00Institution-level percentage of students receiving federal loan aid

Students whose degree-granting institution was in the lowest third in % loan aid (0-36%) 0.29 0.01

Students whose degree-granting institution was in the middle third in % loan aid (37-71%) 0.58 0.01

Students whose degree-granting institution was in the highest third in % loan aid (72-100%) 0.14 0.01

Distance to institution by sector Distance to nearest public 4-year institution, in miles 13.70 0.20

Distance to nearest public 2-year institution, in miles 11.26 0.20Distance to nearest private non-profit 4-year institution, in miles 14.45 0.41Distance to nearest private non-profit less than 4-year institution, in miles 75.04 4.05

Distance to institution, by financial aid measures Distance to nearest institution in the lowest third of students receiving

federal loan aid, in miles 10.16 0.27

Distance to nearest institution in the highest third of students receiving federal loan aid, in miles 12.19 0.35

Covariates Demographics

Gender Male 0.42 0.00Female 0.58 0.00

Race/ethnicity (with multiple) White 0.73 0.01Black or African American 0.08 0.00Hispanic or Latino 0.09 0.00Asian 0.06 0.00American Indian or Alaska Native, Native Hawaiian / other Pacific

Islander, Other, More than one race 0.03 0.00

Dependency status Independent 0.36 0.01 Dependent 0.64 0.01

Age at 2007-08 bachelor's degree award date 25.14 0.10Highest education level attained by either parent as of 2007-08

Don’t know 0.01 0.00High school or less 0.20 0.01Some college 0.24 0.01Bachelor's degree or higher 0.55 0.01

Income percentile (dependents' parents and independents) in 2006 56.26 0.38Undergraduate characteristics/ experiences

College admissions test score 1,084 3.25College admissions test score was missing 0.18 0.01Attendance intensity (all institutions) in 2007-08

Exclusively full time 0.62 0.01Exclusively part time 0.15 0.01Mixed full time and part time 0.23 0.01

Field of study: Undergraduate Computer and information sciences 0.03 0.00Engineering and engineering technology 0.06 0.00Bio and phys science, sci tech, math, agriculture 0.07 0.00General studies and other 0.03 0.00Social Sciences 0.16 0.00

Humanities 0.12 0.00Health care fields 0.07 0.00Business 0.23 0.00Education 0.08 0.00Other Applied 0.15 0.00

Institution sector in 2007-08 Public 4-year 0.59 0.00Private nonprofit 4-year 0.30 0.00For-profit 0.04 0.00Others or attended more than one institution 0.06 0.00

Selectivity of 2007-08 bachelor's degree institution Very selective 0.30 0.01Moderately selective 0.51 0.01

Minimally selective or open admission 0.19 0.01Note: The weight variable used in this table is WTE000. The variance estimation method is BRR.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table 2. OLS First Stage Results

(1) (2) (3) (4) (5) (6) (7) (8) Amount borrowed (continuous) Amount borrowed (quartiles) Percentage of students receiving any financial aid 0.114*** 0.00889*** (0.015) (0.001)Ratio of grant to loan aid -9.798*** -0.648*** (2.823) (0.160)Percentage of students receiving any federal loan aid 0.201*** 0.0143*** (0.015) (0.001)Distance to nearest public 4-year institution 0.025 0.00358** (0.026) (0.002)(Distance to nearest public 4-year institution)2 0.000 0.000 (0.000) (0.000)Distance to nearest public 2-year institution 0.035 0.003 (0.031) (0.002)(Distance to nearest public 2-year institution)2 0.000 0.000 (0.000) (0.000)Distance to nearest private non-profit 4-year institution -0.013 -0.001 (0.025) (0.002)(Distance to nearest private non-profit 4-year institution)2 0.000 0.000 (0.000) (0.000)Distance to nearest private non-profit less than 4-year institution -0.00770** -0.000536*** (0.003) (0.000)

(Distance to nearest private non-profit less than 4-year institution)2 0.000 0.000 (0.000) (0.000)Distance to closest low-aid institution 0.0762** 0.00694***

(0.030) (0.002)(Distance to closest low-aid institution)2 0.000 0.000 (0.000) (0.000)Distance to closest high-aid institution -0.0431*** -0.00259** (0.017) (0.001)(Distance to closest high-aid institution)2 0.000 0.000 (0.000) (0.000)Male -0.809 -1.017** -0.918* -0.963* -0.0516* -0.0672** -0.0646** -0.0683** (0.503) (0.477) (0.517) (0.516) (0.029) (0.028) (0.031) (0.030)Black 3.428*** 3.582*** 3.186*** 3.348*** 0.237*** 0.251*** 0.232*** 0.243*** (0.919) (0.933) (0.977) (0.970) (0.057) (0.057) (0.059) (0.059)Hispanic -2.452** -0.725 -3.339*** -3.106*** -0.167** -0.043 -0.231*** -0.208*** (1.003) (0.867) (0.997) (0.997) (0.064) (0.055) (0.065) (0.066)Asian -2.995*** -2.588*** -4.570*** -4.801*** -0.242*** -0.216*** -0.342*** -0.358*** (0.927) (0.925) (1.013) (0.998) (0.068) (0.068) (0.074) (0.073)Other race -0.106 0.032 -0.344 -0.594 0.041 0.044 0.006 -0.011 (1.148) (1.127) (1.201) (1.190) (0.078) (0.076) (0.081) (0.080)Age -0.104* -0.105* -0.075 -0.071 -0.004 -0.004 -0.002 -0.002 (0.061) (0.058) (0.064) (0.063) (0.004) (0.004) (0.004) (0.004)Dependent -1.829*** -2.648*** -1.204* -1.300** -0.179*** -0.235*** -0.131*** -0.139*** (0.653) (0.635) (0.641) (0.644) (0.046) (0.045) (0.045) (0.045)Parent education - Don’t know -3.166 -2.702 -1.864 -1.833 -0.250* -0.218 -0.170 -0.169 (2.943) (2.922) (3.199) (3.193) (0.151) (0.149) (0.163) (0.162)Parent education - Some college 1.620** 1.541** 2.129*** 2.137*** 0.061 0.054 0.0936** 0.0939** (0.736) (0.705) (0.761) (0.756) (0.044) (0.043) (0.045) (0.045)Parent education - Bachelor's degree or higher -2.878*** -2.674*** -2.960*** -2.964*** -0.210*** -0.197*** -0.218*** -0.219*** (0.752) (0.723) (0.772) (0.765) (0.048) (0.046) (0.048) (0.048)Income percentile (10s) -0.690*** -0.690*** -0.784*** -0.789*** -0.0572*** -0.0574*** -0.0648*** -0.0650*** (0.091) (0.089) (0.094) (0.094) (0.006) (0.006) (0.006) (0.006)College admissions test score (100s) -0.572*** -0.483*** -0.766*** -0.757*** -0.0446*** -0.0387*** -0.0595*** -0.0587*** (0.162) (0.155) (0.165) (0.164) (0.010) (0.009) (0.010) (0.010)College admissions test score missing -5.487*** -4.557** -7.329*** -7.400*** -0.431*** -0.370*** -0.572*** -0.576*** (1.984) (1.910) (1.991) (1.977) (0.122) (0.116) (0.122) (0.122)

Major - Computer and information sciences -2.515* -2.889* -1.759 -1.676 -0.049 -0.073 0.007 0.012 (1.426) (1.495) (1.531) (1.527) (0.096) (0.103) (0.101) (0.101)Major - Engineering and engineering technology -0.427 -0.256 0.340 0.317 -0.057 -0.045 -0.005 -0.006 (1.204) (1.215) (1.226) (1.225) (0.070) (0.072) (0.071) (0.071)Major - Bio and phys science, sci tech, math, agriculture -1.722 -1.734* -0.972 -0.989 -0.111* -0.111* -0.051 -0.053 (1.074) (1.044) (1.076) (1.078) (0.064) (0.062) (0.061) (0.060)Major - General studies and other -1.126 -1.524 -1.402 -1.569 -0.064 -0.092 -0.076 -0.088 (1.637) (1.631) (1.623) (1.611) (0.102) (0.103) (0.098) (0.097)Major - Social Sciences -0.674 -0.610 -0.685 -0.739 0.021 0.025 0.024 0.018 (1.003) (0.962) (0.993) (0.992) (0.054) (0.051) (0.051) (0.051)Major - Health care fields -0.279 -0.588 0.252 0.150 -0.010 -0.034 0.019 0.011 (1.223) (1.201) (1.164) (1.159) (0.066) (0.066) (0.061) (0.061)Major - Business -2.460** -2.720** -2.025* -2.046* -0.116** -0.135** -0.0927* -0.0963* (1.063) (1.047) (1.093) (1.082) (0.057) (0.056) (0.055) (0.055)Major - Education -0.847 -1.066 -0.663 -0.593 -0.027 -0.041 -0.021 -0.016 (1.276) (1.229) (1.268) (1.269) (0.071) (0.068) (0.071) (0.070)Major - Other Applied 0.312 0.126 0.790 0.783 0.036 0.022 0.071 0.069 (1.085) (1.071) (1.061) (1.062) (0.061) (0.060) (0.057) (0.057)Sector - Private nonprofit 4-year 9.158*** 6.342*** 8.708*** 8.720*** 0.448*** 0.260*** 0.418*** 0.420*** (0.678) (0.555) (0.585) (0.564) (0.038) (0.033) (0.034) (0.033)Sector -For-profit 17.15*** 14.00*** 18.64*** 18.53*** 0.953*** 0.720*** 1.052*** 1.041*** (1.711) (1.696) (2.050) (1.999) (0.093) (0.098) (0.114) (0.110)Sector -Others or attended more than one institution 4.179*** 2.694*** 4.364*** 4.302*** 0.233*** 0.133** 0.252*** 0.250*** (0.917) (0.831) (0.844) (0.845) (0.059) (0.054) (0.057) (0.057)Selectivity - Moderately selective -0.471 -1.340** 0.460 0.304 0.005 -0.054 0.0705* 0.0606* (0.576) (0.562) (0.606) (0.594) (0.037) (0.036) (0.037) (0.036)Selectivity - Minimally selective or open admission 0.236 -0.475 1.203 1.100 0.037 -0.016 0.101* 0.0990* (0.953) (0.881) (0.996) (0.960) (0.056) (0.051) (0.059) (0.057)Exclusively part time -1.222 -0.760 -1.344 -1.320 -0.140*** -0.106** -0.149*** -0.147*** (0.790) (0.801) (0.846) (0.844) (0.050) (0.051) (0.053) (0.053)Mixed full time and part time -0.279 0.190 -0.001 -0.063 -0.049 -0.016 -0.031 -0.034

(0.593) (0.591) (0.615) (0.613) (0.037) (0.037) (0.038) (0.038)Constant 26.04*** 20.84*** 30.42*** 30.27*** 2.993*** 2.714*** 3.384*** 3.379*** (3.230) (2.528) (2.761) (2.673) (0.200) (0.170) (0.180) (0.175)Observations 16,046 16,070 15,984 15,984 16,046 16,070 15,984 15,984R-squared 0.137 0.157 0.125 0.126 0.164 0.19 0.151 0.152F statistic of regression 27.62 33.54 23.65 26.11 33.42 41.76 28.15 30.89F statistic of instruments 33.53 175.43 2.64 4.87 49.58 235.81 5.10 6.48Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table 3a – Odds Ratios of Second Stage Results on Employment and Salary

(1) (2) (3) (4) (5) (6) (7) (8)

Worked more than desired due to education cost

Employed in 2012 Employment intensity 2012 Total salary 2012

Amount borrowed (continuous , $1,000s), fitted value using distance by aid 1.127*** 1.126*** 0.992 940.8** (0.037) (0.042) (0.043) (402)Amount borrowed (quartiles), fitted value using distance by aid 3.727*** 5.461*** 0.970 11,370** (1.510) (2.698) (0.509) (5,281)Male 1.044 1.017 1.250** 1.250** 1.421*** 1.430*** 7,121*** 6,986*** (0.077) (0.073) (0.109) (0.108) (0.146) (0.142) (943) (943)Black 0.808 0.883 0.376*** 0.375*** 1.132 1.110 -9,718*** -9,254*** (0.116) (0.120) (0.066) (0.065) (0.279) (0.262) (2,189) (2,188)Hispanic 1.429** 1.299* 1.093 1.092 0.781 0.798 -1020 -1516 (0.241) (0.205) (0.198) (0.190) (0.199) (0.194) (1,887) (1,794)Asian 1.864*** 1.701** 0.735 0.778 1.139 1.174 1591 1266 (0.412) (0.364) (0.179) (0.194) (0.372) (0.376) (3,054) (3,051)

Other race 1.510** 1.425** 0.680** 0.651** 1.042 1.049 750 334 (0.252) (0.232) (0.117) (0.110) (0.278) (0.277) (3,058) (3,038)Age 0.988 0.982** 0.999 0.994 0.978 0.978 120 74 (0.008) (0.008) (0.010) (0.009) (0.019) (0.018) (125) (122)Dependent 0.856 0.880 1.075 1.162 0.735* 0.739* -3,161** -2,813** (0.081) (0.087) (0.112) (0.128) (0.122) (0.126) (1,327) (1,348)Parent education - Don’t know 0.651 0.644 0.459** 0.484** 1.457 1.475 -3963 -3888 (0.249) (0.248) (0.143) (0.153) (1.317) (1.334) (5,500) (5,557)Parent education - Some college 0.822* 0.934 0.769** 0.839 0.919 0.905 -2,696* -1796 (0.094) (0.092) (0.093) (0.090) (0.165) (0.147) (1,550) (1,423)Parent education - Bachelor's degree or higher 1.192 1.112 1.312* 1.336* 0.707* 0.721* 1615 1299 (0.149) (0.131) (0.205) (0.203) (0.137) (0.133) (1,774) (1,695)Income percentile 1.003 1.002 1.011*** 1.013*** 1.005 1.006 216.2*** 216.6*** (0.003) (0.003) (0.003) (0.004) (0.004) (0.004) (38) (40)College admissions test score 1.000 1.000 1.001** 1.001*** 1.000 1.000 12.14*** 11.67*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (4) (4)College admissions test score missing 0.741 0.645 1.901 2.093* 0.496 0.520 9,544** 9,072** (0.259) (0.221) (0.788) (0.891) (0.256) (0.259) (4,161) (4,067)Major - Computer and information sciences 0.453*** 0.368*** 2.602*** 2.113*** 5.594*** 5.673*** 32,086*** 30,444*** (0.103) (0.081) (0.729) (0.575) (2.575) (2.608) (3,843) (3,775)Major - Engineering and engineering technology 0.369*** 0.386*** 2.152*** 2.252*** 3.041*** 3.031*** 27,672*** 28,039*** (0.071) (0.074) (0.536) (0.561) (0.931) (0.926) (2,482) (2,475)Major - Bio and phys science, sci tech, math, agriculture 0.797* 0.759** 0.818 0.793* 1.680*** 1.690*** 4,842** 4,499** (0.096) (0.090) (0.111) (0.107) (0.312) (0.313) (1,880) (1,864)Major - General studies and other 0.906 0.849 1.331 1.291 1.477 1.492 13,169** 12,743** (0.160) (0.146) (0.294) (0.281) (0.479) (0.480) (5,095) (5,071)Major - Social Sciences 0.906 0.809* 1.068 0.947 1.774*** 1.786*** 6,034*** 5,129*** (0.108) (0.097) (0.144) (0.122) (0.279) (0.279) (1,608) (1,593)

Major - Health care fields 0.769* 0.772* 2.631*** 2.616*** 1.289 1.287 21,624*** 21,626*** (0.111) (0.112) (0.488) (0.485) (0.218) (0.218) (1,721) (1,729)Major - Business 0.717*** 0.636*** 2.322*** 2.138*** 3.848*** 3.903*** 18,929*** 18,079*** (0.087) (0.072) (0.367) (0.317) (0.734) (0.734) (1,848) (1,735)Major - Education 0.820 0.778* 1.760*** 1.679*** 2.820*** 2.832*** 8,104*** 7,707*** (0.111) (0.104) (0.270) (0.254) (0.516) (0.519) (1,823) (1,797)Major - Other Applied 0.814 0.817 1.347** 1.312* 2.009*** 1.999*** 6,679*** 6,626*** (0.106) (0.108) (0.195) (0.192) (0.363) (0.361) (1,317) (1,323)Sector - Private nonprofit 4-year 0.448*** 0.738* 0.275*** 0.379*** 1.037 0.975 -8,728** -5,261** (0.131) (0.134) (0.091) (0.085) (0.403) (0.239) (3,642) (2,455)Sector -For-profit 0.173*** 0.410** 0.0686*** 0.106*** 2.179 1.922 -18,407** -12,735** (0.104) (0.175) (0.050) (0.060) (1.852) (1.223) (8,057) (6,404)Sector -Others or attended more than one institution 0.685** 0.829 0.612** 0.669** 1.185 1.152 -2799 -1560 (0.118) (0.121) (0.118) (0.112) (0.304) (0.252) (2,762) (2,555)Selectivity - Moderately selective 1.107 1.058 1.222** 1.139 0.730*** 0.729*** -1550 -1975 (0.091) (0.091) (0.100) (0.098) (0.080) (0.082) (1,114) (1,196)Selectivity - Minimally selective or open admission 1.006 1.004 0.935 0.894 0.755 0.749* -4,349** -4,482*** (0.128) (0.130) (0.128) (0.123) (0.129) (0.129) (1,726) (1,680)Exclusively part time 1.288** 1.340** 1.366** 1.507*** 1.019 1.026 126 589 (0.142) (0.159) (0.171) (0.205) (0.167) (0.175) (1,350) (1,461)Mixed full time and part time 1.071 1.114 1.004 1.058 1.017 1.016 -178 166 (0.080) (0.084) (0.090) (0.095) (0.117) (0.118) (1,174) (1,166)Constant 0.164* 0.0710* 0.104* 0.0117** 13.66* 11.680 -11891 -22035 (0.165) (0.097) (0.126) (0.021) (19.890) (22.310) (12,793) (18,106)Observations 15,605 15,605 15,984 15,984 13,139 13,139 15,984 15,984Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table 3b - Odds Ratios of Second Stage Results on Job Satisfaction, Occupation, and Graduate Enrollment

(1) (2) (3) (4) (5) (6) (7) (8)

Satisfaction with compensation

Satisfaction with job security

Public-service occupation Post-BA enrollment

Amount borrowed (continuous, $1,000s), fitted value using distance by aid 1.063** 1.055* 0.953 0.945* (0.028) (0.034) (0.030) (0.030)Amount borrowed (quartiles), fitted value using distance by aid 2.861*** 2.672** 0.587 0.431** (0.950) (1.062) (0.227) (0.167)Male 1.093 1.106 0.996 1.010 0.529*** 0.535*** 0.813*** 0.811*** (0.072) (0.072) (0.072) (0.072) (0.039) (0.038) (0.062) (0.061)Black 0.488*** 0.468*** 0.525*** 0.499*** 1.612*** 1.556*** 2.749*** 2.774*** (0.067) (0.062) (0.084) (0.076) (0.280) (0.260) (0.427) (0.404)Hispanic 1.078 1.126 0.876 0.924 1.174 1.220 1.036 1.026 (0.155) (0.152) (0.144) (0.142) (0.174) (0.173) (0.141) (0.128)Asian 0.730 0.805 0.919 1.025 0.586** 0.608** 0.755 0.725 (0.145) (0.155) (0.195) (0.212) (0.130) (0.135) (0.159) (0.146)Other race 0.884 0.870 0.727** 0.719** 1.237 1.268 1.389* 1.415* (0.145) (0.140) (0.099) (0.095) (0.211) (0.214) (0.251) (0.252)Age 1.001 0.998 0.984** 0.982** 0.989 0.991 0.964*** 0.966*** (0.009) (0.009) (0.008) (0.008) (0.010) (0.010) (0.009) (0.009)Dependent 1.143 1.216** 1.055 1.124 1.008 0.997 1.130 1.085 (0.104) (0.118) (0.096) (0.109) (0.096) (0.100) (0.116) (0.119)Parent education - Don’t know 1.363 1.446 0.649 0.691 0.639 0.642 1.108 1.071 (0.414) (0.442) (0.195) (0.209) (0.229) (0.230) (0.355) (0.343)Parent education - Some college 1.173 1.207* 0.914 0.931 1.115 1.059 1.048 1.008 (0.125) (0.118) (0.102) (0.092) (0.134) (0.114) (0.123) (0.105)

Parent education - Bachelor's degree or higher 1.283** 1.348*** 1.061 1.124 0.817 0.840 0.848 0.834 (0.144) (0.146) (0.141) (0.141) (0.114) (0.114) (0.106) (0.098)Income percentile 1.007*** 1.010*** 1.007*** 1.010*** 0.998 0.998 0.999 0.998 (0.002) (0.002) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)College admissions test score 1.001*** 1.001*** 1.001 1.001** 1.000 1.000 1.001 1.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)College admissions test score missing 2.206** 2.576*** 1.292 1.536 1.129 1.194 2.763*** 2.582*** (0.674) (0.775) (0.439) (0.514) (0.400) (0.413) (0.978) (0.878)Major - Computer and information sciences 2.420*** 2.171*** 2.088*** 1.898*** 0.315*** 0.343*** 0.277*** 0.306*** (0.553) (0.480) (0.482) (0.431) (0.118) (0.126) (0.065) (0.072)Major - Engineering and engineering technology 1.713*** 1.752*** 1.751*** 1.786*** 0.495*** 0.485*** 0.617*** 0.604*** (0.300) (0.306) (0.276) (0.281) (0.084) (0.083) (0.099) (0.097)Major - Bio and phys science, sci tech, math, agriculture 1.123 1.113 1.230* 1.224* 1.688*** 1.722*** 1.579*** 1.601*** (0.162) (0.159) (0.154) (0.150) (0.206) (0.206) (0.204) (0.201)Major - General studies and other 1.620*** 1.619*** 1.420 1.426* 1.163 1.194 0.798 0.807 (0.292) (0.288) (0.304) (0.301) (0.208) (0.208) (0.161) (0.158)Major - Social Sciences 1.072 1.004 1.154 1.088 1.407*** 1.473*** 1.166 1.236** (0.126) (0.115) (0.133) (0.120) (0.159) (0.163) (0.130) (0.130)Major - Health care fields 1.457** 1.445** 2.330*** 2.310*** 7.242*** 7.229*** 0.738** 0.741** (0.212) (0.209) (0.361) (0.358) (1.085) (1.084) (0.110) (0.110)Major - Business 1.642*** 1.599*** 1.936*** 1.902*** 0.367*** 0.385*** 0.364*** 0.377*** (0.222) (0.202) (0.273) (0.244) (0.056) (0.054) (0.046) (0.043)Major - Education 1.007 0.983 1.338** 1.312** 6.463*** 6.598*** 0.970 0.992 (0.117) (0.113) (0.174) (0.168) (0.887) (0.900) (0.118) (0.120)Major - Other Applied 1.003 0.976 1.162 1.128 1.065 1.064 0.556*** 0.565*** (0.130) (0.127) (0.149) (0.146) (0.131) (0.132) (0.063) (0.064)Sector - Private nonprofit 4-year 0.644* 0.704** 0.604* 0.635** 1.312 1.072 1.463 1.275 (0.164) (0.118) (0.171) (0.111) (0.368) (0.193) (0.430) (0.237)

Sector -For-profit 0.314** 0.326*** 0.294* 0.284*** 1.838 1.297 1.963 1.660 (0.169) (0.132) (0.184) (0.130) (1.158) (0.596) (1.231) (0.753)Sector -Others or attended more than one institution 0.733* 0.734** 0.697* 0.686** 1.461** 1.352** 2.229*** 2.157*** (0.117) (0.098) (0.129) (0.104) (0.245) (0.189) (0.429) (0.342)Selectivity - Moderately selective 1.049 0.999 1.096 1.045 1.160 1.182* 0.911 0.945 (0.080) (0.080) (0.089) (0.090) (0.106) (0.113) (0.063) (0.069)Selectivity - Minimally selective or open admission 1.055 1.010 0.982 0.938 1.257 1.257 0.961 0.987 (0.120) (0.117) (0.112) (0.108) (0.177) (0.180) (0.103) (0.108)Exclusively part time 1.131 1.222* 1.043 1.127 0.730*** 0.719*** 0.638*** 0.606*** (0.110) (0.129) (0.104) (0.123) (0.077) (0.082) (0.070) (0.071)Mixed full time and part time 1.138* 1.176** 1.083 1.117 0.960 0.945 1.037 1.011 (0.081) (0.085) (0.080) (0.083) (0.074) (0.073) (0.070) (0.070)

Constant0.0815**

* 0.0144*** 0.336 0.0594* 1.624 2.281 3.671 11.75* (0.073) (0.017) (0.364) (0.086) (1.673) (3.113) (3.824) (16.300)Observations 15,984 15,984 15,984 15,984 15,984 15,984 15,984 15,984Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Appendix

Table A1a. Odds Ratios of Second Stage Results on Employment using Additional Instruments

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Worked more than desired due to education cost Employed in 2012Amount borrowed (continuous), fitted value using distance by 1.059 1.030

sector

(0.042) (0.041)

Amount borrowed (quartiles), fitted value using distance by sector 1.500 1.970

(0.681) (0.856)

Amount borrowed (continuous), fitted value using % aid & grant ratio (Zhang) 1.040**

1.070***

(0.018) (0.017)

Amount borrowed (quartiles), fitted value using % aid & grant ratio (Zhang) 1.684**

2.530***

(0.401) (0.526)Amount borrowed (continuous), fitted value using % loans

1.052***

1.036***

(0.010) (0.010)Amount borrowed (quartiles), fitted value using % loans

2.032***

1.651***

(0.269) (0.232)Male 0.984 0.958 0.964 0.959 0.981 0.976 1.152* 1.171** 1.158* 1.149* 1.127 1.124

(0.078) (0.072) (0.067) (0.066) (0.067) (0.067) (0.094) (0.093) (0.092) (0.091) (0.090) (0.089)

Black 0.982 1.078 1.128 1.138 1.086 1.0880.501**

*0.473**

*0.442**

*0.446**

*0.498**

*0.499**

*

(0.162) (0.154) (0.132) (0.131) (0.116) (0.116) (0.084) (0.073) (0.060) (0.060) (0.066) (0.066)Hispanic 1.153 1.043 1.028 1.017 1.076 1.069 0.808 0.858 0.957 0.945 0.873 0.870

(0.210) (0.169) (0.128) (0.124) (0.125) (0.124) (0.147) (0.133) (0.127) (0.123) (0.109) (0.108)

Asian 1.374 1.207 1.263 1.273 1.318* 1.349*0.479**

*0.534**

*0.573**

*0.586**

*0.500**

*0.509**

*

(0.333) (0.268) (0.199) (0.203) (0.212) (0.219) (0.125) (0.128) (0.098) (0.101) (0.080) (0.082)

Other race 1.421** 1.372** 1.417** 1.383** 1.480** 1.440**0.627**

*0.626**

* 0.662**0.634**

* 0.656**0.643**

*

(0.228) (0.217) (0.230) (0.223) (0.242) (0.234) (0.107) (0.105) (0.111) (0.106) (0.108) (0.106)

Age 0.984* 0.980**0.978**

*0.977**

* 0.979**0.976**

* 0.993 0.992 0.994 0.991 0.989 0.988

(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.010) (0.009) (0.010) (0.010) (0.009) (0.009)

Dependent 0.798** 0.786**0.761**

*0.777**

*0.776**

* 0.801** 0.970 1.021 1.139 1.185 1.070 1.094

(0.080) (0.083) (0.070) (0.074) (0.069) (0.072) (0.103) (0.109) (0.119) (0.127) (0.108) (0.111)

Parent education - Don’t know 0.582 0.554 0.611 0.612 0.608 0.6140.385**

*0.407**

* 0.483** 0.487**0.426**

*0.429**

*

(0.225) (0.213) (0.217) (0.218) (0.213) (0.215) (0.124) (0.131) (0.141) (0.143) (0.124) (0.125)

Parent education - Some college0.936 1.017 0.981 1.012 0.949 0.985 0.927 0.925 0.902 0.951 0.933 0.959

(0.111) (0.099) (0.089) (0.087) (0.085) (0.086) (0.116) (0.097) (0.085) (0.090) (0.093) (0.095)Parent education - Bachelor's degree or higher 0.989 0.909 0.968 0.964 0.987 0.990 1.007 1.070 1.178 1.176 1.049 1.051

(0.146) (0.119) (0.082) (0.082) (0.081) (0.081) (0.164) (0.154) (0.125) (0.124) (0.100) (0.100)

Income percentile 0.998 0.996 0.997* 0.997 0.998* 0.998 1.004 1.006**1.006**

*1.007**

*1.004**

*1.004**

*

(0.003) (0.003) (0.002) (0.002) (0.001) (0.001) (0.004) (0.003) (0.002) (0.002) (0.002) (0.002)

College admissions test score 0.999**0.999**

*0.999**

*0.999**

*0.999**

*0.999**

* 1.000 1.000 1.001** 1.001** 1.000 1.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)College admissions test score missing 0.463**

0.379***

0.415***

0.418***

0.456***

0.469*** 0.969 1.151 1.495 1.531 1.175 1.199

(0.177) (0.140) (0.124) (0.126) (0.115) (0.119) (0.394) (0.438) (0.470) (0.484) (0.370) (0.380)Major - Computer and information sciences

0.411***

0.373***

0.437***

0.408***

0.435***

0.398***

2.252***

2.132***

2.578***

2.290***

2.396***

2.250***

(0.096) (0.082) (0.094) (0.086) (0.093) (0.085) (0.615) (0.585) (0.650) (0.584) (0.626) (0.584)Major - Engineering and engineering technology

0.380***

0.389***

0.385***

0.391***

0.383***

0.391***

2.232***

2.256***

2.291***

2.351***

2.295***

2.327***

(0.072) (0.073) (0.070) (0.071) (0.069) (0.070) (0.554) (0.560) (0.550) (0.562) (0.554) (0.562)Major - Bio and phys science, sci tech, math, agriculture 0.757**

0.733*** 0.814 0.808* 0.829 0.824 0.757** 0.759** 0.821 0.812 0.787* 0.784*

(0.094) (0.087) (0.102) (0.100) (0.101) (0.101) (0.106) (0.104) (0.107) (0.105) (0.101) (0.101)

Major - General studies and other0.827 0.787 0.840 0.831 0.850 0.843 1.167 1.183 1.303 1.284 1.240 1.232

(0.147) (0.136) (0.145) (0.144) (0.147) (0.146) (0.249) (0.249) (0.274) (0.269) (0.260) (0.258)Major - Social Sciences 0.869 0.827 0.903 0.870 0.918 0.875 1.005 0.970 1.016 0.952 0.996 0.962

(0.104) (0.099) (0.104) (0.099) (0.103) (0.098) (0.135) (0.126) (0.135) (0.126) (0.132) (0.127)

Major - Health care fields 0.787* 0.793 0.813 0.808 0.814 0.8092.709**

*2.687**

*2.628**

*2.601**

*2.581**

*2.570**

*

(0.114) (0.115) (0.127) (0.127) (0.125) (0.124) (0.502) (0.496) (0.494) (0.489) (0.478) (0.476)

Major - Business 0.636***

0.589***

0.614***

0.592***

0.635***

0.610***

1.952***

1.956***

2.278***

2.149***

2.140***

2.078***

(0.087) (0.070) (0.073) (0.068) (0.071) (0.068) (0.324) (0.298) (0.334) (0.314) (0.318) (0.307)

Major - Education 0.796* 0.775* 0.794* 0.779* 0.800* 0.780*1.682**

*1.665**

*1.596**

*1.545**

*1.592**

*1.564**

*

(0.108) (0.103) (0.108) (0.106) (0.107) (0.104) (0.260) (0.251) (0.226) (0.218) (0.224) (0.220)Major - Other Applied 0.861 0.878 0.914 0.909 0.908 0.899 1.455** 1.417** 1.371** 1.356** 1.394** 1.384**

(0.111) (0.113) (0.115) (0.115) (0.113) (0.112) (0.211) (0.205) (0.187) (0.185) (0.188) (0.186)

Sector - Private nonprofit 4-year 0.775 1.085 0.915 1.031 0.823* 0.944 0.6010.585**

*0.427**

*0.520**

*0.575**

*0.633**

*

(0.276) (0.220) (0.162) (0.135) (0.084) (0.079) (0.212) (0.115) (0.067) (0.062) (0.068) (0.062)

Sector -For-profit 0.550 1.052 0.675 0.814 0.570** 0.694 0.358 0.307**0.165**

*0.222**

*0.322**

*0.370**

*

(0.422) (0.531) (0.256) (0.256) (0.150) (0.168) (0.272) (0.154) (0.059) (0.066) (0.086) (0.089)Sector -Others or attended more than one institution 0.897 1.040 0.934 0.973 0.889 0.927 0.900 0.864 0.845 0.901 0.969 0.998

(0.187) (0.167) (0.134) (0.130) (0.106) (0.108) (0.175) (0.131) (0.111) (0.113) (0.121) (0.121)

Selectivity - Moderately selective1.134 1.127 1.176* 1.153* 1.169* 1.136

1.261*** 1.217** 1.250** 1.206**

1.275*** 1.250**

(0.096) (0.102) (0.098) (0.099) (0.093) (0.092) (0.104) (0.102) (0.110) (0.107) (0.112) (0.110)Selectivity - Minimally selective or open admission 1.086 1.113 1.166 1.157 1.157 1.142 1.039 0.998 0.935 0.920 0.982 0.973

(0.146) (0.150) (0.141) (0.142) (0.131) (0.130) (0.139) (0.133) (0.129) (0.128) (0.135) (0.134)

Exclusively part time 1.186 1.168 1.206* 1.236** 1.236** 1.283** 1.218 1.297* 1.287**1.348**

* 1.264** 1.298**

(0.139) (0.148) (0.121) (0.128) (0.123) (0.128) (0.156) (0.173) (0.144) (0.155) (0.141) (0.146)Mixed full time and part time 1.069 1.081 1.115 1.132 1.114 1.137* 1.004 1.026 1.028 1.056 1.028 1.043

(0.079) (0.083) (0.082) (0.085) (0.082) (0.084) (0.089) (0.090) (0.097) (0.100) (0.098) (0.099)

Constant 1.086 1.574 1.920 1.083 1.395 0.585 1.527 0.374 0.451 0.153** 1.287 0.696

(1.357) (2.503) (1.144) (0.903) (0.579) (0.304) (1.984) (0.584) (0.270) (0.123) (0.566) (0.390)

Observations 15,605 15,605 15,666 15,666 15,690 15,690 15,984 15,984 16,046 16,046 16,070 16,070

Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1

SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table A1b. Odds Ratios of Second Stage Results on Employment Intensity and Salary using Additional Instruments

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Employment intensity 2012 Total salary 2012

Amount borrowed (continuous), fitted value using distance by sector 1.102** 277

(0.051) (536)

Amount borrowed (quartiles), fitted value using distance by sector

4.219*** 6320

(2.304) (6,981)

Amount borrowed (continuous), fitted value using % aid & grant ratio (Zhang) 1.004 -453.3*

(0.024) (258)

Amount borrowed (quartiles), fitted value using % aid & grant ratio (Zhang) 1.083 -6,089*

(0.340) (3,519)Amount borrowed (continuous), fitted value using % loans 1.000 -184

(0.014) (149)Amount borrowed (quartiles), fitted value using % loans 1.005 -2581

(0.200) (2,092)

Male 1.563***

1.567***

1.448***

1.448***

1.432***

1.432*** 6,504*** 6,657*** 5,719*** 5,775*** 5,888*** 5,904***

(0.162) (0.157) (0.139) (0.139) (0.137) (0.137) (1,021) (1,002) (859) (859) (871) (872)

Black 0.813 0.805 1.141 1.136 1.161 1.161 -7,616***-

8,137*** -5,654*** -5,737*** -6,428*** -6,435***

(0.200) (0.185) (0.248) (0.245) (0.247) (0.247) (2,597) (2,571) (1,888) (1,871) (1,780) (1,778)Hispanic 1.121 1.130 0.763 0.765 0.735 0.735 -3291 -2725 -5,374*** -5,265*** -4,749*** -4,726***

(0.300) (0.292) (0.156) (0.155) (0.141) (0.141) (2,281) (2,110) (1,673) (1,648) (1,579) (1,571)Asian 1.920* 2.046** 1.047 1.055 1.031 1.031 -1635 -621 -6,256** -6,378** -5,131** -5,215**

(0.652) (0.682) (0.274) (0.278) (0.276) (0.279) (3,318) (3,294) (2,786) (2,840) (2,511) (2,538)

Other race 1.135 1.098 1.145 1.142 1.151 1.150 150 141 -576 -297 -516 -416

(0.301) (0.290) (0.312) (0.311) (0.315) (0.314) (2,955) (2,989) (2,992) (2,962) (2,965) (2,947)

Age 0.985 0.981 0.972 0.972 0.972 0.972 75 66 -38 -16 -20 -11

(0.018) (0.018) (0.019) (0.019) (0.019) (0.019) (126) (122) (126) (126) (126) (127)

Dependent 0.826 0.884 0.755* 0.759* 0.744* 0.745* -3,922***-

3,447*** -4,067*** -4,315*** -3,691*** -3,808***

(0.135) (0.147) (0.119) (0.121) (0.116) (0.116) (1,269) (1,276) (1,334) (1,370) (1,343) (1,376)Parent education - Don’t know 1.906 2.002 0.888 0.893 0.872 0.872 -5299 -4763 -8,996* -9,030* -8046 -8084

(1.757) (1.846) (0.782) (0.788) (0.752) (0.752) (5,662) (5,710) (5,051) (5,055) (5,082) (5,086)

Parent education - Some college0.739* 0.793 0.946 0.948 0.928 0.928 -1306 -1320 242 -118 -245 -382

(0.133) (0.127) (0.155) (0.152) (0.152) (0.151) (1,759) (1,523) (1,418) (1,387) (1,350) (1,355)Parent education - Bachelor's degree or higher 0.979 1.005 0.805 0.808 0.780 0.780 -384 181 -2,449* -2417 -1656 -1665

(0.199) (0.194) (0.127) (0.127) (0.118) (0.118) (2,043) (1,928) (1,474) (1,467) (1,424) (1,428)

Income percentile1.014**

*1.016**

* 1.006** 1.006** 1.006** 1.006** 164.2*** 183.7*** 94.78*** 91.14*** 115.2*** 113.1***

(0.004) (0.004) (0.003) (0.003) (0.002) (0.003) (48) (50) (27) (29) (21) (22)

College admissions test score 1.000 1.001 1.000 1.000 0.999 0.999 7 8.651* 1 1 3 3

(0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (5) (5) (3) (3) (3) (3)College admissions test score missing 1.107 1.223 0.538 0.544 0.509* 0.509* 4499 6105 189 80 2157 2054

(0.616) (0.654) (0.239) (0.243) (0.205) (0.206) (4,717) (4,556) (3,855) (3,904) (3,754) (3,793)Major - Computer and information sciences

6.686***

5.627***

4.477***

4.444***

4.503***

4.500***

30,999***

30,489***

28,647***

29,441***

29,231***

29,554***

(3.089) (2.593) (2.225) (2.201) (2.216) (2.220) (3,826) (3,781) (3,628) (3,659) (3,612) (3,643)Major - Engineering and engineering technology

2.916***

3.028***

3.283***

3.291***

3.291***

3.291***

27,963***

28,059***

30,508***

30,341***

30,563***

30,491***

(0.895) (0.920) (0.975) (0.977) (0.974) (0.974) (2,501) (2,484) (3,162) (3,122) (3,164) (3,158)Major - Bio and phys science, sci tech, math, agriculture

1.848***

1.803***

1.933***

1.935***

1.919***

1.919*** 4,270** 4,291** 3,448* 3,533* 3,827** 3,852**

(0.358) (0.340) (0.363) (0.362) (0.355) (0.355) (1,910) (1,873) (1,909) (1,896) (1,872) (1,870)Major - General studies and other 1.736* 1.701 1.511 1.513 1.498 1.498 12,205** 12,325** 9,776** 9,882** 10,203** 10,237**

(0.568) (0.549) (0.488) (0.488) (0.486) (0.486) (5,033) (5,013) (4,908) (4,897) (4,824) (4,821)

Major - Social Sciences 1.913***

1.732***

1.734***

1.726***

1.729***

1.728*** 5,583*** 5,247*** 4,309*** 4,740*** 4,602*** 4,778***

(0.309) (0.270) (0.268) (0.262) (0.262) (0.262) (1,638) (1,590) (1,574) (1,567) (1,566) (1,570)

Major - Health care fields 1.247 1.238 1.559** 1.557** 1.585** 1.585**21,844**

*21,763**

*22,285**

*22,353**

*22,043**

*22,064**

*

(0.213) (0.211) (0.285) (0.284) (0.293) (0.293) (1,735) (1,738) (1,730) (1,736) (1,701) (1,703)

Major - Business 4.755***

4.457***

3.712***

3.705***

3.628***

3.627***

17,626***

17,636***

16,406***

16,805***

16,951***

17,101***

(1.067) (0.921) (0.745) (0.720) (0.682) (0.680) (2,000) (1,814) (1,731) (1,703) (1,688) (1,685)

Major - Education 2.983***

2.869***

2.793***

2.787***

2.770***

2.770*** 7,774*** 7,675*** 6,673*** 6,889*** 6,746*** 6,835***

(0.550) (0.528) (0.515) (0.514) (0.507) (0.507) (1,841) (1,799) (1,727) (1,721) (1,716) (1,738)

Major - Other Applied 1.840***

1.794***

2.130***

2.127***

2.140***

2.140*** 7,258*** 7,014*** 7,000*** 7,068*** 6,866*** 6,900***

(0.328) (0.320) (0.372) (0.372) (0.374) (0.375) (1,383) (1,397) (1,316) (1,327) (1,290) (1,293)Sector - Private nonprofit 4-year 0.406** 0.520** 0.875 0.879 0.910 0.911 -2881 -3118 3053 1706 685 187

(0.173) (0.136) (0.210) (0.159) (0.154) (0.130) (4,957) (3,300) (2,445) (1,801) (1,539) (1,261)Sector -For-profit 0.306 0.421 1.906 1.905 1.826 1.829 -6084 -7494 7081 5016 1868 1153

(0.280) (0.282) (1.175) (1.027) (0.873) (0.816) (10,819) (8,434) (5,685) (4,845) (3,710) (3,408)Sector -Others or attended more than one institution 0.747 0.796 1.090 1.089 1.113 1.114 81 -296 2922 2473 1862 1709

(0.204) (0.180) (0.235) (0.226) (0.229) (0.226) (3,574) (3,179) (2,147) (2,099) (1,990) (2,010)

Selectivity - Moderately selective 0.702***

0.660***

0.752***

0.749***

0.755*** 0.755** -1293 -1624 -2,056* -1821 -2,152* -2,048*

(0.077) (0.073) (0.079) (0.081) (0.080) (0.082) (1,125) (1,229) (1,191) (1,181) (1,202) (1,198)Selectivity - Minimally selective or open admission 0.662** 0.633** 0.752 0.749 0.758 0.758 -3,536** -3,911** -3,872** -3,776** -4,347** -4,300**

(0.118) (0.114) (0.131) (0.131) (0.131) (0.132) (1,759) (1,710) (1,738) (1,733) (1,772) (1,780)Exclusively part time 1.169 1.276 1.141 1.147 1.113 1.113 -754 -170 -1685 -1983 -1418 -1555

(0.203) (0.237) (0.194) (0.199) (0.186) (0.186) (1,415) (1,581) (1,295) (1,368) (1,259) (1,304)Mixed full time and part time 1.019 1.065 1.072 1.075 1.073 1.073 -200 -1 -84 -258 5 -71

(0.117) (0.124) (0.126) (0.127) (0.127) (0.126) (1,164) (1,132) (1,289) (1,310) (1,279) (1,288)

Constant 0.538 0.07812.30**

* 10.76**14.36**

*14.28**

* 8276 -483033,898**

*40,774**

*25,877**

*29,038**

*

(0.808) (0.151) (11.340) (12.880) (9.460) (11.110) (16,307) (23,440) (8,661) (12,332) (6,468) (8,453)

Observations 13,139 13,139 13,180 13,180 13,201 13,201 15,984 15,984 16,046 16,046 16,070 16,070

Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1

SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table A1c. Odds Ratios of Second Stage Results on Job Satisfaction using Additional Instruments

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Satisfaction with compensation Satisfaction with job security

Amount borrowed (continuous), fitted value using distance by sector 1.013 1.067*

(0.036) (0.037)

Amount borrowed (quartiles), fitted value using distance by sector 1.894

3.419***

(0.752) (1.512)

Amount borrowed (continuous), fitted value using % aid & grant ratio (Zhang) 1.000 1.038**

(0.015) (0.015)

Amount borrowed (quartiles), fitted value using % aid & grant ratio (Zhang) 1.008 1.640**

(0.202) (0.314)Amount borrowed (continuous), fitted value using % loans 1.002 1.006

(0.009) (0.009)Amount borrowed (quartiles), fitted value using % loans 1.035 1.091

(0.127) (0.142)Male 1.046 1.077 0.992 0.993 0.991 0.991 1.007 1.027 0.981 0.976 0.956 0.955

(0.074) (0.072) (0.062) (0.062) (0.063) (0.063) (0.074) (0.075) (0.062) (0.061) (0.058) (0.058)

Black 0.569***

0.514***

0.531***

0.530***

0.534***

0.534***

0.506***

0.473***

0.550***

0.555***

0.620***

0.620***

(0.092) (0.074) (0.064) (0.063) (0.061) (0.061) (0.083) (0.074) (0.070) (0.070) (0.076) (0.076)Hispanic 0.914 1.020 0.919 0.920 0.925 0.925 0.912 0.980 0.828 0.819* 0.749** 0.749**

(0.141) (0.139) (0.103) (0.102) (0.097) (0.097) (0.158) (0.166) (0.100) (0.098) (0.085) (0.085)

Asian 0.578** 0.690*0.594**

*0.595**

*0.599**

*0.599**

* 0.973 1.124 0.871 0.878 0.764** 0.766**

(0.130) (0.141) (0.094) (0.095) (0.092) (0.093) (0.210) (0.243) (0.120) (0.122) (0.101) (0.102)

Other race 0.846 0.857 0.820 0.820 0.804 0.803 0.735** 0.726** 0.720**0.704**

* 0.718** 0.716**

(0.135) (0.135) (0.125) (0.125) (0.119) (0.119) (0.101) (0.098) (0.097) (0.094) (0.096) (0.096)

Age 0.997 0.998 0.998 0.998 0.998 0.998 0.985* 0.982** 0.982**0.980**

*0.978**

*0.977**

*

(0.009) (0.009) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.007) (0.007) (0.007)

Dependent 1.082 1.155 1.128 1.130 1.130 1.132 1.069 1.158 1.071 1.091 1.010 1.014

(0.099) (0.112) (0.098) (0.100) (0.096) (0.097) (0.097) (0.114) (0.094) (0.099) (0.088) (0.089)Parent education - Don’t know 1.236 1.345 1.075 1.077 1.084 1.085 0.663 0.719 0.711 0.711 0.636 0.637

(0.372) (0.408) (0.325) (0.326) (0.325) (0.326) (0.198) (0.215) (0.205) (0.205) (0.183) (0.184)

Parent education - Some college1.297** 1.254**

1.296***

1.296***

1.295***

1.297*** 0.891 0.909 1.038 1.070 1.088 1.093

(0.156) (0.126) (0.120) (0.115) (0.118) (0.116) (0.103) (0.092) (0.096) (0.097) (0.103) (0.101)Parent education - Bachelor's degree or higher 1.109 1.230* 1.079 1.080 1.088 1.088 1.099 1.187 1.103 1.099 0.997 0.997

(0.140) (0.140) (0.089) (0.089) (0.086) (0.087) (0.144) (0.153) (0.097) (0.095) (0.082) (0.082)

Income percentile 1.004 1.007** 1.002 1.002 1.002* 1.002*1.008**

*1.011**

*1.005**

*1.005**

* 1.003** 1.003**

(0.003) (0.003) (0.002) (0.002) (0.001) (0.001) (0.003) (0.003) (0.002) (0.002) (0.001) (0.001)

College admissions test score 1.0001.001**

* 1.000 1.000 1.000* 1.000* 1.001*1.001**

* 1.000** 1.000** 1.000 1.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)College admissions test score missing 1.530 2.022** 1.343 1.348 1.389 1.391 1.413 1.776* 1.346 1.352 1.061 1.064

(0.493) (0.615) (0.356) (0.360) (0.338) (0.340) (0.472) (0.591) (0.338) (0.340) (0.262) (0.265)Major - Computer and information sciences

2.235***

2.177***

2.161***

2.161***

2.159***

2.150***

2.129***

1.895***

2.031***

1.902***

1.897***

1.877***

(0.516) (0.485) (0.470) (0.461) (0.463) (0.459) (0.489) (0.434) (0.451) (0.419) (0.424) (0.416)Major - Engineering and engineering technology

1.749***

1.754***

1.823***

1.824***

1.824***

1.826***

1.742***

1.784***

1.705***

1.728***

1.699***

1.703***

(0.301) (0.303) (0.307) (0.308) (0.308) (0.308) (0.279) (0.281) (0.274) (0.279) (0.274) (0.276)Major - Bio and phys science, sci tech, math, agriculture 1.077 1.094 1.108 1.109 1.112 1.112 1.243* 1.236* 1.201 1.192 1.151 1.150

(0.160) (0.159) (0.162) (0.161) (0.160) (0.160) (0.151) (0.148) (0.148) (0.146) (0.141) (0.141)

Major - General studies and other1.511** 1.564**

1.637***

1.638***

1.643***

1.642*** 1.443* 1.454* 1.482* 1.468* 1.410 1.408

(0.279) (0.278) (0.295) (0.295) (0.295) (0.295) (0.298) (0.298) (0.309) (0.306) (0.298) (0.297)Major - Social Sciences 1.038 1.014 1.023 1.023 1.031 1.028 1.164 1.082 1.148 1.108 1.125 1.118

(0.123) (0.116) (0.119) (0.116) (0.118) (0.117) (0.129) (0.122) (0.126) (0.120) (0.124) (0.122)

Major - Health care fields 1.480***

1.461***

1.487***

1.487***

1.474***

1.473***

2.321***

2.295***

2.332***

2.319***

2.310***

2.308***

(0.215) (0.211) (0.217) (0.218) (0.212) (0.212) (0.364) (0.360) (0.354) (0.353) (0.348) (0.348)

Major - Business 1.494***

1.541***

1.507***

1.508***

1.506***

1.503***

1.982***

1.944***

1.955***

1.891***

1.830***

1.820***

(0.214) (0.197) (0.187) (0.181) (0.178) (0.176) (0.271) (0.243) (0.237) (0.223) (0.221) (0.217)Major - Education 0.983 0.981 0.966 0.966 0.967 0.966 1.346** 1.313** 1.291** 1.269* 1.278* 1.274*

(0.114) (0.113) (0.110) (0.111) (0.110) (0.111) (0.172) (0.168) (0.162) (0.160) (0.162) (0.161)Major - Other Applied 1.046 1.007 1.106 1.106 1.103 1.102 1.149 1.107 1.183 1.177 1.200 1.198

(0.136) (0.130) (0.137) (0.137) (0.138) (0.138) (0.152) (0.149) (0.149) (0.148) (0.150) (0.149)

Sector - Private nonprofit 4-year 0.984 0.839 1.128 1.125 1.109 1.117 0.544*0.572**

* 0.718** 0.805** 0.952 0.968

(0.316) (0.155) (0.174) (0.130) (0.117) (0.098) (0.171) (0.118) (0.103) (0.086) (0.102) (0.086)

Sector -For-profit 0.768 0.501 0.975 0.968 0.967 0.976 0.236**0.220**

*0.378**

*0.452**

* 0.702 0.719

(0.529) (0.232) (0.350) (0.294) (0.267) (0.245) (0.167) (0.120) (0.134) (0.137) (0.175) (0.166)Sector -Others or attended more than one institution 0.903 0.813 0.911 0.909 0.902 0.904 0.662**

0.646*** 0.770** 0.800** 0.875 0.879

(0.171) (0.116) (0.105) (0.100) (0.096) (0.095) (0.129) (0.104) (0.086) (0.086) (0.094) (0.093)

Selectivity - Moderately selective1.069 1.028 1.090 1.090 1.091 1.090 1.091 1.028 1.093 1.073 1.109 1.106

(0.081) (0.081) (0.077) (0.079) (0.076) (0.077) (0.089) (0.087) (0.087) (0.087) (0.088) (0.089)Selectivity - Minimally selective or open admission 1.119 1.058 1.159 1.158 1.149 1.148 0.968 0.912 0.987 0.980 1.036 1.034

(0.134) (0.128) (0.127) (0.127) (0.121) (0.122) (0.112) (0.104) (0.109) (0.109) (0.115) (0.115)Exclusively part time 1.061 1.148 1.038 1.039 1.048 1.050 1.060 1.170 1.060 1.085 1.032 1.037

(0.109) (0.128) (0.096) (0.098) (0.096) (0.096) (0.101) (0.124) (0.086) (0.089) (0.084) (0.085)Mixed full time and part time 1.136* 1.160** 1.167** 1.167** 1.174** 1.175** 1.084 1.126 1.075 1.090 1.072 1.075

(0.081) (0.083) (0.084) (0.085) (0.085) (0.085) (0.080) (0.084) (0.082) (0.085) (0.082) (0.082)

Constant 0.3520.0587*

* 0.490 0.478 0.458* 0.439 0.2350.0256*

* 0.492 0.286* 1.329 1.195

(0.388) (0.081) (0.273) (0.357) (0.188) (0.223) (0.257) (0.040) (0.278) (0.212) (0.607) (0.689)

Observations 15,984 15,984 16,046 16,046 16,070 16,070 15,984 15,984 16,046 16,046 16,070 16,070

Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1

SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table A1d. Odds Ratios of Second Stage Results on Occupation and Graduate Enrollment using Additional Instruments

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Public-service occupation Post-BA enrollment

Amount borrowed (continuous), fitted value using distance by sector 1.008 0.975

(0.035) (0.032)

Amount borrowed (quartiles), fitted value using distance by sector 1.323 0.623

(0.580) (0.256)

Amount borrowed (continuous), fitted value using % aid & grant ratio (Zhang) 1.028 1.012

(0.018) (0.015)

Amount borrowed (quartiles), fitted value using % aid & grant ratio (Zhang) 1.430 1.209

(0.333) (0.239)Amount borrowed (continuous), fitted value using % loans 0.998 0.999

(0.010) (0.009)Amount borrowed (quartiles), fitted value using % loans 0.973 0.984

(0.131) (0.126)

Male 0.558***

0.564***

0.567***

0.565***

0.551***

0.551*** 0.836** 0.830** 0.839** 0.839** 0.837** 0.837**

(0.041) (0.040) (0.039) (0.038) (0.037) (0.037) (0.066) (0.064) (0.060) (0.060) (0.059) (0.059)

Black 1.348* 1.300 1.166 1.174 1.310** 1.310**2.495**

*2.554**

*2.272**

*2.262**

*2.346**

*2.346**

*

(0.234) (0.213) (0.157) (0.157) (0.164) (0.163) (0.417) (0.395) (0.318) (0.313) (0.309) (0.309)

Hispanic 1.425** 1.482**1.524**

*1.511**

*1.376**

*1.377**

* 1.149 1.120 1.274** 1.278** 1.246** 1.246**

(0.236) (0.234) (0.176) (0.171) (0.152) (0.152) (0.173) (0.158) (0.129) (0.128) (0.130) (0.130)Asian 0.772 0.825 0.824 0.828 0.730** 0.730** 0.876 0.831 1.165 1.179 1.101 1.101

(0.186) (0.201) (0.143) (0.146) (0.116) (0.117) (0.191) (0.173) (0.180) (0.184) (0.162) (0.164)

Other race 1.307 1.311 1.371* 1.347* 1.331* 1.332* 1.429** 1.436** 1.522** 1.511** 1.569** 1.570**

(0.221) (0.221) (0.227) (0.222) (0.221) (0.220) (0.256) (0.252) (0.259) (0.256) (0.272) (0.273)

Age 0.992 0.992 1.001 0.999 0.997 0.9970.966**

*0.967**

*0.973**

*0.972**

*0.971**

*0.971**

*

(0.011) (0.010) (0.010) (0.010) (0.010) (0.010) (0.009) (0.009) (0.009) (0.008) (0.008) (0.008)

Dependent 1.075 1.103 1.128 1.143 1.071 1.070 1.170 1.136 1.178 1.191* 1.144 1.143

(0.112) (0.126) (0.105) (0.109) (0.097) (0.097) (0.120) (0.124) (0.118) (0.123) (0.111) (0.114)Parent education - Don’t know 0.717 0.741 0.831 0.830 0.752 0.752 1.177 1.142 1.349 1.359 1.284 1.284

(0.254) (0.262) (0.296) (0.296) (0.265) (0.266) (0.380) (0.367) (0.443) (0.447) (0.411) (0.411)

Parent education - Some college0.991 0.982 0.985 1.007 1.026 1.025 0.983 0.974 0.853 0.860 0.875 0.874

(0.120) (0.104) (0.102) (0.102) (0.103) (0.102) (0.117) (0.102) (0.088) (0.086) (0.087) (0.086)Parent education - Bachelor's degree or higher 0.969 1.006 1.040 1.035 0.944 0.944 0.929 0.904 0.983 0.988 0.946 0.946

(0.136) (0.140) (0.115) (0.114) (0.096) (0.097) (0.118) (0.109) (0.093) (0.093) (0.087) (0.087)

Income percentile 1.002 1.003 1.003 1.003 1.000 1.000 1.001 1.000 1.004** 1.004** 1.003** 1.003**

(0.003) (0.003) (0.002) (0.002) (0.002) (0.002) (0.003) (0.003) (0.002) (0.002) (0.001) (0.001)

College admissions test score 1.000 1.000 1.000* 1.000* 1.000 1.000 1.001** 1.001**1.001**

*1.001**

*1.001**

*1.001**

*

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)College admissions test score missing 1.732 1.920* 1.965** 1.967** 1.594* 1.592*

3.480***

3.205***

4.458***

4.541***

3.907***

3.904***

(0.623) (0.669) (0.569) (0.573) (0.433) (0.435) (1.181) (1.064) (1.208) (1.229) (1.039) (1.044)Major - Computer and information sciences

0.345***

0.340***

0.317***

0.302***

0.298***

0.299***

0.291***

0.305***

0.294***

0.288***

0.286***

0.286***

(0.129) (0.125) (0.114) (0.110) (0.109) (0.109) (0.068) (0.072) (0.068) (0.067) (0.067) (0.067)Major - Engineering and engineering technology

0.482***

0.484***

0.463***

0.468***

0.462***

0.461***

0.609***

0.604***

0.537***

0.540***

0.536***

0.536***

(0.083) (0.083) (0.077) (0.078) (0.077) (0.077) (0.099) (0.097) (0.090) (0.090) (0.089) (0.089)Major - Bio and phys science, sci tech, math, agriculture

1.772***

1.780***

1.743***

1.732***

1.668***

1.669***

1.621***

1.625***

1.589***

1.589***

1.561***

1.561***

(0.219) (0.215) (0.198) (0.196) (0.192) (0.192) (0.200) (0.199) (0.193) (0.193) (0.192) (0.192)

Major - General studies and other1.262 1.277 1.252 1.243 1.192 1.192 0.834 0.832 0.827 0.827 0.810 0.810

(0.221) (0.221) (0.209) (0.207) (0.199) (0.198) (0.163) (0.159) (0.151) (0.150) (0.147) (0.147)

Major - Social Sciences 1.462***

1.444***

1.412***

1.376***

1.385***

1.387*** 1.190 1.225* 1.175 1.161 1.155 1.157

(0.163) (0.161) (0.158) (0.154) (0.156) (0.154) (0.129) (0.130) (0.128) (0.124) (0.125) (0.124)

Major - Health care fields 7.094***

7.059***

6.554***

6.528***

6.545***

6.546*** 0.731** 0.734** 0.709** 0.707** 0.714** 0.714**

(1.078) (1.073) (1.041) (1.037) (1.031) (1.031) (0.109) (0.110) (0.107) (0.106) (0.107) (0.107)

Major - Business 0.409***

0.413***

0.387***

0.377***

0.360***

0.361***

0.387***

0.390***

0.409***

0.406***

0.401***

0.401***

(0.058) (0.055) (0.050) (0.048) (0.047) (0.046) (0.047) (0.044) (0.044) (0.043) (0.044) (0.044)

Major - Education 6.638***

6.624***

6.216***

6.136***

6.142***

6.147*** 0.984 0.994 0.928 0.922 0.925 0.925

(0.919) (0.905) (0.870) (0.862) (0.862) (0.861) (0.117) (0.120) (0.110) (0.110) (0.110) (0.110)

Major - Other Applied 1.013 0.999 0.964 0.961 0.977 0.9770.542**

*0.549**

*0.499**

*0.497**

*0.501**

*0.501**

*

(0.128) (0.129) (0.116) (0.116) (0.117) (0.117) (0.064) (0.065) (0.056) (0.056) (0.056) (0.056)Sector - Private nonprofit 4-year 0.797 0.760 0.682** 0.743** 0.892 0.888 1.120 1.091 0.812 0.832* 0.913 0.910

(0.252) (0.159) (0.116) (0.096) (0.113) (0.098) (0.330) (0.203) (0.117) (0.089) (0.094) (0.079)

Sector -For-profit 0.644 0.559 0.406** 0.464** 0.726 0.721 1.116 1.131 0.467**0.479**

* 0.613* 0.611**

(0.434) (0.278) (0.160) (0.155) (0.210) (0.192) (0.707) (0.521) (0.143) (0.120) (0.153) (0.137)Sector -Others or attended more than one institution 1.143 1.104 1.025 1.056 1.164 1.162

1.953***

1.967***

1.588***

1.597***

1.664***

1.662***

(0.200) (0.159) (0.128) (0.124) (0.128) (0.126) (0.359) (0.298) (0.190) (0.181) (0.185) (0.182)

Selectivity - Moderately selective1.135 1.117 1.060 1.046 1.081 1.082 0.901 0.922 0.882* 0.874* 0.883* 0.884*

(0.103) (0.107) (0.097) (0.097) (0.099) (0.099) (0.064) (0.071) (0.065) (0.067) (0.064) (0.064)Selectivity - Minimally selective or open admission 1.172 1.146 1.104 1.099 1.150 1.151 0.926 0.946 0.908 0.903 0.929 0.930

(0.169) (0.171) (0.151) (0.150) (0.156) (0.156) (0.103) (0.108) (0.094) (0.095) (0.093) (0.094)

Exclusively part time 0.787** 0.812*0.726**

*0.738**

*0.709**

*0.708**

*0.665**

*0.640**

*0.653**

*0.660**

*0.641**

*0.641**

*

(0.085) (0.096) (0.072) (0.074) (0.070) (0.071) (0.071) (0.072) (0.063) (0.064) (0.062) (0.062)Mixed full time and part time 0.961 0.969 0.952 0.962 0.952 0.951 1.038 1.023 1.023 1.029 1.020 1.019

(0.074) (0.077) (0.076) (0.077) (0.076) (0.076) (0.071) (0.070) (0.071) (0.072) (0.071) (0.070)

Constant 0.291 0.1430.166**

*0.113**

* 0.413* 0.427 1.462 3.354 0.472 0.358 0.729 0.743

(0.333) (0.225) (0.098) (0.091) (0.194) (0.248) (1.548) (4.828) (0.253) (0.257) (0.331) (0.425)

Observations 15,984 15,984 16,046 16,046 16,070 16,070 15,984 15,984 16,046 16,046 16,070 16,070

Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1

SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table A2a. Odds Ratios of Second Stage Results on Employment and Salary for Those Not Enrolled in 2012

(1) (2) (3) (4) (5) (6) (7) (8)

Worked more than desired due to education cost

Employed in 2012 Employment intensity 2012 Total salary 2012

Amount borrowed (continuous, $1,000s), fitted value using distance by aid

1.126*** 1.116*** 0.989 859.5**

(0.04) (0.05) (0.05) (387)Amount borrowed (quartiles), fitted value using distance by aid

3.426*** 5.351*** 0.894 10,699**

(1.44) (2.97) (0.51) (5,246)

Male 1.048 1.016 1.320*** 1.330***1.544**

*1.549**

* 7,563*** 7,458*** (0.08) (0.08) (0.14) (0.14) (0.18) (0.17) (984) (997)

Black 0.801 0.887 0.419*** 0.408*** 1.112 1.101-

8,485***-

8,133*** (0.12) (0.13) (0.08) (0.08) (0.29) (0.28) (2,274) (2,304)Hispanic 1.399* 1.253 1.085 1.112 0.691 0.698 -1,162 -1,539 (0.25) (0.21) (0.23) (0.23) (0.19) (0.19) (1,921) (1,869)

Asian 1.880*** 1.673** 0.664 0.730 1.381 1.397 2,495 2,319

(0.42) (0.36) (0.17) (0.20) (0.48) (0.47) (3,099) (3,096)Other race 1.595** 1.500** 0.693* 0.668** 1.003 1.008 1,170 797 (0.29) (0.27) (0.14) (0.13) (0.34) (0.33) (3,543) (3,535)Age 0.990 0.984* 1.000 0.995 0.970 0.971 101 59 (0.01) (0.01) (0.01) (0.01) (0.02) (0.02) (132) (128)Dependent 0.897 0.915 1.200 1.309** 0.724* 0.723* -2,699* -2,341 (0.09) (0.09) (0.14) (0.16) (0.13) (0.14) (1,437) (1,435)Parent education - Don’t know 0.634 0.620 0.501** 0.536* 2.101 2.107 -3,295 -3,181 (0.27) (0.26) (0.17) (0.18) (1.59) (1.59) (5,672) (5,724)Parent education - Some college 0.825 0.942 0.773* 0.828 0.856 0.845 -3,245** -2,458* (0.10) (0.10) (0.10) (0.09) (0.16) (0.14) (1,505) (1,431)

Parent education - Bachelor's degree or higher 1.182 1.086 1.384* 1.441** 0.650** 0.655** 1,764 1,542 (0.15) (0.13) (0.25) (0.25) (0.14) (0.13) (1,844) (1,787)Income percentile 1.002 1.001 1.011*** 1.013*** 1.007 1.007 219.6*** 221.9*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (37) (40)College admissions test score 1.000 1.000 1.001*** 1.002*** 1.000 1.000 16.93*** 16.69*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (4) (4)

College admissions test score missing 0.717 0.601 2.919** 3.398** 0.965 0.98214,921**

*14,678**

* (0.27) (0.22) (1.36) (1.62) (0.58) (0.56) (4,082) (3,957)Major - Computer and information sciences

0.495***

0.404*** 2.342** 1.929**

4.507***

4.593***

30,722***

29,220***

(0.12) (0.09) (0.78) (0.63) (2.30) (2.34) (3,878) (3,802)Major - Engineering and engineering technology

0.393***

0.412*** 2.066*** 2.153***

4.575***

4.557***

27,587***

27,920***

(0.08) (0.08) (0.52) (0.54) (1.80) (1.78) (2,303) (2,297)Major - Bio and phys science, sci tech, math, agriculture 0.867 0.824 1.167 1.139

2.170***

2.181*** 9,159*** 8,855***

(0.11) (0.11) (0.20) (0.20) (0.48) (0.48) (2,062) (2,052)Major - General studies and other 0.926 0.863 1.166 1.145 1.235 1.244 12,131** 11,768** (0.17) (0.15) (0.28) (0.27) (0.45) (0.45) (5,368) (5,348)

Major - Social Sciences 0.977 0.876 1.053 0.9411.806**

*1.825**

* 6,022*** 5,188*** (0.12) (0.11) (0.15) (0.13) (0.35) (0.35) (1,582) (1,575)

Major - Health care fields 0.794 0.798 2.650*** 2.630*** 1.235 1.23521,440**

*21,430**

* (0.12) (0.12) (0.62) (0.61) (0.25) (0.25) (1,810) (1,818)

Major - Business 0.755**0.666**

* 2.028*** 1.896***3.330**

*3.371**

*16,695**

*15,944**

* (0.09) (0.08) (0.35) (0.31) (0.73) (0.72) (1,907) (1,791)

Major - Education 0.830 0.788* 1.642*** 1.572***2.369**

*2.381**

* 6,721*** 6,356*** (0.12) (0.11) (0.29) (0.27) (0.48) (0.48) (2,010) (1,987)Major - Other Applied 0.848 0.855 1.269 1.228 1.800** 1.799** 5,541*** 5,468***

* * (0.11) (0.12) (0.20) (0.19) (0.36) (0.36) (1,399) (1,401)Sector - Private nonprofit 4-year 0.460** 0.776 0.326*** 0.420*** 0.959 0.913 -6,960* -3,925 (0.14) (0.15) (0.12) (0.11) (0.41) (0.24) (3,571) (2,497)

Sector -For-profit 0.172*** 0.434*

0.0809*** 0.108*** 1.993 1.828

-16,998** -12,142*

(0.11) (0.19) (0.07) (0.07) (1.84) (1.24) (7,895) (6,524)Sector -Others or attended more than one institution 0.680** 0.835 0.644* 0.680* 1.226 1.203 -2,024 -972 (0.13) (0.13) (0.15) (0.14) (0.37) (0.31) (2,924) (2,779)Selectivity - Moderately selective 1.076 1.035 1.076 1.001 0.751** 0.754** -2,789** -3,197** (0.10) (0.10) (0.11) (0.11) (0.09) (0.09) (1,162) (1,235)Selectivity - Minimally selective or open admission 1.000 1.006 0.849 0.805 0.711* 0.710*

-5,587***

-5,742***

(0.13) (0.14) (0.14) (0.13) (0.13) (0.14) (1,784) (1,727)Exclusively part time 1.294** 1.331** 1.338** 1.490** 0.932 0.929 -498 -26 (0.15) (0.17) (0.19) (0.23) (0.16) (0.16) (1,348) (1,447)Mixed full time and part time 1.074 1.115 1.010 1.064 0.970 0.967 -208 118 (0.09) (0.09) (0.10) (0.11) (0.12) (0.12) (1,188) (1,173)

Constant 0.154* 0.0850* 0.0898*0.00827*

* 12.520 13.150 -12,974 -23,303 (0.16) (0.12) (0.12) (0.02) (20.74) (27.87) (12,399) (17,938)Observations 13,301 13,301 13,503 13,503 11,616 11,616 13,503 13,503Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).

Table A2b - Odds Ratios of Second Stage Results on Job Satisfaction and Occupation for Those Not Enrolled in 2012

(1) (2) (3) (4) (5) (6)

Satisfaction with compensation

Satisfaction with job security

Public-service occupation

Amount borrowed (continuous , $1,000s), fitted value using distance by aid 1.060** 1.059* 0.952 (0.03) (0.04) (0.03)Amount borrowed (quartiles), fitted value using distance by aid 2.826*** 2.880** 0.591 (0.97) (1.23) (0.25)

Male 1.114 1.129* 1.047 1.0630.528**

*0.534**

* (0.08) (0.08) (0.08) (0.08) (0.04) (0.04)

Black 0.501*** 0.478***

0.565***

0.535***

1.736***

1.672***

(0.08) (0.07) (0.10) (0.09) (0.33) (0.31)Hispanic 1.047 1.101 0.915 0.970 1.205 1.254 (0.16) (0.16) (0.16) (0.16) (0.18) (0.18)Asian 0.670* 0.746 1.026 1.156 0.558** 0.580** (0.14) (0.15) (0.23) (0.26) (0.14) (0.15)Other race 0.882 0.870 0.771 0.762* 1.153 1.182 (0.16) (0.15) (0.12) (0.12) (0.22) (0.22)Age 1.002 1.000 0.982** 0.980** 0.992 0.995 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)Dependent 1.156 1.232** 1.107 1.185 1.017 1.006 (0.11) (0.13) (0.11) (0.12) (0.10) (0.11)Parent education - Don’t know 1.520 1.619 0.729 0.781 0.792 0.799 (0.47) (0.51) (0.22) (0.23) (0.29) (0.29)Parent education - Some college 1.117 1.143 0.869 0.885 1.104 1.047 (0.12) (0.11) (0.10) (0.09) (0.14) (0.12)Parent education - Bachelor's degree or higher 1.237* 1.307** 1.076 1.145 0.817 0.842 (0.15) (0.15) (0.16) (0.16) (0.12) (0.12)Income percentile 1.007** 1.009*** 1.008** 1.011** 0.997 0.997

* * * (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)College admissions test score 1.001** 1.001*** 1.001 1.001** 1.000 1.000 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)College admissions test score missing 2.082** 2.468*** 1.394 1.684 0.840 0.893 (0.68) (0.79) (0.50) (0.61) (0.33) (0.34)Major - Computer and information sciences

2.436*** 2.196*** 1.811** 1.636**

0.319***

0.347***

(0.60) (0.53) (0.45) (0.40) (0.13) (0.14)Major - Engineering and engineering technology

1.718*** 1.756***

1.604***

1.639***

0.290***

0.285***

(0.31) (0.31) (0.26) (0.27) (0.07) (0.07)Major - Bio and phys science, sci tech, math, agriculture 1.203 1.193 1.361** 1.354**

1.612***

1.644***

(0.20) (0.20) (0.20) (0.20) (0.23) (0.23)Major - General studies and other 1.601** 1.605*** 1.310 1.318 1.218 1.252 (0.30) (0.29) (0.30) (0.30) (0.23) (0.23)

Major - Social Sciences 1.010 0.948 1.089 1.0231.479**

*1.548**

* (0.13) (0.12) (0.14) (0.13) (0.17) (0.18)

Major - Health care fields 1.406** 1.394**2.291**

*2.271**

*8.220**

*8.206**

* (0.22) (0.22) (0.39) (0.39) (1.25) (1.25)

Major - Business 1.544*** 1.510***

1.713***

1.682***

0.378***

0.398***

(0.22) (0.20) (0.26) (0.23) (0.06) (0.06)

Major - Education 0.975 0.954 1.226 1.2007.077**

*7.227**

* (0.12) (0.12) (0.17) (0.17) (1.01) (1.03)Major - Other Applied 0.969 0.941 1.058 1.025 1.104 1.102 (0.14) (0.13) (0.16) (0.15) (0.15) (0.15)Sector - Private nonprofit 4-year 0.649 0.696** 0.581* 0.612** 1.297 1.056 (0.17) (0.12) (0.18) (0.12) (0.39) (0.21)Sector -For-profit 0.332** 0.332*** 0.278* 0.266** 1.884 1.318

* (0.19) (0.14) (0.19) (0.13) (1.30) (0.67)Sector -Others or attended more than one institution 0.747* 0.740** 0.697* 0.685** 1.512** 1.397** (0.12) (0.10) (0.14) (0.11) (0.27) (0.21)Selectivity - Moderately selective 1.047 0.997 1.104 1.049 1.177* 1.198* (0.09) (0.09) (0.10) (0.10) (0.12) (0.12)Selectivity - Minimally selective or open admission 1.068 1.020 0.953 0.907 1.318* 1.317* (0.13) (0.12) (0.12) (0.11) (0.20) (0.20)Exclusively part time 1.050 1.137 1.014 1.103 0.753** 0.742** (0.11) (0.13) (0.10) (0.12) (0.09) (0.09)Mixed full time and part time 1.106 1.143* 1.060 1.096 0.946 0.931 (0.08) (0.09) (0.09) (0.09) (0.08) (0.08)

Constant 0.103**0.0173**

* 0.331 0.0508* 1.741 2.383 (0.10) (0.02) (0.38) (0.08) (1.97) (3.54)Observations 13,503 13,503 13,503 13,503 13,503 13,503Note: Standard errors in parentheses. The weight variable used in this table is WTE000. The variance estimation method is BRR. *** p<0.01, ** p<0.05, * p<0.1SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).