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© Copyright by
Mark N. Mitchell
2006
All Rights Reserved
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ESSAYS ON THE DETERMINANTS AND IMPLICATIONS OF THE CHOICE OF
UNDERGRADUATE MAJOR
Abstract
by
Mark N. Mitchell
The following series of essays has been assembled to examine the determinants
and implications surrounding the choice of undergraduate major. Other studies have
analyzed various aspects of the decision making process associated with the choice of
college major, however, many questions remain unanswered. These questions include:
1.) How does the probability of receiving a job offer and expected earnings that
incorporate risk affect a student’s choice of major at a selective university? 2.) What are
some of the implications that the choice of major has upon other college decisions such
as borrowing behavior? 3.) What affect does choosing a second major have upon a
student’s earnings? 4.) How do the answers to these questions change when describing
the choices of men and women? These essays are an attempt to address these
shortcomings in previous research in this area.
There have been several studies that model the college student’s choice of major
as a utility maximizing decision that is primarily based upon the relative expected
earnings that are correlated with different majors. One problem with these studies is that
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Mark N. Mitchell
few incorporate the fact that students face various forms and degrees of uncertainty and
risk in their choice of major. Motivated by this shortfall, this first essay models a
student’s choice of major as being determined primarily by a student’s relative expected
earnings. Students’ choice of major at a selective university is shown to be positively
correlated to relative expected earnings adjusted for earnings uncertainty or risk.
Students at this selective university are less motivated by another form of uncertainty
inherent in the choice of major, the probability of receiving job offers across majors.
Once a student has chosen a major, this choice may have particular importance inother decisions that a student makes while in college. The second essay takes an
alternative look at the relationship between a student’s choice of major and the amount of
loan debt they choose to accumulate during college. Based on a life-cycle model of
consumption and borrowing, this essay suggests that a student’s choice of major could
influence the level of debt that a student will take on. This study finds that some students
in higher earnings majors do tend to have higher total debt levels. The endogenous
nature of major choice and loan debt is also addressed.
The final essay explores an aspect of the choice of college major that has
previously been ignored. While the earnings implications of a student’s primary major
have been the focus of several empirical studies, little attention has been paid to the
earnings implications of a student’s choice of a second major. This essay empirically
tests whether students from a selective university actually receive some type of monetary
return to investing in a second major. Results show that some students do benefit from
choosing to obtain a second major.
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CONTENTS:
ESSAY 1
EXPECTED EARNINGS, EMPLOYMENT, AND THE CHOICE
OF UNDERGRADUATE MAJOR
1.1 INTRODUCTION
Background ……………………………………………….….………..1
Statement of the Problem…………………………………..…………..1
1.2 REVIEW OF THE LITERATURE.………………………….….…………3 Marc Berger (1988)………………………………………………….…4Eric Eide & Geetha Waehrer (1998) ……………………………...…...5Montmarquette, Cannings, Mahseredjian (2002)…….…………….......5
1.3 METHODOLOGY AND DATA
Similarities with Flyer (1997)………………………………………….7
Data Description………………………………………………………..9
Specification of student’s choice of MajorThe effect of expected earnings on Major………………..….....9
Predicting Expected Earnings………………………………………….10
Predicting Probability of Receiving a Job offer……………………….16
Specification of student’s choice of MajorThe effect of Probability of Job offer on Major………….……16
1.4 EMPIRICAL RESULTS
Mixed Conditional Logits
Effect of Expected Earnings no uncertainty ………..…...….…18
Effect of Expected Earnings with uncertainty……………....…19
Effect of Expected Probability of Job offer……….……....…...21
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1.5 SUMMARY AND CONCLUSIONS…………………………….....….…23
1.6 APPENDIX 1
Tables……………………………………..……..…………….85
References……………………………………………………..142
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ESSAY 2
UNDERGRADUATE CHOICE OF MAJOR AND LOAN DEBT
2.1 INTRODUCTION
Background …………………………………………………………...26Statement of the Problem……………………………………………...28Data …………………………………………………………………...30
2.2 REVIEW OF THE LITERATURE
Monks (1999)…………………………………………………….…....31St. John (1994)………………………………………………………...35Weiler (1994)……………………………………………...………......38
2.3 METHODOLOGY
Life-Cycle and Marginal Utility Theory……………………………....40 Method of Analysis…………………………………………….……...45Instrument Choice…………………………………..............................47
2.4 EMPIRICAL RESULTS
First Approximation: Single equation OLSChoice of Major and Loan debt…….………………………....50
Testing for Exogeneity…………………………………………….….53
Instrumental Variable: (2SPLS)Choice of Major and Loans………………………….…….….54
2.5 SUMMARY AND CONCLUSIONS……………….………………...…..56
2.6 APPENDIX 2…………………………………….…………………...…..97
Tables………………………………………..………………….…..…95References…………………………………………………………....144
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ESSAY 3
AN ANALYSIS OF SECOND MAJORS AND EARNINGS
3.1 INTRODUCTION
Background …………………………………………….…………....58
Statement of the Problem…………………………….……………....58
3.2 REVIEW OF THE LITERATURE……………………………………...59
Rumberger & Thomas (1993)………………….………………….…61Hamermesh and Donald (2004) …………………………...………...63
3.3 DATA
Single Selective University…………………………………………..67College and Beyond 1976 Cohort…………………………………....67
3.4 METHODOLOGY
Method of Analysis………………………………………………......68
3.5 EMPIRICAL RESULTS
Log Earnings Regressions
Single University …………………………………...…….…73College and Beyond…………………………………….…...77
3.6 SUMMARY AND CONCLUSIONS……………………….……......…79
3.7 APPENDIX 3
Tables……………………………………………………………....123Educational choice, earnings and happiness……………...……......135References...………………………………………………………..146
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ACKNOWLEDGEMENTS
The journey I began nine years ago as a freshman at the University of Notre Dame
has been an exceptional one. My time at Our Lady’s University has been filled with
wonderful experiences and people. Therefore, I cannot reflect upon this time without
acknowledging that I did not make it to this point without the help of many individuals. I
stand in great appreciation of their support. I owe a large debt of gratitude to both my parents
for not only instilling in me a genuine work ethic and stressing the value of obtaining an
education but also for sacrificing to ensure that I obtained the best. I also give a special thank
you to my mother whose own academic achievements have provided me with the inspiration
to complete my own doctorate. I could not have asked for better parents or role models, thank
you both. I’d also like to thank my brothers, particularly my little brother Matthew for
always being there when I need you. I thoroughly enjoyed spending part of our Notre Dame
journeys together. My beautiful wife Elyse, who I was blessed to meet at Notre Dame, is
also deserving of my unending thanks. Her constant love, support and encouragement
allowed me to keep my sanity while working on this dissertation even to the detriment of her
own. You are the love of my life and I thank you dearly.
In addition to those mentioned above, I owe a word of thanks to a few other
individuals. Thank you to my dissertation committee, especially to my chair Thomas R.
Swartz, for your guidance in developing this research and my subsequent career path. Any
errors that remain herein are my own. I give a final thanks to Institutional Research at the
university whose students are the focus of this research for allowing me access to their
student database. I also express my gratitude to the Andrew Mellon Foundation for allowing
me access to The College and Beyond database. This research would have not been possible
without your generosity.
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ESSAY 1
EXPECTED EARNINGS, EMPLOYMENT, AND THE CHOICE
OF UNDERGRADUATE MAJOR
1.1 Introduction
This essay, which is the first of three essays that examine the choice of
undergraduate college majors, will investigate the importance of two incentives that
impact a student’s choice of major. Previous research has empirically examined the
many incentives economic theory offers as important influences on higher educationdecisions; however, previous research that examines the nature of the relationship
between expected earnings and choice of college major is incomplete. While a few
studies have shown that expected earnings represent a significant influence on higher
educational decision making, one shortcoming of this research has been the inability to
incorporate into the modeling of student choice the idea that a student’s choice of college
major is a decision made under uncertainty and risk. Although there is an established
link between a college student’s expected earnings across all majors and their choice of
major, the absence of uncertainty in models of a college student’s choice of major may
prohibit a more accurate model of how students choose their major from being estimated.
Incorporating the risk that a student faces in their expected earnings allows this study to
more closely model the choice of major as a case of expected utility maximization.
The first question this research will attempt to answer is the extent to which a
student’s choice of major at a selective institution is influenced by their risk adjusted
expected earnings and uncertainty in probabilities of job offers across majors. This essay
will incorporate earnings uncertainty or risk into the student’s utility maximizing decision
1
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by allowing a student’s own abilities across majors and the variance in earnings outcomes
represented by a major’s earnings distribution to affect their earnings projections. This
research will also investigate a question not previously addressed by the current body of
literature. A student’s choice of major is a choice made under uncertainty as a student
will only achieve their expected earnings if they receive a job offer. Therefore, this essay
will investigate whether the expected probabilities of getting a job offer across majors
also affects a student’s choice of major. A unique feature of this essay is that the
analysis focuses on the choice of major at a single selective university. Most other
studies in this area have exclusively used the National Longitudinal Surveys of YoungMen, Youth or High School Class of 1972. So while the results from this study are not
generally descriptive of all college graduates, this essay can determine whether the
relationship between expected earnings and choice of major holds for more recent
graduating cohorts from a selective university. The data used is well suited to address
these questions as it provides descriptive information on four years of graduating seniors
in 1997, 1999, 2001 and 2003 at a single selective university which eliminates problems
with comparing major types across different colleges, a problem encountered by previous
studies.
Like many other incentives theorized to influence higher education decisions,
expected earnings have been shown by the previous literature to influence the decisions
of different demographic groups differently. This study will also find whether there are
differences in the way men and women in this dataset respond to expected earnings in
their choice of major. Any analysis of minority or socioeconomic group differences in
choice of major must unfortunately be abbreviated and left for further research due to the
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small number of minority students and students of socioeconomic disadvantage. In order
to understand whether accounting for risk in expected earnings adds any precision to
modeling student’s choice of major, this study will first estimate a student’s choice major
following the methodology of previous research and then again after incorporating
earnings uncertainty or risk into the model.
1.2 Review of the Literature
The educational choice literature can be split easily into two parts. The first
segment addresses the incentives and costs that influence the demand for education, that
is, the incentives and costs that influence the decision to invest in additional years of
education. This literature rests on the principle that individual educational investment
decisions are rational economic decisions, influenced by the reconciliation of costs—
which include direct costs of a year of education and indirect costs such as wages lost—
and the returns to an extra year of education. Still other incentives have been linked with
this decision, but for the sake of parsimony, they will not be addressed here. The second
segment of the literature that is directly relevant to this research focuses on the qualitative
choices of students selecting into higher education. These decisions include the choice of
what college an individual attends, whether a student will persist in college and finally
what their final field of study will be. The qualitative choice that will be subject to
empirical scrutiny in this essay is the final choice of college major.
Just as the human capital literature assumes that individuals make rational
educational investment decisions by “implicitly calculating whether or not education is
worthwhile, by comparing expected benefits with the expected costs associated with an
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investment” 1 , the literature on this qualitative choice is rather cohesive in its conclusion
that college students can implicitly calculate their own relative expected returns in each
available major when deciding between the types of education in which they will invest.
Demand in the labor market (Firoito and Dauffenbach 1982), pre-college ability (Turner
and Bowen 1999) and socioeconomic background (Leppel et al 2001, Simpson 2003, and
Strasser et al 2002), as well as the ease of obtaining a high grade point average (Cohen
2005) are among other incentives linked to the individual and aggregate choice of college
major.
Freeman (1971), Koch (1972), Berger (1988), Eide and Waehrer (1998) andMontmarquette, Cannings and Mahseredjian (2002) have all found empirical evidence
that suggests that increases in the expected earnings of a major relative to expected
earnings of other majors, enhances the utility of that major and consequently increases
the probability that individuals will choose that major relative to others.
One of the seminal works in this area was completed by Marc Berger (1988).
Berger’s hypothesis—that individuals will choose a major that maximizes his or her
lifetime expected earnings—is supported by his analysis which examines the choice of
major of students from the National Longitudinal Survey of Young Men who graduated
college from 1962-1977. These young men are shown to choose majors with relatively
higher expected earnings streams. However, Berger finds that these men are also less
influenced by initial expected earnings and influenced to a greater extent by the expected
earnings stream of a major. 2
1 Paulsen p.56-572 Berger’s model assumes that individuals respond to life-cycle earnings expectations. His results
support this assumption as the results are consistent with rational expectations frameworks where studentsdo not form their earnings expectations in a naïve or myopic manner.
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expected earnings are estimated with more precision than expected earnings predictions
that don’t account for this earnings uncertainty. Montmarquette et al find that increases
in both the predicted probability of finishing a major, the uncertainty adjusted expected
earnings of a major relative to other majors, increases the utility of that major and
consequently increases the probability that the major will be chosen relative to others. It
should be noted that Montmarquette et al run separate regressions for males and females.
They find that the coefficient on the expected earnings variable is smaller for females but
still positive and significant. The different results for men and women in this analysis
would seem to assert that expected earnings have a greater influence on the choice ofmajor for men than women. This result could also be evidence that women are less likely
to choose majors with higher earnings due to anticipated discrimination or the presence
of other negative attributes of jobs with higher expected earnings; or as many argue,
women choose majors that are less likely to atrophy or receive wage penalties from long
absences from the labor market. However, this last argument might not be as convincing
when describing female students graduating from a selective institution. Montmarquette
et al acknowledge that a more complete model of student choice under uncertainty would
incorporate the idea that students don’t know if they will be able to actually obtain their
expected earnings in the major they choose, due to employment uncertainty. This study
will also address employment uncertainty at graduation which may affect students’
choice of major.
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1.3 Methodology and Data
Most previous research treat expected earnings only as a function of observed
characteristics and treat individuals as predicting these expected earnings with certainty.
Little attention is paid to the various earnings outcomes that a student may face in a
particular major. Expected earnings across majors are in fact risky, being determined by
a student’s abilities as well as the various feasible earnings outcomes within a major.
Therefore, we allow a student’s expected earnings in a major to be affected by both his or
her own abilities as well as the earnings distribution of that major.
In order to develop a model of student choice that integrates students’ own majorspecific abilities, as well as the various earnings outcomes in the earnings distributions of
each major, this study will in part employ a methodology to predict expected earnings
used by Flyer (1997) in his study of occupational choices made under uncertainty. Flyer’s
study assumes that individuals face uncertainty in their own occupational specific
abilities and in the monetary rewards to an occupation. Flyer calculates life-cycle
earnings estimates that reflect occupational specific abilities and various earnings
outcomes of an occupation and investigates how these expected earnings influence a
person’s initial choice of occupation. Flyer finds that these life-cycle earnings
predictions are strongly related to choosing to enter an occupation. He also finds that
because of an individual’s uncertainty in his or her abilities across occupations, increases
in an occupation’s option value of job mobility are strongly related to choosing that
occupation.
Apart from the direct relationship that risk adjusted expected earnings have on the
choice of major, an issue raised by Montmarquette et al, will also be addressed in this
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essay. While all students form some type of earnings expectations in each major
conditional upon their own abilities and earnings distribution of a major, there is nothing
that guarantees with certainty that a student will receive or “realize” those earnings given
that they could be unemployed when they graduate from college. For this reason there is
also uncertainty in a student’s ability to obtain employment in each major. As uncertain
job prospects surely weigh on the thoughts of both student and parent alike, the effect of
unemployment uncertainty on the choice of major will also be addressed by this study.
This essay is unique as it is the first to empirically test whether the probability of
receiving a job offer may influence a student’s choice of major. Just as a student might be motivated to choose the major in which he or she has the highest relative expected
earnings, the student might also be more likely to choose a major in which he or she has
the highest expected probability of receiving a job offer. For example, based upon an
individual’s own abilities, an individual who majored in philosophy might have a higher
probability of getting a job as a philosopher than they would as an engineer, and for this
reason chose to major in philosophy.
The methodology for establishing the relationship between uncertain expected
earnings and major choice will closely follow previous studies in this area. This research,
however, will be unique in the manner in which expected earnings is calculated. In most
previous studies, predicted earnings are only a function of an individual’s observed
characteristics and are treated as certain. Earnings estimates that account for both major
specific ability and the variance in major specific earnings outcomes as well as students’
predicted probabilities of receiving job offers will be calculated using data from a pooled
cross-section which provides information on the educational records, major choice, test
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scores, socioeconomic background, tastes and aspirations of 7,448 undergraduates who
graduated in 1997, 1999, 2001 and 2003 from a single selective university; as well from
the College and Beyond 1976 cohort which includes background and 1995 earnings
information for 22,512 graduates from 30 selective colleges and universities.
Since most variables in the dataset from the single university and in the College
and Beyond dataset are easily matched, and since earnings data from the College and
Beyond is more complete, the College and Beyond data will constitute the sample that
will help provide predictions of both major specific productive probabilities and major
specific earnings distributions for the individuals in the single selective universitysample. 3
Specification of the choice of major
Following a special case of the theory of expected utility maximization, expected
earnings maximization, to explain how students choose their college major, we can
empirically test whether expected earnings of a major is correlated with major choice.
Estimation of the following equation will be carried out using a mixed logit specification,
a special case of the conditional logit proposed by McFadden (1974). In this
specification, major choice is an expected utility maximizing decision, where expected
earnings are characteristics of the dependant variables themselves (the choice
alternatives), and all other independent variables are characteristics of the individual
students.
(1). E(U ij ) = α E ij + Z i δ + u ij
3 A student that falls in a specific quartile of a major’s earnings distribution is assumed for the sake of thisstudy to also fall in that same quartile in the distribution of productive ability for those that enter that major. Soestimating an individual’s major specific productive abilities amounts to predicting the probabilities that an individualwill fall in each of the four quartiles of a major’s earnings distribution.
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E(U ij ) is the expected utility for individual i associated with choosing major j; E ij
is the expected earnings for individual i from choosing major j over the other majors; and
Z i is a vector of student background characteristics including controls for tastes. So the
expected utility for individual i in major j depends upon the expected earnings of major j
and Z i. Individuals choose major j if U ij > U in, for all n ≠ j. McFadden demonstrates
how the logit transformation allows the equation above to be expressed as the log of the
ratio of two probabilities.
(2). ln ( P ij ) = α ( E ij – E in ) + Z ( δ j – δ n)( P in )
This new equation shows that as expected earnings of major j (E ij ) for individual
i increases, the utility of major j increases as does the probability of choosing major j.
This equation demonstrates that the probability of choosing a major should increase as its
expected earnings relative to all other majors increases.
Estimating major specific earnings (E ij ) without uncertainty
Before discussing how this study will incorporate earnings risk into the expected
earnings predictions (E ij ) for each student in each major j, a short description of how the
previous literature chooses to calculate earnings expectations that do not account for any
kind of uncertainty is in order. The way that most of the previous empirical literature
predicts earnings expectations for students is rather straightforward.
Observed earnings of individuals in the dataset are first used to run log earnings
regressions for each major. These major specific earnings regressions are used to
estimate the underlying wage earnings parameters ( βs) for each major. For example the
equation below is estimated for each major j in order to find the vector β j for that major.
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(3). Log earnings ij = β0 + X ij β 1 j + λ ij β 2 j + εij
However, earnings are only observed for each major for those who have selected
into that major, therefore there is usually a correction made for this implied sample
selection. Given this censored data, most studies proceed with Lee’s (1983) or
Heckman’s (1976) two-stage estimation method for sample selection. A first stage probit
model is used to predict the probability of being in major j and in a second stage the
inverse mills ratio calculated from the first stage is included as a regressor. This two
stage estimation is implemented in order to arrive at consistent estimates of β j. This
selection correction is completed for each major. This essay uses a student’s probablemajor at the beginning of their freshman year as the exclusion restriction for the
estimated equation above.
Once the correction is made for selection into each major, the vector of β j is used
to estimate unconditional earnings for every individual in major j. This is done for each
major so that every individual in the data set will have an expected earnings prediction
for each major based on their own levels of X ij. The student’s choice of major is then
modeled using a conditional logit where the choice of major is based upon a student’s
relative expected earnings across majors. The problem here is that these earnings
expectations are assumed to be taken with certainty. However, the very nature of the
prediction of earnings using this method implies that earnings are predicted with some
level of error. There inherently exists some level of uncertainty in these earnings
projections. This essay will compare this way of calculating expected earnings with the
alternative process described below.
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Estimating major specific expected earnings (E ij ) with uncertainty
Alternatively, if we assume that earnings predictions are uncertain and are
determined by both a student’s abilities and the distribution of earnings outcomes in a
major, expected earnings for a student I majoring in major j can be represented.
4(4.) E ij = ∑ ∏ ijm L jm ,
m=1
where L jm is the expected value of earnings for individual I who chooses major j and who
has a productivity level that ranks in quartile m among those also graduating in major j.4
The variance and skewness in the distribution of earnings for major j can affect E ij
through the L jm term. The reason that the I subscript is dropped from L jm is because this
study assumes that all students have the same expectations regarding major specific
earnings distributions. ∏ ijm corresponds to the probability that individual I assigns to
their major specific productivity (earnings) being ranked in quartile m among all
graduates in major j. Together, both ∏ ijm and L jm incorporate a student’s uncertainty and
the variance in feasible earnings outcomes represented by a major’s earnings
distribution. 5
Estimating ∏ ijm
In order to predict the probability ( ∏ ijm ), that an individual assigns to the
likelihood of falling into quartile m of major j’ s productive ability distribution, a simple
prediction method is used. Predicted probabilities that an individual falls into the four
quartiles of each major’s earnings distribution can be found by estimating major specific
earnings equations using a two stage Heckman method for sample selection into a major,
4 m = 1 is equal to the highest quartile and m = 4 is equal to the lowest quartile.5 Earnings are incorporated into this model of student choice following Flyer 1997.
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The data used comes from the College and Beyond 1976 cohort of students, the
majority of whom, graduated in 1980 from a group of colleges and universities of varying
levels of selectivity. In order to arrive at a reasonable approximation of an earnings
estimate L jm for every quartile in every major, major specific earnings regressions must
be estimated using the College and Beyond data. The following equation is estimated by
OLS for each major.
(6). log earnings ij = β 0 + X ij β 1j + λ ij β 2j + εij
Where log earnings ij is the log of 1995 earnings for individuals who graduated in
major j, and X ij is a vector of personal background characteristics for individual i whograduated with major j. λ ij is once again the selection variable that allows the estimation
to produce consistent estimates of β 1j . Once this equation is estimated for everyone in a
particular major, the coefficient vector β j is used to predict an expected earnings in that
major for everyone in the C&B sample. All of these predicted earnings for major j
represent the distribution of earnings for major j.
The midpoint of each quartile in a major’s earnings distribution is used as the
expected earnings (L jm ) for that quartile. The resulting (L jm ) for each quartile in each
major represents a projected earnings estimate for falling into that productivity quartile
15 years after a student’s graduation.
Calculating E ij
“(Under uncertainty) there is no scientific basis on which to form any calculable probability whatever. We simply do not know. Nevertheless, the necessity for actionand for decision compels us as practical men to do our best to overlook this awkward factand to behave exactly as we should as if we had behind us a good Benthamite calculationof a series of prospective advantages and disadvantages, each multiplied by itsappropriate probability waiting to be summed.” John Maynard Keynes “General Theoryof Employment”, 1937 Quarterly Journal of Economics
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In order to calculate an expected earnings variable for individual i in major j, we
take the predicted probabilities that individual i will fall in each quartile of the earnings
distribution for major j and multiply them by the earnings that individual i expects to
receive for being in each quartile of major j (L jm ). Summing across these products will
yield an expected earnings variable E ij for individual i in major j that accounts for
uncertainty or risk by incorporating a variance in earnings outcomes and major specific
abilities.
m
(7). E ij = ∑ ∏ ijm L jm ,m=1
Repeating this process will yield expected earnings for individual i in every major
{1…j}. Every individual in this sample will therefore have five expected earnings
variables that are influenced by the distribution of earnings outcomes, one for each
possible major. 6
Estimating Predicted Probability of Employment at Graduation
An additional source of uncertainty in a student’s choice of major can be derived
from the uncertainty a student faces in finding a job upon graduation from college. A
student will only receive expected earnings in her or his chosen field if he or she is able
to obtain employment after graduation. Higher probabilities of getting a job offer in a
major category are likely to increase the utility of that major and consequently, the
probability that major will be chosen. This study will calculate the predicted probability
of getting a job in each major for every student at the single selective university. Each
6 Majors for which expected earnings are calculated include engineering, business, naturalsciences, social sciences, and humanities.
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student will therefore have five predicted probabilities of receiving a job offer upon
gradation, one for each major. These predicted probabilities will be used in the choice of
major mixed logit estimation in two ways, in an attempt to ascertain whether or not an
increase in the probability of receiving a job offer in a major relative to others affects an
individual’s choice of that major. This predicted probability of a job offer will be used as
a lone independent variable to find its effect independent of earnings and other
exogenous variables and will then added to the mixed logit along the expected earnings
variable calculated above and the remaining control variables to see if predicted
probability of a job offer affects major choice separately from its possible correlationwith expected earnings.
Using actual job offer observations for individuals from the single selective
university, receiving a job offer will be modeled by major specific probit models, with
corrections for sample selection carried out in a two-step Heckman procedure due to
selection into each major category, with freshman major serving as the exclusion
restriction: The equation that will be estimated:
(8). Job offer ij = α 0 + X ij α 1j + λ ij α 2j + u ij
Parameter values from these probit models will be used to calculate a “predicted
probability of getting a job offer” variable for every individual in each major.
Specification of the choice of major: with Predicted Probability of Employment
Under the assumption that an individual maximizes his or her expected utility by
choosing a college major with the highest expected earnings, modeling the expected
utility of choosing major j as depending on the expected earnings in that major relative to
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other majors and the probability of receiving a job offer in that major relative to other
majors can be illustrated.
(9). E (U ij ) = α E ij + γJij + δ Z i + u ij
E(U ij ) is the expected utility for individual i associated with choosing major j; E ij
is the expected earnings from choosing major j for individual i; J ij is the predicted
probability of receiving a job offer if major j is chosen by individual i; and Z i is a vector
of personal background variables including controls for tastes. Individuals choose major
j if U ij > U in, for all n ≠ j.
Estimation of the equation above will follow a mixed logit specification, a specialcase of the conditional logit framework proposed by McFadden (1974) where regressors
are characteristics of the choice alternatives and a generalized multinomial logit where
regressors are characteristics of the individuals who are doing the choosing. The logit
transformation allows the equation above to be expressed as the log of the ratio of two
probabilities.
(10). ln ( P ij ) = α ( E ij – E in ) + γ (J ij - J in ) + Z i (δ j – δ n)( P in )
Therefore, as expected earnings or probability of job offer of major j increases
relative to all other majors n ≠ j, the utility of major j increases as does the log odds of
choosing major j. This equation demonstrates that the probability of choosing a major
should increase as its expected earnings or probability of receiving a job offer relative to
all other majors increases.
The mixed logit specification indicates that the variables E ij and J ij are a
characteristic of the choice alternatives, which in this case are the alternative majors. The
other right hand side variables Z i are characteristics of the individuals in the sample. The
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left hand side variable is a dummy variable indicating that a major was chosen. Whether
a major is chosen or not depends upon the relationship between the expected earnings of
that major and the expected earnings of all other majors ( E ij – E in ). Looking at the logit
transformed equation we see that the higher the expected earnings of a major is relative to
the expected earnings of other majors ( E ij – E in ), the higher the probability will be that
major will be chosen relative to other majors.
For instance, if the engineering major was chosen by individual i, then
major engineer =1, major business =0, major nat.science =0, etc. We expect that the choice of an
engineering major (major engineer =1) should correspond to a higher level of expectedearnings for individual i in engineering relative to his/her expected earnings in other
fields n (a higher value of E i engineer – E in). The higher the expected earnings in
engineering relative to the expected earnings of other majors might have led to an
increase in the probability that major engineer =1 (i.e. the probability that engineering is
chosen over other majors). If the expected earnings variable does have a positive and
significant coefficient in the choice of major estimation, then we would expect that this
individual had higher expected earnings in engineering relative to other majors and
therefore chose engineering. This same relationship between expected earnings and
major choice should apply to the choice of all majors, if the expected earnings parameter
has a positive and significant value. The effect of other variables on the choice of major
can be interpreted in the same way as they would be interpreted in a multinomial logit.
1.4 Empirical Results
The results in Table 1A demonstrate the effect of expected earnings on a
student’s choice of college major at a single selective university. Using the previous
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literature as guide, this expected earnings variable for students at the single university is a
prediction based upon parameter estimates βs from major specific earnings regressions
for students from the College and Beyond sample as well as individual characteristics X i s
from the students at the single selective university. These expected earnings do not
account for various possible earnings outcomes, so this specification closely parallels
most of the previous literature. And as most of the previous literature finds for students
in the National Longitudinal Surveys, expected earnings does have a positive and
significant effect upon choosing a particular major for this sample from a selective
institution as well.
Results from separate regressions for men and women can be seen inTable 1B . The separate regressions for males and females have a full set of controls,
however, only the parameter estimates for the expected earnings variables are shown.
Table 2A displays the effect that expected earnings has on choosing a major for
the full single university sample, when the variance in earnings distributions are allowed
to effect the expected earnings calculation. Results from separate regressions for males
and females can be seen in Table 2B . Just as in the case of expected earnings that did not
account for risk, an increase in the expected earnings of a major relative to the expected
earnings in other majors, increases the probability that major will be chosen.
However, examination of the different estimates for the two expected earnings
variables in Tables 1A and 2A together suggest that adjusting expected earnings for
uncertainty or risk leads to a more accurate explanation of a student’s choice of major.
Not only is the parameter estimate for expected earnings larger but it estimated more
precisely. The parameter estimate for U_EXPECTED EARNINGS is both large and
significant at the 1% level. Other variables which the previous literature found to be
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important in the choice of college major have also been included in these regressions as
control variables. Table 1A and 2A show the structural parameter estimates for these
other control variables in this mixed logit model. The estimates in Table 1A and 2A for
all variables besides EXPECTED EARNINGS and U_EXPECTED EARNINGS can be
interpreted in relation to the fifth-baseline major, humanities . Using the gender variable
female as an example in Table 2A ; females are significantly less likely to choose
engineering (1) and natural sciences (3) over the baseline major humanties (5) than their
male counterparts. Females in this sample are also significantly more likely to choose
social sciences (4) over humanities (5) than their male counterparts.When the sample is split between men and women, the results show that men
are very strongly motivated by their expected earnings. The results in Table 2B show
that women in this sample are far less motivated by expected earnings in their choice of
major. The expected earnings variable for women has smaller affect on the choice of
major than for men and is significant at the 5% level.
These results for men and women corroborate much of the literature devoted to
the role of expected earnings in a student’s choice of major. While Montmarquette et al
(2002) found that their expected earnings accounting for uncertainty did have a
significantly positive effect on students’ choice of major, they found that for women, the
coefficient on expected earnings that accounted for graduation uncertainty was half the
size of the coefficient for men. Eide and Waherer (1998) found a positive correlation
between expected earnings from a terminal degree and choice of college major for men in
their sample, but found that there was a significant negative correlation between expected
earnings and choice of college major for women in their sample. Reasons for this finding
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could include the possibility that women choose majors less likely to atrophy, or that the
majors which have the highest expected earnings for women might also have some type
of negative attribute or risk for women that isn’t captured in the expected earnings
variable. If this is the case, expected earnings in these majors are higher than they would
be if these negative attributes were included in the expected earnings prediction. Also, if
preferences play a role in the choice of major, they may play a greater choice for women
than they do for men. Or finally, because occupational segregation might influence
sorting of females to majors dominated by females. These majors typically receive lower
earnings.Table 3A displays the results from the conditional logits using predicted
probability of obtaining a job offer as the only right hand side independent variable for
the full sample, then for only males and only females. An increase in the predicted
probability of obtaining a job offer in a major has a positive effect on choosing that major
relative to others for males while it has negative effect on the same choice for females.
Females seem to be less impacted by employment uncertainty than males. This result
might be derived from females’ increased probability to major in humanities and social
science majors, majors where post graduate plans are more likely to include graduate
school than job searches. This possibility is born out in the means tables following the
next essay.
Results for the full sample that include the predicted probability of a job offer as
well as all other control variables are shown separately for men and women in Table 4A .
Once these controls are added to the model, the predicted probability of getting a job
offer in a major has little power in explaining the choice of college major for both males
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and females. Keeping in mind that this study predicts the probability of receiving a job
offer by graduation and not in the period shortly following graduation, this insignificant
correlation between probability of receiving a job offer by graduation and major choice
could be due to the fact that predicted probability of receiving a job offer by graduation
might not be as important as receiving a job offer at some point after graduation.
Also, if a student has a high probability of receiving a job offer in a particular
major based on their own abilities, they might also be more likely to be in the upper
quartiles of a major’s earnings distribution and hence to have higher expected earnings.
This means that the probability of receiving a job offer is most likely positively correlatedwith having high earnings expectations in a major or with other control variables
included in the mixed logit since there is no separate effect that the probability of getting
a job offer has on a student’s choice of major in this sample once all controls are added to
the model.
Given the results from previous studies that examined the choices of students
from the National Longitudinal Surveys of Young Men, Youth and High School Class of
1972, the results presented here are not surprising. Students from a selective university
also seem to be very much affected by expected earnings in their choice of major.
Students who have higher expected earnings in one major relative to all others will likely
choose to major in that field. Females are shown to be less influenced by their own
relative expected earnings across fields in their choice of majors. This persistent result
for women is not necessarily confounding due to reasons described above.
The results from previous studies of the choice of college major, all of which
have utilized national survey data on college graduates from a wide range of schools and
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returns. It has also been shown that different groups of students, particularly males and
females, respond to expected earnings differently.
Based on the results provided in this essay, we can conclude that students do
care about the relative expected earnings they can obtain in different majors. Therefore,
anything that changes an individual’s expected earnings in one major relative to other
majors will likely affect their choice of undergraduate major. This result does have
important implications on how we model and understand student’s college major and
labor supply decision.
If for example, there were a large increase in the earnings (wage) received byengineers in the labor market, we could expect that this would raise the expected earnings
in engineering of students beginning their college studies by various magnitudes,
depending on the student. Based upon each individual’s abilities and the shape of the
engineering earnings distribution, some student’s expected earnings are likely to increase
more than others. However, it could easily be assumed that for a large number of
students, this increase in the engineering wage in the labor market would now make their
expected earnings in engineering higher than their expected earnings in other majors.
Since we have just shown in this essay that students choose majors based upon their
relative remuneration, we would expect that a larger number of students in the aggregate,
will choose engineering as a major.
This type of scenario would give some credence to a cobweb-type model of
labor supply. However, the cobweb model, even in a rational expectations framework
must be based upon earnings expectations for students being predicted rather myopically,
so that changes in expected beginning earnings will influence changes in major choice
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behavior. This essay has explored the link between expected earnings 15 years after
graduation and choice of major, and finds a significant correlation between the two
variables. Other studies such as Berger (1988) have also shown that expected beginning
earnings influence the choice of major, but as Berger explains, this may only be because
beginning earnings are positively correlated to life-cycle earnings; and it is expected life-
cycle earnings that people respond to in their choice of major, since life-cycle earnings
take into account differing slopes in earnings growth across majors. If Berger is right,
then the cobweb model’s success in predicting the supply of new entrants into a field
might only be because beginning earnings are closely correlated with life-cycle payestimates.
Since neither beginning nor life-cycle expected earnings were used in this
study, the results from this essay do not address whether students are myopic in their
earnings expectations which would support a cobweb model of labor supply, however,
the results herein do establish that the choice of college major is very much linked to
relative expected earnings across majors.
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incentives, discrimination, cobweb patterns of human capital investment and
occupational shifts. 1
In particular, the one monetary incentive among others which as been proposed to
influence an individual’s utility of choosing a major is student loan debt. In fact, the
literature devoted to studying the relationship between major choice and loan debt
provides the impetus for this essay. The literature hypothesizes that increases in student
loan debt will tend to lead students to choose higher earnings majors. This economic
reasoning assumes that loan debt is exogenously determined and rejects the assumption
that students might be forward thinking beings, who in the process of their educationlearn more of the benefits and costs to their education and consequently might choose
their loan debt based upon the major that they have chosen. Because the previous
literature misses this very probable possibility when they assume that loan debt is
exogenous, there is relatively little literature that examines the impact that a student’s
choice of major might have upon their borrowing behavior. This essay will attempt to
address and fill a gap present in this area of the empirical educational choice literature.
The work included in this paper will empirically test the relationship between a student’s
loan debt and their choice of major at a selective university, in a way that addresses the
1 Choice of major throughout this essay actually refers to the choice of a particular “college” within auniversity. Of course each “college” will represent a specific type or subgroup of individual majors, but the groupingwithin each “college” usually consists of majors that are relatively similar in nature—with the possible exceptions of
social sciences and humanities. Due to the large number of different majors available to students in college, in order tocompare major choices in a clear and more manageable manner, majors are aggregated into several categories. This
procedure is a limitation of many studies in the educational choice li terature, because although majors within eachcategory may be similar, the subject and fields included within a larger category might range widely. For this study,majors are grouped into Engineering, Business, Social Sciences, Humanities, Pre-professional and Architecture. Whilethere might be wide differences in fields—and salaries—within some of these categories, when majors are aggregatedunder these categories there does exist a hierarchy of average salaries between the categories. This result is usefulsince one goal of this essay is to see whether majoring in a category with high average salaries relative to a categorywith lower average salaries has an affect on a student’s loan debt. Since majors are aggregated in this manner,however, we are unable to compare the effects of choosing specific majors within one category relative to specificmajors in another category.
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endogenous nature of loan debt and choice of major that have been up to this point
ignored.
Educational choices, especially those made in higher education, like the choice of
major for example, have immediate implications on a student’s labor market success and
consequently upon their financial abilities to repay debt. With the yearly price tag at
some colleges rising to astronomical heights and at a rate far greater than the average
family’s income is rising, the pursuit of a better understanding of the motivations and
consequences of important educational choices that take place in the presence of
tremendous costs, risks and returns constitutes the primary motivation for this research.Most studies that examine the relationship between loans and the choice of
college major attempt to test the theory that loan debt is a causal determinant of a
student’s choice of major decision. As mentioned above, the problem with modeling
major choice as a function of loan debt arises because of the fact that loan debt is
incorrectly being treated as an exogenous variable. Loan debt, in fact, should not be
assumed exogenous, as a student’s loan debt is not randomly drawn from a hat, but
instead can be influenced by many factors including but not limited to the student’s
choice of major. A brief discussion of the theoretical relationship between major choice
and loan debt explains why loan debt can be considered endogenous within a system of
equations.
Previous studies follow a particular theoretical line of reasoning. If educational
choices are made at the margin and debt load for a student were to rise, the marginal
utility of consumption for that student would be increased during the repayment period
due to the extra income that needs to be devoted to debt repayment rather than
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consumption, giving a student an incentive to choose a more remunerative major. The
conclusion of this theoretical reasoning is simple. An increased debt load may influence a
student to choose a higher earning major, ceteris paribus.
What the previous literature ignores is the idea generated from a life-cycle
hypothesis perspective. Once this alternative perspective is included into the analysis, the
simultaneous relationship between loans and choice of major, and hence the endogenous
nature of choice of major and loans becomes apparent. According to the life-cycle
hypothesis, during the beginning period of an individual’s life-cycle—in this case during
college—an individual should borrow based on their future expected wealth or incomewhich is of course in turn based on their skills, talents, education, etc. This argument
suggests that a student is able to choose at least a portion of the amount of the debt they
take on based on the expected future income that their specific major will provide them
after graduation. This perspective explains why a student who chooses a high paying
major, ceteris paribus, will be more apt to take on greater debt now to pay for college.
The conclusion of this theoretical reasoning is also fairly simple, the choice of major can
be said to affect the level of debt a student incurs. Therefore, loan debt can reasonably be
assumed to be an endogenous explanatory variable in the equation that explains choice of
major. The life-cycle hypothesis perspective shows that while debt may influence the
choice of major, the choice of major may also affect the level of debt incurred. This
relationship can be demonstrated by a system of simultaneous equations.
1. Major choice i = X i+ Loans i + ε i2. Loans i = X i + Major choice i + u i
If the first equation is estimated using regression analysis without accounting for
the endogenous nature of loans (how previous studies proceed) it is possible that
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estimates of the effect of loans on choice of major may suffer from simultaneity bias.
Similarly, if the second equation is estimated in an empirical model that does not take
into account the endogenous nature of major choice, the estimate of the effect of a
students choice of major on loan debt may also be biased due to the correlation between
Major choice and the error u i . The empirical research presented here will reconcile
these two perspectives in a way that addresses the possible endogeneity problem. Also,
while all studies attempt to estimate equation 1 above while ignoring the importance of
estimating the second equation, this paper will alternatively attempt to estimate the effect
that major choice has on loans, to find whether choosing a higher earnings major promptsstudents to take on more loan debt, ceteris paribus. A two-stage instrumental variable
estimator will be presented in order to address the possible endogenous nature of major
choice in equation 2 above that will enable consistent estimates of the effect that choice
of major has on loan debt in this simultaneous context.
Data
The data used in this study comes from a pooled cross-section of graduating
seniors from a single selective university in the years 1997, 1999, 2001 and 2003. The
data used includes information on students’ SAT scores, family background, beginning
and final major, total loan debt, activities during college, graduate school plans, a number
of self-evaluated personal characteristics, as well as whether employment has been
secured at the time of graduation and a salary offered. These data will be used in an
attempt to replicate results of previous research, answer the unique questions proposed in
this paper concerning the relationship between choice of major and loan debt and to
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examine the possibility that choice of college major is endogenous to the equation of
interest (equation 2 above).
2.2 Review of the Literature
In this section, I will briefly review some of the important literature pertaining to
loan debt and higher education choices. While there are a few other studies in this area,
which are tangential to this work, only the studies most closely related to this work are
mentioned here. In addition to literature reviews, replications of the empirical results
from the previous literature have been attempted using the data described directly above.Results of these replications can be found in the tables of Appendix 2.
Replication of Monks (1999): Loan debt and graduate school plans
James Monks (1999) studied the impact of debt on various higher educational
outcomes and concludes that debt is not significantly influential in the educational
outcomes of college students. Theory might suggest that the student with higher debt
will be less likely to attend graduate school because it increases debt repayment and
therefore the student would have a higher marginal utility of consumption once debts
began to be repaid than if that student decided to begin working immediately after
college. He does not find any relationship between debt and graduate school plans or
between debt and changing majors during college from a lower paying field to a higher
paying field; however he does not specifically test whether debt affects the final choice of
major.
In order to determine the applicability of Monks findings to students from the
dataset used in this study, a similar specification has been used to estimate the effect of
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loan debt upon the choice to attend graduate school: Grad i = β 1 X i+ β 2 Loans i + ε i .
Grad is a dummy variable indicating the student is planning on attending graduate school
within six months of graduation, X i is a vector of background characteristics and Loans i
is the log of total loan debt for individual i . It should also be mentioned that the problem
that arises when estimating the relationship between loans and major choice if the
endogenous nature of loans is not accounted for can also be applied to the estimation of
the effect that loans have on a student’s plans to attend graduate school. That being said,
the estimates using these data, like the estimates produced in Monks’ study may be
biased due to the simultaneous nature of loans and graduate school attendance.Unlike the findings from the Monks study, where Monks found that loans did not
significantly affect graduate school plans, I find a slight correlation between the amount
of student’s loan debt and the decision to attend graduate school for students in this
sample. Probit regression results are presented in Table 1A in Appendix 2 . The choice
to attend graduate school is modeled as a decision based on student ability, parental
background, undergraduate major, and loan debt. The probit is estimated twice, first
following Monk’s specification which uses categorical dummy variables that represent
having various levels of loan debt relative to the baseline of no loan debt and then using
the log of a student’s loan debt to replace these dummies as regressors.
College G.P.A. and major have signs that were expected and that were also found in
Monks’ study. “College GPA” has a positive and significant influence on graduate
school choice. So these results illustrate that controlling for choice of major, higher
ability individuals tend to be the students going on to graduate school. The female
dummy variable has a negative coefficient; however this estimate is not significant. Had
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it been significant, it would support the actual raw percentages of males and females who
plan on attending graduate school from this sample. 2 For each major category the
percentage of women choosing to attend graduate school is lower than the percentage of
men in each major who plan on attending graduate school except in Humanities.
Students with higher ability (higher SATs) are more likely to attend graduate school and
African Americans are more likely than their white classmates to attend graduate school. 3
A finding not supported by this data is that Asians and Latinos are more likely to attend
graduate school than their white classmates.
While all loan debt levels have a negative relationship with plans of graduateschool attendance relative to having no debt, having loan totals between $1000-$6000
and between $12,000-18,000 significantly decreases the likelihood that a student plans on
attending graduate school relative to students that have no loan debt.
Variables not included in Monks’ analysis but are nevertheless shown to be
significantly related to plans to attend graduate school include a student’s categorical
self-ranking of their individual “drive to achieve”. Having a high “drive to achieve” is
positively correlated with graduate school plans. Also, having parents with the highest
income decreases the likelihood that students from this sample plan on attending graduate
school upon graduation. It is also interesting to point out that the estimate for “Graduated
in 2003” is positively significant . It is entirely plausible that business cycles might play
an important part in students’ graduate school plans since students who graduated in the
2003 class were more likely to attend graduate school than those who graduated in 1997.
2 Means Table: Appendix 2 3 A finding analogous to those found by Rivkin 1995
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The incoming class of 1997 was ushered into college by an economic expansion
which eventually lasted nearly 10 years. To the extent that graduate school plans are
affected by employment opportunities, the business cycle expansion since 1991 was
likely to cause fewer students to turn to graduate school as an alternative to employment
in the 1997, 1999 and 2001 graduating classes. The class of 2003 entered college in
1999 in the midst of this last expansion. However, the peak in the last business cycle was
pinpointed in March 2001 which was followed by a trough in November 2001. This
downturn in economic activity between March and November of 2001 most likely
influenced many students in the 2003 class to decide to go to graduate school rather thanattempt to enter the workforce when job prospects were bleak.
However, one might think that since the business cycle recovery began in
November of 2001 over a year before the class of 2003 graduated, the short decline in
economic activity would not be enough to influence more students consider applying to
graduate school. The curious thing about the expansion that began in November of 2001
is that it was mainly due to productivity growth, so while real GDP was growing, the
growth was also accompanied by stagnating or falling employment. So even as there was
increasing economic growth by the time the 2003 class neared graduation, employment
prospects might have still been quite low. It is quite possible that for these reasons the
class of 2003 was more likely to have plans to go to graduate school than the class of
1997.
Unlike Monks, however, I do find some evidence that students in this sample
might be influenced by loans in their switching from a lower paying major to a higher
paying major. Table 1D from Appendix 2 shows that log loan debt is positively
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correlated with switching to a higher paying major. When loan dummies are included as
exogenous regressors, having $6000-$12000 in loan debt relative to having no loans is
significantly related to switching to a higher paying major. African Americans, as well as
females are more likely to switch to a higher paying major than their white and male
counterparts. Students who graduated in 1999 and 2001 are more likely to have switched
to a higher paying major than 1997 graduates. However 2003 graduates are no more
likely than those in 1997 to have switched to a higher paying major. Another sign that
business cycles may play a part in how students choose their majors. 4
Replication of St. John (1994): Loan debt and choosing higher earning majors
A study by Edward P. St. John (1994) attempts to uncover a relationship between
loan debt and choice of a college major, specifically whether higher loan debt influences
students to choose higher earnings majors. St. John does not find within a margin of
error that loans are significantly influential in major choice. This study does suffer
twofold from shortcomings in its methodology. On one hand, St. John uses an ordered
ranking of majors (14 majors) ranked by their average salary as his dependant variable
and regresses this dependant variable on all independent variables including loan debt
using OLS. On the other hand, this study like all others in this literature does not address
the endogenous nature of loan debt. 5 And because St. John uses data on individuals who
graduated college in 1984-85, there also remains a motivation to determine whether there
has been a change in the loan-major choice relationship since the mid- 1980s.
4 Majors in this dataset can be ranked from highest to lowest paying based on actual reported beginningsalaries for students in each major. Majors are ranked from highest to lowest in this order: Engineers, Business,
Natural Sciences, Social Sciences, Humanities, Architects, Pre-professional . See Means Table in Appendix 2 5 The exception is Weiler’s study that examines the effect of loans on the choice to attend graduate school.
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Following the methodology laid out by St. John (1994) and using more recent
data from a single selective university, I attempt to establish whether loan debt has no
impact on curriculum choice behavior for this group of students as described by St. John.
The estimates from two OLS regressions of the following equation are presented in
Table 2A and Table 2B in Appendix 2: Major ranking i = β 1 X i+ β 2 Loans i + ε i. In
the first regression the dependant variable is an ordered ranking 1-7 of major chosen by
each student. In the second regression, major categories are disaggregated further to
form an ordered ranking 1-14. 6 Tables 2A and 2B reveal the correlation between certain
independent variables and choosing a higher ranked –higher earnings – major.The result of interest that runs contrary to the findings in St. John’s study is the
positive and significant effect that loan debt seems to have on the choice of higher
earnings majors. When the log of loan debt or loan debt dummy variables are used the
correlation between loan debt and higher earnings majors is evident. We can see in
Table 2B that compared to having very low or no loans, total loan debt between $10,000-
$20,000, and $25,000-$30,000 is associated with choosing higher earnings majors. This
impact is significant at the 10% level. When the log of loan debt is used in the estimation
in place of the loan dummies, log loan debt is also found to be correlated with choosing a
higher earnings major. The coefficient for “ladjloan” suggests that for every unit increase
in log loan debt, major ranking (1-14) increases by .0347. This is also equivalent to
saying that if we multiply actual loan debt (not the log transformed version) by 2.72 (the
unit increment of the natural log) then major ranking increases by 1.04 (the exponent of
6 The ranking 1-7 is based on average remuneration of each major grouping. Engineering being the highest,followed by business, natural sciences, social sciences, humanities, with architecture and pre professional majorshaving the lowest average salary upon graduation. The ordered ranking 1-14 followed a similar pattern except somemajor groupings were disaggregated into more specific major qualifications. More comprehensive BLS average salaryfigures were used to determine the relative rankings among these disaggregated majors.
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mathematically geared. It seems that students who spend the most hours partying during
the week were also the students choosing higher earnings majors. This finding more
appropriately defines students choosing high earning majors within the business college
than students choosing the highest earning engineering majors. Auxiliary regressions do
reveal that high partying habits increase the probability of choosing business majors
while it decreases the likelihood of choosing engineering majors relative to humanities.
Replication of Weiler (1994): Endogenous loan debt and graduate school plans
One study has acknowledged the possible bias that might arise by estimating the
relationship between college debt and higher education choices—specifically collegedebt and graduate study plans—with only a single equation. William C. Weiler (1994)
does not assume—like most other studies—that loan debt is an exogenous determinant of
the choice to attend graduate school. Weiler implements a multi-equation model to test
the effects of debt on the choice to enroll in post-baccalaureate studies. Weiler finds—in
contrast to previous studies—that loan debt significantly reduces the expectation of
enrolling in graduate studies for students based on the High School and Beyond.
I have previously demonstrated through replication of Monk’s study that when
using only a single equation estimation for this sample of students from a selective
university, loan debt negatively influences graduate school plans. However, that finding
may be driven by the possibility of endogenous loan debt. The prospect of this bias
influences Weiler to use a two-stage empirical model to solve the system of equations:
(3). Loan debt = α1 + α2 Grad + α3 X + ε (4). Grad = γ1 + γ2 Loan debt + γ3 X + u
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Table 3C displays the results from a replication of Weiler’s model using this
sample of university students and implementing a two-stage estimation procedure that
attempts to account for the possible endogenous nature of loans. Tables 3A -3B presents
regression results that attempt to ascertain whether loan debt is indeed endogenous in
equation (4) above through a Durbin-Wu-Hausman exogeneity test. The Durbin-Wu-
Hausman exogeneity test is implemented in this case by saving the residuals from the
instrumenting regression equation (3) and including these residuals in the structural
equation (4). 8 The alternative hypothesis that there is significant difference between OLS
and IV estimates is tested against the null hypothesis that the Cov {Loan debt, u} = 0. Ifthe coefficient on the included residuals is statistically different from zero then we can
reject the null of exogeneity, therefore an IV estimator is needed to provide consistent
estimates of equation (4). In Table 3B we can see that the coefficient for the residuals
from the instrumenting regression are indeed significantly different from zero and so this
replication will proceed using instrumental variables to find the affect of loan debt upon
graduate school plans. The results from the second stage of the instrumental variable
probit are given in Table 3C. One can see that even after accounting for the
contemporaneous correlation between loan debt and the error, loan debt is still
significantly negatively related to planning to attend graduate school. Many other
variables significantly related to graduate school plans can also be found in Table 4C.
Using a two-stage IV estimator similar to that used in Weiler’s study along with
data from a single university, we find that simultaneity bias does not seem to drive the
significant negative coefficients on loan debt. Once the endogeneity problem has been
8 Durbin-Wu-Hausman test first proposed by Durbin (1954) and independently by Wu (1973) and Hausman(1978). It is numerically equivalent to the standard Hausman test.
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eliminated or lessened, loan debt does appear to impact grad school choices. This result
runs parallel to Weiler’s study of students in the High School and Beyond where loan
debt was found to significantly effect the decision to go to graduate school, at the 10%
significance level. The estimates found by this replication are somewhat more precise as
loan debt had a significant effect on grad school plans, significant at the 5% level.
The remainder of this essay will develop a model of undergraduate choice of
major and loan debt, where the choice of a higher earnings major is suggested to
influence students to choose higher levels of debt. The model will be empirically tested
using data from a group of students in 4 graduating classes, graduating from a singleselective university between 1997 and 2003. This constitutes the first study that attempts
to estimate the effect of undergraduate choice of major on loan debt.
2.3 Methodology
Economic theory provides two distinct theoretical motivations for modeling the
simultaneous relationship between a student’s loan debt and their choice of
undergraduate major. The first theoretical motivation is drawn from the Life-Cycle
Hypothesis of an individual’s consumption behavior. According to the Life-Cycle
hypothesis, an individual will consume more in the present and hence be more apt to
borrow in the present based on increases in their future expected wealth or income. This
future wealth and income will be all but determined by an individual’s skills, talents,
motivation and education. If we can assume that education and specifically choice of
college major choice affects future income and wealth—an assumption consistent with
the finding of many previous studies—and that the Life-Cycle description of an
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debt from the first stages and saves for retirement. Approaching retirement and then into
post-retirement, income falls below consumption and an individual dissaves or lives off
of savings from the previous periods. This process can be shown in the following
diagram:
Income, Consumption
DebtAccumulation
Dissaving
Savings
YouthYears
RetirementYears
WorkingYears
Consumption
Income
Figure 1
The Life-cycle framework assumes that an individual takes into consideration
future labor income in making consumption decisions in the present, and therefore has a
low discount rate i.e. values future consumption similarly to present consumption. Since
consumption depends on present value of future income, it does not matter that the
increase in income comes in one particular period or another. In valuing current and
future consumption similarly, individuals smooth their consumption, therefore
consumption (and specifically borrowing in the first life-cycle stages) in any period
should rise proportionally due to the increase in expected future income.
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Since expected future income during the “working years” determines
consumption and borrowing decisions in the “youth years” of the life cycle, an increase
in expected future income should increase consumption –i.e. borrowing- in the early
“youth years”, ceteris paribus . Following the logic of this theory, a student’s choice of
major should influence the amount debt they are willing to undertake during their years
as an undergraduate student.
The alternative theoretical motivation to modeling loan debt and major choice as a
simultaneous system comes from marginal utility theory. Based on this theory,
educational choices such as college major are made at the margin. Consequently, if anindividual’s debt load were to rise, their marginal utility of consumption would be
increased during the repayment period due to loan repayment. This would give a student
an incentive to choose a more remunerative major. 1 An objective of this essay will be
to empirically test the application of the life cycle hypothesis—whether choosing a highly
remunerative major as opposed to a low paying major provides incentives to students to
undertake higher amounts of loan debt.
After laying out these theoretical motivations which highlight the need for an
empirical model that takes into account the possible endogeneity of curriculum choice
and loan debt, the natural progression is to represent a student’s choice of higher earning
majors and loan debt by the system of simultaneous equations shown below.
(6). Major choice i = X i+ Loans i + ε i (7). Loans i = X i + Major choice i + u i
1 While it only implies that the effect which loan debt has on major choice may often reveal itself after astudent has graduated, Appendix 1 shows that there is a definite link that runs between loans and major choice. Inanswer to the question posed at the end of a student’s senior year, “Given your level of debt, would you have chosen adifferent major?” 9% of students who responded to this question replied that they would change their major if theycould. This was an even higher 12% among students who responded and had more than $25,000 in loans.
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(9). Loans = β1 + β2Major i + β3Xi + ε iL
Major i = α 1 + α2 (β1 + β2Major i + β3Xi + ε iL ) + α3Xi + ε iMMajor i = α 1 + α2 β1 + α2 β2Major i + α2 β3Xi + α2 ε iL + α3Xi + ε iMMajor i - α2 β2Major i = α 1 + α2 β1 + α2 β3Xi + α2 ε iL + α3Xi + ε iM
Major i ( 1- α2 β2 ) = α 1 + α2 β1 + α2 β3Xi + α2 ε iL + α3Xi + ε iM
Major i = α 1 + α2 β1 + α2 β3Xi + α2 ε iL + α3Xi + ε iM
(1- α2 β2 )
A way to circumvent this simultaneous equation bias problem and ensure that the
error term in equation (9) is not contemporaneously correlated with the choice of a higher
earnings major, is offered by an instrumental variable estimator. A two-stage estimation
can be applied in this case to estimate the effect of choosing a higher earnings major onloan debt.
If major choice is found to be endogenous in this system of equations, the
estimation of the system will be carried out in two ways described below. In order to
ensure that estimation of the relationship between major choice and Loan debt (equation
9) are consistent, the system of equations will first be estimated by two stage least
squares. The drawback to using this first approach would naturally be the fact that the
first stage would estimated via a linear probability model with choice of a high earnings
major being the dichotomous dependant variable. The linear probability model used in
the first stage estimation would treat this dichotomous variable as a continuous variable
and thus allow predicted major choice to fall outside the 0, 1 range of the original
dichotomous variable. However, despite its drawbacks, the two stage least squares
estimation procedure should lead to consistent estimates in the second stage regression
above.
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Two stage Least Squares:
In the first stage, the dichotomous variable Major choice, the choice of the
highest earning major, will be regressed on all exogenous variables (X) in the structural
model (equation 8 above) as well as the variables Z that will serve as instruments for
choosing a higher earnings major, variables which will be uncorrelated with the error
term, ie. Cov (Z, ε iL) = 0.
(10). Major choice= α1 + α2Loans i + α3Xi + α3 Z i + ε iM
The second stage will proceed by replacing major choice with its fitted value*
predicted from the first stage regression. The second stage equation below excludes theinstruments for major choice and is identified. This two-stage process should produce
consistent estimates of the coefficient β2 below.
(11). Log Loans = β1 + β2Major* i + β3Xi + ε iL
Two stage Probit Least Squares:
Estimation of this system of equations above can also be carried out using a
simultaneous equation model that unlike two stage least squares accounts for the
dichotomous nature of the endogenous variable (Major choice) and the continuous nature
of the other endogenous variable (Loans). 11 The estimation will proceed in a similar
fashion as the 2SLS described above, however, the model would allow the first stage to
be estimated by probit and the second by OLS. 12
11 Heckman (1978) and Maddala (1983) present a simultaneous equation model in which one endogenousvariable is dichotomous and the other endogenous variable is continuous. Mroz (1999) also discusses a similar modelwith application to discern marriage effects on wages.
12 The estimation will be carried out using a STATA program developed by Keshk 2003 that implements thetwo stage estimation described in Maddala (1983) . The STATA command provides consistent second stage estimates,correcting biased standard errors.
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therefore should not be correlated with the error. Consequently, a student’s self-
measured “Mathematical ability” may serve as a suitable instrument for a student
choosing a higher earnings major. An argument against the use of self-measured
mathematical ability would be that as measure of ability, it might be influential on loan
debt apart from its affect on a student’s major choice, or that it might be correlated with
some unobserved ability embodied in the error. Students with higher ability would be
expected to have higher expected future earnings and consequently higher debt. This
would present a problem if a student’s ability is not completely controlled for by SAT
scores and other ability measures in the second stage regression. Considering this possible shortfall, the data present two more options for instruments for the choice of
major.
A student’s probable major at the beginning of his or her freshman year is known
for almost all students in the dataset. Whether or not the student’s probable major in
their freshman year was engineering will be used to instrument for whether a student’s
final major is also high earnings engineering major. A student’s probable major in a
high paying major in their freshman year is obviously highly correlated with the choice of
a high paying final major and is most likely not as effected by a student’s total loan debt
as a student’s final major might be since the probable freshman major is chosen before
the student has incurred any significant debt . In addition to using whether a student
chose engineering as a probable major in their freshman year to instrument for a high
earnings final major, the choice of a second major will also be used to instrument for the
choice of a high earnings final major. Having a second major is highly correlated with
not choosing a higher earnings final major most likely due to the fact that students in
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lower paying majors in the business, humanties and social sciences are more likely to
take on a second major. There would be no reason to believe that having a second major
would affect loan debt other than through its effect on an individual’s choice of primary
major.
Testing for the exogeneity of Major
Before using these instruments in any two stage IV estimation, the
appropriateness of an IV estimator—i.e. whether major choice should indeed be treated
as endogenous in equation 13 below—will be tested using a Durbin-Wu-Hausman
exogeneity test previously described in the Weiler replication. This test will be
performed to test whether the assumption that the decision to choose a higher earnings
major is exogenous.
Residuals from the instrumenting regression—equation 12 (minus Loans i )
below—are saved and included as regressors in the loan debt equation—equation 13
below—in order to test the null hypothesis of no contemporaneous correlation between
(Major, ε iL ).
(12). Major = α1 + α2Loans i + α3Xi + ε iM(13). Loans = β1 + β2Major i + β3Xi + ε iL + ( β4 ε iM )
Without adding the residuals from equation 12 ( ε iM ) as regressors in equation 4,
equation 4 produces residuals ε iL . The residuals from equation 13, ε iM , are inserted into
equation 4 in order to see if they produce significant coefficients. In order for the
parameter value ( β4 ) for these residuals ( ε iM ) to be significant they must take some
explanatory power away from ε iL . Therefore, if β4 is significantly different than zero, it
is the case that Major and ε iL are correlated and the null of exogeneity of Major must be
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rejected. Another version of the Hausman test uses the expected value of Major
predicted from the instrumenting equation as a regressor in equation 13 above. The
significance of the coefficient on this predicted Major variable is then used to test the null
hypothesis of exogeneity of Major i in equation 13.
2.4 Empirical Results
First approximation: Single Equation OLS
In order to establish the link that theory suggests should exist between choosing a
higher earnings major and loan debt, the equation below has been estimated twice usingOLS. The first specification includes dummy variables for the 6 categories of majors
with humanities, the lowest paying major grouping acting as the seventh and baseline
group. 14 The second specification only includes one dummy indicating whether a
student chose to major in the highest paying major—engineering—relative to all other
lower paying majors. A range of other variables in (X i) are used to control for race,
gender, parental income and education, ability of the student, year of graduation, years in
college and financial background.
(14). Log Loans i = β1 + β2Major i + β3Xi + ε iL
Tables 4A and 4B display the results from these first two OLS regressions. After
dropping observations with missing information on loan debt or other explanatory
variables, there were a total of 3,545 observations used in these regressions. The results
represented in Table 4A illustrate the finding that majoring in highest paying engineering
14 Architect students are both included and excluded from these regressions. The exclusion of these studentsdoes however cause the effect of choosing a high earnings major to be stronger. They are excluded due to the nature ofthe architecture program where students are in school for five rather than only four years, spending one year abroad.The relationship between loan debt and major choice for these students is inherently different since these studentsnaturally have the highest loan debt but are in the lowest paying major.
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same parental income and education; black, latino and multiracial students have
substantially more loan debt upon graduation. This finding may be due to the possibility
that these minority students may not have as much financial support from extended
families that similar white students have.
Parental background variables have expected correlations with debt levels.
Students with either a mother or father with some graduate education or graduate degree
curtail their borrowing behavior relative to students with either a mother or father with
just a high school education. Also, having a mother with some college education or a
college degree relative to having just a high school education also decreases a student’sdebt levels. Having parents with $50,000-$99,999 annual income increases a student’s
loan debt relative to similar students who have a parental income below $30,000.
Students with parents who make $100,000 or above have significantly less loan debt than
similar students with parents who make less than $30,000. Students with a father who
works in a professional occupation –i.e. doctor or Law Occupationyer—have lower loan
debt than students without fathers working in professional occupations. Each of these
results concerning a student’s parental background point to the ability of higher educated
and income earning parents to subsidize their son or daughter’s educational costs. This
“social capital” enables some students to maintain lower debt levels relative to students
with lower parental education and income.
Results from these two first approximation regressions demonstrate the
correlation that exists between major choice and loan debt. While no other studies have
attempted to empirically find this same link, the literature that examines the possible
effect that loan debt has on major choice does not inspect the possible endogenous nature
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of loan debt. This study will of course address the possibility that the choice of a higher
earnings major is in fact endogenous.
This essay, therefore will not end under the assumption that major choice is an
exogenous choice variable in the equation that determines loan debt, but rather this study
will attempt to ascertain whether instrumental variable estimation is needed to produce
consistent estimates of the impact of choice of major on loan debt. The next section will
describe the results from the Durbin-Wu-Hausman test of the exogeneity of the choice of
major.
Two Stage Least Squares: Testing the exogeneity of the choice of major
Table 5 presents the results from the second stage of the two stage least squares
regression which also includes the Durbin-Wu-Hausman test for exogeneity. This is the
test of significance for the coefficients on the residuals which are taken from the first
stage regression Major = α1 + α2Loans i + α3X i + ε iM and included as regressors in the
second stage regression Loans = β1 + β
2Major
i + β
3X
i + ε
iL+ β
4ε iM .
The test for significance of these residuals ( ε iM ) can be explained simply as
testing the null hypothesis of no contemporaneous correlation between the choice of
Major i and the error ( ε iL) , and consequently whether the ‘endogenous’ regressor’s
effects on the estimates are in fact meaningful.
If the DWH statistic suggests significance of β4 it would necessarily lead to a
rejection of the null hypothesis of the exogeneity of Major and would point to the
necessity of an IV estimator to obtain consistent estimates of Loans = β1 + β2Major i +
β3X i + ε iL. If the DWH test statistic is found to be insignificant, then we cannot reject
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the null hypothesis that there is no contemporaneous correlation and must conclude that
the choice of Major is indeed exogenous and consequently can conclude that we would
receive no additional benefit from using an IV estimator, since OLS and IV estimates
would be similar.
The test statistic for the included residuals from the first stage instrumenting
regression (0.44478) is indeed insignificant at every level. Therefore, the null hypothesis
that the coefficients for these residuals are significantly different than zero could not be
rejected. That also means that the exogeneity of the choice of major could not be
rejected.15
It can be concluded that unlike the theorized simultaneous relationshipoutlined above might suggest, the choice of a high earnings major in this case is not
endogenous and it may not be necessary to implement an IV estimator to consistently
estimate of the effect that choice of major has on loan debt.
Two stage Probit Least Squares Results
Although tests for exogeneity do not reveal the need to use an IV estimator, the
results from a two stage IV regression of Loans on Major are presented here. In this
regression, a student’s choice of a second major and whether they indicated their
probable major as engineering at the beginning of their freshman year will serve as
instruments.
This specification allows the first stage to be estimated by probit and then the
second stage by ordinary least squares. Table 6 A shows the results from the first stage
instrumenting regression. The two variables we will use as instruments for the choice of
15 The omitted variable version of the Durbin-Wu-Hausman test was implemented using two similarmethods. The first method used the residuals from the first stage regression as the ‘omitted variable’ regressor in thesecond stage and the second used the predicted value of the dependent variable from the first stage as the ‘omittedvariable’ regressor in the second stage. Both methods produced the same results for the exogeneity test.
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a high earnings major—engineering—are “Freshman Major is Engineer” and “Double
Major” are both shown to be significantly correlated with the choice of a high earnings
major—engineering—at the .01 level. From these reduced-form estimates, predicted
values for the dependant variable, the choice of a high earnings major are obtained for
use in the second stage. In the second stage the ‘endogenous’ variable Major choice is
replaced with its fitted-value, “Instrument for High Earnings Major”. Since standard
errors from this second stage regression will be based upon “Instrument for High
Earnings Major” and not on the appropriate actual choice a student makes, the estimated
standard errors for the second stage will be incorrect. The standard error corrections thatneed to be made are obtained by STATA procedures and are implemented by the cdsimeq
STATA command. 16 Second stage IV estimates with corrected standard errors are
presented in Table 6B. The instrument for choosing a higher earnings major has a
positive and significant coefficient. Even after controlling for the possibility that major
choice is endogenous in the theorized system of equations, choosing a high earnings
engineering major is correlated with taking on higher amounts of loan debt. This study
proposes that the mechanism for this increased loan debt is the higher expected future
earnings that high earnings majors bring. Assuming credit markets allow shifting of
earnings between periods in the life cycle, these higher expected future earnings allow an
individual to consume and borrow more while they are a student. All of the signs on the
remaining explanatory variables obtained from the single equation loan debt regression
are present in the two-stage method. Both females and those planning to go on to
graduate school take on less debt than their counterpart male and non-graduate school
16 The cdsimeq command is explained by Keshk (2003) in CDSIMEQ: a command to implement two-stage probit least squares.
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students. Black and latino students take on significantly more loan debt than do white
students. The other result that should be noted is that SAT score is still correlated with an
increase in the amount of loans that a student takes on, indicating that student’s abilities
might also influence loan debt through an increase in expected future earnings potential.
2.5 Summary and Conclusions
In all of the previous literature that presents models of the relationship between
choice of college major and loan debt, the level of debt was specified as an exogenous
determinant of the choice of a higher earnings major. The conclusion drawn frommodeling loan debt as a exogenous factor in determining a college major is that a
student’s future plans or income expectations derived from their major do not affect the
amount that they borrow. The argument underlying the specification presented in this
essay is based on the life-cycle hypothesis and makes the claim that debt is not in fact
exogenous and that the choice of a higher earnings major is a determinant of the level of
debt a student incurs. This essay implies that education occurs at distinct stages, where
the student learns more about the costs and payoffs to additional (or specific types of)
education. The choice of major and amount of debt undertaken are both determined
jointly based upon a student’s background, abilities and future earnings expectations.
Therefore, this work attempts to test the importance that a student’s choice of major has
on the loan debt they incur. Not only does this essay propose that choice of major
determines loan debt, but it also does not assume that the choice of a high earnings major
is exogenous.
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The goal of this essay has been to explore more deeply the relationship between
loan debt and an undergraduate student’s major choice. This constitutes the first attempt
to explore whether there is a direct link between the choice of a higher earnings major
and loan debt. A major problem with previous literature derived from its disregard of any
possible endogenous relationship between loan debt and the choice of undergraduate
major. This gap in the previous literature has been addressed in this work by testing the
endogeneity of the choice of major. And while the hypothesis that major choice is
exogenous could not be rejected, an IV estimator was implemented to test the effect of
choosing a higher earnings major on loan debt. Results from this estimation procedure produced similar results to a single equation OLS model. Both regression results, first
from the single equation and then from the two-stage method point to the significance of
the life-cycle hypothesis in the borrowing choices made by students in this sample. Both
the choice of major and SAT score, reasonable proxies for future expected earnings had a
positive correlation with levels of borrowing.
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ESSAY 3
AN ANALYSIS OF SECOND MAJORS AND EARNINGS
3.1 Introduction
The previous two essays have focused on analyzing the relationship between the
choice of college major, the incentives which motivate that choice, as well as some of the
decisions and outcomes that are associated with that choice. These two essays
demonstrate that we can model students as choosing their major based upon their own
uncertain expected earnings and to a lesser extent on their probability of job prospects
across majors. The previous essay also suggests that once a particular major is chosen,
the expected future earnings of that major relative to other majors is correlated with a
student’s borrowing behavior during college. This final essay will not stray too far from
this developing analysis of the choice of college majors. Since the choice of primary
majors seems to be heavily influenced by expected earnings, might there also be an
earnings incentive for a student to choose a secondary major? This is a question that has
been ignored by the current body of literature.
Consequently, this essay will extend some of the well established literature that
estimates the earnings premiums associated with primary majors for college graduates by
attempting to estimate whether there is a premium associated with adding a second major
to a student’s transcript and whether this result holds across all majors and within specific
major categories.
It is easy to find anecdotal evidence of students contemplating the addition of a
second major in the hope of increasing their market value, or as a “backup major” in case
their primary major alone—possibly the major they enjoy—might not readily land them a
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job. It is reasonable to ask, therefore, whether or not a premium exists for students who
choose to invest in a second major. If second majors fail to carry any remunerative value,
we could assume that either second majors hold more of a purely consumption value for
the student as opposed to the evident investment value embodied in a primary major
choice, or that students have acted upon bad market information on how a second major
would affect earnings prospects.
Since the literature is void of studies that analyze the choice of a secondary major,
the following literature review will summarize the findings and concerns addressed by
the previous literature, which investigates the relationship between primary collegemajors and earnings.
3.2 Review of the Literature :
There has been considerable attention paid by the literature to the qualitative
effects that college attendance has on earnings. Of these qualitative effects, college
selectivity, college major and student performance have been the predominant subjects of
study. This area of research is popular among those involved in economic, sociological
and educational research because of the many implications that the choice of a college
major has on labor market outcomes for the general population of college graduates. 1
Studies centering on the choice of college major and earnings have maintained a level of
Highly Popular especially due to continued concern that some groups of students,
specifically women, tend to choose majors with the lowest average earnings. 2 Thus a
large part of the literature attempts to examine of how the choices of college major for
1 Rumberger and Thomas 1993, Eide, 1994, Grogger and Eide, 1995, Weinberger, 1998, Hamermesh andDonald 2004
2 Daymont and Andrisani, 1984
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men and women affect later earnings differences. Other than examining gender wage
differences, tracking earnings differences by major has garnered general appeal because
of the implications that choice of college major might have on upward mobility for low
income students and minorities as well as on the ability to repay student loans. There has
also been a common recurring limitation in the literature that attempts to measure the
effect that educational choices have on earnings. This caveat deals with the fact that
educational choices are not truly exogenous, and modeling them as such fails to
recognize the fact that the apparent relative returns to primary majors and even the
apparent earnings premium that might accompany the choice of a secondary major couldmainly be due to selection into those majors by students with certain levels of unobserved
abilities. While controls for observable ability have been included in all models
estimated in this study, there still exists the possibility that any results that indicate there
are higher returns to double majoring might be driven by positive selection into second
majors based on both observable characteristics and characteristics which are not
observed by the econometrician. Because of this problem, if no appropriate corrections
are made, we are unable to gather what the earnings premium to double majoring might
be if it were possible to have random assignments to secondary majors.
The remainder of this section will focus on the recurring themes found in the
empirical studies that examine the choice of college major and subsequent earnings.
This will provide an entry point for a discussion of the relationship between the choice of
a second college major and earnings. As noted, this is a relationship that has received
little attention in previous empirical studies.
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Rumberger and Thomas (1993) estimate the impact of college major, selectivity
and performance on student’s subsequent earnings. Citing that the majority of the
literature illustrating the significant impact these qualitiative differences have on
student’s salaries had used data on students graduating in the 1970s, 1960s or earlier, this
study set out to find whether these relationships continue to exist for more recent 1987
graduates. They also extend the literature by focusing on gender and racial differences in
the impact of college quality. Rumberger and Thomas also use a statistical technique
Hierarchical Linear Modeling that is more able to properly estimate the effects of
institutional characteristics on a nested sample of students. This modeling attempts toaccount for the nature of their data where observations on students are nested within
many different subsets of college that make up the complete sample. The HLM, with
fixed and random effects, apart from its methodological advantage over OLS, allows the
researchers to examine earnings outcomes at the student level and also allows the effects
of individual level variables on earnings to vary between schools in the dataset.
This study reveals that for students in the Recent Graduate Survey 1987, starting
salaries for females, after controlling for choice of major were about 13% lower than
similar males and 5% lower after controlling for labor market factors. Contrary to the
existing research, which had shown differences in college earnings among racial
categories, Rumberger and Thomas did not find any earning disadvantage for minority
students in their dataset. It should be noted that this study ignores the possible problems
associated with selectivity.
Ignoring selection bias, the authors find that all the variables representing
qualitative differences in a student’s college experience, especially a student’s choice of
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major are significant determinants of earnings. Engineering and health majors enjoyed a
38% and 36% starting salary advantage respectively over the baseline humanities majors.
Science and math majors received a 24% higher salary. Business majors in their sample
received an 18% higher beginning salary while social sciences had an approximate 7%
earnings advantage. These results are consistent with most studies that analyze the
differentials in wages received by college graduates with different majors. The authors
also find that these salary differences among major vary between men and women.
When separate regressions were carried out for males and females, results show that there
are higher earnings advantages among females for majoring in engineering and businessmajors relative to majoring in humanities majors.
Conditional upon the college majors women typically choose, these higher
earnings premiums available to women for majoring in engineering and business could
play a crucial role in determining the overall earnings gap that is present between male
and female college graduates. In fact, Eide 1994, estimated the impact that the increasing
number of women choosing to enter engineering and business majors between the 1970s
and 1980s had upon the shrinking gender wage gap. Eide finds evidence that this
dramatic “skill ugrade” during these decades, especially among women contributed to a
shrinking of the gender wage gap during the 1980s.
Another paper addresses whether between group differences in the quality of or
type of education can account for between group wage differentials that we otherwise
consider to be proof of discrimination. Weinberger, 1998 estimates wage regressions that
account for narrowly defined majors, college performance and college attended, she still
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finds that white and Latino males earn approximately 10-15% more than comparable
women, Black males, and Asian male graduates.
A study by Hamermesh and Donald, 2004 estimates the effect of curriculum
choice (major) on college graduates’ earnings and attempts to account for two types of
sample selection bias, but not selection bias that arises from selection into college majors.
The Hamermesh and Donald study relies on responses to surveys sent out to a random
selection of graduates from the University of Texas at Austin, who graduated in the last
20 years. Due to the nature of their survey data, the authors present a unique model of
earnings determination that accounts for not one but two sources of sample selectivity.They control for the sample selectivity that occurs when survey respondents choose to
respond to the survey as well as selection into the labor force for those who did respond.
A similar correction for selection into the labor force is made when estimating earnings
equations in this essay, which will model the effect of the choice of a second major on
earnings.
After correcting for this double sample selectivity in their data, but also ignoring
the selection that occurs when students choose their major, a problem which might put an
upward bias on earnings premiums, Hamermesh and Donald find that there are still
differences in the earnings between majors; however, these differences are not extreme
after accounting for ability sorting, high school performance, parental economic status
and various demographic characteristics. Since Hammermesh and Donald do not control
for selection bias stemming from endogenous major choice, the difference between their
estimated conditional earnings differentials between majors and the actual unconditional
differentials that would occur if there were random assignments to majors, corresponds to
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the extent to which selection effects reflect differences in unobserved characteristics.
The potential problem of not accounting for endogenous major choice may not be too
troubling since many of the studies that do control for selection bias; whether that
selection is into a college type or college major still have found varied premiums to
choosing some college types relative to others and most college majors relative to
humanities. 3
Like the choice of a primary major, a secondary major can be viewed as an
investment that can bring some level of expected return. The problem with most studies
that examine the choice of a primary major is that they do not make the connection andestimate the possible premium associated with investment in a second major.
The remainder of this paper will turn to the possible impact that a student’s choice
of a second major may or may not have on subsequent earnings. There could be
relatively little earnings effect for taking a second major, in which case we expect there is
an essential non-remunerative incentive to taking a second major. The problem with
estimating the effect of double majoring upon earnings is also the same problem apparent
in much of the educational literature that attempts to empirically test the effect that
educational decisions have on earnings.
The problem is that educational choices such as the choice of first or second
majors are treated as exogenous decisions and are added as regressors in log earnings
regressions, when in fact they are not exogenous choices. The argument might also be
made that choice of a second major may have a greater chance of being exogenous, if as
3 Brewer, Ehenberg, Eide (1999), in a study that estimates the effect of college type on earnings controls forselectivity into college type. They use college net costs to identify selection into college type. They find that there arestill earnings differentials associated with attending different types of colleges, but unconditional earnings differentialare smaller. Arciadiano (2003) controls for selection into college type and college major. This has an ambiguouseffect on the earnings premiums associated with certain majors, increasing the premium for some majors and decreasesthe premium to others.
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systems, finance and accounting; and the lower earnings (or “soft”) business majors of
management and Marketing.
The second part of this empirical analysis is based upon similar data as described
above for the single university, a university that is also sampled by the larger data set .
The College and Beyond data set contains limited information about the post-secondary
records of students enrolling in one of 30 selective colleges and universities in 1976. The
records include information on grade point averages, SAT scores, majors chosen and
family background. These records were also linked with a survey that was completed 16
years after college graduation. This survey gathers information on graduates’ activitiessince graduation through 1995, including their present occupation, income, civic
activities, graduate work and other general information such as individual levels of
satisfaction. Approximately 80 percent responded to the survey. These records were also
linked with student information from the Higher Education Research Institute.
This sample of students includes students from public private and liberal arts
colleges. Data were collected for all 1976 matriculants enrolled in the private colleges
and universities, however a sub-sample of students were taken from the 1976
matriculants of the public colleges. This subsample included all known minority
students, varsity letter winners, those with SAT scores 1350 and above and a random
sample of others. The analysis will follow mainly those who were working full time at
the time the survey was completed. This will restrict the number
of students with available survey information from 22,514 to 16,367.
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3.4 Methodology
In the first part of the empirical analysis, an OLS earnings function is used to
estimate beginning earnings for all students from the single university with beginning
salary observations. Variables are included that capture and control for ability
differences, family background, and demographic variation. The basic model is
presented below, where X i is a vector of student characteristics described above. M i
includes dummy variables representing a students choice of primary major, and D i
indicates the student chose a second major. λ i is the selection variable that controls for
selection into employment.
(1). log earnings i = α0 + α1 Xi + α2 M i + α3 Di + α4 λ i + ε i
Separate regressions will also be run for males and females. The earnings effect
of taking a second major will also be investigated within the business college, since
business courses and majors are intrinsically geared more toward gaining occupation
specific skills and training. As a result, a second major might more readily be viewed as
an investment in human capital.
Attention will then turn to addressing the problem of sample selection that is
found whenever there is selection into the workforce. The sample of students with
earnings observations is non-random, meaning there is a selection equation that
determines who does and does not enter the workforce. Most estimations require some
correction for this type of censoring due to the fact that the error term in the earnings
regression is correlated with the explanatory variables biasing the estimated coefficients.
If the expected value of the error term can be included in the regression as an explanatory
variable, this bias could be avoided. The first stage selection equation, of the Heckman
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method, estimates the expected value of the error term. The original wage regression is
estimated including the expected value of the error as a regressor.
Two variables will be used to identify labor force participation in the selection
equation and will not appear in the second stage of the estimation procedure. The
exclusion restriction will be dummy variables indicating whether the student plans on
attending graduate school within six months of graduation and another indicating whether
the student highly values having a family after graduation. The indication of a student
highly valuing having a family is used in place of the usual identifier of labor force
participation—presence of children—since most students did not have children at thetime of graduation. The log earnings regressions for the full sample of students will be
re-estimated using a Heckman two stage procedure to account for the possible sample
selection bias.
If there does appear to be an earnings premium associated with taking a second
major in the context of the single university, the potential for exploring the possibility
that this result extends beyond a single university is presented by the College and Beyond
dataset. Broadening the lens of this study, we may focus on the choice of second majors
at a wider range of 30 selective colleges and universities. Use of this dataset will serve
a two-fold purpose. Not only will the dataset allow this research to extend the
applicability of the results found for the single university in describing a wider population
but it will also allow further investigation into whether unobservable student abilities
might be driving the estimated earnings premium to investing in a second major .
Students in the College and Beyond dataset answered a series of questions which
allow the researcher to better control for both a student’s observable as well as
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“unobservable” characteristics that might motivate selection. Each student in this dataset
was asked to list his or her top 4 colleges and universities (including the college the
student attended) and to also indicate whether they were accepted or rejected at each of
these institutions.
In their paper “Estimating the Return to Attending a More Selective College: An
Application of Selection on Observables and Unobservables”, Dale and Krueger (1999)
attempt to estimate whether students receive an earnings boost from attending a more
selective school. Dale and Krueger also realize that there exists a selection problem
making the relationship they wish to study more complicated. Discovering that attendinga more selective school is correlated with higher earnings, might be the result of more
“able” students selecting into more selective schools. Therefore, this apparent earnings
premium might be due to higher levels of unobserved ability of students attending more
selective schools and not because a student attended a more selective school.
Dale and Krueger decide to use the screening procedure inherent in the college
admissions process to control for selection of higher ability students into more selective
schools. The logic of their model is rather straightforward. College admissions
committees are often privy to observing what otherwise might be considered
“unobservable ability” in the eyes of the econometrician. Colleges and universities use
this information on unobservable ability along with observable characteristics in the
admissions process to help decide whether or not a student will be accepted into their
institution. 4
Therefore, we assume that students who get accepted and rejected by the same
post-secondary institutions will have similar levels of observed and unobservable
4 Dale and Kruger (1999) present a model of the college selection process on observables and unobservables.
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abilities. For example, student A and student B are both accepted into schools where the
average SAT score of matriculants is 1300 and are both rejected from schools with
average SAT scores of 1350. These two students are assumed to have similar levels of
observed and unobserved abilities since they were both accepted and rejected from
schools of similar selectivity and similar admissions standards. Dale and Kruger proceed
by categorizing and grouping students who have been accepted and rejected by schools
with similar average SAT scores (i.e. schools of equal selectivity) and include each of
these groups of students (represented by dummy variables) in the log earnings equations
in order to control for unobservable ability. So what Dale and Krueger are in factestimating is whether there is an earnings premium to attending a more selective
institution between students with similar levels of both observable and unobservable
skills. What they find is that once this unobserved ability is controlled for, any earnings
premium that was associated with attending a more selective institution is reduced to
zero. This current research will use this methodology in an attempt to control for any
selection that might be driving the apparent premium to choosing a second major.
The difference between, this study and that of Dale and Kruger is how this study
will group students with similar levels of unobserved ability. Dale and Krueger were
able to group students together who had been accepted and rejected by similar schools.
The College and Beyond Survey only reports school codes and not the specific school
names where the students applied. Using a file provided by the Higher Education
Research Institute, D&K were able to link these codes with the actual names of
institutions where students applied. In this way they were able match the average SAT
score of a school to the institution’s code. Therefore, a student who was accepted by a
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school with an average SAT score of 1250 and rejected by a school with an average SAT
score of 1350 could be grouped with another student who was accepted and rejected by
two different schools, but schools that had an approximate average SAT score of 1250
and 1350 respectively.
This study will have access to institution codes for each school that the student
was accepted and rejected by, but does not have access to the file that would allow the
matching of these codes to the name of that specific institution. This makes it
impossible to match groups of students together who were accepted and rejected by
institutions with similar average SAT scores since we cannot garner the average SATscore of a school from only its number code.
Because of this deficiency, this study only matches students who were accepted
by the same exact institutions. This can be done by just comparing the institutional codes
for the schools to which each student applied. While this method is a variation of that
used in the Dale and Krueger study and might not be able to fully capture students’
unobserved ability, it is actually fairly successful in matching students, as many students
in this sample applied to many of the same institutions and therefore should still give
some indication of the premium associated with having a double major if it were possible
to have random assignments to second majors. If a student was not matched to a group,
as in the Dale and Krueger study, they are dropped from the analysis. Due to the inability
to match every student to a group, the sample size in the second College and Beyond
specification drops from 16,367 to 8,528. The estimated log earnings equation that
controls for observed and unobserved ability will be: 5
5 The earnings of each survey respondent appear in categorical form. There are 10 earnings categories. Each
student was assigned a specific earnings value using the midpoint value of the earnings range associated with their
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(2). log earnings 1995 = β0 + β1 X i +β2 d i + β3 ability i + ε i ,
where log earnings is equal to the log of each individuals’ full time 1995 earnings, X i is a
vector of individuals specific variables including G.P.A., SAT scores and demographic
variables. d i is a dummy variable indicating the choice of a second major. Ability i
includes the 196 dummy variables that represent matched groups of students who applied
and were accepted by the same institutions. The model will be estimated both with and
without these dummy variables. Ability i will also include dummy variables indicating
several self revealed evaluations of student’s own abilities.
3.5 Empirical Results
In the first stage of the empirical analysis, an OLS earnings function was used to
estimate beginning salary for students at a single university that capture demographic
characteristics, ability measures, choice of major and family background. The model is
estimated for several groups of students. First the model was estimated for the whole
sample including both males and females. Next the model was estimated for men and
then separately for women. The coefficients and standard errors from these regressions
can be found in Table 1 in Appendix 3.
In the overall sample, the regression estimates reveal significant differences in
beginning salaries associated with some of the demographic variables that characterize
individual students. These results illustrate no earnings disadvantage for minority
students; however this result could be driven by the lack of minority students in the
category. For example if a respondent indicated their earnings fell in a category ranging from $5,000 to $9,999, thatindividual is assigned an earnings of $7,499. The log of each of these midpoint values represents the individuals log1995 earnings. Dale and Krueger (1999) follow the same strategy in order to estimate OLS log earnings regressions forstudents in the College and Beyond. Ordered Probit models using the income categories as dependent variable
produced coefficients with similar significance and direc tion for the variables of interest (esp. double major)
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sample. In fact, we can see that after controlling for a wide range of family and college
background factors as well as occupational attachment, Asian students in this sample earn
significantly more, around 11% more than similar white students. This demonstrates
that earnings differentials between college graduates might be somewhat smaller than
between all educational groups and since we are looking at beginning salaries, these
results might not expose any subsequent earnings disadvantage that college minorities
might face.
While many studies show that family background factors influence the length of
schooling or the type of schooling decision more heavily than the earnings of collegegraduates, it seems that some parental characteristics are associated with higher
beginning earnings. Students who have fathers with high and medium education levels
tend to have around a 5% earnings advantage. This effect that father’s education has on
earnings seems to hold only for women. In the separate regression estimated for females,
we can see that father’s education increases beginning earnings where the same result is
not found in the all male specification. This parental impact may point to the possibility
that students with families with different socioeconomic levels might have different
information about the labor market for educated labor enabling some students to find
better paying jobs. 6
As expected, there are glaring earnings differences across primary majors.
Engineering has the highest wage premium over the baseline humanities majors. The
high earning (hard) business majors, including management information systems,
finance, and accounting majors had the next highest beginning salaries compared to
6 Betts (1996) tests whether students with higher socioeconomic levels do have more accurate information onrelative earnings between occupations than students with lower socioeconomic levels. He finds that they do.
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humanties, followed by natural science majors, social science majors with low earnings
or “soft” business majors having the lowest earnings premium relative to humanities
majors. Pre-professional students naturally have 18% lower beginning earnings than
humanities majors as many pre-professional students would not be expected to be fully
attached to the labor force.
An interesting finding that parallels many other studies that estimate the wage
premiums associated with different majors, is illustrated in the separate regressions for
men and women. Similar to results found by Rumberger and Thomas (1993) women
have a higher earnings advantage for majoring in engineering and high earning businessmajors relative to humanities majors than men do. It should be noted that these
differentials showing earnings advantages for particular major categories relative to all
humanities say nothing in particular about the relative premiums to individual majors
within these broad categories.
One must also keep in mind that these wage differentials are conditional earnings
differentials, meaning that they are conditional upon individuals selecting into certain
majors. Also these estimates illustrate earnings differences for beginning salaries and
don’t speak to the possible differences in earnings growth between majors.
The main variable of concern in this study is the choice of a second major. For
the whole sample, Table 1 demonstrates that there is a 3% earnings premium associated
with choosing a second major, however, this variable is only found to be significant for
women in separate earnings regressions. It seems that it is mainly women in this sample
who benefit from an earnings advantage to taking a second major. The inclusion of the
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female*double major interaction term provides more evidence that female second majors
do have significantly different earnings.
While Table 1 illustrates the likelihood that double majoring across all primary
majors holds some investment return for females, Table 2 reveals how double majoring
effects earnings within the college of business. One could assume that there might be a
higher investment quality to taking a second major within a college that provides
relatively more fields geared toward occupational training. Similar results are found
within the business college; however, there is an even higher premium to a second major
within the business school. Once again, however, only female business students receive a6% higher beginning wages than female business students without a second major.
Tables 3A and 3B present the results from the Heckman two stage corrections for
sample selection, which attempt to control sample selection bias that might be present
due to individual selection into employment. Selection into the labor market is identified
by two variables. The standard identifier of labor force participation is presence of
children, however since at the time of graduation most individuals in this dataset are
without children, a substitute for presence of children was used. Students are asked the
importance they place on raising a family after graduation. Giving a high import to
raising a family as well as having plans to attend graduate school will be used to identify
selection into the labor force. Both of these variables are strongly positively related to
entering into the sample of students with wages. The results from the second stage of the
Heckman procedures do not differ greatly from the uncorrected results in Table 1 and
Table 2. It still appears that only females receive an earnings premium to a second major
at this selective university. But why might second majors act as human capital which
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earns a return for women and not for men? Second majors might possibly act as an
ability signal for female graduates at this institution or maybe these results might be
explained simply by the type of second majors women are choosing. A explanation of
these results is likely related to the different types of primary majors males and females
choose. On one hand, since women are more likely to choose their primary majors based
upon their preferences rather than their earnings prospects (see essay 1), they tend to
choose primary majors in the lower paying humanities and social sciences. These same
female students might feel some type of pressure from those financing their education to
take a remunerative second major. Males on the other hand are more likely to choosemore remunerative primary majors and therefore might choose a secondary major based
upon their preferences—likes and dislikes—rather than upon which second major will
provide them the greatest return. In this way, differences in the choice of primary major
between men and women might produce the positive earnings benefit associated with
second majors for women in this sample.
Whether earnings premiums connected to a second major are received by males or
females, the possibility still exists that these estimated earnings premiums associated with
choosing a second major are driven by selection bias. The attempt to correct the problem
that endogenous choice of a second major and unobserved ability might create has led to
a model that attempts to partially control for unobserved ability in earnings regressions
estimated for students in the College and Beyond dataset. The estimates from two
specifications, one that does not control for unobserved ability and a second that includes
students self-revealed ability rankings as well as the 196 dummy variables representing
groups of students with similar levels of unobserved ability, are shown in Table 4 and
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Table 5 respectively. Controlling for some level of unobserved ability decreased the
earnings premiums associated with choosing engineering, business, natural sciences,
social sciences and other majors relative to humanities. This result would reinforce the
idea that conditional earnings differentials between majors given by conventional OLS
regressions might overstate earnings differentials between majors. One more result
regarding the choice of a primary major that should be noted is the apparent difference in
earnings growth that takes place between majors. When earnings models were estimated
for the single university, there existed a significantly greater beginning earnings premium
associated with choosing engineering. The College and Beyond data has earningsobservations for 15 years after graduation as opposed to beginning earnings and we see
that after controlling for observed and unobserved ability, the highest earnings premium
is now associated with choosing a business major. This indicates that while business
majors might begin with lower relative salaries than engineers, the growth in their
relative earnings profile is steeper than the earnings profile for engineers.
The result of primary interest in this section, however, is the effect that
controlling for unobserved ability has upon the estimated coefficient for double majors.
The results illustrated in Table 5 show that there most likely is some amount of selection
into second majors taking place as controlling for selection into a double major decreases
the earnings premium to taking a second major. This would indicate that there is positive
selection into second majors, where students with higher levels of unobserved abilities
tend to be the students who take a second major. The earnings premium decreases from
4.5% to 3.8% between the two specifications.
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Students with higher unobserved ability in the College and Beyond tended to
choose a second major, and one can see that if this higher unobserved ability also
increases earnings and is not controlled for in the earnings equation, the coefficient for
double majoring will be biased upward. When we do not control for this positive
selection into secondary majors as is the case for the regression producing Table 4 , the
effect of double majoring is higher than what we would see in an experiment where a
student were randomly assigned a second major. But even after controlling for selection
there appears to be an earnings premium associated with a second major.
Other background and schooling variables also play a part in determiningearnings for students in this dataset. Having a father with higher education, attending a
catholic high school relative to a public secondary school, and attending a private
university relative to a public university were associated with an increase in individual
earnings 15 years after graduation, controlling for other background characteristics
including occupation. As expected, obtaining an advanced degree also positively affects
earnings over a decade following graduation.
3.6 Summary and Conclusions
In this essay, estimates have been presented that demonstrate the effect of
choosing a secondary major upon subsequent earnings. Unlike previous studies which
focus on earnings premiums across primary majors, this study used data from both a
single university as well as data from several colleges and universities to study earnings
premiums associated with choosing a secondary major both across all majors and within
a specific college. Also, this study allows for the possibility that students may
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systematically choose a secondary major based upon unobservable abilities. It does not
seem that the positive wage premium associated with choosing a second major is greatly
lowered by correcting for selection bias. However, correcting for selectivity provides
evidence that there might be some amount of positive selection into second majors. After
controlling for selection on observables and unobservables, the earnings impact of having
a second major falls slightly. Also, the second major earnings advantage for students in
the single university of around 3.5% was very close to the selection corrected premium of
3.8% for the College and Beyond. The difference between the two datasets rests
primarily on which gender receives the earnings advantage. For the single university, wefind that women rather than men receive an earnings premium to secondary majors, while
in the College and Beyond, the opposite is true. With approximately 41% of students
from the single university selecting second majors and slightly over half of those students
being female, the choice of a second major is potentially an important decision for many
students. This study also finds that there continues to be varied earnings premiums to
most primary majors especially to engineering and business majors relative to humanities
majors. Earnings premiums in these majors also appear to be larger for females than they
are for male students, leaving the opportunity open for continued minimizing of gender
earnings differentials. 7 Findings also suggest that there are most likely different growth
rates in earnings between majors as some majors have lower beginning earnings
premiums but have higher earnings premiums later in the working period. 8
These results that extend and support much of the previous research on higher
education choices and their effect on post graduate outcomes are of particular importance
7 See Eide (1994)8 Berger finds this is true for individuals in the National Longitudinal Survey of Young Men.
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especially since these choices can vary widely based on gender and socioeconomic status.
As college costs and returns continue to rise, these choices continue to have the potential
to minimize or exacerbate economic differences between student groups.
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SUMMARY AND CONCLUSIONS
The preceding essays have attempted to shed some additional light on a student’s choice
of an undergraduate major. For many students, the choice of major is an important decision
which will have a significant impact upon other higher education decisions as well as on their
later life outcomes; therefore, defining the incentives that determine this choice can be helpful in
discussing why there are different educational and economic outcomes for particular groups of
students.
The first essay attempts to model a student’s choice of primary major in a unique way by
allowing earnings expectations to be determined by various earnings outcomes within a major
and finds that like other groups of students in nationally representative datasets, students from a
selective university also choose their major based upon relative expected returns in each major,
and choose the major that would give them the highest expected returns. Expected earnings,
however, seem to be a more important to the decisions of men than women in this dataset. Little
evidence is found that suggests that students in an environment of uncertainty choose a major
based upon relative probabilities of obtaining a job offer across majors.
Based on these findings we can conclude that increasing the number of individuals
entering a particular field might be achieved by enhancing the monetary returns of that field. Of
course this inference would be truer for men than it is for women. Additional study could focus
on how expected earnings affect the choice of field differently for other minority groups. This
essay did not specifically conclude whether a change in expected beginning earnings or lifetime
earnings would be the most appropriate to affect choice of major or field as the choice of major
was based upon expected earnings 15 years after graduation .
The choice of primary major itself is hypothesized to impact other decisions students
make during college. Therefore, the second essay of this paper focuses on one of the implications
of a student’s choice of major. In the second essay we find that the choice of the highest earning
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majors increases the magnitude of a student’s debt burden. Not only was a student’s major found
to affect the amount that a student chooses to borrow, but we can see that student’s abilities as
measured by their SAT scores also affect borrowing behavior. Consequently, the expected future
earnings of a major and individual ability levels are linked to the amount that a student will
borrow as suggested by the life-cycle hypothesis. Based on the results presented here, we can
also see that students incurring the most debt will also be the students who will be more able
handle their larger debt upon graduation. We also conclude that these results are not driven by
the proposed endogenous nature of the choice of major. The relationship between loans and
choice of major is an interesting one and merits additional study since both the costs of higher
education and also the aid from colleges and universities continue to rise.
The third and final essay fills a void in the choice of major literature by estimating the
premium to investing in a secondary major for students at a selective university and also for
students from a wider range of 30 selective institutions. Estimating the effect of second majors
on log earnings while controlling for selection into secondary majors, evidence is found to
suggest that students from a single selective university who invest in a second major do earn an
earnings premium of about 3.5%. This premium is even greater within the college of business.
However, this positive earnings premium only exists for female graduates in this sample. For the
sample of students from the wider range of colleges and universities, there also exists an earnings
premium of 3.8% that is associated with taking a second major, however, in the College and
Beyond this positive earnings premium was present for only men.
There is also something to be said about controlling for selection into secondary majors.
The College and Beyond dataset allows for a unique way to control for some amount of the
possible selection into second majors that might occur. For the sample of 30 selective institutions
in the College and Beyond, we find that there is apparently a least a small amount of positive
selection into secondary majors, and once selection is controlled for there is a smaller premium to
having a second major than previously estimated. It could be assumed that most individuals
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choosing second majors have higher levels of some unobserved ability that is positively
correlated with earnings and without controlling for this unobserved ability, the premium to
taking a second major is pushed upward.
Results from this third essay give evidence that there is some investment value to
choosing a second major. There do appear to be remunerative reasons for students to choose
second majors. It seems to be reasonable way for students to slightly increase their realized
earnings.
In conclusion, these essays set out to answer four specific questions: 1.) How do
expected earnings affect a student’s choice of major for students from a selective university? 2.)
What are some of the implications that the choice of major has upon other college decisions such
as borrowing behavior? 3.) What affect does choosing a second major have upon a student’s
earnings? 4.) How do the answers to these questions change when describing the choices of men
and women? These three essays do find that there are both strong incentives and implications of
student’s choice of both primary and secondary majors. Expected earnings incorporating
earnings risk significantly affect choice of major; the choice of major and ability are shown to be
significantly correlated with loan debt and there appears to be a monetary remuneration to taking
a second major.
Further research might focus on extending these results that describe qualitative higher
educational choices to the greater population of college undergraduates beyond graduates of
selective colleges and universities. Additional research might be well advised to extend the
analysis in these essays by studying the differences in qualitative educational choices made by
different socioeconomic and demographic groups so that we may better understand the labor
supply, borrowing decisions and earnings outcomes for diverse groups of college graduates.
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1.6 Appendix 1
TABLE 1A:
Conditional (Mixed) Logit:
Estimating the Impact of Expected Earnings on the Choice of CollegeMajor for the Single Selective University
1997, 1999, 2001 and 2003 cohorts
Dependant Variable: Choice of Major
Full Sample
ESTIMATE STD.ERROR
ESTIMATE STD.ERROR
EXPECTEDEARNINGS
.00000921** .00000367 Multiracial 1/5 0.0427 0.3557
Female 1/5 -1.6275** 0.12458 Multiracial 2/5 -0.3865 0.3023Female 2/5 -0.74311** 0.08433 Multiracial 3/5 -0.6564 0.4174Female 3/5 -0.30421** 0.10736 Multiracial 4/5 -0.6688 0.3604Female 4/5 0.1824 0.0984 Unknown Race
1/50.1474 0.6227
Mother Education:College and Above1/5
-0.1885 0.1219 Unknown Race2/5
-0.3570 0.5658
Mother Education:College and Above2/5
-0.2435** 0.0939 Unknown Race3/5
0.3295 0.6104
Mother Education:College and Above3/5
-0.1948 0.1193 Unknown Race4/5
0.0322 0.5808
Mother Education:College and Above4/5
0.0519 0.1004 ParentalIncome 50-100K 1/5
-0.1845 0.1498
FatherEducation:College
and Above 1/5
-0.1897 0.1226 ParentalIncome 50-
100K 2/5
0.3311** 0.1144
Father Education:College and Above2/5
-0.3585** 0.0966 ParentalIncome 50-100K 3/5
-0.1227 0.1446
Father Education:College and Above3/5
-0.0364 0.1214 ParentalIncome 50-100K 4/5
0.1240 0.1233
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Father Education:College and Above4/5
-0.2899** 0.1052 ParentalIncome over100K 1/5
0.2264 0.1441
Black 1/5 -0.3113 0.5500 ParentalIncome over
100K 2/5
0.0246 0.1174
Black 2/5 0.7840* 0.3518 ParentalIncome over100K 3/5
0.1010 0.1436
Black 3/5 -0.2806 0.5135 ParentalIncome over100K 4/5
0.0106 0.1262
Black 4/5 0.6030 0.3771 Father isProfessional1/5
-0.5455** 0.1807
Asian 1/5 0.5193 0.2941 Father is
Professional2/5
-0.2491* 0.1270
Asian 2/5 0.3935 0.2443 Father isProfessional3/5
-0.1891 0.1609
Asian 3/5 0.5833* 0.2795 Father isProfessional4/5
-0.0875 0.1345
Asian 4/5 -0.1942 0.2915 SAT /100 1/5 0.0163 0.0105
Latino 1/5 0.1644 0.2317 SAT /100 2/5 0.0769** 0.0089
Latino 2/5 0.2661 0.1827 SAT /100 3/5 -0.0196 0.0107
Latino 3/5 -0.1890 0.2574 SAT /100 4/5 -0.0107 0.0106
Latino 4/5 0.2977 0.1930
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TABLE 1B:
Conditional (Mixed) Logit: Results of separate mixed logits for males and females
Estimating the Impact of Expected Earnings on the Choice of CollegeMajor for the Single Selective University
1997, 1999, 2001 and 2003 cohorts
ESTIMATE STD. ERRORMales:
EXPECTEDEARNINGS
0.0000152 ** 0.0000048282
Females:EXPECTEDEARNINGS
-0.0000067791 0.00000599054
Significance:
Bold = significantly different from zero at the 10% levelBold* = significantly different from zero at the 5% level Bold ** = significantly different from zero at the 1% level
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TABLE 2A:
Conditional (Mixed) Logit: Full Sample
Estimating the Impact of Risk Adjusted Expected Earnings on the Choice of CollegeMajor for the Single Selective University
1997, 1999, 2001 and 2003 cohorts
ESTIMATE STD.ERROR
ESTIMATE STD.ERROR
U_EXPECTED
EARNINGS
.0000459** .00000677 Multiracial
1/5
0.0049 0.3562
Female 1/5 -1.70327** 0.12122 Multiracial2/5
-0.6015* 0.3047
Female 2/5 -0.03386 0.13137 Multiracial3/5
-0.6780 0.4159
Female 3/5 -0.3908** 0.10779 Multiracial4/5
-0.7043* 0.3606
Female 4/5 0.39191** 0.10175 UnknownRace 1/5
0.1520 0.6227
MotherEducation:College andAbove 1/5
-0.1726 0.1216 UnknownRace 2/5
-0.3087 0.5663
MotherEducation:College andAbove 2/5
-0.0992 0.0962 UnknownRace 3/5
0.3357 0.6103
MotherEducation:College andAbove 3/5
-0.1228 0.1189 UnknownRace 4/5
0.0257 0.5808
MotherEducation:College andAbove 4/5
0.0820 0.1005 ParentalIncome 50-100K 1/5
-0.1903 0.1504
FatherEducation:College andAbove 1/5
-0.1660 0.1215 ParentalIncome 50-100K 2/5
0.2639* 0.1151
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FatherEducation:College andAbove 2/5
-0.3782** 0.0961 ParentalIncome 50-100K 3/5
-0.1224 0.1453
Father
Education:College andAbove 3/5
-0.0262 0.1213 Parental
Income 50-100K 4/5
0.1103 0.1235
FatherEducation:College andAbove 4/5
-0.3036** 0.1048 ParentalIncomeover 100K1/5
0.2213 0.1446
Black 1/5 -0.2675 0.5503 ParentalIncomeover 100K2/5
-0.0138 0.1179
Black 2/5 0.6290 0.3538 ParentalIncomeover 100K3/5
0.1013 0.1442
Black 3/5 -0.1450 0.5126 ParentalIncomeover 100K4/5
0.0001 0.1264
Black 4/5 0.5459 0.3774 Father isProfessional1/5
-0.5456** 0.1806
Asian 1/5 0.4991 0.2941 Father isProfessional2/5
-0.2674* 0.1274
Asian 2/5 0.3307 0.2446 Father isProfessional3/5
-0.1865 0.1608
Asian 3/5 0.6610* 0.2796 Father isProfessional4/5
-0.0977 0.1345
Asian 4/5 -0.2659 0.2891 SAT/1001/5
-0.0181 0.0118
Latino 1/5 0.0436 0.2323 SAT/1002/5
-0.0459* 0.0191
Latino 2/5 0.0805 0.1852 SAT/1003/5
-0.0244* 0.0108
Latino 3/5 -0.2094 0.2555 SAT/1004/5
-0.0574** 0.0128
Latino 4/5 0.3475 0.1933
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TABLE 2B:
Conditional (Mixed) Logit: Results from separate mixed logits for males and females
Estimating the impact of Risk Adjusted Expected Earnings on the Choice of CollegeMajor for the Single Selective University
1997, 1999, 2001 and 2003 cohorts
ESTIMATE STD. ERRORMales:
U_EXPECTEDEARNINGS
0.0000557** 0.000009
Females:
U_EXPECTEDEARNINGS
0.0000186* 0.00000948
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TABLE 3A:
Conditional (Mixed) Logit
Estimating the Impact of the Predicted Probability of Receiving Job Offer on the Choiceof College Major for the Single Selective University1997, 1999, 2001 and 2003 cohorts
Probability of Receiving a Job Offer is the only independent variable
ESTIMATE STD.ERROR
Full Sample :
Predicted Probability ofReceiving Job Offer
0.02704
0.09667
Males :
Predicted Probability ofReceiving Job Offer
1.30941**
0.14874
Females :
Predicted Probability ofReceiving Job Offer
-1.00785**
0.13099
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TABLE 4A:
Conditional (Mixed) Logit:
Estimating the Impact of the Predicted Probability of Receiving Job Offer on the Choice
of College Major for the Single Selective University
1997, 1999, 2001 and 2003 cohorts
All Controls
Males Females
ESTIMATE STD.ERROR ESTIMATE STD.ERROR PROBJOB -0.29518 0.327617 PROBJOB 0.291036 0.38585MotherEducationCollege andAbove 1/5
-0.12286 0.157468 MotherEducationCollege andAbove 1/5
-0.18085 0.223039
MotherEducationCollege andAbove 2/5
-0.2229 0.13684 MotherEducationCollege andAbove 2/5
-0.25636 0.134197
MotherEducationCollege andAbove 3/5
-0.0244 0.177101 MotherEducationCollege andAbove 3/5
-0.28832 0.162058
MotherEducationCollege andAbove 4/5
0.032672 0.163589 MotherEducationCollege andAbove 4/5
0.030606 0.139143
FatherEducationCollege andAbove 1/5
0.083463 0.156214 FatherEducationCollege andAbove 1/5
-0.67015** 0.218915
FatherEducationCollege andAbove 2/5
-0.29248* 0.138643 FatherEducationCollege andAbove 2/5
-0.42557** 0.138079
FatherEducationCollege andAbove 3/5
0.087401 0.182101 FatherEducationCollege andAbove 3/5
-0.13079 0.170944
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FatherEducationCollege andAbove 4/5
-0.18748 0.165825 FatherEducationCollege andAbove 4/5
-0.304* 0.139321
Black 1/5 -0.10529 0.638962 Black 1/5 -14.3583 887.6594
Black 2/5 0.534541 0.518019 Black 2/5 0.950181* 0.496095
Black 3/5 -0.87311 0.888238 Black 3/5 0.322548 0.733261
Black 4/5 0.428625 0.583048 Black 4/5 0.69312 0.510563
Asian 1/5 0.497099 0.402941 Asian 1/5 0.680807 0.486174
Asian 2/5 0.319002 0.358281 Asian 2/5 0.573311 0.335015
Asian 3/5 0.593185 0.440299 Asian 3/5 0.634177 0.390491
Asian 4/5 0.067902 0.435 Asian 4/5 -0.1486 0.386503
Latino 1/5 0.007405 0.31018 Latino 1/5 -0.08952 0.45561
Latino 2/5 -0.05263 0.279596 Latino 2/5 0.52622 0.275074
Latino 3/5 -0.44543 0.381882 Latino 3/5 -0.12603 0.353962
Latino 4/5 0.058342 0.325334 Latino 4/5 0.458926 0.301524
Multicultural1/5
0.129137 0.474689 Multicultural1/5
-0.37358 0.648638
Multicultural2/5
-0.29095 0.468186 Multicultural2/5
-0.39725 0.408714
Multicultural3/5
-0.26278 0.627013 Multicultural3/5
-0.82005 0.579908
Multicultural4/5
-1.2535 0.796099 Multicultural4/5
-0.55644 0.416851
Unknown
Race 1/5
0.176487 0.679139 Unknown
Race 1/5
-14.1746 1681.736
UnknownRace 2/5
-0.51531 0.681318 UnknownRace 2/5
0.007754 1.01453
UnknownRace 3/5
0.232874 0.772362 UnknownRace 3/5
0.690047 1.008966
UnknownRace 4/5
-0.19569 0.770388 UnknownRace 4/5
0.361579 0.921614
Parental -0.177 0.196676 Parental -0.03609 0.258944
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Income 50-100K 1/5
Income 50-100K 1/5
ParentalIncome 50-100K 2/5
0.447544** 0.169398 ParentalIncome 50-100K 2/5
0.159619 0.159038
ParentalIncome 50-100K 3/5
-0.15326 0.223972 ParentalIncome 50-100K 3/5
-0.04721 0.190671
ParentalIncome 50-100K 4/5
0.125531 0.196713 ParentalIncome 50-100K 4/5
0.133192 0.159769
Parentalincome over100K 1/5
0.118056 0.186102 Parentalincome over100K 1/5
0.245453 0.255417
Parental
income over100K 2/5
-0.1463 0.168864 Parental
income over100K 2/5
0.161462 0.165691
Parentalincome over100K 3/5
0.050443 0.213484 Parentalincome over100K 3/5
0.100906 0.1972
Parentalincome over100K 4/5
-0.26078 0.197675 Parentalincome over100K 4/5
0.227157 0.166343
Father isProfessional1/5
-0.55936** 0.230426 Father isProfessional1/5
-0.60926 0.359707
Father isProfessional2/5
-0.30276 0.188568 Father isProfessional2/5
-0.29415 0.188734
Father isProfessional3/5
-0.47577 0.256117 Father isProfessional3/5
-0.15108 0.217415
Father isProfessional4/5
0.061324 0.21374 Father isProfessional4/5
-0.1869 0.184003
SAT/100 1/5 0.037607* 0.017725 SAT/100 1/5 -0.03896 0.023616
SAT/100 2/5 0.047112** 0.017951 SAT/100 2/5 0.003379 0.019061
SAT/100 3/5 -0.00965 0.019629 SAT/100 3/5 -0.0334 0.018821
SAT/100 4/5 0.006458 0.018252 SAT/100 4/5 -0.00361 0.016114
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2.6 Appendix 2
Summary statistics from this data would suggest that loan debt plays some role in
higher educational choices, or at least is an underlying determinant in education and
career decisions for students from this university. In the graduating class of 2003, the
only graduating class to answer the relevant survey questions, only 9 % of students would
have chosen a different major considering the loans they accumulated. The fact that such
a low number wish to change their major demonstrates one of two behaviors : Either most
students are forward looking enough to choose their major based in part based upon the
loan debt they were to accumulate; or students—and relatively few students at that—finally consider loan debt only closer to graduating. In constructing the system of
simultaneous equations estimated in this study, we show how the former is most likely
the case—that loan debt does influence final major choice. This is one reason underlying
the argument that major choice is endogenous in the equation below. However, it still
might be the case that the latter—loan debt does not influence major choice for most
students until they are about to graduate—might be a more accurate description of the
effect loan debt has on major. If this is the case major choice would not be endogenous
through the system of equations presented in this paper.
Loans i = X i + Major choice i + u i
An even higher 12% of students with $25,000 or more worth of debt said that they
would have chosen a different major given the total loan debt they accumulated by their
senior year. Other statistics from the 2003 graduating class would suggest that loan debt
also plays a part in student’s post-graduation choices. When asked whether loan debt
will play a part in delaying post baccalaureate plans, 40% of students responded that their
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post-bac plans would be delayed due to their accumulated loan debt. When asked
whether loan debt played a part in their career choices, 46% of students responded that
their debt played a part in the career path they will take.
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GENDER AND ETHNICITY OF GRADUATES FROM SELECTIVE UNIVERSITY
1997, 1999, 2001 and 2003 graduating cohorts
MALE FEMALE TOTAL
WHITE 3375 2821 6196
Percent of Row 54.47% 45.53% 100.00%Percent of Column 83.75% 82.53% 83.19%
BLACK 107 97 204
Percent of Row 52.45% 47.55% 100.00%Percent of Column 2.66% 2.84% 2.74%
ASIAN 158 141 299
Percent of Row 52.84% 47.16% 100.00%Percent of Column 3.92% 4.13% 4.01%
LATINO 244 223 467
Percent of Row 52.25% 47.75% 100.00%Percent of Column 6.05% 6.52% 6.27%
MULTIRACIAL 64 68 132
Percent of Row 48.48% 51.52% 100.00%Percent of Column 1.59% 1.99% 1.77%
OTHER RACE 50 47 97
Percent of Row 51.55% 48.45% 100.00%Percent of Column 1.24% 1.38% 1.30%
UNKNOWN RACE 32 21 53
Percent of Row 60.38% 39.62% 100.00%
Percent of Column 0.79% 0.61% 0.71%
TOTAL 4030 3418 7448
Percent of Row 54.11% 45.89% 100.00%Percent of Column 100.00% 100.00% 100.00%
97
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DESCIPTIVE STATISTICS & M E A N S
Males and Females from Selective University1997, 1999, 2001 and 2003 graduating cohorts
MEN: N =4030
Percent of allmajors
Percentgoing to
gradschool
Salary(2003 $)
Total Loans(2003 $)
Engineers 14.76% 16.97% 47,213 21,969
Business 34.19% 9.65% 44,297 20,670
NaturalSciences
9.23% 35.75% 41,577 21,527
Social Sciences 15.24% 27.03% 41,389 20,199
Humanities 15.88% 19.84% 36,921 18,625
Architecture 2.16% 3.44% 32,702 28,830(5yr progr)
Pre Professional 7.62% 55.70% 30,232 20,699
WOMEN: N = 3418Engineers 5.38% 11.96% 47,530 22,074
Business 24.20% 8.95% 42,980 19,709
NaturalSciences
12.00% 32.19% 38,400 20,590
Social Sciences 24.02% 27.28% 38,572 19,750
Humanities 22.91% 21.58% 35,901 19,420
Architecture 2.19% 1.33% 33,793 29,420(5yr progr)
Pre Professional 8.66% 51.01% 35,213 18,358
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TABLE 1A: MONKS REPLICATION:
Probit results of the impact of loan debt on graduate school attendanceSelective University Sample
DEP VAR:GRAD SCHPLANS
ESTIMATE STD.ERR
DEP VAR:GRAD SCH.PLANS
ESTIMATE STD.ERR
Intercept -3.3095 0.3877 Intercept -3.2951 0.3870Female -0.0807 0.0516 Female -0.0800 0.0515College GPA 0.6440** 0.0779 College GPA 0.6385** 0.0774Father’sEducation:Grad School
-0.0744 0.1058 Father’sEducation:Grad School
-0.0778 0.1057
Father’sEducation:College
-0.0991 0.0999 Father’sEducation:College
-0.0997 0.0997
Mother’sEducation: GradSchool
-0.1313 0.0918 Mother’sEducation: GradSchool
-0.1342 0.0916
Mother’sEducation:College
-0.0613 0.0819 Mother’sEducation:College
-0.0648 0.0818
SAT / 100 0.0547* 0.0257 SAT / 100 0.0556* 0.0257Black 0.3726* 0.1782 Black 0.3626* 0.1783
Asian -0.0050 0.1305 Asian -0.0025 0.1304Latino 0.1012 0.0993 Latino 0.1036 0.0992Multiracial 0.0703 0.1732 Multiracial 0.0611 0.1729Unknown Race -0.0032 0.3572 Unknown Race 0.0043 0.3571Drive to Achieve:Medium
0.1835 0.1426 Drive to Achieve:Medium
0.1839 0.1422
Drive to Achieve:High
0.3321* 0.1456 Drive to Achieve:High
0.3321* 0.1453
Parental Income:30K -50K
-0.2152 0.1222 Parental Income:30K -50K
-0.2075 0.1219
Parental Income:50K -100K
-0.1953 0.1092 Parental Income:50K -100K
-0.1980 0.1087
Parental Income:over 100K
-0.1902 0.1159 Parental Income:over 100K
-0.2038 0.1151
Engineer -0.1748 0.0922 Engineer -0.1730 0.0919Business -0.4771** 0.0757 Business -0.4727* 0.0755Social Sciences 0.1684* 0.0759 Social Sciences 0.1701 0.0757Natural Sciences 0.5092** 0.0884 Natural Sciences 0.5128** 0.0882
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PreprofessionalStudies
1.0929** 0.0994 PreprofessionalStudies
1.0988** 0.0993
Architecture -1.0115** 0.2300 Architecture -1.0120** 0.2302Loans (2,000-4,999)
-0.2717* 0.1365 Log Loans -0.0107 0.0064
Loans (5,000-9.999)
-0.1429 0.1053 Graduated in 1999 0.0864 0.0890
Loans(10,000-14.999)
-0.1566 0.0865 Graduated in 2001 0.0882 0.0841
Loans(15,00019.999)
-0.0768 0.0831 Graduated in 2003 0.1986* 0.0798
Loans(20,000-24.999)
-0.0656 0.0890
Loans(25,000-29.999)
-0.0622 0.0934
Loans(30,000-49.999)
-0.0614 0.1491
Loans (50,000and above)
-0.2135 0.1604
Graduated in1999
0.0860 0.0891
Graduated in2001
0.0918 0.0843
Graduated in2003
0.1991 0.0803
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TABLE 1 B:MONKS REPLICATION
Probit model for estimating the impact of loans on attending professional schoolSelective University Sample
DEPENDENT VAR: PLANS TOENROLL IN PROFESSIONALSCHOOL
ESTIMATE STANDARD ERROR
Intercept -3.7954 0.7934Female -0.0758 0.1477College GPA 0.2659 0.0922Father’s Education: Grad School 0.0875 0.1922Father’s Education: College -0.0254 0.1827Mother’s Education: Grad
School-0.1354 0.1639
Mother’s Education: College -0.1120 0.1454SAT / 100 0.2107** 0.0490Black 0.3272 0.3467Asian 0.4856* 0.2425Latino 0.4948** 0.1852Multiracial -0.0444 0.3159Unknown Race 0.4912 0.6450Drive to Achieve: Medium 0.0271 0.3084Drive to Achieve: High 0.3508 0.3119Parental Income: 30K -50K 0.0177** 0.2130Parental Income: 50K -100K 0.0594 0.1510
Parental Income: over 100K 0.1205 0.2099Engineer -1.3110** 0.2155Business -0.2147 0.1519Social Sciences 0.2170 0.1340Natural Sciences 0.1908 0.1425PreProfessional Studies 1.2943* 0.1566Architecture -6.3765 4679.194Loans (2,000 -4,999) -0.0567 0.2798Loans (5,000-9.999) -0.1438 0.1906Loans (10,000-14.999) -0.1871 0.1615Loans (15,000-19.999) -0.1299 0.1500Loans (20,000-24.999) -0.0882 0.1634Loans (25,000-29.999) 0.0663 0.1712Loans (30,000-49.999) 0.2154 0.2609Loans (50,000 and above) -0.5419 0.2925Graduated in 1999 -0.0328 0.1713Graduated in 2001 -0.2836 0.1603Graduated in 2003 -0.2776 0.1546
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TABLE 1C:
MONKS REPLICATION:
Probit estimating the impact of loan debt upon the decision to change major
Selective University Sample Obs: (4713)DEPENDENT VAR:CHANGE IN COLLEGEMAJOR
ESTIMATE STANDARD ERROR
Intercept 1.0081 0.2799College GPA -0.4620** 0.0592Female 0.0747 0.0396Father’s Education:
Grad School-0.0684 0.0890
Father’s Education:
College
-0.0877 0.0855
Mother’s Education:Grad School
0.0498 0.0743
Mother’s Education:College
0.0803 0.0677
SAT / 100 0.0516 ** 0.0197Black 0.2682 0.1568Asian 0.0593 0.1005Latino 0.0100 0.0843Nonwhite 0.1070 0.1466Unknown Race -0.0491 0.2597Engineer -0.9984** 0.0759Business -0.1693** 0.0564Natural Sciences -0.0913 0.0723Social Sciences 0.1901** 0.0619PreProfessional Studies -0.8079** 0.0781Architecture 6.3018 2787.357Loans (2,000 -4,999) -0.0173 0.1185Loans (5,000-9.999) 0.0579 0.0894Loans (10,000-14.999) 0.0078 0.0703Loans (15,000-19.999) 0.0266 0.0636Loans (20,000-24.999) 0.0340 0.0720
Loans (25,000-29.999) -0.0167 0.0791Loans (30,000-49.999) 0.2196 0.1357
Loans (50,000 and above) 0.1878 0.1386Parental Income: 30K -
50K0.1948 0.1107
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Parental Income: 50K -100K
0.1201 0.0995
Parental Income: over100K
0.1629 0.1032
Graduated in 1999 0.0242 0.0618
Graduated in 2001 -0.0295 0.0613Graduated in 2003 -0.0395 0.0614
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ParentalIncome: 50K-100K
0.0037 0.1197 ParentalIncome: 50K -100K
-0.0790 0.1692
ParentalIncome:
over 100K
0.1656 0.1271 ParentalIncome: over
100K
0.0511 0.1778
Loans (2,000-4,999)
0.0610 0.1495 Loans (2,000 -4,999)
0.1218 0.2100
Loans(5,000-9.999)
0.2262* 0.1107 Loans (5,000-9.999)
0.2182 0.1530
Loans(10,000-14.999)
0.1143 0.0943 Loans (10,000-14.999)
0.1164 0.1312
Loans(15,000-19.999)
-0.0020 0.0937 Loans (15,000-19.999)
-0.0825 0.1278
Loans(20,000-24.999)
0.1337 0.0982 Loans (20,000-24.999)
0.1494 0.1381
Loans(25,000-29.999)
0.1594 0.1037 Loans (25,000-29.999)
0.3300* 0.1481
Loans(30,000-49.999)
0.1814 0.1654 Loans (30,000-49.999)
0.0658 0.2226
Loans(50,000 andabove)
0.1997 0.1651 Loans (50,000and above)
0.1178 0.2221
Graduatedin 1999
0.3042** 0.0959 Graduated in1999
0.4956** 0.1341
Graduatedin 2001
0.2009* 0.0922 Graduated in2001
0.2485* 0.1259
Graduatedin 2003
0.1112 0.0895 Graduated in2003
0.1297 0.1227
Log Loans 0.0128 0.0072
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TABLE 2.AREPLICATION OF ST. JOHN:
OLS regression: Estimating the impact of loan debt on choosing a higher earnings major
Obs. (3270)R-Square 0.2240Adj R-Sq 0.2137
DEPENDANT VAR: RANKINGOF MAJORS (1-7)
ESTIMATE STD. ERROR
Intercept 3.80096 0.43628College Gpa -0.23531** 0.08577Female -0.33315** 0.05809Sat / 100 0.09322** 0.02864Father’s Education: Grad
School-0.20042 0.11774
Father’s Education: College -0.08247 0.11155Grad -0.79031** 0.06042Double Major -0.57105** 0.06091Mother’s Education: Grad
School-0.19998 0.10253
Mother’s Education: College -0.10498 0.09175Black 0.22168 0.20011Asian -0.06031 0.14318Latino 0.21820 0.11084Unknown Race 0.75926 0.43557Multiracial 0.12169 0.18684Varsity athlete 0.03740 0.06415Drive To Achieve: Medium 0.39257* 0.15784Drive To Achieve: High 0.34293* 0.16285Parental Income: 30k -50k 0.07291 0.13622Parental Income: 50k -100k 0.12517 0.12265Parental Income: Over 100K 0.10627 0.13058Loans (2,000-4,999) 0.21109 0.18523Loans (5,000-9,999) -0.15620 0.14592Loans (10,000-14,999) 0.43253* 0.11503Loans (15,000-19,999) 0.17969 0.09441
Loans (20,000-24,999) -0.01685 0.10178Loans (25,000-29,999) 0.20338 0.12264Loans (30,000-34,999) 0.04632 0.17754Loans (35,000-39,999) 0.04632 0.17754Loans (40,000-49,999) 0.05113 0.13059Loans (2,000-4,999)0 0.15237 0.11462Graduated In 1999 0.13628 0.09752
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Graduated In 2001 0.12745 0.09266Graduated In 2003 0.24937** 0.08863Party 11 hours/ week 0.26247** 0.08301Party 3-10 hours/ week 0.13719* 0.06570Clubs 11 hours/ week 0.16040 0.11465Clubs 3-10 hours/ week 0.04352 0.06905Being Well off highly important 0.87480** 0.11246Being Well off somewhat
important0.48847** 0.08271
Math ability High 0.56260** 0.05854Writing ability High -0.36250** 0.06042Artistic ability High -1.14412** 0.11760Artistic ability Medium -0.42113** 0.05907
Log Loans
R-Square 0.2189Adj R-Sq 0.2107
0.01313 0.00728
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TABLE 2BREPLICATION OF ST. JOHN:
OLS regression: Estimating the impact of loan debt on choosing a higher earnings major
R-Square 0.2697Adj R-Sq 0.2600DEPENDENT VAR: RANKINGOF MAJORS 1-14
ESTIMATE STD. ERROR
Intercept 4.31554 1.07179College GPA -0.54447** 0.21072Female -1.15595** 0.14271Sat / 100 0.28681** 0.07035Father’s Education: Grad School -0.56545 0.28926Father’s Education: College -0.26918 0.27405Graduate School Plans -1.96552** 0.14843Double Major -1.53073** 0.14963Mother’s Education: Grad
School-0.41327 0.25189
Mother’s Education: College -0.12938 0.22541Black 0.56814 0.49161Asian 0.00006158 0.35175Latino 0.70342** 0.27230Unknown Race 1.52908 1.07006Multiracial 0.55126 0.45900
Varsity athlete 0.18906 0.15761Drive To Achieve: Medium 1.20286** 0.38776Drive To Achieve: High 1.24173** 0.40007Parental Income: 30k -50k 0.13114 0.33464Parental Income: 50k -100k 0.30486 0.30131Parental Income: Over 100K 0.27595 0.32080Loans (2,000-4,999) 0.51879 0.45506Loans (5,000-9,999) -0.42003 0.35848Loans (10,000-14,999) 0.98056** 0.28259Loans (15,000-19,999) 0.49903* 0.23194Loans (20,000-24,999) 0.04273 0.25004Loans (25,000-29,999) 0.46669 0.30129Loans (30,000-34,999) 0.62190* 0.30902Loans (35,000-39,999) 0.03813 0.43616Loans (40,000-49,999) 0.14188 0.32082Loans (50,000 and up) 0.40052 0.28159Graduated in 1999 0.29572 0.23958Graduated in 2001 0.36659 0.22763
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Graduated in 2003 0.50417* 0.21774Party 11 Hours/ Week 0.69944** 0.20394Party 3-10 Hours/ Week 0.41455* 0.16141Clubs 11 Hours/ Week 0.22114 0.28167Clubs 3-10 Hours/ Week 0.06292 0.16963Being Well off highly important 2.45204** 0.27629Being Well off somewhat
important1.42909** 0.20320
Math ability High 1.72403** 0.14381Writing ability High -1.14722** 0.14844Artistic ability High -2.94318** 0.28892Artistic ability Medium -1.08188** 0.14511
Substitution Log Loans For LoanDummies
Log Loans
R-Square 0.2653Adj R-Sq 0.2576
0.03469* 0.01789
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Table 3A: Weiler Replication
Estimating the impact of log loan debt on graduate school attendance
Durbin-Wu-Hausman test: Testing the exogeneity of loan debt
1st stageNo. Obs = 2640R-squared = .2948
DEP VAR:LOG OF LOANS
ESTIMATE STD.ERROR
female -0.06881 0.138829SAT / 100 0.182128** 0.06456Black 0.700674 0.475069Asian 0.355823 0.367557Latino 0.993278** 0.27387Unknown Race -0.21952 1.024618Multiracial 1.071572* 0.465564Father’s Education: GradSchool
-0.04589 0.287243
Father’s Education: College 0.542643* 0.268598Mother’s Education: GradSchool
-0.15802 0.252851
Mother’s Education: College -0.33765 0.224476
Parental Income: 30K -50K -0.19812 0.323518Parental Income: 50K -100K -0.24438 0.293014Parental Income: over 100K -2.19328** 0.318177Graduated in 1999 1.675134** 0.242151Graduated in 2001 -0.11083 0.233296Graduated in 2003 -1.43723** 0.219318Has HAS FGSL 1.987134** 0.155551
_cons 4.526947 0.888368
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TABLE 3B.WEILER REPLICATION
Estimating the impact of log loan debt on graduate school attendance
Durbin-Wu-Hausman test: Testing the exogeneity of loan debt
2nd stage
No. Obs = 2638Pseudo R2 = .1445
DEP VAR:GRAD SCHOOL PLANS
ESTIMATE STD. ERROR
COLLEGE GPA 0.735347 0.083836female -0.10467 0.056036
SAT / 100 0.022059 0.02886Black 0.240639 0.192009Asian -0.00342 0.146686Latino 0.142365 0.111231Unknown Race -0.06754 0.373759Multiracial 0.224625 0.18765Father’s Education: GradSchool
-0.15818 0.112275
Father’s Education: College -0.11834 0.104781Mother’s Education: GradSchool
-0.11683 0.098898
Mother’s Education:College
-0.08124 0.087862
Parental Income: 50K -100K
-0.06709 0.076903
Parental Income: over 100K -0.22016 0.116687Graduated in 1999 0.00854 0.106167Graduated in 2001 0.029335 0.091752Graduated in 2003 0.112003 0.091521Engineer -0.15998 0.099105Business -0.5060** 0.082904
Social Sciences 0.197393* 0.083547Natural Sciences 0.429425** 0.097166Architecture -0.92574** 0.22651Preprofessional Studies 1.259394** 0.112618Log of Loans -0.06233* 0.02607Loan Residuals 0.055757* 0.027322
_cons -2.41021 0.410821
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TABLE 3C:WEILER REPLICATION:
Two Stage Probit IV: Estimating the impact of log loan debt on graduate schoolattendance
2 ND StageObs 3545 Wald chi2(24) = 414.88Prob > chi2 = 0.0000
Wald testof exogeneity Chi2 (1) = 4.18 Prob > chi2 = .0410
The null hypothesis that loan debt is exogenous can be rejected
DEP VAR: GRAD SCHOOLPLANS
ESTIMATE STD.ERROR
Log Loans* -0.0613* 0.025809College GPA 0.701218** 0.085383Female -0.09829 0.056496Sat / 100 0.026581 0.029771Black 1/5 0.234507 0.193515Asian -0.00642 0.148075Latino 0.137621 0.111714Unknown Race -0.06672 0.3771Multiracial 0.221568 0.189314Father’s Education: GradSchool
-0.15238 0.113253
Father’s Education: College -0.11511 0.105978Mother’s Education: Grad
School
-0.11996 0.100026
Mother’s Education: College -0.08179 0.088758Parental Income: 50k -100k -0.06476 0.077562Parental Income: Over 100K -0.2149 0.116055Graduated In 1999 0.008992 0.107243Graduated In 2001 0.030011 0.092643Graduated In 2003 0.113012 0.092214Engineer -0.13391 0.100266Business -0.49227** 0.083864Social Sciences 0.221775** 0.084774Natural Sciences 0.428035** 0.098259Architecture -0.85046** 0.228837PreProfessional Studies 1.251413** 0.113812
_Cons -2.38503 0.418382
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TABLE 4A:CHOICE OF MAJOR AND LOAN DEBT: FIRST APPROXIMATION
OLS Results: Estimating the impact of a student’s choice of major on log loan debt
Baseline major: Humanities Number of obs = 3545R-squared = 0.3061Adj. R-square=0.2977
DEP VAR:LOG OF LOANS
ESTIMATE STD.ERROR
Female -0.23951 0.13348Grad school plans -0.26621 0.141814Black 1.167971* 0.462729Asian 0.240029 0.331428Latino 1.049024** 0.254314Multiracial 0.733922 0.438544Unknown Race -0.59166 0.95848Engineer 0.533094* 0.267187High earning business majors 0.195567 0.216624Low earning business majors 0.354227 0.302296Social Sciences 0.102273 0.207714Natural Sciences 0.198805 0.25415PreProfessional Studies -0.02774 0.276134Architecture 1.04142* 0.436006
SAT / 100 0.186429** 0.062854Varsity athlete -0.19128 0.168537Father’s Education: Grad School -0.51415 0.269396Father’s Education: College 0.071526 0.254501Mother’s Education: Grad School -0.68133** 0.234436Mother’s Education: College -0.47977* 0.210795Drive to Achieve: High 0.1621 0.375761Drive to Achieve: Medium 0.047486 0.366065Parental Income: 50K -100K 0.765719** 0.164182Parental Income: over 100K -1.35257** 0.178858Graduated in 1999 1.634034** 0.221744Graduated in 2001 -0.05698 0.210804Graduated in 2003 -0.08189 0.344086Being Well off highly important -0.90338** 0.258933Being Well off somewhatimportant
-0.4428* 0.190023
Worked for pay 11hrs/week 1.641308** 0.163802Worked for pay: 3-10 hours / 1.2305** 0.147626
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weekFather has professionaloccupation
-0.74681** 0.230174
HAS FGSL 1.767108** 0.130366Has several credit cards 1.605265** 0.312073
Catholic 0.316313* 0.157179Writing ability high 0.103568 0.320701Writing ability medium 0.466451 0.284604Math ability High -0.25572 0.264143Math ability High -0.02838 0.202533Artistic ability High 0.206299 0.280877Artistic ability Medium 0.065315 0.136393
_cons 2.365577 1.023251
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TABLE 4B:CHOICE OF MAJOR AND LOAN DEBT: FIRST APPROXIMATION
OLS Results: Estimating the impact of a student’s choice of major on log loan debt
Baseline majors: All majors other than Engineering
DEP VAR:LOG OF LOANS
ESTIMATE STD.ERROR
Female -0.25679 0.132144Graduate School Plans -0.33584* 0.132473Black 1.164073* 0.462134Asian 0.244038 0.33112Latino 1.042904** 0.254131
Multiracial 0.729212 0.43845Unknown Race -0.59754 0.958083High Earnings Major (Engineer) 0.362775 0.212842Sat / 100 0.17811** 0.062241Varsity Athlete -0.19707 0.168455Father’s Education: Grad School -0.53709* 0.268856Father’s Education: College 0.063284 0.254373Mother’s Education: Grad School -0.67269** 0.23406Mother’s Education: College -0.48646* 0.210568Drive To Achieve: High 0.208687 0.374626
Drive To Achieve: Medium 0.085111 0.365439Parental Income: 50k -100k 0.754038** 0.164071Parental Income: Over 100K -1.36737** 0.178572Graduated In 1999 1.621105** 0.221443Graduated In 2001 -0.04892 0.210508Graduated In 2003 -0.08998 0.343303Being Well Off Highly Important -0.8608** 0.252046Being Well Off SomewhatImportant
-0.41501** 0.187321
Worked For Pay 11hrs/Week 1.629238** 0.163181
Worked For Pay: 3-10 Hours PerWeek
1.224886** 0.147253
Father Has ProfessionalOccupation
-0.76157** 0.229982
Has FGSL 1.765193** 0.130248Has Several Credit Cards 1.578678** 0.311767Catholic 0.319159* 0.156936
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Writing Ability High -0.00882 0.313811Writing Ability Medium 0.408444 0.283408Math Ability High -0.21307 0.253976Math Ability High 0.011776 0.197303Artistic Ability High 0.292008 0.268939Artistic Ability Medium 0.072422 0.1342585 th Year Graduate 0.471838 0.532068
_Cons 2.668051 1.009106
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TABLE 5:Choice of Major and Loan Debt
Two Stage Least Squares: Estimating the impact of a student’s choice of major on logloans
with Durbin-Wu-Hausman exogeneity test: (Null: Choice of Major is Exogenous)
DEP VAR:LOG OF LOANS
ESTIMATE STD.ERROR
High Earnings Major Choice* 0.712715* 0.429467Female -0.29707* 0.143904Grad -0.4263** 0.142172Black 0.964777* 0.519058Asian 0.18007 0.354188Latino 0.902722** 0.272586Multiracial 0.597105 0.466551Unknown Race -0.75685 1.000013Sat / 100 0.155001* 0.06909Varsity Athlete -0.18644 0.180032Father’s Education: Grad School -0.63473* 0.284629Father’s Education: College 0.10437 0.268246Mother’s Education: Grad School -0.58597* 0.248691Mother’s Education: College -0.41611 0.222641Drive To Achieve: High 0.392079 0.417978
Drive To Achieve: Medium 0.314514 0.405459Parental Income: 50k -100k 0.768499** 0.172594Parental Income: Over 100K -1.26166** 0.188861Graduated In 1999 1.503542** 0.236784Graduated In 2001 -0.17546 0.224982Graduated In 2003 -0.20538 0.373843Being Well Off Highly Important -0.69844** 0.271452Being Well Off SomewhatImportant
-0.3408 0.200734
Worked For Pay 11hrs/Week 1.539012** 0.17319Worked For Pay: 3-10 Hours /
Week
1.120036** 0.156681
Father Has ProfessionalOccupation
-0.76384** 0.24644
Has FGSL 1.74362** 0.1381Has Several Credit Cards 1.640512** 0.340022Catholic 0.313729 0.166894Writing Ability High 0.238026 0.345335
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Writing Ability Medium 0.475658 0.306071Math Ability High -0.34481 0.2993Math Ability High -0.04158 0.212055Academic Ability Medium -1.09014 0.660583Academic Ability High -1.13421 0.67441Helping Others Highly Important 0.949686* 0.4610015 th Year Graduates 0.058143 0.164145Helping Others SomewhatImportant
0.914796 0.472624
Party 11 Hours/ Week -0.58078* 0.201726Party 3-10 Hours/ Week -0.31346* 0.157728
_cons 3.002653* 1.366015
Tests of endogeneity of: high earnings major choiceHo: Regressor is exogenousDurbin-Wu-Hausman chi-sq test: 0.45006 Chi-sq(1) P-value = 0.50231
Cannot reject the Null of exogeneity
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Worked For Pay: 3-10 Hours PerWeek
0.034307 0.091717
Father Has Professional Occupation -0.09534 0.15848Has FGSL -0.08937 0.083935Has Several Credit Cards -0.23133 0.227076Catholic -0.0127 0.102556Writing Ability High -1.22105** 0.191921Writing Ability Medium -0.38574** 0.140427Math Ability High 1.921804** 0.320525Math Ability High 1.086049** 0.30994Artistic Ability High -0.42087* 0.2083Artistic Ability Medium -0.00215 0.085232Academic Ability Medium -0.42818 0.38177Helping Others Highly Important -0.13816 0.231282
Helping Others in SomewhatImportant
0.075818 0.235056
Party 11 Hours/ Week -0.4421* 0.129861Party 3-10 Hours/ Week -0.00456 0.093031Academic Ability High -0.45475 0.389575Freshman Major is Engineer 1.66056** 0.082009Double Major -1.56548** 0.204351
_Cons -4.4132 0.792879
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TABLE 6 B:CHOICE OF MAJOR AND LOAN DEBT: TWO STAGE PROBIT LEAST SQUARES
Estimating the impact of the choice of a high earnings major (engineering) on loans
Second Stage OLS results:
with instrument (*) and corrected standard errors
DEP VAR:LOG OF LOANS
ESTIMATE STD.ERROR
Instrument for the choice of a highearnings major*
0.140969* 0.065436
Female -0.2359 0.138335
Grad -0.32827* 0.13511Black 1.024991* 0.465086Asian 0.208722 0.331177Latino 0.944675** 0.25574Multiracial 0.674296 0.438452Unknown Race -0.59181 0.960057Sat / 100 0.142821* 0.066028Varsity Athlete -0.18261 0.168617Father’s Education: Grad School -0.48984* 0.269132Father’s Education: College 0.128819 0.255157Mother’s Education: Grad School -0.65774** 0.23412Mother’s Education: College -0.48883* 0.210658Drive To Achieve: High 0.090343 0.377771Drive To Achieve: Medium -0.01665 0.367538Parental Income: 50k -100k 0.75545** 0.164256Parental Income: Over 100K -1.29866** 0.179548Graduated In 1999 1.597018** 0.221667Graduated In 2001 -0.01553 0.211803Graduated In 2003 -0.05449 0.345022Being Well Off Highly Important -0.73522** 0.255849Being Well Off Somewhat Important -0.39075** 0.190523
Worked For Pay 11hrs/Week 1.679261** 0.165387Worked For Pay: 3-10 Hours / Week 1.229246** 0.147374Father Has Professional Occupation -0.74424** 0.230427Has FGSL 1.771085** 0.130344Has Several Credit Cards 1.608836** 0.312651Catholic 0.319107* 0.157586Writing Ability High 0.152333 0.336062
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Writing Ability Medium 0.442156 0.286945Math Ability High -0.50452 0.306972Math Ability High -0.17213 0.218889Artistic Ability High 0.274973 0.270998Artistic Ability Medium 0.035867 0.134679Academic Ability Medium -0.07111 0.147404Helping Others very Important 0.960914* 0.442739Helping Others somewhat Important 0.952965* 0.453213Party 11 Hours/ Week -0.49046* 0.192654Party 3-10 Hours/ Week -0.29951* 0.1491165 th Year Graduate 0.294502 0.310483
_Cons 2.915122 1.14624
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3.7 Appendix 3:
TABLE 1:
SECOND MAJORS AND EARNINGS: SELECTIVE UNIVERSITY
OLS earnings regression: Estimating the impact second majors on log earnings
Observations: 1013 553 460R-Squared: 0.3045 0.3163 0.355Sample: Full Sample Men WomenDEP. VAR:
Log earningsESTIMATE STD.
ERRESTIMATE STD.
ERR ESTIMATE STD.
ERR
Sat / 100 0.010526 0.0089 0.0269826* 0.0130 -0.00799 0.0128
Female -0.00979 0.0175Black 0.065761 0.0703 0.0632492 0.1059 0.121849 0.0957Asian 0.11814** 0.0424 0.0813796 0.0667 0.1618311** 0.0565Latino 0.05899 0.0398 0.0865975 0.0592 0.011741 0.0548Unknown Race -0.27282** 0.1123 -.379051** 0.1322 0.165702 0.2580Multiracial 0.102919 0.0851 .094268 .08576 0.127187 0.0874Engineer 0.315165** 0.0401 0.2816976** 0.0611 0.388684** 0.0574High EarningsBusiness Major
0.213208** 0.0341 .2016237 ** 0.0562 0.2407143** 0.0465
Low EarningsBusiness Major
0.073548 0.0409 0.1185221 0.0718 0.04521 0.0507
NaturalSciences
0.112698** 0.0452 0.1485652* 0.0748 0.111698* 0.0586
Social Sciences 0.08782* 0.0380 0.1691139 * 0.0658 0.020044 0.0480Architecture -0.0429 0.0524 -0.100735 0.0879 0.003301 0.0674PreProfessionalStudies
-0.18701* 0.0807 -0.2126754* 0.1046 -0.08675 0.1418
Father’sEducation:Grad School
0.07561 0.0398 0.0418978 0.0525 0.107985 0.0647
Father’s
Education:College
0.068386 0.0382 0.0378438 0.0506 0.095612 0.0622
Mother’sEducation:Grad School
-0.02331 0.0316 -0.0051143 0.0435 -0.05208 0.0470
Mother’sEducation:College
-0.03647 0.0290 -0.0354316 0.0396 -0.03804 0.0431
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Drive ToAchieve: High
0.132769 0.0967 0.1466106 0.1138 -0.00302 0.2373
Drive ToAchieve:Medium
0.071376 0.0962 0.0468647 0.1132 -0.03349 0.2358
ParentalIncome: 50k -100k
0.025867 0.0235 0.0544013 0.0342 0.009613 0.0324
ParentalIncome: Over100K
0.025835 0.0234 0.0453248 0.0353 0.001712 0.0317
Graduated In1999
0.083121** 0.0232 0.0727867* 0.0334 0.1210564** 0.0327
Graduated In2001
0.154184** 0.0243 0.1643106** 0.0354 0.1604022** 0.0343
Graduated In2003
0.078271** 0.0233 0.0602118 0.0334 0.1496579** 0.0333
Father Is AProfessional
0.00657 0.0265 0.0295735 0.0372 -0.01396 0.0381
AcademicAbility High
-0.11996 0.1080 -0.133958 0.1245 0.074655 0.3003
AcademicAbility Medium
-0.16031 0.1067 -0.1668744 0.1224 0.015383 0.2999
Math AbilityHigh
-0.01352 0.0369 -0.0168518 0.0555 -0.00797 0.0518
Math AbilityMedium
0.006434 0.0310 0.0113146 0.0496 0.032414 0.0402
Writing AbilityHigh
-0.02859 0.0379 -0.0103427 0.0520 -0.02379 0.0603
Writing AbilityMedium
-0.00984 0.0321 -0.0152354 0.0451 0.029908 0.0501
Party 11Hours/Week
0.035263 0.0249 -0.0009769 0.0367 .0859686** 0.0278
Artistic AbilityHigh
-0.01717 0.0395 -0.0066764 0.0511 -0.02193 0.0651
Party 3-10Hours/ Week
0.021079 0.0213 0.0089901 0.0320 0.036121 0.0296
Double Major 0.0360090.0200 0.0022149 0.0290
.0859686 **0.0279
Female*DoubleMajor
.0638185* 0.0276
College GPA 0.039197 0.0277 0.0379307 0.0385 0.040499 0.0417Catholic 0.056493** 0.0215 0.0284122 0.0311 .0940555** 0.0303High SchoolGPA(A)
0.022119 0.0193 0.0243718 0.0275 .0082743 0.0278
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Study 11Hours/Week
-0.12084** 0.0509 -0.138075* 0.0620 0.029631 0.1119
Study 3-10Hours/Week
-0.12869** 0.0511 -0.1392717* 0.0625 .0142317 .03037
Being Well Off
HighlyImportant
0.140282** 0.0377 0.1834507** 0.0528 0.04185 0.0576
Being Well OffSomewhatImportant
0.105765** 0.0321 0.139653** 0.0465 0.02404 0.0462
Work CloselyRelated ToMajor
0.060047** 0.0200 0.0933143** 0.0284 .0141093 0.0294
_Cons 9.997501 0.157512 9.824364 0.2134 9.999237 .28743
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Graduated In1999
0.118421 0.0242 0.1294301** 0.0351 0.091266** 0.0330
Graduated In2001
0.261104 0.0250 0.2752559** 0.0359 0.216032** 0.0347
Graduated In
2003
0.134633 0.0242 0.1457118** 0.0346 0.106649** 0.0362
Father Is AProfessional
-0.00994 0.0274 0.012788 0.0389 -0.05096 0.0342
Academic AbilityHigh
0.113995 0.1269 0.192299 0.1583 -0.12325 0.0388
Academic AbilityMedium
0.071676 0.1258 0.164275 0.1568 -0.19338 0.2616
Math AbilityHigh
-0.05849 0.0418 -0.10107 0.0607 -0.04789 0.2607
Math AbilityMedium
-0.02461 0.0360 -0.08328 0.0544 0.00099 0.0613
Writing AbilityHigh
-0.0271 0.0393 -0.05764 0.0521 0.044418 0.0483
Writing AbilityMedium
0.005936 0.0334 -0.02911 0.0467 0.071061 0.0662
Party 11Hours/Week
0.037448 0.0279 0.068341 0.0432 0.008681 0.0487
Artistic AbilityHigh
0.13816 0.0468 0.102191 0.0568 0.2179585* 0.0386
Party 3-10Hours/ Week
0.018815 0.0256 0.06261 0.0412 -0.02682 0.0964
Double Major 0.052653 0.0188 0.030943 0.0264 0.061694 0.0332College GPA 0.040281 0.0292 0.050315 0.0392 0.013018 0.0278Catholic -0.00221 0.0238 -0.02317 0.0334 0.035183 0.0457High School GPA(A)
-0.01232 0.0206 0.010502 0.0287 -0.03546 0.0346
Study 11Hours/Week
-0.05528 0.0535 -0.02003 0.0605 0.078437 0.0299
Study 3-10Hours/Week
-0.06381 0.0531 -0.047 0.0592 0.08746 0.1958
Being Well OffHighly Important
0.015326 0.0595 0.056352 0.0884 -0.01931 0.1966
Being Well OffSomewhatImportant
0.012481 0.0568 0.053283 0.0858 -0.01128 0.0898
Work CloselyRelated To Major
0.08653 0.0553 0.058612 0.0324 -0.01331 0.0850
_Cons 9.935025 0.1784 9.842021 .228314 10.27238 0.3379
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TABLE 3A:SECOND MAJORS AND EARNINGS: SELECTIVE UNIVERSITY
Heckman Procedure for sample selection into labor marketEstimating the impact of second majors on log earnings
Censored Obs 41622nd Stage Uncensored Obs 1013Obs 5175
DEP VAR:LOG
EARNINGS
ESTIMATE STD.ERROR
ESTIMATE STD.ERROR
Sat / 100 0.009749 0.0088 Graduated
In 19990.0849467** 0.0228
Female -0.010593 0.0172 GraduatedIn 2001
0.1581227** 0.0240
Black 0.0639923 0.0689 GraduatedIn 2003
0.0782258** 0.0228
Asian 0.1164025** 0.0416 Father Is AProfessional
0.0076877 0.0260
Latino 0.0610624 0.0390 AcademicAbility High
-0.109246 0.1059
Unknown Race -0.26143* 0.1102 AcademicAbilityMedium
-0.150163 0.1046
Multiracial 0.1030749 0.0833 MathAbility High
-0.013088 0.0361
Engineer 0.3096871** 0.0395 MathAbilityMedium
0.0079662 0.0304
High EarningsBusiness Major
0.2093238** 0.0336 WritingAbility High
-0.025639 0.0372
Low EarningsBusiness Major
0.069689 0.0401 WritingAbilityMedium
-0.006816 0.0316
NaturalSciences
0.1099744** 0.0442 Party 11Hours/Week
0.0337308 0.0244
Social Sciences 0.0864755* 0.0372 ArtisticAbility High
-0.011773 0.0389
Architecture -0.047383 0.0514 Party 3-10Hours/Week
0.0195053 0.0209
PreProfessionalStudies
-0.177535** 0.0791 DoubleMajor
0.0355955 0.0196
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Father’sEducation:Grad School
0.0758558* 0.0390 CollegeGPA
0.0358322 0.0273
Father’s
Education:College
0.0695532 0.0374
Female*
DoubleMajor
.0692613 .03659
Mother’sEducation:Grad School
-0.023386 0.0310 Catholic 0.0564466** 0.0211
Mother’sEducation:College
-0.036333 0.0284 High SchoolGPA (A)
0.0208387 0.0190
Drive ToAchieve: High
0.1200668 0.0951 Study 11Hours/Week
-0.119831** 0.0499
Drive To
Achieve:Medium
0.0616468 0.0944 Study 3-10
Hours/Week
-0.127645** 0.0500
ParentalIncome: 50k -100k
0.0241581 0.0231 Being WellOff HighlyImportant
0.1368227** 0.0370
ParentalIncome: Over100K
0.0232303 0.0230 Being WellOffSomewhatImportant
0.1015959** 0.0315
_Cons 10.07679 0.1649 WorkCloselyRelated ToMajor
0.0137518 0.0395
SEPARATEMALE &
MALES: DoubleMajor
-.0069323 .0280998
FEMALEREGRESSIONS
FEMALES: DoubleMajor
.0843825** .0266341
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TABLE 3B:SECOND MAJORS AND EARNINGS: SELECTIVE UNIVERSITY
Heckman Procedure for sample selection into labor market
Estimating the impact of second majors on log earnings
Business College Only
DEP VAR:LOG EARNINGS
ESTIMATE STD. ERROR
FULL SAMPLE: Double Major .0459689* .0180954MALES: Double Major .0317302 0.025004FEMALES: Double Major .0711934* .0277767
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TABLE 4:SECOND MAJORS AND EARNINGS: COLLEGE AND BEYOND 1976 COHORT
OLS Regression: Estimating the impact of second major on log earnings
(No ability controls)
Number of Obs 12195R-Square 0.2975
DEP VAR:LOG
EARNINGS
ESTIMATE STD.ERROR
ESTIMATE STD.ERROR
Intercept 10.19352** 0.11405 Private High
School0.03208* 0.01644
SAT average ofuniversity
0.05742 * 0.01100 Catholic HighSchool
0.04987** 0.01696
SAT /100 -0.02553** 0.0072 Women’sCollege
0.05934* 0.02925
Female -0.28144** 0.01202 PrivateUniversity
0.08837** 0.01708
Black -0.04228 0.02646 Liberal ArtsCollege
-0.00657 0.02328
Hispanic 0.04495 0.04696 ObtainedAdvancedDegree
0.06008** 0.01220
Asian 0.11269** 0.03765 ClergyOccupation
-0.51408** 0.05020
Other 0.15044 0.0880 ClericalOccupation
-0.42410** 0.04519
Unknown Race -0.03736* 0.01585 ComputerOccupation
0.02057 0.02785
Top 10% ofH.S. class
0.01057 0.01478 EngineerOccupation
-0.04419 0.02675
Varsity Athlete 0.09279** 0.01895 ExecutiveOccupation
0.26122** 0.01607
Engineer 0.20068** 0.02047 FinanceOccupation
0.40369** 0.02576
NaturalSciences
0.15886** 0.02192 DoctorOccupation
0.60300** 0.02306
Social Sciences 0.14287** 0.01595 HealthOccupation
-0.04993 0.03339
Business 0.20279** 0.02001 InsuranceOccupation
0.15611** 0.05156
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Other Majors 0.06694** 0.01684 Law Occupation 0.33587** 0.02093Double Major 0.04473* 0.02105 Consulting
Occupation0.32055** 0.04874
Mother’sEducation:
Grad School
0.02606 0.01502 MarketingOccupation
0.13317** 0.02500
Father’sEducation:Grad School
0.07295** 0.01607 MilitaryOccupationOccupation
-0.10924 0.10146
Father’sEducation:College
0.05076** 0.019 Math & ScienceOccupation
-0.17412** 0.03470
Mother’sEducation:College
0.02081 0.01557 Social ScienceOccupation
-0.22711** 0.04141
Job SatisfactionHigh
0.15509** 0.01047 Writer ArtistAthleteOccupation
-0.12271** 0.03039
College GPA 0.33930** 0.02168 OtherOccupation
-0.23936** 0.03224
Results for separate wage regressions for males and females are not shown here butresults from these regressions include the finding that similar to what was observed forthe single selective university, female engineering and business majors have a muchhigher earnings advantage over female humanities majors than male engineering and
business majors have over male humanities majors. The same result was found forfemale natural science majors. Unlike results found for the single university, doublemajoring for men was correlated with having 5% higher earnings over non-doublemajors. Female double majors in the College and Beyond sample received no earnings
premium.
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TABLE 4:SECOND MAJORS AND EARNINGS: COLLEGE AND BEYOND 1976 COHORT
OLS Regression: Estimating the impact of second major on log earnings
(With ability controls: Selection on observable and unobservable characteristics)
Number of Observations 6224Missing Values 2304R-Square 0.3469
Dependant:Log Earnings
ESTIMATE STD.ERROR
ESTIMATE STD.ERROR
Intercept 10.33672** 0.12646 InsuranceOccupation
0.13884** 0.05163
Sat Average Of
University
0.04375** 0.01184 Law Occupation 0.30689** 0.02117
Sat /100 -0.02416** 0.00503 ConsultingOccupation
0.28517** 0.04872
Female -0.25840** 0.01256 MarketingOccupation
0.12108** 0.02502
Black -0.05244** 0.02701 MilitaryOccupation
-0.22638 0.14447
Hispanic -0.06603 0.04747 Math & ScienceOccupation
-0.16923** 0.03475
Asian 0.11405** 0.03821 Social ScienceOccupation
-0.21679* 0.04146
Other Race 0.13881 0.08813 Writer ArtistAthleteOccupation
-0.11083** 0.03042
Unknown Race -0.03570 0.01631 OtherOccupation
-0.24112** 0.03221
Top 10% OfH.S. Class
0.15044 0.02199 Job SatisfactionHigh
0.14569** 0.01448
Varsity Athlete 0.08164** 0.01930 ObtainedAdvanced Degree
0.05989** 0.01223
Engineer 0.19427** 0.02147 College GPA 0.35164** 0.02209NaturalSciences
0.15349** 0.02246 Writing AbilityHigh
-0.02834 0.01490
Social Sciences 0.11729** 0.01617 Math AbilityHigh
-0.00157 0.02328
Business 0.20113** 0.02908 Academic AbilityHigh
-0.03668* 0.02063
Other Majors 0.06506** 0.02042 Athletic AbilityHigh
0.03669* 0.01448
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Double Major 0.03859* 0.02104 Artistic AbilityHigh
-0.02634 0.01572
Mother’sEducation:Grad School
0.01717 0.01505 Highly cheerful -0.04498* 0.02207
Father’sEducation:Grad School
0.05963** 0.01614 Highly Driven 0.02818 0.01761
Father’sEducation:College
0.04660** 0.01901 High LeadershipAbility
0.02762 0.01614
Mother’sEducation:College
0.01684 0.01558 HighlyMechanical
-0.01106 0.02286
Private HighSchool
0.03627* 0.01669 Highly Attractive 0.07267** 0.02449
Catholic HighSchool
0.04414** 0.02462 Highly Popular 0.02428 0.02751
Women’sCollege
0.06899* 0.03136 Speaking AbilityHigh
-0.01394 0.0226
PrivateUniversity
0.08521** 0.02789 Highly Confidentin Intellect
-0.04081 0.0252
Liberal ArtsCollege
-0.0227 0.0367 Highly Sensitive -0.00949 0.0215
ClergyOccupation
-0.52298** 0.05022 Highly Stubborn 0.02301 0.01376
ClericalOccupation
-0.42776** 0.04522 Highly SociallyConfident
0.03615* 0.01676
ComputerOccupation
0.00574 0.03829 HighlyUnderstanding
-0.03903 0.01536
EngineerOccupation
-0.04854 0.03822 Highly Popularwith OppositeSex
0.05633 0.01942
ExecutiveOccupation
0.25682** 0.01613
FinanceOccupation
0.39230** 0.02581 DummyGroups(196)
DoctorOccupation
0.59279** 0.02313
HealthOccupation
-0.07048 0.04768
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At the individual level, the goal of the economic agent is to maximize one’s self-
interest, and therefore individuals will make choices that will maximize their own self
interest, pushing them to consume more subject to constraints. Holding preferences
constant, economic theory assumes that if an individual is able to consume more, they are
better off. Income then becomes a determinant of an individual’s well being or
happiness.
For better or worse, at the national aggregate level, national income is often used
as an indicator of happiness or well being. However, as national income increases, many
economists would argue that a nation’s well being will not necessarily increase, as thereare many costs associated with economic activity and progress. Only if the “winners”,
those who benefit from these economic activities can compensate the “losers”, those who
bear the costs of economic progress, could national income be used to proxy for well
being. This compensation test, can the “losers” be compensated by “winners”?—and
this does not necessarily mean that they will be—is often used to allow national income
proxy for national well being. 1
Both at the individual and at the national level, income or earnings changes are
assumed to change our levels of happiness and satisfaction. There are many that might
say that income is a relatively unimportant factor in determining our happiness or
satisfaction. In a search for a combined science that attempts to discern what actually
does makes people happy, so that more can be done to make our society better off,
economist Richard Layard relays this observation; “economics equates changes in the
happiness of society with changes in its purchasing power—or roughly so. I have never
accepted that view, and the history of the last fifty years has disproved it, on average
1 Layard, 134
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people are no happier today than people were 50 years ago. Yet at the same time average
incomes have more than doubled.” 2
So why might changes in absolute income or earnings be ineffectual in
determining happiness? According to Layard, increases in an individual’s absolute
income may only raise that individual’s reference group of comparison. In other words
when an individual gets more income, the “Joneses” they want to keep up with also
become wealthier. Also, decreases in absolute income can at times be associated with
increases in happiness if at the same time our relative income status improves. For
Layard, an individual’s relative income, or their perceived relative income instead ofabsolute income is a more relevant determinant of happiness. So, if our absolute
income—or purchasing power—does not add to the overall happiness of individuals or
society, then what things actually do increase our overall satisfaction with life? Other
variables which Layard says might influence happiness more than income include family,
work, community and friendships, health, personal freedoms and values.
This paper does not attempt to answer the question of how much changes in
income effects overall happiness, but instead will use data from the College and Beyond
to determine if there is empirical evidence of a relationship between increased earnings
and overall satisfaction with life among a group of college graduates who we assume are
strongly influenced in their college choices by expected earnings. An ordered probit is
estimated that attempts to explain the effect that higher earnings has on an ordered scale
that measures “overall satisfaction with life” for the sample of students in the College and
Beyond 1976 cohort. The results are shown in Table 6 of this Appendix.
2 Layard, 3
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affect people’s happiness more than absolute earnings. He sites a study which analyzes
the income-happiness issue more deeply and finds that an individual’s perceived relative
income has a stronger effect on individual happiness than absolute income. 3 In other
words, people are happier when their incomes are higher relative to people in their
comparison group—“the Joneses”. The results provided here cannot untangle whether an
individuals increased satisfaction is driven strictly by increases in absolute income or
perceived relative income, but they do seem to support some basic economic
assumptions, that increases in earnings are correlated with an increased measure of
happiness. The significant correlation between happiness and family, friends, work,health and even obtaining an advanced degree also demonstrate the complexity of
variables beyond just earnings that determine individual happiness.
What is important, however, and what motivated this postlude was the pursuit to
find evidence that might indicate that increased levels of earnings are correlated with
increases in levels of individual happiness. This correlation which did appear in this data
seems to corroborate the conclusion that students are acting rationally when they make
decisions based upon expected returns because their later realized earnings will in fact be
influential to their happiness after college.
3 Layard , 46
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REFERENCES : ESSAY 1
Betts, Julian R. 1996. “What do students know about wages? Evidence from a survey ofUndergraduates” The Journal of Human Resources Winter, 27-56
Berger, Mark C. 1988 “Predicted Future Earnings and Choice of College Major” Industrial and Labor Relations Review Vol. 41 No. 3
Cohen, Ed. 2005 “Enrollment Trends: Too many students are choosing the sameacademic path. What’s a College to do?” Notre Dame Magazine Winter 2005-2006
Eide, Eric and Geetha Waehrer 1998 “The Role of the Option Value of CollegeAttendance in College Major Choice” Economics of Education Review Vol. 17
No.1 73-82
Fiorito, Jack; Dauffenbach, Robert C. 1982 “Market and Non-Market Influences onCurriculum Choice by College Students” Industrial and Labor Relations Review ,Vol36:No1 88-101
Flyer, Fredrick A. 1997 “The Influence of Higher Moments of Earnings Distributions onCareer Decisions” Journal of Labor Economics Vol. 15 No. 4 Oct
Freeman, Richard B. 1971 The Market for College Trained Manpower. Cambridge, MAHarvard Univerisity Press
Koch, James 1972. “Student Choice of Undergraduate Major Field of Study and PrivateInternal Rates of Return” Industrial and Labor Relations Review Oct. Vol26:No1
Leppel, Karen; Williams Mary L. and Charles Waldauer 2001 “The Impact of ParentalOccupation and Socioeconomic Status on Choice of College Major” Journal of
Family and Economic Issues Vol. 22(4) Winter
McFadden, Daniel F. 1973 “Conditional Logit Analysis of Qualitative Choice Behavior”in Economic Theory and Mathematical Economics ed. Zarembka, AcademicPress, 1974 pp.105-139
Montmarquette, Claude; Cannings, Kathy and Sophie Mahseredjian 2002 “How doyoung people choose college majors” Economics of Education Review 21 543-556
Paulsen, Michael B. 2001 “The Economics of Human Capital and Investment in HigherEducation” in The Finance of Higher Education: Theory, Research, Policy, andPractice, eds. Paulsen and Smart, John; Agathon Press pp. 55-94.
Ribar, David C. 2001 “The effects of local employment opportunities on youth’s workand schooling” Economics of Education Review 20: 401-413
142
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Rivkin, Steven G. 1995 “Black White Differences in Schooling and Employment” The
Journal of Human Resources Autumn Vol30:No.4 826-852
Simpson, Jaqueline C. 2003 “Mom Matters: Maternal Influence on the Choice of
Academic Major” Sex Roles Vol. 48 Nos. 9/10
Strasser, Sandra E; Ozgur, Ceyhun and David Schroeder 2002 “Selecting a BusinessMajor: An Analysis of Criteria and Choice Using the Analytical HierarchyProcess” Mid-AmericanJournal of Business , Fall
Turner, Sarah E. and William Bowen 1999 “Choice of Major: The Changing(Unchanging) Gender Gap” Industrial and Labor Relations Review , Vol 52 No. 2
143
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REFERENCES: ESSAY 2
Clark, Warren, 1999 “Paying off Student Loans” Perspective, spring 1999
Eyermann 1999 (dissertation) “The Effects of Loan Indebtedness on Students’
Educational Attainment, Career Choice, and Post-Collegiate Income” Universityof California
Fox, Marc 1992 “Student debt and enrollment in graduate and professional school” Applied Economics 24, 669-677
Fox, Marc 1993 “Medical Student Indebtedness and Choice of Specialization” Inquiry 30:84-94
Hausman, J.A., 1978 “Specification tests in Econometrics” Econometrica 46: 1251-1271
Hausman, J.A., 1983 “Specification and estimation of simultaneous equation models” InZ. Griliches and M. Intriligor (eds), Handbook of Econometrics Vol. 1,Amsterdam: North Holland, ch. 7
Heckman, J. J., 1978 “Dummy endogenous variables in a simultaneous equationssystem” Econometrica 46: 931- 959
King, Tracey and Ellynne Bannon 2002 “The Burden of Borrowing: A Report on theRising Rates of Student Debt” The State PIRG’s Higher Education Project ,Washington D.C.
Kennedy, Peter 1998 A Guide to Econometrics Fourth Edition Cambridge, MIT Press
Keshk, Omar M. G., 2003 “CDSIMEQ: A program to implement two-stage probit leastsquares” The STATA Journa l, 3 Number 2 pp. 1-11.
Maddala, G.S., 1983 Limited Dependant and Qualitative Variables in EconometricsCambridge, Cambridge University Press
Monks, James 2001 “Loan Burdens and Educational Outcomes” Economics of Education Review 20 545-550
Mroz, Thomas, 1999 “Discrete factor approximations in simultaneous equation models:Estimating the impact of a dummy endogenous variable on a continuous outcome.
Journal of Econometrics 92: 233-274
Rivkin, Steven G. 1995 “Black /White Differences in Schooling and Employment” The Journal of Human Resources Autumn Vol30:No.4 826-852
144
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St. John, Edward P. 1994 “The Influence of Debt on Choice of Major” Journal of Student Financial Aid Vol.24 No. 1
Thorton, James 2000 “Physician choice of medical specialty: do economic incentivesmatter?” Applied Economics 32, 1419-1428
Weiler, William 1994 Expectations, Undergraduate Debt and the Decision to AttendGraduate School: a Simultaneous Model of Student Choice, Economics of
Education Vol. 13 pp 29-41
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REFERENCES: ESSAY 3
Arciadiano, Peter 2003 “Ability Sorting and The Return to College Major” Duke Economics Working papers.
Betts, Julian 1996 “What do Students Know About Wages? Evidence from a Survey ofUndergraduates” Journal of Human Resources Vol. 31 No: 1 27-56
Brewer, Dominic; Eide, Eric; and Ehrenberg, Donald 1999, “Does it Pay to Attend andElite Private University: Cross-Cohort Evidence on the Effect of College Type onEarnings” Journal of Human Resources Vol. 34, Issue 1, pp104-123
Card, David, 2001 “Estimating the Return to Schooling: Progress on Some PersistentEconometric Problems” Econometrica 69 pp 1127-1160
Dale, Stacy Berg. and Krueger, Alan B. 1999 “Estimating the Payoff to Attending a more
Selective College: An Application of Selections on Observables andUnobservables” NBER Working Paper series , Working Paper 7322.
Eide, Eric, 1994 College Major Choice and Changes in the Gender Wage Gap”Contemporary Economic Policy , 12: 2 pp55
Gerhart, Barry, 1990 “Gender Differences in Current Starting Salaries. The Role ofPerformance, College Major, and Job Title” Industrial and Labor Relations
Review, Vol. 43. No 4.
Hamermesh, Daniel S. and Donald, Stephen G., 2004 “The Effect of College Curriculumon Earnings: Accounting for Non-ignorible Non-respons Bias” NBER Working
Paper Series Working Paper 10809
Jackson, John D. and Jones, Ethel B., 1990 “College Grades and Labor MarketRewards” Journal of Human Resources Vol. 25 No. 2 pp 253-266
Layard, Richard 2005, Happiness: Lessons from a New Science. The Penquin Press , New York
Loury, Linda Datcher and Garman, David, 1995 “College Selectivity and Earnings” Journal of Labor Economics Vol. 13 No. 2 pp289-308
Rumberger, Russell W. and Thomas, Scott L., 1993, “Economic Returns to CollegeMajor, Quality and Performance: A Multilevel Analysis of Recent Graduates”
Economics of Education Review , Vol. 12, No. 1 pp 1-19
Slater, Robert B. 1996, “The College-Course Majors Offerings Blacks the BestFinancial Rewards” Journal of Blacks in Higher Education No. 12, 84-87
146
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Strayer, Wayne, 2002, “The Returns to School Quality: College Choice andEarnings” Journal of Labor Economics , Vol 20: No. 3
Weinberger, Caherine, 1998, “Race and Gender Gaps in the Market for Recent CollegeGraduates” Industrial Relations , Vol. 37 No. 1