Risk and Career Choice?
Raven E. Saksyand Stephen H. Shorez
June 8, 2004
Choosing a type of education is one of the largest …nancial de-
cisions we make. Educational investment di¤ers from other types
of investment in that it is indivisible and non-tradable.
di¤erences lead agents to demand a premium to enter careers
with more idiosyncratic risk.Since the required premium will
be smaller for wealthier agents, they will tend to enter careers
with more idiosyncratic risk.
After developing a model of career choice, we use data from
the Panel Study of Income Dynamics (PSID) to estimate the risk
associated with di¤erent careers. We …nd education, health care,
and engineering careers to have relatively safe streams of labor
income; business, sales, and entertainment careers are more risky.
By choosing a college major, many students make a costly hu-
man capital investment that allows them to enter a speci…c career.
To examine the link between wealth and college major choice im-
plied by the model, we use data on choice of college major from
the National Postsecondary Student Aid Survey (NPSAS). Con-
trolling for observable measures of ability and background, we
…nd evidence that wealthier students tend to choose riskier ca-
reers, particularly business.
?We thank John Campbell, Caroline Hoxby, Lawrence Katz, Howard Stone, and
Joshua White. We also thank seminar participants at Harvard, Oxford, NYU,
Northwestern, the University of Chicago, the Federal Reserve Board, and the Federal
Reserve Bank of New York for helpful comments.
excellent research assistance.
yHarvard University. Cambridge, MA.
zUniversity of Pennsylvania, Wharton School of Business, 3012 Steinberg
Hall-Dietrich Hall, 3620 Locust Walk, Philadelphia, PA 19104, (215) 898-7770,
We thank David De Remer for
This paper explores educational investment as a portfolio choice deci-
sion. Educational investment is one of the largest investments a person
makes in his lifetime. This investment allows him to earn a stream of
labor income whose properties depend on the career he has chosen. In
this way, educational investment is similar to investment in many other
…nancial assets, where purchasing the asset today gives its owner the
right to receive a stream of risky dividend payments in the future. Tra-
ditional asset pricing and portfolio choice models assume that assets are
tradable and divisible. By contrast, educational investment is lumpy
and not tradable. An agent can attend law school precisely once, can-
not sell his diploma if he decides to switch careers, and cannot practice
medicine and law at the same time. This lack of divisibility and trad-
ability makes human capital correlated with marginal utility. Therefore,
unlike traditional asset pricing models, idiosyncratic labor income risk
This paper examines how the …nancial risks associated with di¤erent
careers in‡uence who goes into which careers.
choose a type of education given the risk and return attributes of educa-
tional opportunities? How should di¤erences in wealth and risk aversion
impact who enters which profession? In this setting, agents demand a
premium to enter careers with more idiosyncratic risk.
required size of that premium depends on wealth. Controlling for ability
and preferences, wealthier agents should demand smaller risk premiums
and consequently be more willing to choose riskier careers.
ition for this result is that by investing a larger fraction of his wealth to
obtain an education, a poor agent is putting all of his eggs in one basket.
If he ends up with a low paying job, he has nothing else to fall back on.
By contrast, a wealthier agent will be able to rely on other sources of
income and is therefore less worried about labor income risk.
After developing a model of risk and career choice, we look for evi-
dence that its predictions are consistent with observed behavior. Ceteris
paribus, are wealthier agents more likely to enter riskier careers? Since
the theory predicts that risk is important to career choice, our …rst step
is to measure the riskiness of di¤erent careers. Using the Panel Study
of Income Dynamics (PSID), we measure the exposure of individuals in
di¤erent careers to career-wide and individual-level shocks.
that teaching, health care, and engineering professionals have less risky
labor income streams than sales, management, and entertainment pro-
fessionals.Most of the risk we observe in the PSID is idiosyncratic.
Conditioning on background and ability, these di¤erences in risk should
be more important to poor agents than rich ones.
How should an agent
Next, using the
National Postsecondary Student Aid Survey (NPSAS), we examine how
the riskiness of di¤erent careers might impact who goes into which …elds.
The analysis centers on estimating how di¤erent personal attributes in-
‡uence the likelihood of going into di¤erent careers. Consistent with our
theory, we …nd evidence that wealthier agents are more likely to go into
riskier …elds. This …nding is driven primarily by the fact that business
…elds tend to be high-risk and also tend to be chosen by richer students.
For example, we …nd that doubling a student’s family wealth increases
by roughly 20% his likelihood of studying business.
2 Related Literature
This paper is part of a large literature that analyzes education as an
investment. Education is seen as an investment because it has an up-
front cost, but allows an individual to receive a stream of labor income
in the future.
There are many empirical papers which try to measure the returns
to educational investment. A standard approach to measuring these
returns is associated with Mincer (1974), who regresses income on years
of education, age, and a variety of control variables. The focus of this
literature has been on obtaining an unbiased estimate of the coe¢cient
on schooling despite problems stemming from individual heterogeneity
and selection bias. Prominent examples are Ashenfelter and Krueger
(1994) and Angrist and Krueger (1991).
for example Card and Krueger (1992) addresses quality as well as quan-
tity of education. In addition to estimating the expected returns to
education, a few papers have tried to estimate the risks of di¤erent
types of occupations. For example, Topel (1984) estimates the proba-
bility of becoming unemployed as well as compensating wage di¤eren-
tials. Palacios-Huerta (2003) estimates the impact of human capital on
the mean-variance frontier of assets.
follow, Carroll and Samwick (1997) estimate the wage risks associated
with di¤erent industries.
Rather than examining the reduced form relationship between educa-
tion and income, another strand of the literature examines the problem of
choosing the optimal quantity of educational investment. Becker (1964)
notes that since human capital is both risky and illiquid, it should de-
mand a premium over safer assets. Williams (1978), Levhari and Weiss
(1974), Levhari and Weiss (1974), Williams (1979), and Judd (2000)
model the decision about what quantity of education to receive when
investment in education is risky.Our paper di¤ers from these models
of education choice in two respects: …rst, we focus on the type of ed-
ucation people choose instead of just the quantity; second, we test the
A related body of literature,
Also, using a technique that we
predictions of our model by looking at how the probability of entering
risky careers varies with initial wealth.
Previous research estimating the career decision as an unordered
choice has focused on the e¤ects of the level of income.
Boskin (1974) …nds that an occupation with higher lifetime earnings and
lower training costs is more likely to be chosen. Relating occupational
choice to college major, Berger (1988) …nds that predicted future earn-
ings in‡uence the choice of college major of young men. Neither of these
papers analyzes the di¤erential impact of initial income on di¤erent ca-
reers. Another framework often used in the career choice literature is
a cobweb model. For example, Freeman (1976) shows how current and
expected wages impact the supply and demand for speci…c classes of
engineers. Again, he focuses on the e¤ects of the level of income rather
than variance or risk.
The impact of risk on career choice could also be compared to the
decision about whether to become an entrepreneur. Just as this paper
predicts that richer agents are more likely to enter riskier careers, richer
agents may also be more likely to become entrepreneurs, an occupation
that carries large non-diversi…able risks.
Gentry and Hubbard (2002) document that entrepreneurs tend to have
larger …nancial wealth than other agents. However, Rosen and Willen
(2002) argue that the wage premium associated with self-employment is
too large to be explained by risk.
Finally, recent research in …nancial economics has looked at how tra-
ditional portfolio choice theory should be modi…ed by the introduction
of risky labor income. This literature takes labor income risk as ex-
ogenous and determines the optimal …nancial portfolio given this risk.
Merton (1971) has a HARA model of portfolio choice with non-tradable
labor income and stochastic raises. Viceira (2001), Koo (1998), Bertaut
and Haliassos (1997), Cocco, Gomes, and Maenhout (1998), Gakidis
(1997), Heaton and Lucas (1997), and Storesletten, Telmer, and Yaron
(1998) have also explored the impact of labor income on …nancial port-
folio choice. Davis and Willen (2000b) estimate the covariance of the
occupation-level component of income using CPS and use these as inputs
to compute the optimal portfolio of …nancial wealth for a given occupa-
tion. Davis and Willen (2000a) also compute the gains to risk-sharing
across professions and …nd that it is large. The goal of this paper is to
endogenize the career choice that these papers take as exogenous. While
endogenizing career choice is new in …nance models, endogenizing other
parts of the labor decision is not. Bodie et al. (1992) endogenizes the
decision about the quantity of labor supplied jointly with the …nancial
Consistent with this theory,
While there is a great deal of literature about risk, career choice, and
educational investment, this is the …rst paper to tie them together by
looking at the impact of career risk on type of educational investment.
3 A Simple Model of Career Choice
We begin by presenting a stylized model to illustrate why di¤erent agents
might have di¤erent preferences over risky careers.
agent has initial …nancial wealth, W0. Initially, the agent must choose
a career c, which will generate risky labor income,~Yc.
period, the agent merely consumes initial wealth plus labor income, so
his problem is to maximize expected utility:
Labor income depends on the agent’s career and can be decomposed
into risky and deterministic components, ~ "cand ?c:
Imagine that an
In the next
U = Eu
~Yc? ?c+ ~ "c;
E [~ "c] = 0:
We consider career c to be safer than career c0if ~ "csecond order stochas-
tically dominates ~ "c0.
We assume the agent has decreasing absolute risk aversion (DARA),
which implies that he becomes less concerned about any speci…c amount
of risk as he gets richer. The impact of this type of utility function
on career choice is relatively straightforward. Wealthier agents will be
more comfortable undertaking risky careers than poorer agents. While
this assumption is common and quite realistic, it is critical in driving
our results. An agent with constant absolute risk aversion utility would
demand the same-risk premium for undertaking a speci…c risk regardless
Formally, the use of DARA utility means that absolute risk aversion,
A ? ?u00=u0, is decreasing in consumption. Therefore, A0< 0 implies
The key result of the model is that if the agent gets richer, he will not
choose a career that is safer.
Proposition 1 Assume that an agent with DARA preferences over con-
sumption prefers career c over all others when his initial wealth is W.
If that agent’s wealth increases to W0> W, the agent chooses a career
c0. Then, career c0cannot be strictly safer than career c.
Proof. See Appendix A.
This proposition shows that agents become (weakly) more likely to
enter risky careers as they become wealthier.
premium to enter a risky career over a safe one.1However, if agents have
DARA utility, the demanded wage premium falls with wealth. Thus, ca-
reers with wage premiums that are insu¢cient at a given wealth become
su¢cient as an agent gets richer. Since an analytic solution describing
the consumption and career choice of an agent with DARA utility does
not exist, it is di¢cult to quantify the importance of wealth for career
choice. Therefore, we perform a simple calibration exercise to explore
the magnitude of the impact of wealth on career choice.
We consider the stylized career choice problem of two agents, one with
a greater initial wealth than the other. We assume that the wealthier
agent has $100,000 of initial wealth and that the poorer agent has $10 of
initial wealth. Each considers the choice between two careers, one riskier
than the other. The proposition above implies that the richer agent
would be more likely than the poorer agent to choose the riskier career
over the safer one. To illustrate this point, we assume a simple, discrete-
time model and calibrate numerically the impact of risk on career choice.
First, we assume that labor income follows a simple process:
Agents demand a wage
where the lognormal shocks, ", are identically and independently dis-
tributed with mean, ?", and standard deviation, ?".
common choice of simple stochastic process, any process for " will gen-
erate the similar results. The annual standard deviations for the safe
and risky career are set to 10% and 20%, respectively.
correspond roughly to the estimates of permanent time-series variability
of wages for safe and risky careers that we will estimate below in Section
4. For simplicity, we set ?"= 0 and do not allow agents to buy risky
assets. These assumptions can be loosened without changing the results
substantially. If the expected wages of the two careers were equal, all
agents would prefer the safer career. To show the impact of di¤erences
in risk on di¤erences in choice, we must assign a wage premium to the
risky career. We set the initial annual wages to $20,000 for the safe
career and $30,000 for the risky career, so that the expected wages are
50% higher for the risky career.
Similar to Viceira (2001), we assume that agents have power utility,
which exhibits decreasing absolute risk aversion, over lifetime consump-
While this is a
1The equilibrium wage is set by the wage premium demanded by the marginal
worker. The existence of this wage premium is con…rmed by Topel (1984), who
shows that jobs with higher unemployment risk demand wage premiums.
U (W;Y;t) = Et
1 ? ?
Wt+1= (1 + r)(Wt? Ct) + Yt+1
We set the parameters to the following plausible values: interest rate,
r = 0:05; discount rate, ? = 0:9; and risk aversion parameter, ? = 5.
We assume the agent works for 40 years.
optimization and numerical procedures used to estimate this problem.
Table 1 shows the certainty equivalent of a safe and a risky career
for both a rich and a poor agent. Given the parameters assumed, the
richer agent prefers the risky career while the poorer agent prefers the
safe career. For the richer agent, the 50% wage premium of the high-
risk career outweighs its increased risk. For the poorer agent, the higher
wages associated with the high-risk career are insu¢cient to make that
career attractive. This example emphasizes the role of risk because the
agents have a high degree of risk aversion and social insurance is not
included. In practice, social insurance prevents incomes from falling
too low and increases consumption in very bad states of the world. The
absence of social insurance from this calibration makes the risky career
look even more unappealing. This example demonstrates that, even for
individuals facing the same set of choices, initial wealth can cause people
to make substantially di¤erent decisions regarding the risk-reward trade-
Appendix B describes the
4 Estimating Career Risk
4.1 Cross-sectional Dispersion of Wages
In the remaining sections, we use several data sources to look for evidence
that wealthier people choose riskier careers.
the impact of risk on career choice, we must …rst determine the risk
associated with di¤erent careers.The simplest way to measure career
risk would be to look at the cross-sectional dispersion of wages. Within
an occupation, di¤erences in wages are likely to re‡ect heterogeneity in
ability. If people did not know their ability before entering a career, then
the cross-sectional dispersion of wages in that career would provide one
measure of the labor income risk they face. However, to the degree that
agents do know their ability before choosing a career, the cross-sectional
dispersion of wages will overstate the degree of risk.
While cross-sectional measures of wage dispersion are far from per-
fect as a measure of risk, we begin with these estimates because they are
Before we can measure
Estimated Variances of Shocks to Wages
Note. Numbers in parentheses are standard deviations calculated by Monte Carlo simulation. See Section 4 for
details. Includes data with between 1 and 5 years between observations, estimating variances for each skip length
and calculating a weighted average.
1. Includes individuals of all education levels.
2. Includes individuals in any college-educated occupation.
Percentage of Graduates in Each Major
Taking a Given Job After Graduation
Source: Baccalaureate and Beyond Longitudinal Study. Each row represents a college major. Majors are grouped based on the Baccalaureate and Beyond major categories. Each column
represents a type of job held one year after graduation. These jobs are grouped based on 2-digit SIC codes. The column reports the percentage of graduates from a given major who
entered each type of job. Cells are in bold when the major and job closely correspond.
Arts Sciences Computers Engineering Education Business Health Clerical Sales Other Unemployed Not in Labor Force Total % Total #
25.19% 6.23% 12.57%
25.11% 5.45% 21.90%
18.72% 5.66% 10.82% 100.00%
453 1193 875 8089
Graduate’s Self-Reported Link Between College Major
and First Job After Graduation
Source: Baccalaureate and Beyond Longitudinal Study. Each row represents
a college major. Majors are grouped based on the Baccalaureate and Beyond
major . Each column represents the link that a recent graduate reports
between their major and their first job after graduation. Each cell refers to the
percentage of graduates in a given major who identify a given amount of
connection between their major and work.
Distribution of College Majors in the NPSAS
Data is from the NPSAS. Each row refers to a category of major as grouped in the NPSAS. Income is defined as parent's income if
the student is a dependent and own income if he files his taxes as an independent. Lifetime wealth = wealth+10*annual income. The
Barron’s rating takes as a student’s rating the rating of their school, and averages over students. Schools without a Barron’s rating
are given a rating of 10. Lower numbers indicate more selective schools. Test scores are the average percentile of the student’s
ACT or SAT score. The score is reported as the student’s school’s average score when the student’s score is missing.
Coefficient on ln(lifetime wealth) from Multinomial Logit Regressions
Arts/entertainment - .086
Control for institution
Control for SAT score?
Only dependents under 25?
Control for parent occupation?
Number of observations
Note. Standard errors in parentheses. Lifetime wealth = wealth+10*income. See text for details. All
regressions include dummy variables for race, sex, age, and parents’ education. All coefficients represent
the impact of a change in log wealth on the log odds ratio of choosing a given major over education.
Humanities are excluded from the fixed-effects regression to save processing time. Fixed effects
regression excludes all schools with fewer than 15 students in the sample and fewer than 3 education
majors in the sample.
Estimated variance of
permanent shock to labor wealth
Percent change in odds ratio given a 1% increase in wealth
0 .05.1 .15.2
The x-axis shows the coefficients on ln(lifetime wealth) from column 1 of Table 8, the impact of a change in log income on the log
odds ratio. Since the base choice in each regression is the education major, these results indicate how the likelihood of choosing a
given major over education changes with wealth. See Section 5.4 for details.