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Federal Reserve Bank of Chicago

The Value and Risk of Human Capital
Luca Benzoni and Olena Chyruk

July 2015
WP 2015-06

The Value and Risk of Human Capital∗
Luca Benzoni† and Olena Chyruk‡
July 20, 2015

Abstract
Human capital embodies the knowledge, skills, health, and values that contribute
to making people productive. These qualities, however, are hard to measure, and quantitative studies of human capital are typically based on the valuation of the lifetime
income that a person generates in the labor market. This article surveys the theoretical and empirical literature that models a worker’s life-cycle earnings and identifies
appropriate discount rates to translate those cash flows into a certainty equivalent of
wealth. The chapter begins with an overview of a stylized model of human capital
valuation with exogenous labor income. We then discuss extensions to this framework
that study the underlying economic sources of labor income shocks, the choices, such
as work, leisure, retirement, and investment in education, that people make over their
life, and their implications for human capital valuation and risk.

1

Introduction

We think of human capital broadly as the set of knowledge, skills, health, and values that
contribute to making people productive (e.g., Becker 1964). In a free society, any contract
written against future labor services is not strictly enforceable, and ownership of human
capital is restricted to the person who embodies it, i.e., labor income is a non-traded asset.
Hence, any quantitative analysis of human capital is typically based on the valuation of
the income flow that a person generates by ‘renting’ services in the labor market. While
intuitive, this definition is hardly operational without sufficient knowledge of the worker’s
earnings over the life cycle and appropriate discount rates to translate those cash flows into
a certainty equivalent of wealth.
Modeling a worker’s life-time earnings is challenging. Heterogeneity in ability and preference for leisure results in cross-sectional income dispersion. Moreover, people adjust their
∗

We are grateful to Dan Aaronson, Gadi Barlevy, Marco Bassetto, Cristina De Nardi, Eric French, Bob
Goldstein, Debbie Lucas, Bhash Mazumder, and Anna Paulson for helpful comments and suggestions. Helen
Koshy and Vidur Snood provided excellent editorial assistance. The views expressed herein are those of the
authors and not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve System.
†
Federal Reserve Bank of Chicago, 230 S. LaSalle St., Chicago, Il 60604, 312-322-8499, lbenzoni@frbchi.org.
‡
Federal Reserve Bank of Chicago, 230 S. LaSalle St., Chicago, Il 60604, ochyruk@frbchi.org.

1

2
labor supply and the amount of effort exerted at work to buffer wealth, health, and labor
income shocks. Further, a worker can choose his retirement date to optimally smooth consumption. Finally, career choices and human capital investment in the form of education
have a significant impact on life-time earnings. Due to the complexity of a structural model
of labor income that accommodates all these features, researchers often focus on a reducedform specification to estimate the distribution of life-time earnings. They then rely on this
statistical model to determine labor income flows in the valuation of human capital.
Identifying a discount rate for the valuation of human capital also presents a challenge.
The appropriate rate should reflect the riskiness of workers’ labor income flows, which are
subject to unexpected fluctuations. Some of these fluctuations are linked to changes in general economic conditions. For instance, wages and employment rates are typically lower when
the economy does poorly, especially during a long recession. Similarly, some health problems
could increase in the population during an extended economic downturn and contribute to
lower aggregate earnings.1 However, labor income also contains a significant idiosyncratic
component. For example, many health shocks that are unrelated to aggregate economic conditions could force a person out of the labor force. Similarly, individual productivity shocks
could affect a worker’s earnings regardless of the overall state of the economy.
In a perfect market, workers could fully insure earnings against shocks by trading explicit
claims written against future labor income flows. In practice, market imperfections such as
moral hazard, adverse selection, transaction costs, and legal restrictions preclude trading such
claims. Self-insurance via precautionary savings and government-sponsored programs (e.g.,
unemployment insurance and Medicaid in the U.S.) help workers to smooth consumption.
Moreover, traded assets like stocks, bonds, and real estate fluctuate with general economic
conditions and therefore provide a partial hedge against labor income shocks. Yet, workers
remain exposed to idiosyncratic shocks that are not spanned by traded assets. Since such
shocks cannot be hedged, the appropriate rate at which to discount future earnings depends
on the workers’ preferences.
In this article, we discuss how the recent literature has tackled these issues. The chapter begins with a stylized life-cycle model of consumption-saving decisions in which labor
income is exogenous. Section 2 lays out a widely-used statistical model that explains lifecycle earnings in terms of three main components: a deterministic function of individual
characteristics, persistent shocks, and transient shocks. We then review the studies that
explore the empirical properties of these three components. Next, we derive a rate at which
a utility-maximizing worker discounts future earnings flows to determine the value of human
capital. The section ends with a synthesis of the financial economics literature that uses this
setup to study the value and risk of human capital.
Section 3 extends the framework along several dimensions. First, we review literature that
explicitly models work/leisure and retirement decisions rather than treating labor income as
1

See, e.g., The Economist for a non-technical overviews of the literature that studies the linkage between
economic downturns and health outcomes: “Body of research: The impact of downturns on physical and
mental health,” August 24, 2013.

3
an exogenous process. Second, we discuss human capital investment in the form of schooling,
training, and early childhood education. We review theory and evidence with an emphasis
on the role of education and credit constraints in explaining heterogeneity in earnings and,
therefore, in human capital valuation. Third, we explore various sources of risk that are
underlying persistent and transient earnings shocks. Examples of such risks include shocks
to individual productivity, health shocks, job arrival shocks, and firm-level shocks that may
lead to job creation or destruction. Fourth, we provide an analysis of the linkage between
labor income shocks and aggregate economic fluctuations and discuss the implications of
time-varying correlations between earnings and stock returns for the value and risk of human
capital. Finally, Section 4 concludes the chapter with summary points and suggestions for
future work.

2

A Model of Human Capital Valuation

Valuing human capital involves the pricing of non-tradable future cash flows that a worker
will earn in the labor market. Such an exercise requires two basic ingredients: a model
for the worker’s earnings over the life cycle and an appropriate rate at which to discount
those cash flows. Section 2.1 outlines a standard model for the worker’s labor income and
surveys the studies that provide empirical support for it. Section 2.2 presents a stylized
life-cycle model of consumption and savings decisions that determines a discount rate for
human capital valuation. In Section 2.3 we provide an overview of the financial economics
literature that combines these two ingredients to study the value of human capital.

2.1

A Statistical Model of Labor Income

For simplicity, here we follow much of the literature in assuming that the labor income
process is exogenous. That is, we ignore workers’ ability to adjust their labor supply, to
optimally choose a retirement date, and to invest in their human capital by pursuing more
years of education. While these considerations are important, the assumption that labor
income is exogenous is a reasonable starting point as long as the earnings profiles of the
model used for human capital valuation closely match the outcomes of workers’ decisions
reflected in labor income data. We will return to these issues in the second part of the article.
The labor income model of MaCurdy (1982) and Abowd and Card (1989) has become
a standard tool in the literature on life-cycle consumption and investment decisions (e.g.,
Carroll and Samwick (1997), Campbell et al. (2001), Gourinchas and Parker (2002), Cocco,
Gomes, and Maenhout (2005), Brown, Fang, and Gomes (2013)). It assumes that the worker
i’s age-t labor income, Yi,t , is the sum of deterministic and stochastic components,
log(Yi,t ) = f (t, Zi,t ) + νi,t + εi,t ,

t≤K,

(1)

where K is the last year before worker i retires.
The first term, f (t, Zi,t ), is a deterministic function of age t and other individual characteristics Zi,t . Empirical evidence suggests that a third-order polynomial in age provides

4
a good fit for age effects (e.g., Cocco, Gomes, and Maenhout (2005), Brown, Fang, and
Gomes (2013)). Estimation on household-level or individual labor income data delivers a
hump-shaped life-cycle earnings profile, consistent with stylized facts on income dynamics
(e.g., Attanasio (1995), Hubbard, Skinner, and Zeldes (1995), and Gourinchas and Parker
(2002)). Other individual characteristics Zi,t help to explain the life-cycle profile of earnings.
For instance, higher levels of education are typically associated with higher lifetime earnings.
Other covariates considered in the literature are gender, marital status, household size, and
other demographic variables (e.g., Hubbard, Skinner, and Zeldes (1995), Cocco, Gomes, and
Maenhout (2005), Guvenen (2009), Brown, Fang, and Gomes (2013)).
The next two terms in equation (1) capture idiosyncratic shocks. The labor income
literature distinguishes between persistent shocks, denoted here by νi,t , and transient shocks
εi,t . It is common to posit that νi,t follows a first-order autoregressive process,
νi,t = ϕ νi,t−1 + ui,t ,

(2)

where ui,t is a Gaussian i.i.d. mean-zero error uncorrelated across households with constant
variance σu2 . As for the transient shocks, the literature assumes that the εi,t process is uncorrelated across households and independent over time with identical Gaussian distribution
and constant variance σε2 .
Much effort has gone into determining the persistence of the νi,t process. Early work
has estimated νi,t to have only modest persistence and has attributed most of the observed
cross-sectional variation in earnings to heterogeneity in the deterministic component of labor
income profiles (e.g., Lillard and Weiss (1979), Hause (1980)). More recently, several studies
have revisited this question and have estimated the autoregressive coefficient ϕ to be close to
one (e.g., MaCurdy (1982), Abowd and Card (1989), Hubbard, Skinner, and Zeldes (1995),
Storesletten, Telmer, and Yaron (2004), and Guvenen (2009)). This evidence has motivated
several authors to assume that the νi,t shocks are permanent. For instance, Carrol (1997),
Carroll and Samwick (1997), Campbell et al. (2001), Gourinchas and Parker (2002), Cocco,
Gomes, and Maenhout (2005), and Brown, Fang, and Gomes (2013)) assume that νi,t follows
a random walk, ϕ = 1.
Carroll and Samwick (1997) develop a method for decomposing innovations into transitory and permanent components. Using Panel Study of Income Dynamics (PSID) data from
1981 to 1987, they estimate the variance σε2 of the transient component to be approximately
twice as large as the variance of the permanent shocks σu2 . They go on to document wide
heterogeneity in their estimates across workers’ characteristics, such as education, occupation, and age. For instance, they find an inverse-U pattern in permanent shocks as a function
of education: Their σu2 estimates increase till workers reach a high-school degree, and they
progressively decline for workers who have a higher level of education. In contrast, they find
a U-shaped pattern in transitory shocks σε2 as a function of education. Further, they show a
mild U-shaped pattern of both σu2 and σε2 in age.
Several studies after Carroll and Samwick (1997) have followed a similar approach to
investigate the properties of labor income risk. Differences in data sources, sample periods,
labor income measures, and model specification make it difficult to directly compare their

5
findings. This might explain why the conclusions of this body of research are often conflicting.
For example, Hubbard, Skinner, and Zeldes (1994) estimate lower σu2 and σε2 coefficients for
more educated workers. Campbell et al. (2001) document an increasing pattern in permanent
shocks as a function of education and a decreasing relation for transitory shocks. Finally,
Meghir and Pistaferri (2004) and Brown, Fang, and Gomes (2013) show a U-shaped pattern
for permanent shocks, while transitory shocks are decreasing in education.
In this model, workers retire at a fixed age K. From that moment, they receive retirement
income that may come in different forms. In the U.S., Social Security is the primary source
of retirement income for the majority of retirees; benefits are calculated based on a worker’s
average indexed monthly earnings during the 35 years of highest earnings. While defined
benefit pension plans are becoming less common, there is still a significant portion of the
U.S. population that is entitled to such benefits through their former employer; also in this
case, benefits are typically computed as a fraction of income earned during the last few years
prior to retirement. Alternative pension plans (e.g., defined contribution 401K and thrift
plans) are also funded by workers by setting aside a fraction of their annual income. Hence,
it is reasonable to model retirement income as a constant fraction λ(Zi,K ) of permanent labor
income in the last working year K, where λ(Zi,K ) is a function of individual characteristics
Zi,K :
log(Yi,t ) = log(λ(Zi,K )) + f (t, Zi,K ) + νi,K , t > K .
(3)
Among others, Cocco, Gomes, and Maenhout (2005), and Brown, Fang, and Gomes (2013)
follow this approach in their empirical investigation of life-cycle labor income profiles.

2.2

The Private vs. Market Valuation of Human Capital

We can think of labor income as the cash flow generated by the worker’s human capital.
Most people rely on their wages to pay for food, shelter, and other necessities. While human
capital is possibly their most valuable asset, market imperfections such as moral hazard,
adverse selection, transaction costs, and legal restrictions preclude them from borrowing
against it by trading explicit claims to their future earnings.
The same market imperfections prevent people from insuring fully against labor income
shocks. Self-insurance via precautionary savings and government-sponsored programs (e.g.,
unemployment insurance and Medicaid in the U.S.) help workers to smooth consumption.
Moreover, traded assets like stocks, bonds, and real estate provide a partial hedge for labor
income shocks. Yet, the worker remains exposed to idiosyncratic shocks that are not spanned
by traded assets. Since these shocks cannot be hedged, the discount rate for future labor
income flows may differ from the market discount rate.
In general, the implicit value of a non-traded asset is specific to a person and depends
on individual preferences. Only when the cash flows from the non-traded asset are spanned
by traded assets, i.e., the market is complete, would the value of the non-traded asset be
independent of preferences (e.g., Svensson and Werner (1993), He and Pearson (1991)).
To illustrate the private valuation of human capital, it is useful to start from the life-cycle
consumption-savings problem of a person who does not earn labor income. We then consider

6
the hypothetical scenario in which the person can trade a claim to future labor income flows,
and we determine the shadow price at which he is willing to hold that claim based on his
preferences (e.g., Svensson and Werner (1993)).
A person i with time-separable utility is endowed with positive initial wealth Wi,0 and
lives for T years. He begins the year with wealth from the previous year plus accumulated
interest, Wi,t−1 (1 + r), where r is a constant rate of return on invested wealth. Each year he
consumes Ci,t to maximize the utility function
[
E1

T
∑

]
β t−1 U (Ci,t ) ,

(4)

t=1

where β is an intertemporal discount factor, subject to the budget constraint
Ci,t = (1 + r) Wi,t−1 − Wi,t .

(5)

Further, we assume that the worker has no access to borrowing, i.e., for each year t, Wi,t ≥ 0.
Suppose now that the same person is allowed to trade a hypothetical asset that generates
the labor income flow Yi,t with dynamics in equations (1)-(3). The implicit value of human
capital is then the price Vi at which he would hold that asset if it were traded on the market.
Denote with θ the amount of the human capital asset the person chooses to buy subject to
the augmented budget constraint
Ci,t = (1 + r) Wi,t−1 − Wi,t + θ Yi,t − θ Vi

for t = 1,

Ci,t = (1 + r) Wi,t−1 − Wi,t + θ Yi,t

for 1 < t ≤ T .

(6)

In equation (6), θ Vi denotes the time-1 cost incurred to ‘acquire’ the human capital asset,
while θ Yi,t denotes the time-t cash flow associated with that claim. The person will then
choose θ and his consumption flow Ci to maximize intertemporal utility. The first-order
condition of the utility function (4) with respect to θ yields
[ T
∑

]
∗
∂C
i,t
∗
β t−1 U ′ (Ci,t
)
0 = E1
∂θ
t=1
]
[ T
∑
∗
∗
)Yi,t ,
) Vi + E 1
β t−1 U ′ (Ci,t
= −U ′ (Ci,1

(7)

t=1

(7) yields the standard optimality
where Ci∗ denotes the optimal
]
[∑consumption plan. Equation
T
t−1 ′
∗
′
∗
U (Ci,t )Yi,t , which equates the marginal utility cost
condition, U (Ci,1 ) Vi = E1
t=1 β
of acquiring one additional unit of the human capital asset to the marginal benefit of receiving
the associated additional labor income cash flows. Rearranging equation (7) gives the implicit
value of human capital:
[
Vi = E1

T
∑
t=1

β t−1

]
∗
)
U ′ (Ci,t
Yi,t .
∗
)
U ′ (Ci,1

(8)

7

2.3

The Value and Risk of Human Capital

The setup of Section 2.2 abstracts from a number of important issues. First, it assumes that
people invest their savings exclusively in a safe asset, while in reality they face an array of
investment options, e.g., stocks, bonds, and real estate. Second, the life span is fixed at T
years, while people face mortality risk. Third, preferences exclude a bequest motive.
Cocco, Gomes, and Maenhout (2005) consider a more general setting that overcomes these
limitations. They explicitly model the life-cycle portfolio choice problem of a worker with
constant relative risk aversion (CRRA) preferences who splits his financial wealth between
a risk-free bond and a portfolio of stocks. Along the way, they account for mortality risk
as in, e.g., Hubbard, Skinner, and Zeldes (1994) and introduce a bequest motive in the
worker’s utility function. The labor income dynamics are similar to equations (1)-(3) with
permanent idiosyncratic shocks (ϕ = 1 in equation (2)) and coefficients estimated on PSID
data. They assume that the worker retires at age 65 and calibrate survival probabilities
using the mortality tables of the National Center for Health Statistics. The relative risk
aversion coefficient, intertemporal discount rate, risk-free rate, and equity premium are fixed
at values commonly found in the literature.
Cocco et al. solve their model numerically and evaluate the implicit value of human
capital by discounting labor income flows at the worker’s intertemporal marginal rate of
substitution (equation (8)). They find the value of human capital to be hump-shaped over
the life cycle with a peak around age 35. That is, although young people face a longer stream
of future labor income, they discount such cash flows more than older agents. This occurs
for two reasons. First, the predictable labor income component has a hump-shaped profile:
Higher labor income cash flows occur at older ages and therefore are subject to greater
time discounting. Second, as the worker ages, he faces lower idiosyncratic labor income
uncertainty. Consistent with this intuition, Benzoni, Collin-Dufresne, and Goldstein (2007)
document that the worker’s discount rate associated with the permanent idiosyncratic labor
income shocks has a downward-sloping profile.
Brown, Fang, and Gomes (2013) consider a similar setup that abstracts from portfolio
choice decisions and focuses instead on the linkage between the worker’s education and the
value of his human capital. The authors consider three levels of education: workers without
a high school diploma, those who have it, and college graduates. They estimate the labor
income model on PSID data from 1968 to 2007 on a sample of PSID household heads, who are
still in the labor force and aged between 20 (22 if college graduates) and 65. The measure of
income includes post-tax labor income, unemployment income, and Social Security benefits.
As in equation (3), after retirement workers receive a constant fraction of permanent labor
income in the last working year.
For a range of preference parameters, Brown et al. find that the typical high school
graduate will enjoy a level of consumption that is 24% higher than those who did not attend
high school. When expressed in terms of life-time certainty equivalent (i.e., risk-adjusted)
wealth, this corresponds to an increase of $220,000 in 2010 dollars. The gain from college
education is larger. The increase in present-value of human capital of the average college

8
graduate is $432,000, which corresponds to a 38.5% increase in annual certainty-equivalent
consumption. These estimates are smaller than the numbers often found in the popular
press.2 Brown et al. attribute the differences to the measure of labor income they use, which
is net of income and payroll taxes, and to the risk-adjusted discount rate they apply to value
future labor income flows.
Further, differences in risk aversion, unemployment risk, and earnings volatility can produce significant heterogeneity in estimated returns to education. This sets the Brown et al.
results apart from the labor economics literature that studies the linkage between education
and earnings via linear regressions without giving full consideration to risk (e.g., Card 1999).
In particular, Brown et al. estimate a U-shaped pattern in permanent labor income shocks
as a function of education. They find that high school graduates face lower idiosyncratic
risk than workers without a high school degree and college graduates. Hence, they discount
future earnings at a lower rate, which increases the present-value of their human capital.
College graduates experience higher expected earnings and lower unemployment risk. However, they also face a much more skewed distribution of lifetime earnings due to, e.g., career
earnings heterogeneity. Thus, they discount future earning at a higher rate.3

3

Extensions and Directions for Future Work

The discussion so far has focused on statistical models that treat labor income as an exogenous process. Here we discuss the literature that extends the analysis to account for
work-leisure and retirement decisions, as well as the choice to invest in human capital in
the form of education. Further, the framework of Section (2) is silent about the economic
sources of risk that underlie shocks to earnings and, thus, cannot be used to disentangle
fluctuations in income due to exogenous shocks from those due to the effects of actions (e.g.,
adjusting hours worked or retirement date) taken by workers in response to such shocks. To
shed light on these issues, we review recent studies that explicitly model shocks to individual
productivity, health shocks, job arrival shocks, and firm-level shocks that may lead to job
creation or destruction, and their role in driving labor income fluctuations. Moreover, we
discuss work that explores the link between labor income shocks and aggregate economic
fluctuations. Delving more into these issues is important to obtain a more precise estimate
of the value of human capital and to better understand the sources of its risk.
2

For instance, the 2011 “Education and Synthetic Work-Life Earnings Estimates” report by the U.S.
Census estimates that a college degree increases lifetime earnings by around one million dollars compared
with lifetime earnings of a worker with a high school diploma.
3
The conclusions of Brown et al. hinge on their estimate of a U-shaped pattern in the variance of permanent idiosyncratic shocks with respect to education. This pattern, however, is controversial in the literature
(see the discussion in Section 2.1). Reaching a consensus on the decomposition of labor income shocks into
permanent and transitory components remains an important area of work.

9

3.1

Models of Labor Supply

The labor income model in Section 2.1 specifies a univariate process of earnings. Thus,
its ability to identify the various sources of fluctuations in income is limited. In contrast,
recent work in labor economics focuses on multivariate models that include the distinct
determinants of earnings. For instance, Altonji et al. (2013) consider specifications that
incorporate hours and wages, as well as discrete events such as job changes, employment
loss, interactions between job changes and wages, and the effects of these discrete events on
the variance of wage and hours shocks.
The framework of Altonji et al. (2013) is very rich. While not structural, their equations
can be viewed as approximations to the decision rules relating work and leisure choices to
state variables that would arise in a fully specified model of lifetime utility maximization. In
this interpretation, the parameters in the Altonji et al. (2013) equations depend on an underlying set of coefficients that describe a worker’s preference for leisure, job search technology,
and other economic fundamentals.
Human capital valuation, however, requires a discount rate to translate future labor income flows into a certainty equivalent of wealth (equation (8)). To this end, the hours/wages
processes estimated by Altonji et al. (2013) could be used in the calibration of a life-cycle
model that is more general and flexible than those discussed in Section 2.3. Alternatively,
one could pursue the estimation of a structural life-cycle model that includes some of the
features of the Altonji et al. (2013) specifications. Recent work by Low, Meghir, and Pistaferri (2010) is a major step in this direction. The authors explore a structural life-cycle
model of consumption, labor supply, and job mobility in an economy with search frictions.
The fundamental sources of risk in their model include shocks to individual productivity,
job arrival shocks when employed and unemployed, and firm-level shocks that lead to job
destruction. They go on to quantify empirically such risks in the presence of labor market
frictions and conclude that allowing for job mobility has a large effect on the estimate of
productivity risk. Exploring the implications of these results for the value and risk of human
capital is a promising area of future work.4

3.2

Human Capital Investment

The results of Brown, Fang, and Gomes (2013) that we discussed in Section 2.3 show that a
great deal of cross-sectional variation in the value of human capital is linked to the worker’s
education. Brown et al., however, take age-earnings profiles conditional on education as
given, rather than focusing on the worker’s choice to pursue education over the life cycle.
Related, they abstract from other forms of investment in human capital, such as training,
early childhood intervention, and medical care.
4

Several studies in the financial economics literature explore life-cycle portfolio choice models with a
leisure/labor trade-off and retirement decisions, e.g., Bodie, Merton & Samuelson (1992), Bodie, Detemple,
Otruba, & Walter (2004), Farhi, Emmanuel & Panageas (2007). Their focus is on the effect of variable labor
supply on stock market holdings.

10
The idea of human capital investment goes back to the seminal work of Becker (1964). To
understand the incentives for such investment and its implications for wages and earnings,
it is useful to articulate the uses of human capital in the production process of a firm. For
example, Becker (1964) thinks of human capital as the stock of knowledge and skills embodied
in a worker and used by the firm as an input. Subsequently, other views have emerged (e.g.,
Acemoglu and Autor 2013). The first is the Gardener view, named after the proponent of
the multi-intelligence theory that distinguishes between, e.g., mental and physical abilities,
which attributes similar dimensions to human capital. Acemoglu and Autor dub the second
as the Schultz/Nelson-Phelps view, which thinks of human capital as the capacity to adapt
to a changing environment. Third, the Bowles-Gintis view identifies human capital with
the ability to follow directions and work in hierarchical organizations. While these previous
interpretations are similar in many respects, the fourth one, which Acemoglu and Autor
call the Spence view, stands out as it considers observable measures of human capital (e.g.,
education) as a signal of ability rather than a useful production factor.
Alternative interpretations of the uses of human capital imply different investment incentives. For instance, in the Spence view a costly investment in education is worthwhile if it
allows a high-ability worker to signal his type to an imperfectly informed employer; while in
the Becker view education produces knowledge that directly increases a person’s productivity. More broadly, this distinction determines the way we think of the different components
of human capital. These include innate ability, schooling and training, and other factors
such as school quality, environment, and peer effects.
3.2.1

Schooling and Training

Most of the theoretical work on schooling and training draws on Becker’s view that education
makes people more productive. The basic idea in this literature is that people with different
innate ability face the trade-off between paying for education (in the form of tuition fees
and foregone income) to acquire skills that will increase their future productivity and salary.
People with higher ability experience a higher return to education (other things being equal)
and therefore have a stronger incentive to invest in human capital. With perfect capital
markets, workers can finance the cost of schooling and thus obtain their optimal level of
education. In the presence of market imperfections such as credit constraints, access to borrowing is limited. This causes constrained individuals to invest less, stopping their schooling
when the marginal return is still relatively high (e.g., Lochner and Monge-Navarro 2012).
In addition to attending school, the worker can pursue more education after the beginning
of his employment. The seminal contribution of Ben-Porath (1967) includes knowledge
depreciation and studies human capital accumulation in the form of post-school training.
His model underscores the continuity of the investment in education during and after the
school years. He finds that the optimal investment in human capital has a declining pattern
that goes to zero as the worker approaches retirement, since at that time the opportunity
cost of foregoing wages for more training outweighs the benefit of future productivity gains.
These results are consistent with the hump-shaped earnings profile that workers typically

11
experience over the life cycle and the hump-shaped profile in human capital.
In contrast to the notion that schooling produces skill, Spence (1973) views education
as a signal that high-ability workers use to convey their type to uninformed employers and
therefore secure a higher salary. To obtain a separating equilibrium, it is important that
high-ability workers face a lower cost to invest in human capital than low-ability people,
so that they can attain a level of education that is too costly for the low-ability workers
given the salary differential between the two ability types. However, pooling equilibria are
also possible, though they can often be ruled out by appropriately restricting the concept of
equilibrium (e.g., Cho and Kreps 1987).
There is a positive correlation between the education level of a worker and his earnings.
However, labor economists have been careful about drawing the conclusion that more education causes higher earnings. First, the most productive workers are those who also have
the strongest incentive to pursue more years of education, i.e., there could be an ability
bias. This is consistent with Becker’s view of human capital theory, where more able workers benefit more from longer schooling. But it is also consistent with Spence’s view, where
workers use education as a signal. Second, there could be a selection bias, as people tend
to study fields in which they are most efficient and motivated. A vast literature deals with
these biases by studying the linkage between earnings and education in a sample of identical
twins. Based on the evidence of these studies, Card (1999) concludes that the ability bias is
rather small.5
The findings of this empirical literature suggest that one additional year of schooling
yields a 6–15% increase in a worker’s annual salary (e.g., Card 1999).6 This evidence is consistent with Becker’s view that education makes workers more productive. Testing Spence’s
signaling view is more challenging. In both Becker’s and Spence’s views, part of the difference in earnings between high- and low-education workers is due to differences in ability.
However, in Becker’s theory ability may be unobserved by the econometrician but it is known
to the employer; while in the signaling theory it is unobserved by employers, as well as by
the econometrician. Acemoglu and Autor (2011) review empirical studies that attempt to
gauge the signaling return to education. Overall, there is some evidence for the signaling
channel, although the strength of the effect is hard to quantify.
The evidence of credit constraints on schooling decisions is also mixed. The comprehensive survey article by Lochner and Monge-Navarro (2012) concludes that borrowing constraints had little effect on college attendance in the early 1980s. Since then, in the U.S.,
5

Other research has relied on instrumental variable (IV) regressions based on institutional changes in the
education system to establish a causal link between education and earnings. Card summarizes the evidence
in this literature and reports that the estimated returns to schooling via IV regressions are 20-40% above the
corresponding ordinary least squares estimates. Card’s interpretation of this evidence is that the marginal
returns to schooling for certain subgroups of the population—particularly those whose schooling decisions
are most affected by structural innovations in the school system—are somewhat higher than the average
marginal returns to education in the population as a whole.
6
While these estimates imply that education significantly raises future earnings, it is worth underscoring
that the R2 of these regression is typically small, which suggests that there are determinants other than
years of schooling that explain variation in earnings.

12
both college attendance and tuition have risen, much faster than credit limits of governmentsponsored student loan programs. For instance, Lochner and Monge-Navarro (2012) report
that 26% of all dependent undergraduate students at four-year public universities in the
United States were borrowing the maximum allowable amount from the Stafford Loan Program in 1999-2000, compared with fewer than 4% of students ten years earlier. The private
sector has stepped in to meet, at least partly, the increasing credit demand. Lochner and
Monge-Navarro note that private student credit increased rapidly from virtually zero in the
early 1990s to 9% of all student loan dollars distributed in 1999-2000. Taken together, this
evidence suggests that, in recent years, credit constraints have become more important for
higher education decisions in the United States. These constraints could impact not only
educational attainment, but also college quality, work/consumption decisions while in school,
and saving/investment decisions (e.g., Palacios-Huerta 2003, Saks & Shore 2005, Roussanov
2010, Athreya et al. 2013). Finally, extending credit for human capital requires repayment
enforceability and raises other incentive problems (e.g., Lochner and Monge-Navarro 2011).
3.2.2

Early Childhood Investment

While there is extensive work on the returns to school and college education investment, only
recently has the human capital literature started to focus on the long-term effects of early
childhood education (e.g., Almond & Currie 2011 and Cunha et al. 2006). This research finds
that early childhood intervention among children with disadvantaged background leads to
higher test scores, decreased grade retention, decreased time in special education, decreased
crime and delinquency, and increased high school graduation rates. This new wave of work
stresses the need for a better understanding of the life-cycle skill formation process. It differentiates between early and late investments in human capital. Moreover, it recognizes
the roles of both cognitive and noncognitive (e.g., perseverance, self-control, reliability, consistency, motivation, and optimism) abilities in determining the returns to human capital
(Cunha & Heckman 2008, Cunha & Heckman 2010, and Cunha et al. 2010). In this setup, the
skill production technology exhibits dynamic complementarity (early investments increase
the productivity of later investments) and self-productivity (the skills acquired in the early
stage augment the skills acquired in later periods). These two features produce multiplier
effects as skills acquired today beget more skills in the future. According to this literature, effective public policies would focus on young children’s human capital investments
as they have the highest return compared with investments in later years. Moreover, such
policies may potentially reduce lifetime inequality as differences in early life conditions have
been found to explain a significant portion of the variation in lifetime earnings and wealth
(Huggett et al. 2006, 2011).
Yet, some low-income families do not make the same investment in early childhood programs as higher-income households do. One possible explanation is the presence of borrowing
constraints. Indeed, an increase in family income at early childhood ages has a greater effect
on educational achievement than income received at later ages (e.g., Dahl & Lochner 2012).
This finding is consistent with the dynamic complementarities discussed above: Higher early

13
investment leads to higher returns for later investments in education, while it is difficult to
amend inadequate levels of early investments with higher investments later in life (Keane
& Wolpin 2001, Cameron & Heckman 1998). In an overlapping generation model of human capital production, Caucutt & Lochner (2012) show that relaxing credit constraints on
young parents would increase both early investments in young children and late investments
in older children. In contrast, a policy that focuses on subsidizing college education alone
might not be as effective in increasing human capital investment. The effect on future generations, however, is more ambiguous. Increased borrowing causes higher debt levels that
result in parents transferring less resources to their children in the long run. This in turn
could limit the ability of future generations to sustain the same increased level of human
capital investment.
This discussion underscores that the timing of human capital investment over the life cycle is important. Early childhood investment is critical, but it is highly illiquid and requires
significant time and financial costs. Lochner and Monge-Navarro (2012) argue that the constraints faced by parents when investing in early childhood education are more pervasive and
harmful than the constraints people face at college ages. The findings of this literature provide support for public policies that facilitate access to early childhood education, especially
for underprivileged households. For instance, Restuccia and Urrutia (2004) conclude that
social programs that help finance early education have a larger impact on earnings mobility
than an increase in college subsidies.

3.3

Health Shocks

Individual health has a broad effect on most lifetime labor market outcomes, including
wages, earnings, labor force participation, hours worked, and retirement. As such, the
literature views health as one of the human capital components (e.g., Becker 1964). While
it is intuitive that a large negative health shock can lead to a decline in lifetime earnings,
numerous empirical studies have struggled to gauge the magnitude of these changes.
In their survey, Currie & Madrian (1999) discuss three main issues that are important
to disentangle the effect of health status on labor market outcomes. First, it is difficult
to measure health shocks. Thus, the estimates differ based on measures of health being
used (e.g., mental health, heart diseases, external accidents). Second, there is a vast crossdisciplinary literature that argues that individual socioeconomic status (e.g., education and
wealth) determines the investment in a person’s health and, thus, health capital (e.g., Smith
1999). Third, since health and labor market outcomes are endogenous variables, estimates
of the effect of a health shock on wages, and vice versa, are sensitive to the identification
assumptions (e.g., Lee 1982, Haveman et al. 1994, Riphahn 1999, Au et al. 2005, Disney et
al. 2006).
In general, these studies focus on the adult population to determine the relationship
between health and labor market activity. But there is also growing evidence that the early
childhood environment significantly influences later life outcomes. Almond & Currie (2011)
provide an extensive summary of recent work. The main finding of this literature is that

14
shocks before the age of five years lead to significant long-term consequences. In particular,
poor health in childhood affects both adult health status and investments in other forms
of human capital (such as education). And even a compromised prenatal environment can
have long-term negative effects on future health outcomes (e.g., Almond & Mazumder 2011,
Barker & Osmond 1986, Kraemer 2000). Furthermore, there is evidence that poor health
in childhood is associated with reduced educational attainment as well as lower wages and
labor force participation (e.g., Grossman 1975, Perri 1984, Wolfe 1985, Wadsworth 1986,
and Smith 2009).
This discussion suggests that health shocks could have a significant impact on lifetime
earnings. Moreover, the dynamics of health and health insurance coverage (e.g., De Nardi et
al. 2013) affect the price of health care relative to non-health consumption and, therefore, the
rate at which workers discount future labor income flows. Yogo (2007) builds on these ideas
with a life-cycle model in which a retiree faces stochastic health depreciation, which affects
his marginal utility of consumption and his life expectancy. The retiree receives income
(including Social Security) and chooses consumption, health expenditure, and the allocation
of wealth among bonds, stocks, and housing to maximize lifetime utility. Yogo’s focus is on
explaining the cross-sectional variation and the joint dynamics of health expenditure, health,
and wealth for retired workers.7 Extending his work to the life-cycle problem of a young
worker is a promising step toward a better understanding of the value and risk of human
capital in the presence of health shocks.

3.4

Aggregate vs. Idiosyncratic Labor Income Shocks

Extended periods of strong economic activity are likely to be associated with sustained
earnings growth, and vice versa. Hence, it is natural to conjecture the presence of dependence
between labor income shocks and aggregate economic fluctuations. Here we review the
literature that studies different channels through which economic shocks might propagate
into a worker’s earnings and discuss the implications for the value and risk of human capital.
We focus in particular on articles that use stock market performance as a proxy for economic
conditions and explore short- vs. long-run correlations between stock returns and earnings.
3.4.1

Contemporaneous Correlation

Campbell et al. (2001), Cocco, Comes, and Maenhout (2005), Gomes and Michaelides (2005),
and Viceira (2001) explore the effect of contemporaneous correlation between labor income
and stock market innovations. They assume that logarithmic labor income is the sum of a
deterministic component, permanent idiosyncratic shocks νi and transient shocks εi —similar
to equations (1)-(2) but with random walk dynamics for νi :
νi,t = νi,t−1 + ui,t .
7

(9)

French (2005), French & Jones (2011), and van der Klaauw & Wolpin (2008) study the interaction
between retirement decision and uncertain health status.

15
These authors decompose the permanent shock ui,t into an aggregate component ξt distributed as N (0, σξ2 ) and an idiosyncratic component ωi,t distributed as N (0, σω2 ):
ui,t = ξt + ωi,t .

(10)

They then introduce the stock market portfolio S with total return
rt = log St − log St−1 = µs + ηt ,

(11)

where ηt denotes N (0, ση2 ) innovations that are independently and identically distributed
over time.
This framework allows for contemporaneous correlations between stock market shocks
ηt and aggregate labor income shocks ξt . Empirical support for this channel, however,
is limited. Early work by Davis and Willen (2000) obtains estimates for the coefficient
ρ = corr(ηt , ξt ) that range from 0.1 to 0.3 for college-educated males and −0.25 for male high
school dropouts. More recently, Cocco, Gomes, and Maenhout (2005) estimate ρ to be nearly
zero, i.e., they do not find a significant contemporaneous correlation between stock market
returns and permanent labor income shocks. Campbell et al. (2001) also find extremely
low and insignificant contemporaneous correlations across households with different levels
of education; their estimates are higher only when computed on excess stock returns lagged
one year. Entrepreneurial risk, however, correlates more highly with stock market risk; for
instance, Heaton and Lucas (2000) find that the correlation between the quarterly growth
rate of real nonfarm proprietary income (as reported in the NIPA) and the CRSP valueweighted return is 0.14, whereas the correlation between the value-weighted return and the
quarterly growth rate of real aggregate wages is −0.07.
3.4.2

Countercyclical Volatility of Idiosyncratic Shocks

Storesletten et al. (2004) consider a model with persistent idiosyncratic shocks ui,t (as in
2
equation (2)) with variance σu,
t that is common across agents and varies over the business
cycle:

σ 2 , if aggregate expansion at date t
uE
2
(12)
σu, t =
σ 2 , if aggregate contraction at date t .
uC
They estimate the model on PSID data from 1968 to 1993 via the generalized method of
moments (GMM). They find idiosyncratic risk to be (i) highly persistent, with an annual
autocorrelation coefficient ϕ = 0.95, and (ii) strongly countercyclical, with a conditional
standard deviation that increases by 75% (from σu2E = 0.12 in an expansion to σu2C = 0.21 in
a contraction) as the macroeconomy moves from peak to trough. Their estimates translate
into contraction/expansion conditional standard deviations of $9,500 and $5,300, based on
the average worker’s 2002 dollar earnings of $45,000. These computations are useful to
gauge the impact of idiosyncratic risk on a person’s earnings and how the shocks vary over
the business cycle.

16
3.4.3

Procyclical Mean of Labor Income Growth

Lynch & Tan (2008) extend the Storesletten et al. (2004) framework to allow for fluctuations
in the conditional mean of the labor income flow at business cycle frequencies. They use the
dividend yield D on a stock market portfolio as a proxy for the business cycle and assume
that dt = log(1 + Dt ) follows a mean-reverting process:
dt = µd + bd dt−1 + εt ,

(13)

where εt denotes a N (0, σε2 ) i.i.d. shock. In their model, the conditional mean of both stock
market return and the permanent component of labor income is a linear function of the dt
predictor:
rt = µs + bs dt + ηt
νi,t = νi,t−1 + µν + bν dt + ui,t ,

(14)

where ηt and ui,t are mean-zero Gaussian i.i.d. innovations with variances ση2 and σu2 , respectively.
Using data on retail trade income growth and the 12-month dividend yield on the valueweighted NYSE index, they estimate bν < 0, bs > 0, and bd close to one. That is, they find
the permanent component of labor income to be procyclical, while the dividend yield is countercyclical. Further, they find countercyclical fluctuations in the volatility of idiosyncratic
shocks similar to Storesletten et al. (2004).
3.4.4

Cointegration Between Labor Income and Stock Market Returns

Several studies explore the effect of long-run co-dependence between aggregate economic conditions and labor income. It is straightforward to incorporate this feature into the framework
that we review here in the form of cointegration between labor income and stock returns,
or labor income and aggregate economic activity (e.g., Campbell 1996, Baxter & Jermann
1997, Lucas & Zeldes 2006, Santos & Veronesi 2006, Benzoni et al. 2007, and Huggett &
Kaplan 2013). To this end, it is convenient to specify the logarithmic labor income process
yi,t = log(Yi,t ) as the sum of aggregate y A and idiosyncratic yiI components,
yi = y A + yiI .

(15)

Similar to equation (1), the idiosyncratic labor income component contains deterministic
and stochastic terms,
I
yiI = f (t, Zi,t ) + νi,t
+ εi,t .
(16)
I
As in, e.g., Cocco, Gomes, and Maenhout (2005), the νi,t
process follows a random walk with
i.i.d. error ωi,t ,
I
I
νi,t
= νi,t−1
+ ωi,t ,
(17)

while εi,t is a transient i.i.d. shock.
To capture deviations between aggregate labor income and stock market values, we introduce a variable h = y A − s, where s = log(S) is the logarithmic price of the stock market

17
portfolio with return rt = ∆st given in equation (11). While there may be short-run deviations between aggregate earnings and stock market values, it is natural to conjecture that
a sustained period of high economic growth will result in strong stock and labor market
performance, so that these two markets will move together in the long run. To generate this
type of co-movement in the model, we assume that y A and s are cointegrated with a time
trend, i.e., h is trend-stationary with dynamics8
∆ht = κ(θ t − ht ) + zt − σhη ηt ,

(18)

where zt is a N (0, σz2 ) i.i.d. shock that is independent of the stock market innovation ηt .
The coefficient κ measures the speed of mean reversion for the cointegration relation. In
particular, τ = log(2)/κ is the half-life of shocks to h; larger values of κ imply a faster decay
toward the long-run trend θ t.
In first differences, the labor income process is
I
∆yi,t = ∆ytA + ∆yi,t
.

(19)

Substituting ∆y A = ∆h + ∆s into equation (19) yields an expression for the total labor
income dynamics,
∆yi,t = −κht + κθ t + ∆f (t, Zi,t ) + ξt + ωi,t + ∆εi,t ,

(20)

where, as in Cocco, Gomes, and Maenhout (2005), we can interpret ξt = zt + (1 − σhη ) ηt
as a Gaussian i.i.d. shock to the aggregate component of labor income with mean zero and
variance σξ2 = σz2 + (1 − σhη )2 ση2 .
The model of Cocco, Gomes, and Maenhout (2005) is a special case of this framework. In
the limit when the mean reversion parameter κ → 0, equation (20) collapses into the expression for their labor income process in first differences. As in Cocco, Gomes, and Maenhout
(2005), the labor income model described by equation (20) allows for contemporaneous correlation between labor income and stock market shocks,
(1 − σhη )ση
.
corr(ξt , ηt ) = √
σz2 + (1 − σhη )2 ση2

(21)

This correlation can be set equal to the value of the ρ correlation coefficient estimated by,
e.g., Cocco, Gomes, and Maenhout (2005) with an appropriate choice of σhη . However,
this framework also allows for non-zero long-run correlations between labor income and
stock market returns that can be large even if the contemporaneous correlation is small. In
particular, when σhη = 1, the contemporaneous correlation in equation (21) is zero, yet the
long-run correlations driven by the cointegrating variable h are positive when κ > 0.
The model in equation (18) is mathematically equivalent to an alternative specification with h = y A −d−
y A d, where d is the logarithmic dividend process, y A d is the long-run log-ratio of aggregate labor income to
dividends, and h is a stationary mean-reverting process without time trend (e.g., Benzoni, Collin-Dufresne,
and Goldstein 2007).
8

18
Several articles explore a cointegration relation similar to equation (18). For instance,
Baxter and Jermann (1997) study the correlation in returns to human and physical capital
using annual data on labor income and capital income for Japan, Germany, the United
Kingdom, and the United States over the period 1960-1993. They measure labor income
as total employee compensation while capital income is GDP at factor cost minus employee
compensation. For each country, they find that domestic human capital returns are strongly
correlated with the returns to domestic physical capital. However, they report only weak
evidence that labor and physical capital income are cointegrated. Benzoni, Collin-Dufrense,
and Goldstein (2007) explore the cointegration relation between labor income and aggregate
stock market dividends, while Huggett and Kaplan (2013) focus on labor income and stock
market returns. Also in these cases, the evidence for cointegration is limited, with results
that are sensitive to the choice of the sample period.
While the empirical evidence in its support is weak, it is nonetheless worthwhile to investigate cointegration between labor income and stock market returns as a possible source
of aggregate risk in individual earnings. First, the tests used to reject the null hypothesis
of a unit root in equation (18) are notorious for lacking power. This limitation could well
explain the lack of empirical support for cointegration. Second, economic intuition suggests
that returns to human and physical capital should be correlated.9 Third, empirical evidence
suggests that the capital and labor income shares fluctuate over time. A model with cointegration accommodates this evidence by allowing for short-term variation in factor shares
while retaining the long-run restriction that factor shares are stationary. This is a desirable
feature; as Baxter and Jermann (1997) point out, if the labor and capital income were allowed
to have independent trends, then the ratio of labor income to capital income would either
grow without bound or approach zero asymptotically, and the labor share would approach
either zero or one. This seems unlikely and counterfactual.
3.4.5

Implications for Human Capital Valuation and Risk

One of the channels through which economic conditions could affect individual labor income
is the contemporaneous correlation between aggregate earning shocks and stock market returns, denoted by the coefficient ρ in Section 3.4.1. Empirical support for this channel is
weak. For instance, Cocco et al. (2005) report estimates of ρ that are close to zero and
statistically insignificant. They then go on to solve the life-cycle problem of a worker when
the correlation coefficient between labor income and stock market risk is set at their small
estimate. Their main conclusion is that in this setting labor income acts as a substitute for
risk-free asset holdings, a finding that confirms earlier results obtained by Heaton and Lucas
9

Lustig and Van Nieuwerburgh (2006) reach conclusions at odds with this intuition. They use a representative agent model with recursive utility and observed aggregate consumption to back out consumption
innovations that cannot be attributed to news about current or future financial returns. They attribute
such innovations to the returns on human wealth and find them to be negatively correlated with shocks to
financial asset returns. Their results, however, may be sensitive to model assumptions; for instance, Bansal
et al. (2013) argue that extending their model to include stochastic macroeconomic volatility results in a
positive correlation between returns to human capital and financial wealth.

19
(1997) in an infinite-horizon model.
In contrast, several studies find comovements between labor income and stock returns
at business cycle frequencies to have a significant effect on the value and risk of human
capital. For instance, Storesletten et al. (2007) consider an overlapping generations model
in which idiosyncratic labor income risk is subject to countercyclical variation, a feature
documented in Storesletten et al. (2004) and discussed in Section 3.4.2. In this setting, they
show that the distribution of aggregate risk affects portfolio holdings in the economy, giving
rise to a hump-shaped risky asset share of financial wealth over the worker’s life cycle. This
is because, in their model, human capital acquires stock-like features due to the cyclical
component in the variance of earnings. For a worker exposed to stock market risk via the
implicit stock position embedded in human capital, it is optimal for him to reduce his direct
stock holdings, especially when he is young and most of his wealth is tied up in future labor
income. Lynch and Tan (2011) confirm the results of Storesletten et al. (2007) and extend
them to a model that, in addition to countercyclical idiosyncratic volatility, also features
procyclical labor income growth (see the discussion in Section 3.4.3).
Benzoni et el. (2007) obtain similar results in a model that features cointegration between
the aggregate component of labor income and dividends. Also in this case, long-run comovements between stock returns and earnings expose workers to market risk and human capital
displays a stock-like component, especially when the worker is young.10 Huggett and Kaplan
(2013) consider a more general framework that includes all channels of exposure of labor
income to aggregate risk, among which they favor countercyclical variation in idiosyncratic
labor income risk. They also find a stock component in human capital, though not as large
as the one reported in Benzoni et el. (2007).
Overall, the evidence discussed here suggests that labor income risk contains an aggregate
component that affects long-run fluctuations in earnings. It is difficult, however, to assess
the magnitude of this component with data that span only a few decades. For the same
reason, it is hard to disentangle the different channels through which aggregate risk spills
into individual labor income. In spite of these challenges, these issues are worth further
investigation. These efforts help us to better understand the sources of risk in human capital
and to identify a discount rate that accurately reflects the riskiness of future labor income
flows. Further, different occupations may expose workers to various degrees of aggregate
shocks. This applies to people employed across different industries as well as self-employed
workers and entrepreneurs (Heaton and Lucas 2000). Hence, heterogeneity in the exposure
to aggregate risk could be an important source of cross sectional variation in human capital
valuation.
10

Lucas & Zeldes (2006) consider a similar model for the valuation and hedging of defined-benefit plans,
while Geanakoplos & Zeldes (2010) study the effect of cointegration between labor income and stock returns
on the valuation of Social Security obligations. Retirement benefits for active workers depend on the realization of the future economy-wide wage level. If wages and stock returns correlate positively over the long
run, retirement benefits contain an aggregate risk component and should therefore be discounted at a rate
that accounts for that risk.

20

4

Conclusions

In this chapter, we review the theoretical and empirical literature that models a worker’s
life-cycle earnings and identifies appropriate discount rates to translate those cash flows into
a measure of human capital value. The main points of our discussion can be summarized as
follows:
1. Due to various market imperfections, there is no trading in explicit claims on future
labor income flows. Since workers are exposed to labor income shocks that cannot
be hedged with traded assets, the valuation of human capital is specific to the person
who embodies the set of values and skills that contribute to making him productive.
Hence, we focus on the worker’s private valuation of human capital, defined as the
implicit value of future earnings discounted at the worker’s intertemporal marginal
rate of substitution.
2. A vast literature abstracts from work/leisure decisions and models the worker’s earnings
as an exogenous univariate process that includes a deterministic component as well
as persistent (or permanent) and transient shocks. The estimated earnings process
discounted at risk-adjusted rates implies that the value of human capital exhibits a
hump-shaped profile over the worker’s life cycle. Variation in workers’ characteristics
such as gender, race, risk preferences, marital status, and education account for a great
deal of cross-sectional heterogeneity in human capital valuation.
3. A new wave of research explores the economic sources of risk that underlie shocks to
earnings and explicitly models the worker’s work-leisure and retirement decisions. This
literature has been successful at linking labor income fluctuations to shocks to individual productivity, health shocks, job arrival shocks, and firm-level shocks that may
lead to job creation or destruction. In turn, these findings help us better understand
the sources of risk in a worker’s human capital.
4. The value of a person’s human capital depends on the education and training that he
acquires over life. The benefits are not limited to college education, as recent research
stresses the importance of early childhood investment. The rise in college tuition has
deepened concerns that people might be obtaining a sub-optimal level and quality of
education, and that they could be forced to consume less and work more during their
college years. While the evidence on the effect of borrowing limits on college attendance
is mixed, credit constraints are likely to be a bigger impediment for early childhood
investment. Public policy experiments suggest that social programs that help finance
early education have a larger impact on earnings mobility than an increase in college
subsidies.
5. There is little evidence of contemporaneous correlation between labor income fluctuations and stock market returns. In contrast, long-run dependencies between labor

21
income and stock returns are a potentially important channel that exposes human capital to aggregate economic risk. These low-frequency comovements are hard to quantify
with data that span a relatively short period of time, but in model calibrations they
can have a significant impact on the valuation of human capital. When the stock and
labor markets comove in the long-run, human capital acquires stock-like properties,
especially for young workers who are exposed the most to long-run labor income risk.
In spite of the progress that has been made, much work is still to be done. Issues that
are worth further investigation include the following:
1. While our discussion focuses on private valuation, human capital could also include
a significant social component. For instance, an increase in the human capital of an
individual worker might create positive non-pecuniary externalities, such as better
citizens or lower crime, and raise the productivity of other workers around him and
therefore increase aggregate productivity in the economy. Related, in the presence of
complementarity in the production factors, wider availability of human capital might
induce firms to increase their investment in physical capital and thus result in higher
wages (e.g., Acemoglu 1996). For these reasons, the social return to human capital
could exceed the private return. In contrast, if education is merely a signal of a worker’s
ability, then the social return to the investment in education could be lower than the
private return, because high-ability workers might generate a negative externality for
other workers by pursuing more schooling, which will be rewarded with higher wages.11
2. The measure of human capital that we discuss here is based on the valuation of lifetime
earnings generated by the worker. However, earnings are an imprecise measure of
a worker’s skills. While attributing differences in earnings, and therefore the value
of human capital, to unobserved skill heterogeneity is a useful starting point, this
approach is reductive (e.g., Acemoglu and Autor 2011). For instance, differences in
compensation may be explained by hard-to-measure job characteristics that could make
a job appealing, even if it pays a low salary. Second, the productivity of two jobs may
be different, and workers in those two positions could be paid a different salary in
spite of having similar skills. Third, workers might face wage discrimination due to
employers’ prejudices against, e.g., race, gender, or religion. These considerations
suggest that human capital valuation should be based on a broader measure of cash
flows associated with workers’ output, rather than on observed earnings.
3. Over the years, education has been one of the main targets of social policies aimed
at fostering the production of human capital and bridging income inequality. The
success of these programs hinges on the ability of policymakers to use limited resources
effectively. Understanding how to better design such programs is the topic of much
ongoing research, including many of the contributions we discuss here. An important
11

See, e.g., Lange and Topel (2006) for a review of the literature on the social returns to the accumulation
of human capital and, in particular, the social returns to education.

22
part of this agenda is the study of the role of education in the production of human
capital (early childhood investment versus schooling and subsequent job training).
Related, possible credit constraints motivate policies (e.g., student loans and various
subsidies) that promote the accessibility of education. A better grasp of the human
capital production function and skill formation could form the basis for more effective
school programs and curricula, teachers’ training, and investment in premises and
teaching technology.
4. Finally, the studies that we review suggest that there is a linkage between health shocks
and earnings. More work on this topic could help policymakers develop improved health
and preventive care programs aimed at raising the private and social value of human
capital and improving the management of human capital risks.

References
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Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Examining Macroeconomic Models through the Lens of Asset Pricing
Jaroslav Borovička and Lars Peter Hansen

WP-12-01

The Chicago Fed DSGE Model
Scott A. Brave, Jeffrey R. Campbell, Jonas D.M. Fisher, and Alejandro Justiniano

WP-12-02

Macroeconomic Effects of Federal Reserve Forward Guidance
Jeffrey R. Campbell, Charles L. Evans, Jonas D.M. Fisher, and Alejandro Justiniano

WP-12-03

Modeling Credit Contagion via the Updating of Fragile Beliefs
Luca Benzoni, Pierre Collin-Dufresne, Robert S. Goldstein, and Jean Helwege

WP-12-04

Signaling Effects of Monetary Policy
Leonardo Melosi

WP-12-05

Empirical Research on Sovereign Debt and Default
Michael Tomz and Mark L. J. Wright

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Credit Risk and Disaster Risk
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WP-12-07

From the Horse’s Mouth: How do Investor Expectations of Risk and Return
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WP-12-08

Using Vehicle Taxes To Reduce Carbon Dioxide Emissions Rates of
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WP-12-09

Spending Responses to State Sales Tax Holidays
Sumit Agarwal and Leslie McGranahan

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Micro Data and Macro Technology
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The Effect of Disability Insurance Receipt on Labor Supply: A Dynamic Analysis
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Medicaid Insurance in Old Age
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Fetal Origins and Parental Responses
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1

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Speculative Runs on Interest Rate Pegs
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Institutions, the Cost of Capital, and Long-Run Economic Growth:
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Emerging Economies, Trade Policy, and Macroeconomic Shocks
Chad P. Bown and Meredith A. Crowley

WP-12-18

The Urban Density Premium across Establishments
R. Jason Faberman and Matthew Freedman

WP-13-01

Why Do Borrowers Make Mortgage Refinancing Mistakes?
Sumit Agarwal, Richard J. Rosen, and Vincent Yao

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Bank Panics, Government Guarantees, and the Long-Run Size of the Financial Sector:
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Benjamin Chabot and Charles C. Moul

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Fiscal Consequences of Paying Interest on Reserves
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Properties of the Vacancy Statistic in the Discrete Circle Covering Problem
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Financial Incentives and Educational Investment:
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The Global Welfare Impact of China: Trade Integration and Technological Change
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2

Working Paper Series (continued)
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The Earned Income Tax Credit and Food Consumption Patterns
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Agglomeration in the European automobile supplier industry
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Human Capital and Long-Run Labor Income Risk
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The Effects of the Saving and Banking Glut on the U.S. Economy
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

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A Portfolio-Balance Approach to the Nominal Term Structure
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Gross Migration, Housing and Urban Population Dynamics
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3

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Firm Dynamics and the Minimum Wage: A Putty-Clay Approach
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Can Intangible Capital Explain Cyclical Movements in the Labor Wedge?
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Early Public Banks
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Mandatory Disclosure and Financial Contagion
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The Stock of External Sovereign Debt: Can We Take the Data at ‘Face Value’?
Daniel A. Dias, Christine Richmond, and Mark L. J. Wright

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Interpreting the Pari Passu Clause in Sovereign Bond Contracts:
It’s All Hebrew (and Aramaic) to Me
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AIG in Hindsight
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On the Structural Interpretation of the Smets-Wouters “Risk Premium” Shock
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Human Capital Risk, Contract Enforcement, and the Macroeconomy
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Adverse Selection, Risk Sharing and Business Cycles
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4

Working Paper Series (continued)
Saving Europe?: The Unpleasant Arithmetic of Fiscal Austerity in Integrated Economies
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Quantitative Easing in Joseph’s Egypt with Keynesian Producers
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Constrained Discretion and Central Bank Transparency
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Escaping the Great Recession
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More on Middlemen: Equilibrium Entry and Efficiency in Intermediated Markets
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Preventing Bank Runs
David Andolfatto, Ed Nosal, and Bruno Sultanum

WP-14-19

The Impact of Chicago’s Small High School Initiative
Lisa Barrow, Diane Whitmore Schanzenbach, and Amy Claessens

WP-14-20

Credit Supply and the Housing Boom
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-14-21

The Effect of Vehicle Fuel Economy Standards on Technology Adoption
Thomas Klier and Joshua Linn

WP-14-22

What Drives Bank Funding Spreads?
Thomas B. King and Kurt F. Lewis

WP-14-23

Inflation Uncertainty and Disagreement in Bond Risk Premia
Stefania D’Amico and Athanasios Orphanides

WP-14-24

Access to Refinancing and Mortgage Interest Rates:
HARPing on the Importance of Competition
Gene Amromin and Caitlin Kearns

WP-14-25

Private Takings
Alessandro Marchesiani and Ed Nosal

WP-14-26

Momentum Trading, Return Chasing, and Predictable Crashes
Benjamin Chabot, Eric Ghysels, and Ravi Jagannathan

WP-14-27

Early Life Environment and Racial Inequality in Education and Earnings
in the United States
Kenneth Y. Chay, Jonathan Guryan, and Bhashkar Mazumder

WP-14-28

5

Working Paper Series (continued)
Poor (Wo)man’s Bootstrap
Bo E. Honoré and Luojia Hu

WP-15-01

Revisiting the Role of Home Production in Life-Cycle Labor Supply
R. Jason Faberman

WP-15-02

Risk Management for Monetary Policy Near the Zero Lower Bound
Charles Evans, Jonas Fisher, François Gourio, and Spencer Krane

WP-15-03

Estimating the Intergenerational Elasticity and Rank Association in the US:
Overcoming the Current Limitations of Tax Data
Bhashkar Mazumder

WP-15-04

External and Public Debt Crises
Cristina Arellano, Andrew Atkeson, and Mark Wright

WP-15-05

The Value and Risk of Human Capital
Luca Benzoni and Olena Chyruk

WP-15-06

6