Cognitive Economics and
Human Capital
Robert J. Willis
University of Michigan
Presidential Address
Society of Labor Economics, Chicago, May 4-5, 2007
Organizing Committee for
2007 SOLE Meeting
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Martha Bailey
John Bound
Charlie Brown
Mike Elsby
Mike Hurd
David Lam
Justin McCrarry
Bob Schoeni
Gary Solon
• Frank Stafford
• Rebecca Thornton
1/65
Thanks for Helpful Discussions and
Comments on this Talk
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Dan Benjamin
Jim Heckman
Miles Kimball
Jack McArdle
2/65
Goal of My Talk
• Describe a new research program integrating
psychological and economic theories and new
data collection that I am pursuing with
colleagues.
• The research that I describe has been motivated
by my role during the past dozen years in
overseeing the design of the Health and
Retirement Study, a longitudinal survey of over
20,000 Americans over the age of 50 that began
in 1992 with the support of the National Institute
on Aging and Additional Support from the Social
Security Administration
3/65
Goals (cont.)
• The HRS has given me an appreciation of the
value of embedding economics within the broad
scope of the social, biological and medical
sciences
• On the one hand, among all of these sciences,
economics offers the most coherent theoretical
framework, including
– life cycle theories of individual and family behavior
– static and dynamic theories of markets, general
equilibrium and economic growth
– tight linkage of positive theories of behavior and
normative theories of welfare and policy evaluation
4/65
Goals (cont.)
• On the other hand, theory and measurements
from other sciences can increase the power of
economics to deal with the issues that the HRS
is designed to address
• In turn, I will argue, the power of cognitive
psychology will be enhanced by integration with
human capital theory
• In this talk, I focus on the value of bringing
theory and measurement from cognitive
psychology and cognitive economics into the
HRS
5/65
Why Cognition?
• Increased complexity of decisions faced by older
Americans
– increased longevity, advances in medical technology
– increased scope for choice due to decline of defined
benefit pensions, growth of 401(k), etc.
• Decisions concerning savings and wealth
management, health care decisions, retirement
decisions are cognitively demanding
– The cognitive abilities of older Americans are highly
heterogeneous and changing as they age
6/65
Cognition and Survey Data
• Methodogical reason for measuring
cognition
– Information provided by survey responses to
the HRS are related to cognitive status of
respondent
– Cognitive factors that influence survey
response may also influence behavior in the
real world
– Implies need to jointly model behavior and
survey response in analyzing data
7/65
Outline
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What is Cognitive Economics?
Cognition and Probabilistic Thinking
Cognition and Human Capital
Use it or Lose It? Retirement and Cognition
8/65
Cognitive Economics
The Economics of What is in
People’s Minds
Three Themes of Cognitive
Economics
1. New Types of Data
2. Heterogeneity
3. Finite and Scarce Cognition
10/65
1. Innovative Survey Data
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survey measures of expectations
survey measures of preferences
happiness data
fluid intelligence data
crystallized intelligence data
11/65
2. Individual Heterogeneity
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heterogeneous expectations
heterogeneous preferences
heterogeneous emotional reactions
heterogeneous views on how the world
works (folk theories)
12/65
3. Finite and Scarce Cognition
• Finite cognition=the reality that people
are not infinitely intelligent.
• Scarce cognition=some decisions
required by our modern environment—at
work and in private lives—can require
more intelligence for full-scale
optimization than an individual has
13/65
Some Research Questions
in Cognitive Economics
• Seek to make innovations in economic theory and
measurement to address:
– What are people’s limitations in knowledge, memory,
reasoning, calculation?
– What is the role of emotion, social context, conscious
vs. unconscious judgments and decisions?
– What is the role of health as determinant, outcome
and context for economic activity, decisions and well
being?
– What is connection between economic welfare and
measures of well being?
– Etc.
14/65
Cognition and Probabilistic
Thinking
What is the mapping between
probability beliefs in people’s minds
and the decisions they make?
Probabilistic Thinking and Behavior
in the Economy and on Surveys
• The HRS provides detailed measurement
of the variables that enter into a broad life
cycle economic model
16/65
HRS Measures Components of
Lifetime Budget Constraint
• Income
– from labor, assets, government transfers, family
transfers
• Wealth/Portfolio Composition/Insurance
– from personal assets, future streams of employer
pensions, social security benefits, value of
inheritances received or bequests given
• Labor Supply and Earnings
– hours, weeks, occupation, retirement
• Consumption and Time Use
– goods and services, out-of-pocket medical expenses,
transfers to children and others
17/65
But Conventional Life Cycle Economic
Models Include More Variables
• Current decisions based on effect of decision on
lifetime expected utility
– Separation of Beliefs and Preferences
• Product of utilities and probabilities in each period
– Forward-Looking
• Sum over time from now through (uncertain) end of life
• Discounting by time preference
– Feasible utilities depend on current and future
resources
– Future resources, in turn, depend on current
decisions
18/65
Probabilistic Thinking
• Traditionally, economists make assumptions
about agent’s probability beliefs (e.g. rational
expectations)
• The HRS provides detailed questions about
those probability beliefs that enter into a broad
life cycle economic model
• These questions have stimulated a growing
body of research in which probability beliefs
become part of the data of economic models
19/65
Direct Measurement of Subjective
Probability Beliefs in HRS
Probability questions use a format pioneered by
Tom Juster and Chuck Manski
(Manski, 2004)
HRS Survival Probability Question:
“Using a number from 0 to 100, what do you think
are the chances that you will live to be at least [target
age X]?”
X = 80 for persons 50 to 70 and increases to 85, 90,
95, 100 for each five year increase in age
20/65
Two Key Findings From Previous
Research on HRS Probability Questions
1. On average, probabilities make sense
– Survival probabilities conform to life tables and are
predictive of actual mortality
(Hurd and McGarry 1995, 2002; Sloan, et. al., 2001 )
– Bequest probabilities behave sensibly
(Smith 1999), Perry (2006)
– Retirement incentives can be analyzed using expectational data
(Chan and Stevens, 2003)
– People can predict nursing home entry
(Finkelstein and McGarry, 2006)
– Early Social Security Claiming Depends on Survival Probability
(Delevande, Perry and Willis, 2006) , (Coile, et. al., 2002)
2. Individual probabilities are very noisy with heaping on focal
values of "0", "50-50" and "100“
(Hurd, McFadden and Gan, 1998)
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Survival Probabilities: Accurate on
Average with Heaping on Focal Values
Figure 1. Distribution of Survival Probabilities to Target Age
by Age of Respondent
.15
50-64
0
.05
.1
Subjective Mean= 66%
Life Table
= 59%
Subjective Mean= 57%
= 58%
0
.05
.1
Life Table
.15
75-90
Subjective Mean= 36%
.1
Life Table
= 23%
0
.05
Density
.15
65-74
0
50
100
Survival Probability
Graphs by Age
22/65
10 Year Mortality Rate vs. Subjective
Survival Probability to Age 75
3.00
2.76
2.49
2.50
Odds Ratio (50%=1.0)
Odds Ratio
of Death
by t+10
2.00
1.47
1.50
1.43
1.22
1.00
1.00
0.86
0.85
0.79
0.68
0.70
80
90
0.50
0.00
0
10
20
30
40
50
60
70
100
Probability of survival to age 75
Subjective Survival Probability at Time t
Source: Mortality Computations from HRS-2002 by David Weir
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10 Year Mortality Rate vs. Subjective
Survival Probability to Age 75
3.00
2.76
Strongest relationship between
subjective and objective risks for people
with low subjective survival beliefs
2.49
2.50
Odds Ratio (50%=1.0)
Odds Ratio
of Death
by t+10
2.00
1.47
1.50
1.43
1.22
1.00
1.00
0.86
0.85
0.79
0.68
0.70
80
90
0.50
0.00
0
10
20
30
40
50
60
70
100
Probability of survival to age 75
Subjective Survival Probability at Time t
Source: Mortality Computations from HRS-2002 by David Weir
24/65
. Histograms of Responses to Probability Questions in the HRS
.3
.2
.2
F r a c tio n
Social Security less
generous
Double digit inflation
F r a c tio n
A. General Events
.1
.1
0
0
0
100
0
.3
.2
.2
0
0
0
0
50
W ill L iv e T o B e 7 5
100
.3
.4
.2
100
.1
.2
Leave inheritance
Work at age 62
50
In c o m e W ill K e e p U p W ith In fla tio n
F r a c tio n
.6
F r a c tio n
C. Events with
Personal Control
100
.1
.1
Survival to 75
Income increase
faster than inflation
50
D o u b le -D ig it In fla tio n
F r a c tio n
.3
F r a c tio n
B. Events with
Personal Information
50
S o c ia l S e c u rity T o B e L e s s G e n e ro u s
0
0
0
50
W ill L e a ve In h e rita n c e > = 1 0 ,0 0 0
100
0
50
W ill W o rk A t A g e 6 2
100
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Are Benefits of Greater Individual Choice
Influenced by Quality of Probabilistic
Thinking?
• Trend of increasing scope for individual choice in public
and private policy, especially as it affects those planning
for retirement or already retired
– Private sector shift from defined benefit to defined contribution
pension plans
– Proposals for “individual accounts” in Social Security
– Choice of when/whether to annuitize
– Choice of medical insurance plans and providers by employers
and by Medicare, new Medicare Prescription Drug program
• Economists generally view increased choice as a good
thing, but …
– General public wonders whether people will make wise use of
choice
– Decisions faced by older individuals balancing risks and benefits
of alternative financial and health care choices are genuinely
difficult
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Quality of Probabilistic Thinking and
Uncertainty Aversion
• Lillard and Willis (2001) began to look at the
pattern of responses to probability questions as
indicators of the degree to which they indicate
people’s capacity to think clearly about
subjective probability beliefs
• We explored the idea that focal answers of “0”,
“50” and “100” were perhaps indicators of less
coherent or well-formed beliefs than non-focal
(or “exact”) answers.
27/65
Index of Focal Responses
We treated the probability questions like a
psychological battery and constructed an empirical
propensity to give focal answers of “0”, “50” or “100”
number focal answers
Index of Focal Answers =
total number of probability questions
We found that people who had a lower propensity to
give focal answers tended to have higher wealth,
had riskier portfolios, and achieved higher rates of
return, controlling for conventional economic and
demographic variables
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Uncertainty Aversion
• We hypothesized that people who give more
focal answers are more uncertain about the true
value of probabilities
• If the uncertainty is about a repeated risk, such
as the return to a stock portfolio held over time,
we show that people who have more imprecise
probability beliefs (i.e. are more uncertain about
the “true” probability) will behave more risk
aversely
• Intuition can be shown with coin flipping
example
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Example: Increasing Uncertainty about
Pr(Heads) Leads to Increasing Risk with
Repeated Coin Tosses, Causing Risk Averse
Person to Prefer Coin with Known Risk
Flip two coins. $100 prize for each heads
Payoff
Belief about “success parameter”
$0
$100
$200
Fair coin
0.25
0.50
0.25
Uniform Distribution
0.33
0.33
0.33
Two Head or Two Tails
0.50
0.00
0.50
•Increased uncertainty causes mean-preserving spread in payoffs
because payoff probability is a non-linear function of success parameter,
ie.. Pr(200), P(0) are concave functions of p squared, q squared
30/65
Theory of Survey Response to
Probability Questions
• What is the relationship between the probability
beliefs that people have in their head and the
answer that they give to probability questions on
the HRS?
• To answer this question, requires a theory of
survey response.
• It would be convenient if people gave the
expected value of their subjective prior belief.
But this is cognitively difficult and the high
fraction of focal answers seems inconsistent with
this interpretation.
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Modal Response Hypothesis
• An alternative hypothesis is that when asked to give a
single number between "0" and "100", the individual
gives the "most likely" probability among all possible
probabilities. This is the mode of the subjective prior.
• Lillard-Willis (2001)
• Under this hypothesis, people are more likely to give
focal answers the greater their uncertainty about the true
probability
• Reversing this idea, under the MRH, we can estimate
the degree of uncertainty from the pattern of probability
responses on the survey.
• Hill-Perry-Willis (2005)
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Some Further Results on
Subjective Probabilities
• There is “optimism factor” common across all probability questions
which is correlated with stock-holding and associated with being
“healthy, wealthy and wise”
• Kezdi and Willis (2003)
• HRS has added direct questions on stock returns
– stockholding is related to probability beliefs
• Kezdi and Willis (2003) and Dominitz and Manski (2006)
– most people do not believe that stocks have positive returns, despite the
equity premium that economists know about
• Persons who provide more precise probability answers also exhibit
less risk aversion on subjective risk aversion questions in the HRS,
and they save a higher fraction of their full wealth.
• Sahm (2007), Pounder (2007)
• In 2006, HRS added questions to those who answer “50” to see
whether they mean “equally probable” or “just uncertain”. 75%
indicate they are uncertain.
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Measurement of Cognition
in the HRS
• HRS has included cognitive measures from the
outset, but mostly focused on memory in order
to trace cognitive decline.
• Re-engineering HRS cognitive measures
– Led by Jack McArdle, a cognitive psychologist and
HRS co-PI, we have begun a project to “re-engineer”
our cognitive measures in order to improve our
understanding of the determinants of decision-making
about retirement, savings and health and their
implications for the well-being of older Americans
34/65
Measurement of Cognition
in the HRS (cont.)
• Separate HRS-Cognition Study
– Begins with a separate sample of 1200 persons age 50+ who will
receive about three hours of cognitive testing of their fluid and
crystallized intelligence plus parts of the HRS questionnaire on
demographics, health and cognition
– Followed a month later by administration of an internet or mail
survey of questions designed by economists on financial literacy,
ability to compound-discount, hypothetical decisions about
portfolio choice, long term care
– Finally, telephone follow-up with HRS cognition items and
subjective probability questions
– Analysis of data will guide re-engineering of cognitive items for
HRS-2010
35/65
Cognition and Human Capital
integration of cognitive psychology
and human capital theory
Cognition and Human Capital
• Understanding the connection between how
people think and their economic behavior has
recently emerged as an important topic in
behavioral economics, experimental economics
and neuroeconomics. It has even provoked a
counter-reaction in the recent case for “Mindless
Economics” by Gul and Pesendorfer
• Cognitive economics, the term I shall use for this
broad area, emerged as an important theme in
labor economics nearly half a century ago with
the theory of human capital
37/65
Cognition and Human Capital (cont.)
• Cognitive capacity is producible human capital
– Indeed, much of the theory of human capital is a
theory of the demand and supply of cognitive capacity
• Cognitive psychology has a theory of the
development of “fluid and crystallized”
intelligence over the life cycle which is largely
unknown to economists
• Cognitive psychologists who work in this field
appear never to have heard of the theory of
human capital
• In the last portion of this talk, I want to identify
the potential gains from trade of the two theories
38/65
Theory of Fluid and Crystallized
Intelligence (Gf/Gc)
• (Near) consensus theory of intelligence is
embodied in Woodcock-Johnson battery of
cognitive tests used in HRS-cognition study.
– McArdle, et. al. (2002), Horn and McArdle (2007)
• Primary abilities structured into two principal
dimensions
– Fluid intelligence (Gf) represents measurable
aspects of the outcome of biological factors on
intellectual development (i.e., heredity, injury to the
central nervous system)
– Crystallized intelligence (Gc) is considered the main
manifestation of influence from education, experience
and acculturation
39/65
What is fluid intelligence?
• Fluid cognitive functioning can be thought
of as all-purpose cognitive processing not
necessarily associated with any specific
content domain
• Aspects of fluid cognition
– Working memory
– Executive function or cognitive control
– Ability to abstract, to do hypothetical thinking
40/65
numerical
How fluid intelligence is
related to psychometric g
or IQ
Ravens matrix score is
most loaded on g. Figure
shows correlation of
other tests with g.
Colors indicate nature of
test
Distance from center
indicates progressively
weakening correlations
verbal
visual
Source: J. R. Gray and P. M. Thomson (2004) Nature, reproduced
41/65
From Snow, et. al. (1984)
Life Cycle Pattern of Fluid and
Crystallized Intelligence
Me
42/65
Life Cycle Earnings
by Education and Ability
Me
Source: L.A. Lillard, “Earnings vs. Human Wealth,” American Economic Review, 1977 43/65
Data from NBER Thorndike, tests designed by one of pioneers of multiple intellingence
Cross-Fertilization of Human Capital
and Gc/Gf Theory
• Biological fixity is implicit in many discussions of
intelligence, most notoriously in HerrnsteinMurray’s Bell Curve
• Human capital theory allows for ability
differences, but primary emphasis is on
malleability of human agent and role of choice
and incentives in determining skills
• Key idea in human capital theory going back to
Ben Porath is the human capital production
function.
44/65
Human Capital Production Function
Ben Porath (1967)
Increment
to Human
Capital in
Period t
Ability
Parameter
% Time
Spent
Learning
Stock of
Human
Capital
Purchased
Inputs
Optimal Life Cycle Pattern of Time Allocation
s = 1 during school
s<1 on-the-job training during
labor market career
1-s
time spent earning
s
declines over career,
reaching 0 at retirement
45/65
Relationship to Gf/Gc Theory of
Intelligence
Learning?
Increment
to Human
Capital in
Period t
Fluid
intelligence?
Ability
Parameter
Crystallized
intelligence?
% Time
Spent
Learning
Stock of
Human
Capital
Environment?
Purchased
Inputs
human capital
is self-productive
46/65
Two Important Qualifications
Learning?
Increment
to Human
Capital in
Period t
Fluid
intelligence?
Ability
Parameter
Crystallized
intelligence?
% Time
Spent
Learning
Stock of
Human
Capital
Environment?
Purchased
Inputs
1. Heckman and colleagues, in a series of papers, have shown that the
sequence of investments matters in early childhood- K t is not
homogenous stuff.
Creates irreversibility in investments, making remediation difficult
47/65
Cunha-Heckman Model
• Proposes human capital technology that goes
beyond Ben-Porath in a number of ways
– multiple abilities
• cognitive (fluid/crystallized)
• non-cognitive (motivation, self-discipline, time preference)
– self-productive
– allows dynamic complementarity
• implies that time sequence of investment matters
– consistent with variety of empirical evidence on child
development
Detailed Summary: Cunha and Heckman, “Interpreting the Evidence on
Life Cycle Skill Formation,” In Hanushek and Welch, eds. Handbook of
the Economics of Education, 2006
48/65
Second Qualification
Learning?
Increment
to Human
Capital in
Period t
Fluid
intelligence?
Ability
Parameter
Crystallized
intelligence?
% Time
Spent
Learning
Stock of
Human
Capital
Environment?
Purchased
Inputs
2. James Flynn has shown that mean intelligence has risen across
cohorts. Suggests average  0 is not a constant in the population.
Recently, Flynn and Dickens (2001) suggest gene-environment
interaction to account for this. I will argue that their explanation
is consistent with theory and evidence from human capital
49/65
The Flynn Effect: Secular Change in IQ
(note: there is little cross-cohort change in crystallized intelligence)
source: Blair, et. al. (2005) Intelligence
50/65
The Flynn Effect: Secular Change in IQ
(note: there is little cross-cohort change in crystallized intelligence)
However, many psychologists (including
Flynn) increasingly believe a substantial
part of this growth may artifactual, due to
growing “test-wiseness”
source: Blair, et. al. (2005) Intelligence
51/65
“Heritability Estimates Versus Large Environmental
Effects: The IQ Paradox Resolved”
Dickens and Flynn, Psychological Review (2001)
• Cross-cohort growth in IQ is not consistent with
hereditarian view nor with plausible environmental
change within additive G + E model.
• Can resolve paradox within model of GxE interaction in
which environments are correlated with genes and
amplify genetic effects
• Correlation generated by matching of genetic abilities
and environments conducive to reinforcement of these
abilities.
• Show how these interactions can lead to cross-cohort
growth in IQ that is consistent with high heritability.
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Human Capital and Gene-Environment
Interaction
• Human capital theory provides a model, supported by a
huge empirical literature, of the kind of environmental
amplification of genetic differences of the type
hypothesized by Dickens and Flynn
• Competitive markets in the presence of a population with
heterogeneous abilities leads to a matching of innate
ability and training opportunities that amplifies skill
differentials
• However, human capital is never mentioned in this paper
(even though Dickens is an economist) nor is it
mentioned elsewhere in the cognitive psychology
literature
53/65
Self-Selection Matches Environment and
Abilities in Acquisition of Skills
– Marriage and Family via Quality/Quantity of
Children
• Willis (1973), Becker-Lewis (1973), Becker-Tomes
(1976)
– Formal Schooling, Occupational Choice and
On-the-Job Training
• Becker (1962), Willis-Rosen (1979)
• Rosen (1972), Johnson (1978), Jovanovic (1979)
– Migration
• Sjastaad (1962)
54/65
Economic Growth and Increasing
Cognitive Ability
• Striking new element in Dickens-Flynn is endogenous
growth in IQ across cohorts, somewhat reminiscent of
Fogel’s analysis of secular growth in height
• Related theoretical and empirical work in economics by
Galor and Moav (2000) and Taber (2001) suggests that
economic growth is “ability biased” in the sense the
rising return to education, is an increase in the return to
ability rather than to college
• The Dickens-Flynn argument suggests that the
aggregate supply of ability has a positive elasticity in the
long run
55/65
“Use it or Lose it?”
Retirement and Cognition
Does being on the job prevent
cognitive decline?
“Use or Lose It”
Retirement and Cognitive Reserve
• The HRS is now being replicated around the world, with
the ELSA study in England, the SHARE project in 15
countries in Europe, new studies in Korea, Japan and
planned ones in China and India
• Availability of cross-nationally comparable data promises
to create many new research findings
• I will illustrate this promise with some recent research by
a Belgian team of economists and psychologists who
use the SHARE, ELSA and HRS data to investigate the
idea that people can avoid cognitive decline at older
ages by being in a more stimulating work environment
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“Use or Lose It” (cont.)
• They use a cross-country, cross-sectional
analysis to examine the relationship
between early retirement for men and
cognitive decline between age 50-54 and
60-64
• For each country, they calculate the mean
values of
Early Retirement = LFPR(60-64)/LFPR(50-54)
Cognitive Decline = Cog(60-64)/Cog(50-54)
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Use it or Lose It: Cognitive Reserve
Employment rate and cognitive performance
Relative difference between 60-64 and 50-54 years old men
0%
Cognitive performance (relative difference)
Decreasing
Cognition
United States
-5%
Denmark
Greece
Germany
Belgium
-10%
The Netherlands
Italy
Sweden
Switzerland
Spain
United Kingdom
Austria
-15%
Earlier retirement
France
-20%
-25%
-100%
-90%
-80%
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
Employment rate (relative difference)
Source: S. Adam, E. Bonsang, S. Germain and S. Perelman (2007), “Retirement and cognitive
reserve: A stochastic frontier approach applied to survey data”, CREPP DP 2007/04, University
of Liège.
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Is the Relationship Causal?
• Plausibly, yes.
– Very strong cross-country relationship
between retirement rates and government
policy found by Gruber and Wise (1999)
– Policy variations are likely to be exogenous to
the country-specific average cognitive status
of men age 50-54 relative to those age 60-64
60/65
What Will Happen to Retirement for the Early
Boomers? (cont.)
• Trend toward
lower labor force
participation at
older ages is
much sharper in
a number of
European
countries
Source: J. Gruber and D. Wise, Social Security Programs and Retirement Around the World,
U. Chicago Press, 1999.
61/65
Retirement Policy Shapes Retirement Behavior
Percent Early Retirement
70
Belgium
France Italy
Holland
60
UK
50
Germany
Spain
Canada
40
US
Sweden
30
20
40
60
80 100
Percent Penalty for Continued Work
Source: J. Gruber and D. Wise, Social Security and Retirement Around the World (NBER, 2000)
62/65
Recent Cohorts of Americans in HRS
Expect to Work Longer
Expectations of Working After Age 65
Males and Females Age 51-56
40
42.4
36.4
30
33.0
29.5
24.5
0
10
20
22.7
1992
1998
Male
2004
1992
1998
2004
Female
63/65
Maybe that will be good for their brains
as well as their wallets
Expectations of Working After Age 65
Males and Females Age 51-56
40
42.4
36.4
30
33.0
29.5
24.5
0
10
20
22.7
1992
1998
Male
2004
1992
1998
2004
Female
64/65
Conclusions
• Human capital theory points to the importance of finite,
but not fixed cognition
• Cognitive economics argues for using new types of data
to measure cognition in many dimensions and to
understand the mapping between what is in people’s
minds and decision making
• This will enrich the traditional concerns of human capital
theory:
– Determining the value of cognition at home and at work
– Understanding the market and non-market determinants of the
match between cognitive endowments and learning
environments
– Exploring the demand and supply of cognition in the short and
the long run
65/65