0. Miles Kimball University of Michigan Presentation at Osaka University
Cognitive Economics
Miles Kimball
University of Michigan
Presentation at Osaka University

1. Definition of Cognitive Economics:
The Economics of What is in People’s Minds

2. Named by Analogy to “Cognitive Psychology”
Cognitive Psychology = the area of psychology that examines internal mental processes such as problem solving, memory and language.
Cognitive Psychology was a departure from Behaviorism--the idea that only outward behavior was a legitimate subject of study.

3. How is “Cognitive Economics” Different from “Behavioral” or “Psychological” Economics?
Cognitive economics is narrower.
Much of cognitive economics is inspired by the internal dynamic of economics rather than by psychology.
Cognitive economics is a field of study, not a school of thought.

4. Areas of Economics by Distinctive Data Type
Standard Economics (including “Mindless” Psychological Economics a la Gul and Pesendorfer): actual market choices only.
Experimental Economics: choices in artificial situations but with real stakes.
Neuroeconomics: FMRI, saccades, skin conductance, …
Bioeconomics: genes, hormones
Cognitive Economics: mental contents (based on tests and self-reports) and hypothetical choices.

5. Four Themes of Cognitive Economics
New Types of Data
Heterogeneity
Finite and Scarce Cognition
Welfare Economics Revisited

6. 1. Innovative Survey Data
fluid intelligence data
crystallized intelligence data
happiness data
survey measures of expectations
survey measures of preferences

7. 2. Individual Heterogeneity
heterogeneous expectations
heterogeneous preferences
heterogeneous emotional reactions
heterogeneous views on how the world works (folk theories)

8. 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

9. 4. Welfare Economics Revisited
Scarce cognition means that people sometimes make mistakes. Thus, one can no longer use naïve revealed preference for welfare economics.
Kimball and Willis, in “Utility and Happiness,” argue that happiness data is not a magical touchstone for diagnosing mistakes.
Then, what does count as evidence of mistakes?
Internal inconsistencies, such as lack of transitivity? But which choice then deserves respect?
Regret?
Modification of choices after experience?
Differences in choices between those with high cognitive ability and those with low cognitive ability?
e.g., Dan Benjamin and Jesse Shapiro show that low IQ students had more low-stakes risk aversion and short-horizon impatience

10. 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.

11. New Types of Data: 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

12. New Types of Data: 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

13. New Types of Data: Survey Measures of Expectations
What is the mapping between probability beliefs in people’s minds and the decisions they make?
(Robert Willis, Charles Manski, Mike Hurd, Jeff Dominitz, Adeline Delavande)

14. 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

15. 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)

16. 10 Year Mortality Rate vs. Subjective Survival Probability to Age 75
Odds Ratio
of Death
by t+10
Subjective Survival Probability at Time t
Source: Mortality Computations from HRS-2002 by David Weir

17. 10 Year Mortality Rate vs. Subjective Survival Probability to Age 75
Strongest relationship between
subjective and objective risks for people
with low subjective survival beliefs
Odds Ratio
of Death
by t+10
Subjective Survival Probability at Time t
Source: Mortality Computations from HRS-2002 by David Weir

18. . Histograms of Responses to Probability Questions in the HRS
A. General Events
Social Security less
generous
Double digit inflation
B. Events with
Personal Information
Survival to 75
Income increase
faster than inflation
C. Events with
Personal Control
Leave inheritance
Work at age 62

19. 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

20. 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.

21. 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”
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

22. 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

23. 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.

24. New Types of Data: Survey Measures of Preferences Based on Hypothetical Choices
Examples:
Labor Supply Elasticities,
Altruism,
Social Rivalry,
Risk Aversion,
Elasticity of Intertemporal Substitution

25. Does Risk Tolerance Change?
Claudia Sahm
University of MichiganBoard of
Governors

49. Miles S. Kimball, Claudia R. Sahm and Matthew D. Shapiro
Measuring Time Preference and the Elasticity of Intertemporal Substitution
Miles S. Kimball, Claudia R. Sahm and Matthew D. Shapiro
September 6, 2006
Internet Project Meeting

50. Behavioral Model c is consumption, r is the real interest rate,
s is the elasticity of intertemporal substitution, and
ρ is the subjective discount rate

51. Estimate Parameters : s, ρ
Research Design
Vary Treatment : r
Observe Response : c
Estimate Parameters : s, ρ

52. Implementation Vary Interest Rate Measure Consumption Choice
Vary cost of current consumption
Vary length of time periods
Measure Consumption Choice
Choose among small set of paths
Actively form a desired path
Infer Preferences
Summary statistics of responses
Statistical model with response error

53. Previous Survey Measures
HRS 1992 Module K, N = 198
Analyzed by Barsky, Kimball, Juster, and Shapiro (QJE 1997)
HRS 1999 Mailout, N = 1,210
Similar content to part of Internet Survey
Questions explicitly vary the cost of current consumption and offer a discrete choice over a small set of consumption paths

54. MS Internet Survey Wave 2 (Fall 2004)
Use graphics on Internet to test other measures:
Version 1, N = 350
Vary cost of consumption
Choose from set of pairs
Version 2, N = 155
Move bars to create pair
Version 3, N = 183
Vary length of period

55. Series Introduction - Version 1 -
Series includes four questions with varying interest rates

56. Introduction – 0% Interest Rate
Sequence r = {0%, 4.6%, 9.2%, 13.8%} is random
Introduction repeated for each interest rate

57. Patterns – 0% Interest Rate
Asked to choose two patterns
Above screen (1 of 6) is identical to HRS Mail Out

58. Expansion Screen Follow-up if first choice on boundary (A or E)
New feature on Internet

59. Randomize Pair C Choice C positive, zero, negative growth rate
3 values to the parameter
New feature on Internet
Top screen on mail out

60. Randomize Left-to-Right
Growth rates increase or decrease left-to-right
New feature on Internet
Top screen on mail out

61. Randomize Shifts with Interest Rate
Example with r = 9.2%
Choice of ($2750, $3900) moves from E to C to A
3 values to the parameter
New feature on Internet
Middle screen on mail out

62. Summary of Innovations in Internet Question Series
18 different screen groups
6 different sequences of interest rates
11 discrete choices per question
Purpose of Innovations
Encourage active choices
Increase informative responses
Isolate framing effects

63. Response Statistics Internet lower completion rate
Internet fewer second choices
Internet fewer non-informative responses

64. Consumption Growth at 0% Interest Rate
Constant consumption is modal choice

65. Change in Consumption Growth as Interest Rate to 13.8% from 0%
Interest rates change consumption more on Internet

66. Change in Consumption Growth as Interest Rate Increases - Internet
Decrease in growth is a sign of survey response error

67. Estimates of Parameters
Responses reveal low time preference and IES
Median and modal values in both surveys equal 0

68. Effect of Changes in Choice Set

69. Estimates by Screen Group

70. More Graphical Questions - Version 2 -
Move bars to select a consumption path

71. More Graphical Questions - Version 3 -
Vary length of current and future periods

72. Extensions / Renewal Measure complementary parameters
Diminishing marginal utility
Labor supply elasticities
Retirement elasticity

73. Four Themes of Cognitive Economics
New Types of Data
Heterogeneity
Finite and Scarce Cognition
Welfare Economics Revisited

74. “Bounded Rationality” vs. “Scarce Cognition”
Same meaning, but “bounded rationality” seems a misnomer, since it is rational to recognize one’s own cognitive limitations.
Two obstacles have prevented “Bounded Rationality” from becoming part of the mainstream
theoretical difficulties stemming from the importance of constrained optimization as a theoretical tool in economics
paucity of data
“Scarce Cognition” is meant to label a data-rich research agenda, using new theoretical tools.

75. The Reality of Finite Cognition
Computers beat us at chess
People don’t get perfect scores on tests, even after they have studied the material
For hundreds of years, we had no proof of Fermat’s last theorem

76. The Reality of Scarce Cognition
Many people …
spend time and money learning math
pay others with higher wage rates to do their taxes
pay others to read law books for them
pay for financial advice

77. Modeling Scarce Cognition is Hard: The Infinite Regress Problem (Conlisk)
It is natural for economists to assume a cost of computation, just like any other cost—so why not more such models?
Answer: figuring out how hard to think about a problem is always a strictly harder problem than the original problem
Need the solution to the original problem to calculate the benefit
Need to know how to solve the problem to know how many computational steps it needs

78. Dodging the Infinite Regress Problem by Breaking Taboos
Ignoring computational costs at the outer level. (Maybe OK if the original problem is a repeated choice.)
Using limited information transmission capacity as a metaphor for limited intelligence. (“A thick skull.”)
Subhuman intelligence:
--agent-based modeling
--rules of thumb (adaptive expectations, consume income, statistical models)
Modeling folk theories ignorant of the maintained hypotheses

79. Modeling Unawareness Requires a Subjective State Space Distinct from the True State Space
(Dekel, Lipman and Rustichini)
economic actor: subjective state space
analyst: state space maintained as true

80. Two Levels of Theory
Folk theory: economic actor’s theory modeled in the subjective state space
May look like an “accounting framework” in the sense of Herrnstein and Prelec in The Matching Law
Metatheory: the analyst’s theory which includes a description of the relevant folk theories.
Preferences
Technology
Available Strategies
Active Information Structure
Folk Theories

81. Desirable Properties for a Model of a Folk Theory
Accuracy in describing how people actually view the world
Providing a clear prediction for how people will behave in various circumstances
Representing clearly how people are confused and what they do understand.
NOT REQUIRED: deep logical consistency

82. An Example of Folk Physics
Many people believe that if they swing a stone around on a string and let it go, then the stone will curve sideways in the direction they were swinging it around.
Other than going up and down in the vertical direction, it actually goes straight once released.

83. An Example of Folk Finance
It Misses These Ideas:
Link of diversification to the
variance/covariance matrix
2. Diversification makes it safe
enough to hold a lot of the
risky asset
3. Role of human capital
4. Consumption as the ultimate
objective
This Folk Theory Models
Three Ideas:
Mean return is good
Risk is bad
Diversification is good I

92. Household Finance and Welfare Economics: The Possibility of Strong Normative Statements
Fungibility: money is money
At the end of the day, only the total value of the portfolio matters, not the separate value of its constituent parts.
Fungibility is a legal term: OK to pay back a different piece of currency as long as the value is the same. (Not like a diamond ring)
Very basic principle in economics: fungibility of money is assumed in standard treatments of revealed preference
Noneconomists do not always understand fungibility: “mental accounts”

93. High and Low Savers? Circumstances, Patience, and Cognition
Laurie Pounder

94. Differences in Consumption (Saving) Rates Across Households:
Circumstances?
Such as income shocks; differences in pensions (income replacement rate in retirement); income profiles; etc.
Or Types of Savers?
By “inherent” characteristics such as preferences or ability/cognition

95. Getting Past Circumstances
Simple Lifecycle
Net worth
Consumption
Income
Circumstances create cross-household variation when measuring rates C/Income or C/NetWorth
Difficult to isolate role of preferences or other “inherent” differences across households

96. The Right Rate: Consumption and “Full” Wealth
Lifecycle/PIH theory since Modigliani says consumption should depend on all current and future resources (including financial and human wealth.)
Like a stock value of permanent income from today forward
I call this PV of all resources:
“Modigliani full wealth” = M

97. Data Allows Comparison & Testing
A credible estimate of M for older households, together with consumption, available for the first time in the HRS
Compare observed propensity to consume C/M to neoclassical model of optimal consumption rates
Use survey estimates of model’s factors that vary across households to indicate which factors play largest role in explaining observed variation in C/M

98. Neoclassical Model (Merton 1971)
Mortality and rate of return the only sources of uncertainty
Subject to:
Estimate mortality with Gompertz function: age = 1 e(2*age)

99. Average Propensity to Consume
Infinite Horizon (no mortality):
Implications:
C is proportional in M
C/M depends only on preferences, stochastic return characteristics, and mortality.
C/M does not depend directly on M, income profile, or outcome of past income shocks

100. Findings
Survey estimates of model factors matter in expected direction
Heterogeneity in observed C/M! Rich save more (lower propensity to consume)
“Inherent” characteristics or types important in explaining C/M
Within neoclassical/rational model must have heterogeneous time preference to explain C/M
Looking outside standard model: cognition & planning matter to C/M

101. Model Factors Dependent Variable: ln(C/M) (1) (2)
Log Predicted C/M with variation by mortality only
0.013**
(0.002)
Log Predicted C/M with variation by mortality and expected risky returns
0.017**
N=1842
R2=0.023
R2=0.027

102. Adding Survey Measures of Model Factors
Dependent Variable: ln(C/M)
with additional
demographics
Subjective Life Expectancy Ratio
-0.137***
(0.044)
-0.080*
(0.043)
Probability of Bequest >$10k (Continuous)
-0.002***
(0.001)
Probability of Bequest >$100k (Continuous)
-0.003***
Risk Aversion Survey Measure
-0.022***
(0.007)
Model Prediction
1.33***
(0.363)
1.04***
(0.359)
1.45***
(0.454)
Constant
3.69
-3.55
-5.77
R2=0.231
R2=0.287
R2=0.226
N=1190
N=894

103. Rich Save More: C/M Varies by Income or Wealth Level

104. Beyond the Neoclassical Model Abilities: Cognition & Planning
Bounded cognition
Propensity to plan
Expectations formation
(Lusardi, Lillard&Willis, Caplin&Leahy)

105. Measures in HRS
HRS asks questions on basic cognition (recall, counting, subtraction) plus planning horizon and subjective expectations
Lillard & Willis “focal point” answers; precision of expectations formation related to financial decisions
Measures matter such that lower cognition, less precision, and shorter planning horizons all imply higher propensities to consume

106. Cognition/Planning Predict C/M
Dependent Variable:
Residual of ln(C/M) after full regression
Long Financial Planning Horizon
-0.070**
(0.028)
Fraction of Precise Answers
-0.088*
(0.053)
High Word Recall
-0.062**
(0.030)
Counting Backwards
-0.124**
(0.048)
Hardest Subtraction Problem
-0.049*
N=1645
R2=0.027

107. Further Evidence: Loosely Related Preference Covariates
Dependent Variable:
Residual of ln(C/M) after full regression
Ever Smoked
0.046*
(0.028)
Reports would Spend all of hypothetical income increase
0.091*
(0.053)
Reports would Save all of hypothetical income increase
-0.055*
(0.031)
N=1645
R2=0.01
Dependent Variable:
Residual of ln(C/M) after full regression
Personality Questions
Seldom apprehensive about future
0.028
(0.022)
0.048**
(0.023)
Strive for excellence
-0.097**
(0.024)
-0.109**
(0.029)
Clear set of goals and work toward them
-0.026
-0.006
(0.025)
Work hard to accomplish goals
-0.063*
(0.034)
-0.011
(0.039)
N=235