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

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

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.

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.

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.

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

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

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

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

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

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.

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

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

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)

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

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)

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

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

. 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

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

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.

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Effect of Changes in Choice Set

Estimates by Screen Group

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

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

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

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

“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.

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

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

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

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

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

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

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

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.

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

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”

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

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

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

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

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

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)

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

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

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

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

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

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

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

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  

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