Cognitive Economics and Human Capital

1. Cognitive Economics and Human Capital Robert J. Willis University of Michigan Presidential Address Society of Labor Economics, Chicago, May 4-5, 2007

2. Martha Bailey John Bound Charlie Brown Mike Elsby Mike Hurd David Lam Justin McCrarry Bob Schoeni Gary Solon Frank Stafford Rebecca Thornton Organizing Committee for 2007 SOLE Meeting

3. Thanks for Helpful Discussions and Comments on this Talk • Dan Benjamin • Jim Heckman • Miles Kimball • Jack McArdle

4. 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

5. 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

6. 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

7. 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

8. 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

9. Outline • What is Cognitive Economics? • Cognition and Probabilistic Thinking • Cognition and Human Capital • Use it or Lose It? Retirement and Cognition

10. Cognitive Economics The Economics of What is in People’s Minds

11. Three Themes of Cognitive Economics • New Types of Data • Heterogeneity • Finite and Scarce Cognition

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

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

14. 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

15. 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.

16. Cognition and Probabilistic Thinking What is the mapping between probability beliefs in people’s minds and the decisions they make?

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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)

23. Survival Probabilities: Accurate on Average with Heaping on Focal Values

24. 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

25. 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

26. . 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

27. 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

28. 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.

29. 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

30. 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

31. 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

32. 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.

33. 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)

34. 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.

35. 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

36. 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

37. Cognition and Human Capital integration of cognitive psychology and human capital theory

38. 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

39. 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

40. 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

41. 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

42. 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 From Snow, et. al. (1984)

43. Life Cycle Pattern of Fluid and Crystallized Intelligence Me

44. Life Cycle Earnings by Education and Ability Me Source: L.A. Lillard, “Earnings vs. Human Wealth,” American Economic Review, 1977 Data from NBER Thorndike, tests designed by one of pioneers of multiple intellingence

45. Cross-Fertilization of Human Capital and Gc/Gf Theory • Biological fixity is implicit in many discussions of intelligence, most notoriously in Herrnstein-Murray’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.

46. Human Capital Production Function Ben Porath (1967) Increment to Human Capital in Period t % Time Spent Learning Stock of Human Capital Purchased Inputs Ability Parameter 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

47. Relationship to Gf/Gc Theory of Intelligence Fluid intelligence? Crystallized intelligence? Environment? Learning? Increment to Human Capital in Period t % Time Spent Learning Stock of Human Capital Purchased Inputs Ability Parameter human capital is self-productive

48. Two Important Qualifications Fluid intelligence? Crystallized intelligence? Environment? Learning? Increment to Human Capital in Period t % Time Spent Learning Stock of Human Capital Purchased Inputs Ability Parameter • Heckman and colleagues, in a series of papers, have shown that the • sequence of investments matters in early childhood- is not • homogenous stuff. • Creates irreversibility in investments, making remediation difficult

49. 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

50. Second Qualification Fluid intelligence? Crystallized intelligence? Environment? Learning? Increment to Human Capital in Period t % Time Spent Learning Stock of Human Capital Purchased Inputs Ability Parameter 2. James Flynn has shown that mean intelligence has risen across cohorts. Suggests average 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