The Economics and Psychology of Personality Traits...Abil/Out The Economics and Psychology of...

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The Economics and Psychology of Personality

Traits

By Lex Borghans, Angela Lee Duckworth, and James J. Heckman.

James J. HeckmanUniversity of Chicago

AEA Continuing Education ProgramASSA Course: Microeconomics of Life Course Inequality

San Francisco, CA, January 5-7, 2016

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Introduction

There is ample evidence from economics and psychology thatcognitive ability is a powerful predictor of economic and socialoutcomes.

The power of traits other than cognitive ability for success inlife is vividly demonstrated by the Perry Preschool study.

As demonstrated in Figure 1, by age ten, treatment groupmean IQs were the same as control group mean IQs.

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Figure 1: Perry Preschool Program: IQ, by Age and Treatment Group

79.6

95.5 94.9

91.3 91.7

88.1 87.7

85

78.5

83.3 83.5

86.3 87.1 86.9 86.884.6

75

80

85

90

95

100

IQ

4 5 6 7 8 9 10EntryAge

Treatment Group Control Group

Source: Perry Preschool Program. IQ measured on the Stanford Binet Intelligence Scale (Terman & Merrill, 1960).Test was administered at program entry and each of the ages indicated.

Perry Preschool Program: IQ, by Age and Treatment GroupFigure 7A

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Perry did not raise IQ.

It raised noncognitive skills.

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Perry preschool program: IQ, by age and treatment group

79.6

95.5 94.9

91.3 91.7

88.1 87.7

85

78.5

83.3 83.5

86.3 87.1 86.9 86.884.6

75

80

85

90

95

100

IQ

4 5 6 7 8 9 10EntryAge

Treatment Group Control Group

Source: Perry Preschool Program. IQ measured on the Stanford Binet Intelligence Scale (Terman & Merrill, 1960).Test was administered at program entry and each of the ages indicated.

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Perry preschool program: educational effects, by treatment group

45%

66%

15%

49%

34%

15%

0% 10% 20% 30% 40% 50% 60% 70%

On Time Grad.from HS

High Achievementat Age 14*

SpecialEducation

Source: Barnett (2004).Notes: *High achievement defined as performance at or above the lowest 10th percentile on the California AchievementTest (1970).

Perry Preschool Program: Educational Effects, by Treatment GroupFigure 7B

Treatment Control

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Perry preschool program: economic effects at age 27, by treatment group

14%

29%

13%

36%

7%

29%

0% 10% 20% 30% 40%

Never on Welfareas Adult*

Own Home

Earn +$2,000Monthly

Source: Barnett (2004). *Updated through Age 40 using recent Perry Preschool Program data, derived from self reportand all available state records.

Perry Preschool Program: Economic Effects at Age 27, by Treatment GroupFigure 7C

Treatment Control

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Perry preschool program: arrests per person before age 40, by treatmentgroup

1.2 3.9 .4

2.1 6.7 .6

0 2 4 6 8 10

Treatment

Control

Source: Perry Preschool Program. Juvenile arrests are defined as arrests prior to age 19.

Perry Preschool Program: Arrests per Person before Age 40, by Treatment GroupFigure 7D

Felony Misdemeanor Juvenile

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Scope of the Analysis

Focus analysis on personality traits, defined as patterns ofthought, feelings, and behavior.

Do not discuss motivation, values, interests, and attitudeswhich give rise to personality traits.

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Personality psychologists have developed measurement systemsfor personality traits which economists have begun to use.

Most prominent is the “Big Five” personality inventory.

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Is It Conceptually Possible to Separate Cognitive Ability fromPersonality Traits?

Many aspects of personality are a consequence of cognition,and cognition depends on personality. Nonetheless, one canseparate those two aspects of human differences.

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Is It Possible to Empirically Distinguish Cognitive from PersonalityTraits?

Measures of economic preferences are influenced by numeracyand intelligence. IQ test scores are determined not only byintelligence, but also by factors such as motivation and anxiety.

Moreover, over the life cycle, the development of cognitiveability is influenced by personality traits such as curiosity,ambition, and perseverance.

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What Are the Main Measurement Systems in Psychology forIntelligence and Personality, and How Are They Validated?

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Figure 2: Competing taxonomies of personality

Evsenck Costa & McCrae Tellegen Zuckerman Cloninger Big NineBig Three NEO-PRF Big Five MPQ

Neuroticism Neuroticism Negative Emotionality Neuroticism-Anxiety Harm Avoidance AdjustmentAnxious Anxiety Stress reaction

VulnerabilityDepressed DepressionGuilt-feelingLow self-esteemTenseIrrationalShy Self-consciousness AlienationMoodyEmotional Impulsiveness

PsychotismAggressive Hostility Aggression Aggression-Hostility Cooperativeness AgreeablenessColdEgocentric Rugged IndividualismImpersonal AgreeablenessAnti-social AltruismUnempathic ComplianceTough-minded Tendermindedness

StraightforwardnessTrustModesty

Figure XCompeting Taxonomies of PersonalityNote: Figure reproduced from Bouchard and Loehlin (2001). Figure used with permission of the publisher

(cont.)

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Conscientiousness Constraint Self-Directedness DependabilityDeliberationDutifulness

Impulsive Self-discipline Control Locus of ControlOrderCompetence Persistence AchievementAchievement striving

TraditionalismExtraversion Extraversion

Sensation-seeking Excitement seeking Harm avoidance Impulsive Sensation Novelty SeekingSeeking

VenturesomeActive Activity ActivitySurgentCarefree

Positive emotionalityAchievement Reward Dependence

Social ClosenessSociable Gregariousness Sociability AffiliationLivelyAssertive AssertivenessDominant Social Potency Potency

Positive emotions Well-beingWarmth


Psychotism (cont.)

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Openness Self-TranscendenceFantasyAesthetics AbsorptionFeelingsActionsIdeasValues


Note: Figure reproduced from Bouchard and Loehlin (2001), with kind permission fromSpringer Science and Business Media.

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What is the Evidence on the Predictive Power of Cognitive andPersonality Traits?

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Personality traits are important in explaining performance inspecific tasks, although different personality traits are predictivein different tasks.

The classical model of factor analysis, joined with the principleof comparative advantage, helps to organize the evidence ineconomics and psychology.

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How Stable Are Personality Traits Across Situations and Across TheLife Cycle? Are They More Sensitive than Cognitive Traits toInvestment and Intervention?

We present evidence that both cognitive and personality traitsevolve over the lifecycle-but to different degrees and atdifferent stages of the life cycle.

Cognitive processing speed, for example, tends to rise sharplyduring childhood, peak in late adolescence, and then slowlydecline.

In contrast, some personality traits, such as conscientiousness,increase monotonically from childhood to late adulthood.

We develop models in which traits are allocated differentiallyacross tasks and activities.

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Do the Findings from Psychology Suggest That ConventionalEconomic Theory Should Be Enriched? Can Conventional Models ofPreferences in Economics Explain the Body of Evidence fromPersonality Psychology? Does Personality Psychology Merely RecastWell-Known Preference Parameters into Psychological Jargon, or isThere Something New for Economists to Learn?

Conventional economic theory is sufficiently elastic toaccommodate many findings of psychology.

However, our analysis suggests that certain traditional conceptsused in economics should be modified and certain emphasesredirected.

Some findings from psychology cannot be rationalized bystandard economic models and could fruitfully be incorporatedinto economic analysis.

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The evidence from personality psychology suggests a moreradical reformulation of classical choice theory than is currentlyenvisioned in behavioral economics which tinkers withconventional specifications of preferences.

More fundamentally, conventional economic preferenceparameters can be interpreted as consequences of theseconstraints.

For example, high rates of measured time preference may beproduced by the inability of agents to delay gratification,interpreted as a constraint, or by the inability of agents toimagine the future.

We develop a framework that introduces psychological variablesas constraints into conventional economic choice models.

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Definitions And A Basic Framework Of Measurement AndInterpretation

We distinguish between cognitive ability on the one hand andpersonality traits on the other.

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Factor Analysis

Let Ti ,j denote performance on task j for person i . There are Jtasks.

Output on tasks is generated in part by latent “traits” orfactors.

Factors or psychological traits for individual i are represented ina vector fi , i = 1, , I , where I is the number of individuals.

The vector has L components so fi = (fi ,1, . . . , fi ,L).

Ui ,j is other determinants of productivity in task j for person i .

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The task performance function for person i on task j

Ti ,j = µj + λj fi + Ui ,j , i = 1, . . . , I , j = 1, . . . , J . (1)

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Linear factor models

Ti ,j = µj + λj fi + Ui ,j , i = 1, . . . , I , j = 1, . . . , J . (2)

The number of components in fi , L, has to be small relative toJ (L� J) if the factor model is to have empirical content.

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Factor models (1) and (2) capture the notion that:1 latent traits fi generate a variety of outcomes,2 task outputs are imperfect measures of the traits (fi ), and3 that tasks other than tests may also proxy the underlying traits.

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Cognitive Ability

Intelligence (or cognitive ability) has been defined by an officialtaskforce of the American Psychological Association as the“ability to understand complex ideas, to adapt effectively to theenvironment, to learn from experience, to engage in variousforms of reasoning, to overcome obstacles by taking thought”(Neisser et al. 1996, p. 77).

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Most psychologists agree that cognitive abilities are organizedhierarchically with “g” as the highest-order factor (Spearman1904).

In this context, the order of a factor indicates its generality inexplaining a variety of tests of cognitive ability with differentemphases (for example, verbal ability, numeracy, coding speed,and other tasks).

A first-order factor is predictive in all tasks, j = 1, . . . , J inequation (1).

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A lower order factor is predictive in only some tasks. There isless agreement about the number and identity of lower-orderfactors.

Cattell (1971; 1987) contrasts two second-order factors: fluidintelligence (the ability to solve novel problems) and crystallizedintelligence (knowledge and developed skills).

The relative weighting of fluid versus crystallized intelligencevaries among tests according to the degree to which priorexperience is crucial to performance.

These factors operate in addition to the first-order factor, g .

Achievement tests, like the Armed Forces Qualifying Test usedby economists and psychologists alike, are heavily weightedtowards crystallized intelligence, whereas tests like the RavenProgressive Matrices (1962) are heavily weighted towards fluidintelligence.

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Personality Traits

A distinction between personality and cognition is not easy tomake. Consider, for example, so-called “quasi-cognitive” traits.

These include creativity, emotional intelligence, cognitive style,typical intellectual engagement, and practical intelligence.

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The problem of conceptually distinguishing cognitive traits frompersonality traits is demonstrated in an analysis of executivefunction which is variously described as a cognitive function ora function regulating emotions and decision, depending on thescholar.

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Many measures of executive function do not correlate reliablywith IQ.

However, measures of one aspect of execution function -working memory capacity in particular - correlate very highlywith measures of fluid intelligence.

Currently there is a lively debate among psychologists as to theprecise relationship among working memory, other aspects ofexecutive function, and intelligence.

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This paper focuses on personality traits that are more easilydistinguished from cognitive ability. They are distinguished fromintelligence, defined as the ability to solve abstract problems.

Most measures of personality are only weakly correlated withIQ. There are, however, a small number of exceptions.

IQ is moderately associated with the Big Five factor calledopenness to experience, with the trait of sensation seeking, andwith measures of time preference. The reported correlations areof the order r = .3 or lower.

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Operationalizing Concepts

Intelligence tests are routinely used in a variety of settingsincluding business, education, civil service, and the military.

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Personality Tests

The Big Five factors are Openness to Experience (also calledIntellect or Culture), Conscientiousness, Extraversion,Agreeableness, and Neuroticism (also called EmotionalStability). A convenient acronym for these factors is“OCEAN”. See John (1990) and Costa and McCrae (1992a).

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The Big Five domains and their facets

Borghans, Duckworth, Heckman, and ter Weel 136

Table 1 The Big Five domains and their facets

Factor Facets Definition of Factor

ACLa Marker Items for Factor

I. Openness to Experience (Intellect)

Fantasy, Aesthetics, Feelings, Actions, Ideas, Values

The degree to which a person needs intellectual stimulation, change, and variety.

Commonplace, Narrow-interest, Simple- vs. Wide-interest, Imaginative, Intelligent

II. Conscientiousness Competence, Order, Dutifulness, Achievement striving, Self-discipline, Deliberation

The degree to which a person is willing to comply with conventional rules, norms, and standards.

Careless, Disorderly, Frivolous vs. Organized, Thorough, Precise

III. Extraversion Warmth, Gregariousness, Assertiveness, Activity, Excitement seeking, Positive emotions

The degree to which a person needs attention and social interaction.

Quiet, Reserved, Shy vs. Talkative, Assertive, Active

IV. Agreeableness Trust, Straight-forwardness, Altruism, Compliance, Modesty, Tender-mindedness

The degree to which a person needs pleasant and harmonious relations with others.

Fault-finding, Cold, Unfriendly vs. Sympathetic, Kind, Friendly

V. Neuroticism (Emotional Stability)

Anxiety, Angry hostility, Depression, Self-consciousness, Impulsiveness, Vulnerability

The degree to which a person experiences the world as threatening and beyond his/her control.

Tense, Anxious, Nervous vs. Stable, Calm, Contented

Source: Hogan and Hogan (2007)

Note: a. ACL = Adjective Check List (Gough and Heilbrun, 1983)

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Note: a. ACL = Adjective Check List (Gough and Heilbrun, 1983) Source: Hogan and Hogan (2007)Note: a. ACL = Adjective Check List (Gough and Heilbrun, 1983)

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The most stinging criticism of the five-factor model is that it isatheoretical. The finding that descriptions of behavior asmeasured by tests, self-reports, and reports of observers clusterreliably into five groups has not so far been explained by a basictheory.

Research is underway on determining the neural substrates ofthe Big Five (see Canli 2006).

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The five-factor model is silent on an important class ofindividual differences that do not receive much attention in therecent psychology literature: motivation.

A practical problem facing the analyst who wishes to measurepersonality is the multiplicity of personality questionnaires.

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Measure of Temperament

The question of how to measure personality in adults leadsnaturally to a consideration of personality traits in childhood.

Compared to adults, there seem to be fewer ways that youngchildren can differ from one another.

Child psychologists often refer to the “elaboration” or“differentiation” of childhood temperament into the full flowerof complex, adult personality.

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Measurement and Methodological Issues

There are two general types of measurement schemes:1 those that seek to measure or elicit conventional economic

preference parameters, and2 those that measure personality with self-reports or observer

reports.

Personality psychologists focus primarily on the latter.

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Personality psychologists marshal three types of evidence toestablish the validity of their tests:

1 content-related,2 construct-related, and3 criterion-related evidence.

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Convergent and Discriminant Validity

“convergent” referring to the intercorrelations within a clusterand the “discriminant” referring to lack of correlation acrossclusters.

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The Factor Model for Test Scores

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Let Mni ,l be the nth measurement (by test or observer report) on

trait l for person i . Using a linear factor representation,

Mni ,l =µn

l + λnl fi ,l + εni ,l , (3)

n = 1, . . . ,Nl , i = 1, . . . , I , l = 1, . . . L.

fi ,l is assumed to be statistically independent of themeasurement errors, εni ,l , n = 1, . . . ,Nl .

Different factors are assumed to be independent(fl independent of fl ′ for l 6= l ′).

The measurement errors (or “uniquenesses”) are assumed to bemutually independent within and across constructs.

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In general

Mni ,l = µn

l + λnl fi + εni ,l , n = 1, . . . ,Nl , (4)

λn is a vector with possibly as many as L nonzero components.

εni ,l are assumed to be independent of fi and mutuallyindependent within and across constructs (l and l ′ are twoconstructs).

The test has discriminant validity if λnl is the only nonzerocomponent of fi . The µn

l and λnl can depend on measuredcharacteristics of the agent, Qi .

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The Psychometric Approach and Its Limits

The standard approach to defining constructs in personalitypsychology is based on factor analysis.

fl ,i , l = 1, . . . , L

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Conventional psychometric validity of a collection of item ortest scores for different constructs thus has three aspects.

1 A factor fl is assumed to account for the intercorrelationsamong the items or tests within a construct l .

2 Item-specific and random error variance are low(intercorrelations among items are high within a cluster).

3 Factor fl for construct l is independent of factor fl ′ forconstruct l ′.

4 Criteria (1) and (2) are required for “convergent validity.”5 Criterion (3) is “discriminant validity.”

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An alternative approach to constructing measurement systemsis based on the predictive power of the tests for real worldoutcomes, that is, on behaviors measured outside of the examroom or observer system.

The Hogan Personality Inventory, the California PersonalityInventory, and the Minnesota Multiphasic Personality Inventorywere all developed with the specific purpose of predictingreal-world outcomes. Decisions to retain or drop items duringthe development of these inventories were based, at least inpart, upon the ability of items to predict such outcomes.

This approach has an appealing concreteness about it.

Yet this approach has major problems.

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First, all measurements of factor fi ,l can claim incrementalpredictive validity as long as each measurement is subject toerror

(εni ,l 6= 0

).

Proxies for fi ,l can appear to be separate determinants (or“causes”) instead of surrogates for an underlyingone-dimensional construct or factor.

As long as there are measurement errors for construct l, there isno limit to the number of proxies for fi ,L that will show up asstatistically significant predictors of an outcome.

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A second problem is reverse causality.

Measurements of latent factors may be corrupted by “faking.”

There are at least two types of false responses: those arisingfrom impression management and those arising fromself-deception.

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A Benchmark Definition of Traits

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f is a vector of latent traits and fl is a particular trait in the listof L traits (extraversion, for example).

The manifestation of trait l , as opposed to the trait itself fl , isobtained by measurement n, n = 1, . . . ,Nl and may depend onincentives to manifest the trait.

Let R ln be the reward for manifesting the trait in situation n.

Other latent traits besides l may affect the manifestation of atrait for l .

Let f∼l be the components of f apart from fl . Let wnl denote

other variables operating in situation n that affect measuredperformance for l .

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Measured traits are imperfect proxies for true traits:

Mnl = hl (fl , f∼l ,R

nl ,W

nl ) , n = 1, . . . ,NL, l = 1, . . . , L. (5)

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Mischel (1968) claims that hl does not depend on fl becausethere is no fl (or for that matter f∼l) and indeed that themanifestation Mn

l is solely a function of situational incentivesRnl and context W n

l .

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Only meaningful to define measurements on fl at benchmarklevels of Rn

l , f∼l , and W nl .

Define these benchmarks as Rl , f∼l , and Wl .respectively.

At these benchmark values, one can define fl :

Mnl = fl for Rn

l = Rl , f∼l = f∼l , W nl = Wl , n = 1, . . . ,Nl , l = 1, . . . , L. (6)

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IQ Scores Reflect Incentives and Measure Both Cognitive andPersonality Traits

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Incentives and Performance on Intelligence Tests


Table 2 Incentives and Performance on Intelligence Tests

Study Sample and Study Design

Experimental Group

Effect size of incentive (in

standard deviations)

Summary

Edlund (1972)

Between subjects study. 11 matched pairs of low SES children; children were about one standard deviation below average in IQ at baseline

M&M candies given for each right answer

Experimental group scored 12 points higher than control group during a second testing on an alternative form of the Stanford Binet (about .eight standard deviations)

“…a carefully chosen consequence, candy, given contingent on each occurrence of correct responses to an IQ test, can result in a significantly higher IQ score.”(p. 319)

Ayllon & Kelly (1972) Sample 1

Within subjects study. 12 mentally retarded children (avg IQ 46.8)

Tokens given in experimental condition for right answers exchangeable for prizes

6.25 points out of a possible 51 points on Metropolitan Readiness Test. t = 4.03


Within subjects study 34 urban fourth graders (avg IQ = 92.8)


t = 5.9


Within subjects study of 12 matched pairs of mentally retarded children

Six weeks of token reinforcement for good academic performance

Experimental group scored 3.67 points out of possible 51 points on a post-test given under standard conditions higher than at baseline; control group dropped 2.75 points. On a second post-test with incentives, exp and control groups

“…test scores often reflect poor academic skills, but they may also reflect lack of motivation to do well in the criterion test…These results, obtained from both a population typically limited in skills and ability as well as from a group of normal children (Experiment II), demonstrate that the use of reinforcement procedures applied to a behavior that is tacitly regarded as “at its peak” can significantly alter the level of performance of that behavior.” (p. 483)

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Table 2 Incentives and Performance on Intelligence Tests

Study Sample and Study Design

Experimental Group

Effect size of incentive (in

standard deviations)

Summary

Edlund (1972)

Between subjects study. 11 matched pairs of low SES children; children were about one standard deviation below average in IQ at baseline

M&M candies given for each right answer

Experimental group scored 12 points higher than control group during a second testing on an alternative form of the Stanford Binet (about .eight standard deviations)

“…a carefully chosen consequence, candy, given contingent on each occurrence of correct responses to an IQ test, can result in a significantly higher IQ score.”(p. 319)


Within subjects study. 12 mentally retarded children (avg IQ 46.8)


6.25 points out of a possible 51 points on Metropolitan Readiness Test. t = 4.03


Within subjects study 34 urban fourth graders (avg IQ = 92.8)


t = 5.9


Within subjects study of 12 matched pairs of mentally retarded children

Six weeks of token reinforcement for good academic performance

Experimental group scored 3.67 points out of possible 51 points on a post-test given under standard conditions higher than at baseline; control group dropped 2.75 points. On a second post-test with incentives, exp and control groups

“…test scores often reflect poor academic skills, but they may also reflect lack of motivation to do well in the criterion test…These results, obtained from both a population typically limited in skills and ability as well as from a group of normal children (Experiment II), demonstrate that the use of reinforcement procedures applied to a behavior that is tacitly regarded as “at its peak” can significantly alter the level of performance of that behavior.” (p. 483)


increased 6.25 and 7.17 points, respectively

Clingman and Fowler (1976)

Within subjects study of 72 first- and second-graders assigned randomly to contingent reward, noncontingent reward, or no reward conditions.

M&Ms given for right answers in contingent cdtn; M&Ms given regardless of correctness in noncontingent condition

Only among low-IQ (<100) subjects was there an effect of the incentive. Contingent reward group scored about .33 standard deviations higher on the Peabody Picture Vocabulary test than did no reward group.

“…contingent candy increased the I.Q. scores of only the ‘low I.Q.’ children. This result suggests that the high and medium I.Q. groups were already functioning at a higher motivational level than children in the low I.Q. group.”

Zigler and Butterfield (1968)

Within and between subjects study of 40 low SES children who did or did not attend nursery school were tested at the beginning and end of the year on Stanford-Binet Intelligence Test under either optimized or standard conditions.

Motivation was optimized without giving test-relevant information. Gentle encouragement, easier items after items were missed, and so on.

At baseline (in the fall), there was a full standard deviation difference (10.6 points and SD was about 9.5 in this sample) between scores of children in the optimized vs standardconditions The nursery group improved their scores, but only in the standard condition.

“…performance on an intelligence test is best conceptualized as reflecting three distinct factors: (a) formal cognitive processes; (b) informational achievements which reflect the content rather than the formal properties of cognition, and (c) motivational factors which involve a wide range of personality variables. (p. 2) “…the significant difference in improvement in standard IQ performance found between the nursery and non-nursery groups was attributable solely to motivational factors…” (p. 10)

Breuning and Zella (1978)

Within and between subjects study of 485 special

Incentives such as record albums, radios (<$25) given for

Scores increased by about 17 points. Results were consistent across the

“In summary, the promise of individualized incentives on an

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education high school students all took IQ tests, then were randomly assigned to control or incentive groups to retake tests. Subjects were below-average in IQ.

improvement in test performance

Otis-Lennon, WISC-R, and Lorge-Thorndike tests.

increase in IQ test performance (as compared with pretest performance) resulted in an approximate 17-point increase in IQ test scores. These increases were equally spread across subtests The incentive condition effects were much less pronounced for students have pretest IQs between 98 and 120 and did not occur for students having pretest IQs between 121 and 140.” (p. 225)

Holt and Hobbs (1979)

Between and within subjects study of 80 delinquent boys randomly assigned to three experimental groups and one control group. Each exp group received a standard and modified administration of the WISC-verbal section.

Exp 1-Token reinforcement for correct responses; Exp 2 – Tokens forfeited for incorrect responses (punishment), Exp 3-feedback on correct/incorrect responses

1.06 standard deviation difference between the token reinforcement and control groups (inferred from t = 3.31 for 39 degrees of freedom0

“Knowledge of results does not appear to be a sufficient incentive to significantly improve test performance among below-average I.Q. subjects…Immediate rewards or response cost may be more effective with below-average I.Q. subjects while other conditions may be more effective with average or above-average subjects.” (p. 83)

Larson, Saccuzzo, and Brown (1994)

Between subjects study of 109 San Diego State University psychology students

Up to $20 for improvement over baseline performance on cognitive speed tests

“While both groups improved with practice, the incentive group improved slightly more.” need to calculate effect size, but it was not large

2 reasons why incentive did not produce dramatic increase: 1) few or no unmotivated subjects among college volunteers, 2) information processing tasks are too simple for ‘trying harder’ to

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education high school students all took IQ tests, then were randomly assigned to control or incentive groups to retake tests. Subjects were below-average in IQ.

improvement in test performance

Otis-Lennon, WISC-R, and Lorge-Thorndike tests.

increase in IQ test performance (as compared with pretest performance) resulted in an approximate 17-point increase in IQ test scores. These increases were equally spread across subtests The incentive condition effects were much less pronounced for students have pretest IQs between 98 and 120 and did not occur for students having pretest IQs between 121 and 140.” (p. 225)

Holt and Hobbs (1979)

Between and within subjects study of 80 delinquent boys randomly assigned to three experimental groups and one control group. Each exp group received a standard and modified administration of the WISC-verbal section.

Exp 1-Token reinforcement for correct responses; Exp 2 – Tokens forfeited for incorrect responses (punishment), Exp 3-feedback on correct/incorrect responses

1.06 standard deviation difference between the token reinforcement and control groups (inferred from t = 3.31 for 39 degrees of freedom0

“Knowledge of results does not appear to be a sufficient incentive to significantly improve test performance among below-average I.Q. subjects…Immediate rewards or response cost may be more effective with below-average I.Q. subjects while other conditions may be more effective with average or above-average subjects.” (p. 83)

Larson, Saccuzzo, and Brown (1994)

Between subjects study of 109 San Diego State University psychology students

Up to $20 for improvement over baseline performance on cognitive speed tests

“While both groups improved with practice, the incentive group improved slightly more.” need to calculate effect size, but it was not large

2 reasons why incentive did not produce dramatic increase: 1) few or no unmotivated subjects among college volunteers, 2) information processing tasks are too simple for ‘trying harder’ to

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matter Duckworth (2007)

Within subjects study of 61 urban low-achieving high school students tested with a group-administered Otis-Lennon IQ test during their freshman year, then again 2 years later with a one-on-one (WASI) test

Standard directions for encouraging effort were followed for the WASI brief test. Performance was expected to be higher because of the one-on-one environment.

Performance on the WASI as juniors was about 16 points higher than on the group-administered test as freshmen. Notably, on the WASI, this population looks almost “average” in IQ, whereas by Otis-Lennon standards they are low IQ. t (60) = 10.67, p < .001

The increase in IQ scores could be attributed to any combination of the following 1) an increase in “g” due to schooling at an intensive charter school, 2) an increase in knowledge or crystallized intelligence, 3) an increase in motivation due to the change in IQ test format, and/or 4) an increase in motivation due to experience at high performing school

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Contextualizing Personality

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The Evidence On Preference Parameters

Many economists and some psychologists estimate thetraditional preference parameters in economics: timepreference, risk aversion and preference for leisure.

More recently, altruism and social preferences have beenstudied.

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Many of the measurements we survey in this and next sectiondo not standardize for incentive and contextual effects.

This lack of standardization creates a serious problem inisolating true traits and making comparisons across studies.

In economic choices, market settings play a crucial role inpolicing behavior.

Even if individuals seek to exhibit irrational behavior, they mustlive within their constraints.

Different incentives and context act on agents (Rnl and W n

l inEquation (5)).

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Time Discounting

Evidence from animal and human experiments suggests thatfuture rewards are discounted non-exponentially as a functionof delay.

Under hyperbolic discounting, future utility A is discounted tocurrent utility value V :

V =A

1 + kd

where d is the delay and k is the discount rate parameter.

Under exponential discounting, V = A(1+ρ)d

, where d is thediscount rate.

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Frederick, Loewenstein, and O’Donoghue (2002) point out thatsuch field data may be complicated by the effects of factorssuch as imperfect information on the part of the subject aboutfuture rewards or credit constraints.

These limitations build the case for experimental elicitations,but they have their own set of problems.

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The most widely-used experimental approach to themeasurement of discount rates poses a series of choices betweensmaller, immediate and larger, delayed monetary rewards.

“Would you choose $1500 now or $4000 in five years?” (Fuchs1982).

These choices are typically among hypothetical items, but it isunclear whether discount rates for real and hypotheticalrewards are identical.

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Frederick, Loewenstein, and O’Donoghue (2002) surveymethods for measuring discount rates.

They document that across studies, estimated discount rates ofadult respondents range from -6 percent to infinity.

No attempt is made to standardize for incentives, market forces,personality, cognitive traits, and context in this literature.

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A meta-analysis by Shamosh and Gray (2007) of 24 studies inwhich both IQ and discount rates were measured shows the twotraits are inversely related (r = -.23).

If the cost of making calculations exceeds the expected benefitof such deliberation, the individual may choose by default theimmediate, certain reward.

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Discount rates vary inversely with the size of reward.

Insofar as estimates of discount rates are sensitive to context orframing effects, they may fail the definitional requirements forseparable economic preferences

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Frederick et al. suggest that time preference is tri-dimensional,comprising three separate underlying motives:

1 impulsivity, the tendency to act spontaneously and withoutplanning,

2 compulsivity, the tendency to stick with plans, and3 inhibition, the ability to override automatic responses to urges

or emotions.

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A parallel effort to define impulsivity and to decompose thistrait into its constituent components is underway in thepsychology literature.

An uninvestigated empirical question is whether estimates ofthe constituent components that give rise to time preferencewill prove more useful for economic models than currently usedspecifications.

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Risk Preference

The risk preference parameter (also referred to as “riskaversion” or “risk tolerance”) represents the curvature of theutility function.

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Survey questions assessing risk preference usually pose a seriesof questions involving the choice between a lottery and acertain outcome:

“Which would you prefer: $100 dollars today, or a 50 percentchance of receiving nothing and a 50 percent chance ofreceiving $200?”

Two recent studies have introduced measures of riskpreferences in field experiments.

Harrison, Lau, and Rutstrom (2007) use real stakes to elicit riskpreferences on a representative sample of 253 people inDenmark.

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Dohmen et al. (2005) use a lottery experiment with arepresentative sample of 450 German adults to validate surveyresponses on risk preference from the Socioeconomic Panel(SOEP).

Also, such a general question is free from framing effects thatshape behavior in presence of risk (Kahneman 2003).

As for time preference, in fact, there appears to be an inverserelationship between cognitive ability and risk aversion, wherehigher IQ people have higher risk tolerance (Benjamin, Brown,and Shapiro 2006; Dohmen et al. 2007).

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Risk preference also varies with socioeconomic characteristics.

No general consensus on the direction of such differences: somestudies find a negative relationship between education and riskaversion.

Most of the studies find that women are more risk averse thanmen.

Parents and their children are similarly risk averse, and thiseffect is stronger among children with fewer siblings andfirst-born children.

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The empirical findings summarized in this section assume thatrisk preference can be modelled with a single parameter acrosssituations.Yet, like time preference, risk preference may bemultidimensional rather than unitary.Weber (2001) shows that risk preference varies by domain, anda scale that assesses risk taking in five different domains showslow correlations across these domains (Weber, Blais, and Betz2002).One can be quite risk-averse when it comes to financialdecisions but risk-loving when it comes to health decisions(Hanoch, Johnson, and Wilke 2006).Weber’s risk-return model of risk taking (Weber and Milliman1997; Weber and Hsee 1998) finds that low correlations amongrisk taking preference across domains can be explained bydomain-specific perceptions of riskiness and return.

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A behavioral task and self-report measure from the psychologyliterature are of interest.

Lejuez and colleagues’ (2002, 2003) Balloon Analogue RiskTask (BART) is a computer game in which participants makerepeated choices between keeping a certain smaller monetaryreward and taking a chance on an incrementally larger reward.

Scores on the BART correlate with real-world risk behaviorssuch as smoking, stealing, and not wearing a seatbelt.

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BART scores also correlate with sensation seeking, a traitproposed by Zuckerman (1994) and defined as “the tendencyto seek novel, varied, complex, and intense sensations andexperiences and the willingness to take risks for the sake ofsuch experience.”

More than 2000 published articles have incorporated sensationseeking self-report questionnaires, and collectively these studieshave established that sensation seeking predicts risky driving,substance use and abuse, smoking, drinking, unprotected sex,juvenile delinquency, and adult criminal behavior.

Unfortunately, few, if any studies, have included typical riskpreference propositions of the sort relevant to economicdecision making when sensation seeking is estimated.

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Preference for Leisure

There is a large literature on estimating leisure preferences.

Survey-based estimates of preferences for leisure are lesscommon, probably because direct measurement is complicated.

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Most omnibus measures of personality include scales closelyrelated to preference for leisure or, more frequently, the obversetrait of preference for work.

The widely used Big Five (or NEO-PI-R, Costa and McCrae1992b), whose components and facets are summarized in Table1, includes an Achievement Striving subscale ofConscientiousness, which describes ambition, the capacity forhard work, and an inclination toward purposeful behavior.

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Altruism and Social Preferences

There is a large literature in economics on altruism and anemerging literature in economics on social preferences.

Bergstrom (1997) and Laitner (1997) discuss models ofinterdependent family preferences.

Andreoni (1995) shows that pure models of altruism areinconsistent with his evidence.

Villanueva (2005) and Laferrre and Wolff (2006) summarize themixed evidence on altruism in families.

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A recent literature explores social preferences which are distinctfrom altruism per se.Altruism is based on the assumption that the preferences of oneagent depend on the consumption or utility of other agents.Social preferences are preferences that depend on agent’sevaluations of a social condition (inequality, for example) or theintentions of other agents.Fehr and Schmidt (1999) analyze inequality aversion (in whichpeople dislike inequality rather than valuing the consumption orutility of agents per se).Fehr and Gachter (2000), and Falk and Fischbacher (2006)present evidence on reciprocity and conditional cooperation, inwhich agents act in a pro-social or antisocial manner dependingon the behavior of others with whom they interact.Fehr and Schmidt (2006) summarize the theory and empiricalsupport for social preferences.

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Both cognitive and socio-emotional abilities explain manyfeatures of economic and social performance and theemergence of health differentials.

Evidence from the second chance GED program in America(Heckman and Rubinstein, 2001).

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Density of age adjusted AFQT scores, GED recipients and high schoolgraduates with twelve years of schooling

White Males White Females(a) White males

0

5

10

15

20

25

30

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

(b) White females

0

5

10

15

20

25

30

35

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

HS graduates

GEDs

Source: Heckman, Hsee and Rubinstein (2001)

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Black Males Black Females

0

5

10

15

20

25

30

35

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

HS graduates

GEDs

0

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10

15

20

25

30

35

40

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5


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Hispanic Males Hispanic FemalesHS graduates

GEDs

0

5

10

15

20

25

30

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0

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35

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5


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GEDs earn at the rate of high school dropouts.

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Look at effects of both cognitive and noncognitive skills onmany measures of social performance.

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Ever been in jail by age 30, by ability (males)Pr

ob

abili

ty

Percentile

.15

.10

.05

.0

0 - 20 21 - 40 41 - 60 61 - 80 81 - 100

Cognitive Noncognitive

Note: This figure plots the probability of a given behavior associated with moving upin one ability distribution for someone after integrating out the other distribution. Forexample, the lines with markers show the effect of increasing noncognitive ability afterintegrating the cognitive ability.

Source: Heckman, Stixrud, and Urzua (2006).96 / 224

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Probability of being single with children (females)Pr

ob

abili

ty

Percentile

.10

.08

.06

.04

.02

0 - 20 21 - 40 41 - 60 61 - 80 81 - 100

Cognitive Noncognitive

Note: This figure plots the probability of a given behavior associated with moving upin one ability distribution for someone after integrating out the other distribution. Forexample, the lines with markers show the effect of increasing noncognitive ability afterintegrating the cognitive ability.

Source: Heckman, Stixrud, and Urzua (2006).97 / 224

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Probability of being a 4-year college graduate by age 30 (males)

24

68

10

123456789100

0.2

0.4

0.6

0.8

1

Decile of Noncognitive

Figure 1C. Probability of Being a 4-yr College Graduate by Age 30 - Malesi. By Decile of Cognitive and Noncognitive Factors

Decile of CognitiveP

rob

abili

ty

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1ii. By Decile of Cognitive Factor

Decile

Pro

bab

ility

an

dC

on

fiden

ce I

nte

rval

(2.

5-97

.5%

)

Notes: The data are simulated from the estimates of the model and our NLSY79 sample. We use the standard convention that higher deciles are associated with higher values of the variable.The confidence intervals are computed using bootstrapping (200 draws).

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1iii. By Decile of Noncognitive Factor

Decile

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Probability of daily smoking by age 18 (males)

24

68

10

24

68

10

0

0.2

0.4

0.6

0.8

1

Decile of Noncognitive

Figure 1F. Probability Of Daily Smoking By Age 18 - Malesi. By Decile of Cognitive and Noncognitive Factor

Decile of Cognitive

Pro

bab

ilit

y

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1ii. By Decile of Cognitive Factor

Decile

Pro

bab

ilit

y a

nd

Co

nfid

en

ce I

nte

rval (2

.5-9

7.5

%)

Notes: The data are simulated from the estimates of the model and our NLSY79 sample. We use the standard convention that higher deciles are associated with higher values of the variable.The confidence intervals are computed using bootstrapping (200 draws).

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1iii. By Decile of Noncognitive Factor

Decile

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Mean log wages by age 30 (males)

24

68

10

123456789102

2.2

2.4

2.6

2.8

3

Decile of Non-Cognitive

Mean Log Wages by Age 30 - Males i. By Decile of Cognitive and Non-Cognitive Factors

Decile of Cognitive

Lo

g W

ages

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Predictive Power of Personality Traits

The importance of personality traits can be inferred from thefailure of cognitive measures to predict certain outcomes.Heckman and Rubinstein (2001) use evidence from the GeneralEducation Development (GED).

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Figure 3 summarizes correlations for the predictive validity ofIQ and Big Five personality factors on leadership ratings, jobperformance, longevity, college grades, and years of education.

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IQ surpasses any single Big Five personality factor in theprediction of the two academic outcomes, college grades (r =.45) and years of education (r = .55).

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Contemporary psychologists suggest that self-control,perseverance, and other aspects of conscientiousness as themajor personality contributors to success in school and in life.

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As Heckman, Stixrud, and Urzua (2006) and Judge and Hurst(2007) show that among participants in the NLSY 1979 cohort,positive self-evaluations measured in young adulthood (withself-report questions of self-esteem, locus of control, andrelated traits) predict income in mid-life and, further, enhancethe benefits of family socioeconomic status, and academicachievement on mid-life income.

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Limitations of Current Evidence on Predictive Validity

There are five reasons why effect size estimates summarized inFigure 3 may underestimate the impact of personality traits.

First, whereas the benefits of IQ are monotonically increasing(that is, more is always better), the optimal level of mostpersonality traits may lie somewhere between the two extremes(see Benson and Campbell 2007 and LaHuis, Martin and Avis2005).

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Second, short-term personality measures that yield a singlescore for each Big Five domain are too blunt an instrument tocapture relationships between personality and outcomes.

Stronger relationships between personality and outcomes oftenemerge when more narrowly defined facets are used (Paunonenand Ashton 2001).

Roberts et al. (2005a) show that lower-level facets ofConscientiousness (for example, the traits of industriousness,self-control) have differential relationships with labor marketand other outcomes, and, further, that these traits consideredindividually may predict outcomes better than a broad measureof Conscientiousness.

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An example of the dramatic impact of a very specificallydefined and carefully measured personality trait comes fromMischel and colleagues, who show that delay of gratification(measured as the number of seconds children can wait for alarger treat in lieu of a smaller, immediate treat) at age fourpredicts higher academic and social functioning in adolescence

Hogan (2005) makes a related point regarding the specificityand appropriateness of outcome variables: “Researchers oftenfail to align predictors with criteria; this results in usingmeasures of conscientiousness to predict service orientation, ormeasures of extraversion to predict training performance . . . ”

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Third, personality in large-sample studies is almost invariablymeasured by brief, self-report questionnaires, and this approachyields less reliable and less precise estimates of the effects ofpersonality on outcomes than do IQ tests.

Dunning, Heath, and Suls (2004) show that the limitations ofself-report questionnaires extend beyond vulnerability to fakingand include the tendency of most individuals to overrate theirskills.

Despite general recognition of these limitations and thehandicap they present for assessing the importance ofpersonality traits, few novel measurement approaches havebeen validated for many traits of interest.

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Thus, whereas multi-source, multi-method approaches topersonality measurement are superior, they are difficult if notimpossible to implement in many research contexts.

To the extent that IQ is more accurately measured thanpersonality, estimates of the effects of personality on outcomeswill be disproportionately attenuated (see Duckworth andSeligman 2005).

Accounting for measurement error is empirically important inusing psychometric measurements in empirical work. Cunhaand Heckman (2008) estimate substantial measurement errorcomponents in both cognitive and noncognitive test scores.

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A fourth limitation of the estimates in Figure 3 is that they donot capture interaction effects.

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Finally, standard measures of predictive power are effect sizeand variance explained.

However, R2, or goodness of fit measures, are only one way tomeasure the importance of variables.

Heckman, Stixrud, and Urzua (2006) develop a differentmeasure of predictive power based not on variance explained,but on the response of outcomes to a change in the variable.

They examine the effect of moving people from differentpercentiles in the latent factor distributions of cognitive andpersonality skills on the following outcomes.

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Mean log wages by age 30 (males)

2 4 6 8 102

2.5

3i. By Decile of Cognitive Factor

Decile

Lo

g W

ages

and

Con

fid

en

ce In

terv

al (

2.7

5-9

7.5

%)

2 4 6 8 102

2.5

3ii. By Decile of Non-Cognitive Factor

Decile

Notes: The data are simulated from the estimates of the model and our NLSY79 sample. We use the standard convention that higher deciles are associated withhigher values of the variable. The confidence intervals are computed using bootstrapping (50 draws).

Mean Log Wages by Age 30 - Males

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Changing Preference Parameters and Psychological Variables

If they change, to what extent do environments andinvestments influence the developmental trajectories ofpersonality traits?

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The malleability of personality can be defined and measured inseveral ways: Mean-level change refers to change over time inabsolute levels of a trait and is measured by changes in scoresover time.

Rank-order change, in contrast, refers to changes in the ordinalranking of a trait in a population and is measured by test-retestrank correlations.

Cognitive abilities exhibit dramatic mean-level change fromearly childhood through adolescence, but, over the sameperiod, strong rank-order stability.

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A second useful dichotomy contrasts normative change, definedas changes that are typical of the average individual in a givenpopulation, and caused either by biological programming(ontogenic) or by predictable changes in social roles(sociogenic), and non-normative change, encompassing bothintentional change, caused by deliberate, self-directed efforts,deliberately chosen changes in social roles and atypical lifeevents (trauma, for example).

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Mean Level Changes

People typically become more socially dominant

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Social Vitality

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lue

Figure 4a

Cumulative mean-level changes in personality across the life course

Note: Figure taken from Roberts, Walton and Viechtbauer (2006). Reprinted with

permission of the authors. Social vitality and social dominance are aspects of Big Five

extraversion. Cumulative d values represent total lifetime change in standard deviations.

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Figure 4a





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Figure 4a





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Figure 4a





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Figure 4b shows mean-level changes in cognitive skills using alongitudinal analysis, and the bottom panel of Figure 4b showsmean-level changes using a cross-sectional analysis.

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Rank-Order Change in Cognitive and Personality Skills

Figure 5a shows graphs of rank order stability of personality byage.

Figure 5b shows rank order stability of IQ over broad ageranges.

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144

Figure 4c

Fluid intelligence decreases and crystallized intelligence increases across the lifespan

Note: Figure from Horn (1970). Used with permission of Elsevier.

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Mechanisms of Change for Cognitive and Personality Traits

What mechanisms underlie stability and change in personality?

Behavioral genetics studies typically estimate the effect ofparental environments to be near zero, but Turkheimer et al.(2003) find estimates from such studies to be biased downwardby the over-representation of middle and upper-class families.

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Genes exert their influence in part through the selection andevocation of environments that are compatible with one’sgenotype-a phenomenon sometimes referred to as“gene-environment correlation” or “nature via nurture” (seeRutter 2006).

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It is important to note that the family studies of geneticinfluence measure only the effects of shared environments,which become less similar as children age.

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What other than preprogrammed genetic influences mightaccount for mean-level changes in personality?

Personality change in adulthood may be precipitated by majorshifts in social roles (for example, getting a job for the firsttime, becoming a parent).

Clausen and Gilens (1990) claim that female labor forceparticipation increases self-confidence.

Gottschalk (2005) presents experimental evidence that womenforced to work due to welfare reform showed gains inself-confidence and self-esteem.

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One difficulty with many of these studies is the problem ofreverse causality discussed in Section III and analyzed inHansen, Heckman, and Mullen (2004) and Heckman, Stixrud,and Urzua (2006).

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Are the effects of environment on personality changelong-lasting? Do changes endure after the environmental causeis removed?

At the moment, the prevailing view in psychology is relativelypessimistic.

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The enduring effects of environment are greater earlier in life.

Knudsen et al. (2006) and Cunha and Heckman (2007)summarize this evidence and relate it to models of investmentin economics.

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Cunha and Heckman (2007)

latent traits are time (or age) subscripted: fi ,t .

fi ,t+1 = ψ (fi ,t , INi ,t) , t = 1, . . . ,T , (7)

INi ,t is a vector of experience related to inputs which caninclude the parental and school environments, experiences inthe workplace, and the like.

The initial condition reflects genetic material and the in uteroenvironment that determines the initial stock of traits.

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Both cognitive and personality skills can be affected by parentalinvestment and schooling, which are components of INi ,t .

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For example, the Perry Preschool Program, an enriched earlychildhood intervention evaluated by random assignment wheretreatments and controls are followed to age 40, did not boostIQ but raised achievement test scores, schooling, and socialskills.

It raised personality skills but not cognitive skills, at least asmeasured by IQ. Effects were not uniform across gender groups(Heckman, 2004; Heckman, Stixrud, and Urzua, 2006).

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Stability of Economic Preference Parameters

Less is known about the stability of economic preferences.

No longitudinal study has measured the mean-level orrank-order stability of time preference over the life cycle(Frederick, Loewenstein, and O’Donoghue. 2002).

A handful of cross-sectional studies using relatively smallsamples have examined mean-level stability, and their findingsare mixed.

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In summary, the answer to the question of whether change inpersonality is possible must be a definitive yes, both in terms ofmean-level and rank-order change.

However, change may be more difficult later in the life cycle,

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Frameworks for Integrating Personality Psychology and Economics

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Psychological Variables as Constraints

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A constraint-driven model need not produce a unique choiceoutcome for all persons with the same constraints.

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Thurstone (1927), Block and Marschak (1960), Bock andJones (1968), and McFadden (1974, 1981), write the utility ofagent i for choice l as Ui ,l .

Ui ,l is the motivation for choice (goal) l by agent i .

Choice sets, Bi , differ among persons depending on theircapacities.

Agent i chooses li as the maximal element in the choice set Bi :

li = arg maxl∈Bi

{Ui ,l}

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A familiar model writes Ui ,l = Vi ,l + εi ,l , where Vi ,l is agent ivaluation for l and εi ,l is a random “taste” shock.

When Vi ,l = Vl , and εi ,l is iid extreme value type 1, theprobability that l is selected from choice set Bi is

Pr(l | Bi) =exp(Vl)∑j∈Bi

exp(Vj)for l ∈ Bi

= 0, for l /∈ Bi .

If agents have zero mean scale preference among the choices(Vl = 0) so that all choices (goals) have the same mean utility,we obtain a version of Becker’s (1962) model.

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Depending on how the constraints are determined, one can capturea variety of aspects of choice behaviour.

A shy person may limit her options in a way an extravert doesnot.

An intelligent person may have a much richer choice set notonly because of greater earnings capacity but also because ofmuch greater imagination.

Much like greater pixel resolution in imaging machines, thosewith higher IQ may resolve reality in a more fine-grained andless biased way.

We capture the effect of these traits on the choice sets, whichmay also depend on material endowments.

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Incorporating Personality and Cognitive Ability into ConventionalEconomic Models: A Simple Framework for Organizing the Evidence

How should one incorporate psychological traits intoconventional economic models?

One could think of them as public goods.

This is the approach implicitly adopted by most personalitypsychologists.

One could also think of psychological traits as excludableprivate goods.

More of a trait used in one activity means less of the traitavailable for use in other activities.

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In addition, one might augment, complement or override thesupply of a trait to any activity by supplying more time, orenergy, to the activity in which the trait is used.

On the other hand, “energy,” e, which can be vector valued,may be used to moderate the manifestation of the trait (forexample, energy may be spent controlling anger in a givenactivity).

Individuals differ in their endowment vector of the trait f .

Thus there may be a time constraint as in Becker (1965) ormore generally there may be energy constraints (constraints oneffort capacity).

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A Simple One-period Model

Assume that there are J activities with outputs Zj ,j = 1, . . . , J + 1.

We add one activity to account for market earnings. Zj isproduced by combining tasks, Tj , defined in section II, withpurchased market goods, Xj .

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We augment the task functions defined by equation (8) toinclude levels of energy, and time, in vector e j

Tj = hj(f j , e j

)for j = 1, . . . , J + 1 (8)

f j is to be distinguished from fj , the j th component of vector f .

Parallel notation for e j .

For a fixed input of psychological traits, higher levels of e j mayraise the output of the task.

Thus if e j = 0, the trait f j may be switched off. However, ifsome traits have negative productivity in some tasks moreenergy may be allocated to those tasks to offset the negativetrait.

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Output in activity Zj is

Zj = ϕj (Tj ,Xj) for j = 1, . . . , J + 1 (9)

The outputs in activity j depend on the task output Tj and thegoods input Xj .

Agents have preferences over Zj and ej .

The effort expended in an activity may have psychic costs orbenefits.

There may be psychic costs in using ej to suppress theexpression of a trait.

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Preferences may also depend on f as well as other variableswhich we keep implicit.

The utility function is

U = U(Z1, . . . ,Zj , e

1, . . . , eJ+1, f)

(10)

Income is return on asset flow Y plus labor earnings which wedenote ZJ+1 = ϕJ+1 (TJ+1,XJ+1).

J+1∑j=1

PjXj = Y + ZJ+1 (11)

ZJ+1 is a hedonic earnings function which prices out traits andenergy in the market.

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It is possible to distinguish two different cases for f .

For psychological traits, we can distinguish the case where f isa public good, f j = f for all j = 1, . . . , J + 1.

When it is a private good,∑J+1

j=1 f j = f

People are not stuck with their personality in all activities.

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For simplicity, we consider the pure private goods case and thepure public goods case. Assume that e is private.

fPublic Private

e Private case I case II

In case I, the additional constraint operating on the consumerbeyond the budget constraint (11) is

f j = f ,J+1∑j=1

e j = e, for all j = 1, . . . , J + 1. (12)

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In case II, the operative constraints are

J+1∑j=1

f j = f ,J+1∑j=1

e j = e (13)

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Case I: Traits as Public Goods

In case I, different bundles of f across persons createcomparative advantages for agents in different tasks and thusproduce comparative advantages in different activities.

Case I is a version of Michael’s (1973) model of environmentalvariables in a household production framework.

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For analytical simplicity, suppose that Zj and Tj ,j = 1, . . . , J + 1, display constant returns to scale in non-publicinputs.

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In terms of the technologies (16), when f is a public good, weassume constant returns to scale in e j but that f j = j is afixed, environmental variable.

Different levels of f produce different productivities in differenttasks.

Feeding f into the activity functions (9), which are alsoassumed to be constant returns to scale, we can analyze theagent’s problem of allocating effort among tasks and goodsamong activities using the analysis of Michael (1973).

Financial and energy resources are not changed by f except forits effect on ZJ+1.

Holding energy and money resources fixed, changes in fproduce reallocations across budget categories.

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Several Cases

Consider an increase in conscientiousness.This will likely increase earnings (via ZJ+1), and will enhanceproductivity in some tasks intensive in conscientiousness andactivities based on those tasks more than other tasks andactivities.The increased income will support more of all activities.The differential shift in productivity across tasks and activitieswill reduce the prices of activities that are more intensive in theuse of conscientiousness.If the demands for those activities are price elastic compared tothe demands for the less conscientiousness-intensive activities,the demand for the inputs used in those activities will increase.If the demands are relatively inelastic, the demands willdecrease because of the greater productivity for the inputs.

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If a trait reduces productivity, the chain of logic just presentedruns in reverse.

With increases in, for example, neuroticism, shadow prices ofactivities intensive in that trait will increase.

Labor earnings will tend to decrease.

In the price-elastic case, consumers will tend to substitute awayfrom activities intensive in the trait and the demand for inputswill decrease.

In the inelastic case, input demands will increase as agentssubstitute goods and energy inputs into the activities that areinelastically demanded.

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The same level of the traits is found in all activities, but ingeneral, energy or time will be allocated differentially amongactivities.

A person who allocates more energy or time to a task willmanifest more of the trait.

If inputs are complementary, at the same scale of output moreof the task will be demanded.

Unless one controls for these inputs, one may fail to capturethe uniformity of traits across tasks and activities.

In all of these cases, purchase patterns of market goods willprovide information on endowments and allocation of energyand traits.

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Case II: Traits as Private Goods

The case when traits are private goods produces the possibilityof different levels of traits being used in different tasks andactivities.

Responses of activity levels to changes in rewards acrossactivities will be more price-elastic when traits can be allocatedacross activities than when traits are fixed.

Equiproportionate expansions in (f , e) differentially expand theconsumption possibility set for activities intensive in (f , e) andreduce their shadow prices, producing substitution effects intask production and activity consumption that promoteconsumption in activities intensive in the traits.

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The public goods case imposes more constraints on the systemthan the private goods case.

Compared to the case of public goods for traits, agents willreduce their allocation of the trait from activities where theirproductivity is negative and will spend less effort (e) inoverriding the effects of negative traits in productivity.

The trait will be shifted into less costly activities and lessenergy will be spent controlling it.

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The evidence summarized in sections IV and V of this lecturewould seem to favor case II, since different levels of traits areoften found in different activities.

However, since most of the estimates reviewed in this paper donot adjust for the inputs that affect the manifestation of thetraits, one must be cautious in reaching this conclusion.

Such adjustments are indicated by the theory but are not yetstandard in economics or psychology.

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The roles of time and energy in amplifying or reducing theeffects of the traits in activities needs to be systematicallyexplored to make the theory empirically operational as are theeffects of traits on the purchase of related goods (for example,shy people may seek to live in secluded areas, houses with highwalls and seek jobs with little human contact).

In the private goods specification of the model (case II), themotivation for the supply of traits to different activitiesdepends on preferences (utility rewards U), on productivity inZj , and in productivity in the tasks Tj . In this framework, it ispossible to formalize many of the currently disparate conceptsof personality psychology.

It would be very informative to estimate both versions of themodel and to test between them.

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Integrating Psychology into More General Economic Models

Economic theory at the single agent level separates two distinctaspects of behavior and decision making: preferences andconstraints.

Included among the constraints are (a) information acquisitionconstraints; (b) static budget constraints and endowments thataffect the flow of resources available for consumption in anyperiod; and (c) dynamic constraints connected with asset, skilland trait formation.

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Preferences are central to conventional economic choice models.

In their most general form, we may write utility for an agentwith decision horizon T over bundles of goods (attributes),Xt , t = 1, ...,T , in an environment of perfect certainty withcognitive and personality attributes f as

U(X1, . . . ,XT ; f ), (14)

where it is assumed that U is neoclassical.

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A general non-separable intertemporal preference function isconsistent with substantial departures from standard utilitytheory such as hyperbolic discounting (Phelps and Pollak 1968;Ainslie 1991; Laibson 1998) and a variety of ”exotic” ornonstandard preferences as discussed in, for example, Backus,Routledge, and Zin (2005) and Hansen (2005).

Preference specifications (14) is consistent with different ratesof time preference for different goods and across differentperiods as is found in the literature reviewed in Section IV.

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Few economists would embrace the high level of generality ofspecification (14).

Fruitful economic models are more tightly structured.Specification (14) can characterize a one-shot model of lifetimedecision making under certainty.

Agents choose their lifetime consumption bundles at thebeginning of life and are fully committed to those choices.

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A basic problem with these specifications is time inconsistency.

In open markets, persons are not committed to their initialdesired choices.

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More generally, agents may look at future decisions differentlyin period 2 than they did in period 1.

Let U t be the utility of the agent at stage t for the remainderof life U t = G t(Xt , . . . ,XT ; f ).

Without further restrictions, there is no reason why in period t,the agent is compelled to value the utilities of previous periodconsumption bundles or account for past consumption behaviorin the way done prior to period t in evaluating futureconsumption streams.

The problem of preferences changing over time is distinct fromthe problem of revised information sets although both producepossible departures from initial decisions based on (14).

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The conventional specification of the general preferencefunction assumes a constant rate of discount for utility acrossperiods:

U(X1, . . . ,XT , f ) =T∑t=1

1

(1 + ρ)t−1U(Xt , f ). (15)

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Specification (15) is not required to achieve time consistency ofchoices.

This is an important point, because there is a lot of evidencethat speaks against (15) as previously noted in section IV.

Notice that (15) is just a special case of equation (14), which isalso a standard model of economic preferences.

A more general form of discounting than specification (15) thatis consistent with (14) is

U(X1, . . . ,XT , f ) =T∑t=1

t∏j=2

(1

1 + ρj

)U(Xt , f ), (16)

where discount rates may vary with age.

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Let ft denote personality and cognitive traits at age t.

Can use Ut(Xt , ft) in place of U(Xt , f ), allowing for personaltraits to evolve over time, and we can allow for utility in periodt itself to change, even after controlling for ft and Xt .

The analysis of Becker and Mulligan (1997) and Mulligan(1997) models the evolution of the discount rate throughinvestment decisions.

Becker and Murphy (1988) model the evolution of preferencesfor addiction where ft is a stock of addictive capital.

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A wide variety of special cases of lifetime preferences aresubsumed in specification (14).

Personality factors like deliberation, future time perspective,and the capacity to inhibit impulses likely determine discountfactors or preferences more generally.

So may aspects of cognitive ability.

Loewenstein et al. (2001) discuss how decisions are affected bymoods and emotions, which are influenced by personalityvariables.

There is some evidence that higher IQ persons have lowerdiscount rates (see Frederick 2005 and Dohmen et al. 2007).

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The standard model of social interaction in economics isinteraction through markets (See Arrow and Hahn 1971).

This aspect of human interaction is not captured byspecifications (14)-(16) unless the include outcomes, choices orutilities of other persons.

As noted previously in section IV, a large literature ineconomics discusses the implications of altruism (see Becker,1981 and Laferrre and Wolff, 2006, for a survey).

Fehr and Gachter (2000) discuss the consequences of socialpreferences for economic decisions.

Models of social preferences have been developed by Fehr andSchmidt (1999) and Falk and Fischbacher (2006).

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Surveys by Fehr and Schmidt (2006) and Meier (2007).

One of the major findings of personality psychology noted insection V is that sociability, empathy, and the capacity to getalong with others are important predictors of success in manyactivities.

These traits are not the same as altruism or social preferences,but they are facets related to Big Five agreeableness andextraversion.

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Sociability and empathy may affect preferences for groupactivity which may be a source of pleasure (or displeasure) forsome and which may also affect productivity in group activitiesin the workplace or in learning environments.

Dohmen et al. (2008) present evidence on how trust, positivereciprocity, and negative reciprocity relate to Big Fivepersonality traits.

These and other personality traits play dual roles.

They are a source of pleasure and they can also be a source ofproductivity in certain contexts.

Agents making choices under any of the standard preferenceschemes, including those that recognize social interactions, areconstrained in their information, the resources required tosupport consumption and in their ability to accumulatefinancial assets and skills.

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Uncertainty and risk are essential aspects of life. Economistshave devoted much attention to the specification of thepreferences of agents and the effect of uncertainty on choice(see Mas-Colell, Whinston, and Green 1995).

Individuals who are more intelligent or more open to experience(that is, more intellectually curious and motivated to learn)may acquire information more cheaply.

Other personality traits may affect the basic attribute spacesperceived by agents.

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The conventional model of uncertainty in economics is theexpected utility model.

Break X into values that occur in different states a s and timet, (X11, . . . ,Xts , . . .X1ST ).

Expected utility represents preferences

U(X ) =T∑t=1

St∑s=1

Pt,sU(Xt,s) whereSt∑s=1

Pt,s = 1, t = 1, . . . ,T

(17)Xt,s is a state s, time t-specific bundle of traits.

Pt,s is the probability that state s occurs in period t.

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Considerable empirical evidence against this model. Manydepartures from it have been proposed to rationalize theavailable evidence.

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Personality factors may affect the arrival and processing ofinformation. People not open to experience fail to learn from it.

Impulsive people who do not act with deliberation may processinformation inefficiently (Frederick 2005).

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There are far richer models of decision making underuncertainty in economics than the standard expected utilitymodel or models based on decision making under uncertaintygenerated from objective distributions.

These specifications allow for preferences over the temporalresolution of uncertainty about states of the world (Kreps andPorteus 1978; Epstein and Zin 1991), uncertainty aboutdistributions over states of the world (ambiguity) and differenttypes of risk and uncertainty aversion in preferences (seeStarmer 2000).

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Personality traits are likely to prove useful in economic modelsof decision making under ambiguity.

Individuals may differ in their capacities to deal with poorlydefined situations.

Greater intelligence may help define situations, but person withgreater self control, openness to experience, lower levels ofanxiety and those who seek excitement may better cope withambiguity.

Personality traits may also affect the resources available toagents.

As emphasized by Bowles, Gintis, and Osborne (2001), certainpersonality and character traits may be more highly valued thanothers in the labor market (trustworthiness, perseverance,outgoingness, for example).

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They present evidence for Germany and the United States thatthe increased importance of people skills has affected thelabor-market outcomes of blacks and women.

They find that the relative employment of women is higher inoccupations in which people tasks are more important inBritain, Germany and the United States.

The reverse is true for racial, ethnic, cultural, and linguisticminorities in the United States.

Diligent or trustworthy employees require less supervision.

More generally, different personality and cognitive traits may bemore highly valued in some activities than in others.

In any activity, whether it is learning, information processing orperformance of a workplace task, those who exert higher levelsof effort will be more productive.

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Comparative advantage in the labor market: Roy (1951),Mandelbrot (1961, 1962), Tinbergen (1956), Rosen (1974),Sattinger (1979, 1993), Willis and Rosen (1979), Heckman andSedlacek (1985), and Teulings (1995; 2005).

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Borghans, ter Weel, and Weinberg (2007) develop a model inwhich personality traits are included in an assignment model.

Productivity of a person in occupation (pursuit) j at time t asYj ,t = αj ,t(f

jt , e

jt), j = 1, . . . , Jt , where we adjoin t subscripts to

the trait and energy levels.

Different occupations or tasks require (or weight) differenttraits differently.

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In subsection B, we analyzed specifications of marketproductivity functions that are used in the efficiency wageliterature (see Weiss 1991).If agents choose or are assigned to tasks on the basis ofmaximal output Yj ,t and pursuit of one occupation precludespursuit of other occupations, the occupation (task) selected attime t among the possible assignments at time t is j∗t , definedas

j∗t argmaxjt{Yj ,t}Jtt=1. (18)

In this case, Yi ,j∗t corresponds to ZJ+1,t for the period t asintroduced in subsection C.This framework captures the notion of comparative advantagein the labor market where agents sort into sectors based ontheir comparative productivity.Hogan (2005) and Hogan and Hogan (2007) show thepredictive power of personality traits in different occupations.

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Over time, persons may also accumulate assets and skills, andmay change their personality characteristics and cognitive traits.

Preference parameters affect asset and skill accumulation.

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Section VI presents evidence that cognitive and personalitytraits can be changed (see Cunha and Heckman 2007 andFraley and Roberts 2005).

Both are influenced by experience and current stocks of thecharacteristics and other determinants.

To formalize these notions, define Ct as a capacity vector thatincludes ft and et but encompasses a wider notion of capacities.

Motivation can be affected by intelligence and other capacitiesof human beings (see Cunha and Heckman 2008).

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Interventions can affect preferences, information, opportunitysets, and the formation of skills and preferences.

Personality and cognitive ability evolve over time throughinvestment, through learning by doing or through other lifeexperiences (see Cunha and Heckman 2007; Cunha, Heckman,and Schennach 2007).

Among the characteristics or capacities Ct can be health,motivation, personality traits and ability (Heckman 2007).

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Using the technology of skill formation capacities evolve via thefollowing recursive technology

Ct+1 = τ(Ct , INt), t = 1, . . . ,T − 1,C0 = c0 (19)

c0 is an initial condition for capacities and is investment atstage t and where is concave in τ , and is assumed to bedifferentiable in Ct and INt .

In one version, ft = Ct

Cognitive and personality skills can evolve over time.

Characteristics may be self-productive(∂τ(Ct ,INt)

∂Ct> 0)

.

Investment, which can include experience and other inputs, mayaffect the evolution of abilities and personality, that is,(∂τ(Ct ,INt)

∂INt> 0)

.

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Do Personality Parameters and Economic Preference ParametersCorrespond?

It is tempting to relate the personality traits to conventionaleconomic preference parameters.

Omits cognition.

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The Big Five captures traits that seem relevant but are notexclusive determinants of economic preference parameters.

A single agent economic model cannot fully capture theoperation of traits that foster social interactions.

Positive social interactions can produce benefits in terms oflearning and information processing.

Participation in social groups provides a form of insurance andmay promote risk taking (through insurance), even if it doesnot change risk aversion.

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Economic models of contracting emphasize unobserved effort(a component of e), as an important dimension of economictransactions in the presence of imperfect information (Salanie1997).

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Individuals may put in more effort in a task (a component ofTj , j = 1, . . . , J + 1) and will be more productive than otherindividuals at the task whether the task is a job, learning inschool or acquiring information.

Persons for whom the utility cost of effort is low, and henceexert more effort, will be more productive in a variety ofactivities.

Behavior is affected by incentives and is not necessarilyconstant across settings.

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“Warmth” (a facet under extraversion) may be a productivetrait in some settings, but it may be unproductive in certainsettings (for example, an assembly line, on the battlefield or ina seminar).

Fantasy (under Openness) can be counterproductive in routinetasks but very productive in creative work, providing that theperson is also self-disciplined and open to criticism.

There is wisdom in considering traits that have domain-specificproductivities.

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Do the traits discussed by personality psychology cause us torethink the standard economic model?

The evidence on the predictive power of sociability, effort andconscientiousness and the evidence on altruism and otherpro-social preferences should lead to a reemphasis of traditionaltheory.

Social interactions tend to be neglected in standard economictheory, although there is a lot of recent research on this topic(see Durlauf and Young 2001, Brock and Durlauf 2001, and theevidence in Fehr and Schimdt, 2006).

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Is it possible that conventional economic preference parametersfully explain all of the personality traits uncovered bypsychologists?

Implausible that conventional leisure preference, risk aversion,and time preference parameters explain all of the traitsidentified in Table 1.

For one thing, it is likely that these parameters are producedboth by cognition and personality as we have previously noted.

However, certain traits associated with Big Fiveconscientiousness might be rationalized by basic preferenceparameters.

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Low taste for leisure and a low discount rate would contributeto making persons more conscientious.

The Big Five traits alone cannot explain diligence unless theperson has some goal (or goals) or preferences motivating effortand self-discipline in a particular situation.

Most of the traits in Table 1 (for example, hostility, warmth,anxiety, trust) are less easily explained by standard economicpreferences.

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Summary and Suggestions for Future Research

Whereas the significance of personality traits for success inmany aspects of life has long been appreciated at an intuitivelevel, it was not until recently that a substantial body ofempirical analysis has documented this intuition.

However, recognizing the importance of traits other thanintelligence is not enough. It is also essential to identify whichtraits are important for which outcomes.

Such an understanding not only leads to better measures andricher models, but ultimately provides direction for policy andintervention.

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Economists are not alone in their interest in the description,prediction, and explanation of human behavior.

Psychologists, too, have approached these challenges.

Economists can profitably leverage research from psychology onthe measurement, prediction, and malleability of personalitytraits organized in the widely-accepted Big Five taxonomy.

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Answer to Question 1

Cognitive and personality traits are conceptually distinct if onedefines cognitive traits to mean general intelligence and specificcognitive abilities.

Aspects of personality-shyness, sociability, time preference,impulsivity, extraversion, agreeableness, empathy, sense ofhumor, and so on-involve cognitive processes but can beseparated from raw problem solving abilities for abstractproblems.

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Distinguishing cognitive and personality traits empirically is adifficult task. Measurements of IQ and achievement areaffected not only by the knowledge of the test taker, but alsoby their motivation.

Responses on self-report personality questionnaires are affectedby strategic responses of the persons being examined whichdepend, in part, on their perceptions of gain from a responseand hence their basic intelligence.

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Econometric methods have been developed to isolate ”pure”intelligence and personality from the effects of environment andexperience and to account for measurement error.

Their application will enable both psychologists and economiststo isolate relevant psychological traits as well as test amongcompeting specifications of how personality traits should entereconomic models.

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We distinguish a priori definitions of personality traitsconstructed using factor analyses from predictive definitions.

Definitions of personality traits based on internal consistency ofclusters of test scores are widely used in personality psychology.

The tests used in these exercises are devised on a priori groundsto ”tap” certain trait spaces that are intuited to be important.

Clusters of traits arrived at through factor analysis are lessappealing than definitions based on the predictive power oftests in real world settings.

Each approach has its limitations.

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The concordance between the measures of personalitypsychology and the parameters of economic theory is far fromperfect.

Personality psychology instructs us that many traits, even thosebeyond altruism and social preferences, are important factorsthat should be given more emphasis in the economic theory ofpreferences and constraints.

Motivation and effort deserve a renewed emphasis applied tobroader aspects of social life than just the labor market.

Economists explicitly model motivation through preferences.

The evidence suggests that performance on tests can beaffected by incentives but only for certain personality types.

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Economists have long emphasized that organizations cansucceed by aligning the interests of the workers with those ofmanagers.

This can be achieved by selecting persons with compatiblepersonality traits (for example, on the basis of trustworthiness,cooperativeness, and the like) or by giving incentives to workersof each personality type or by a mixture of the two strategies.

However, implementing both types of strategies in the sameworkplace may be counterproductive because of envy and othersocial effects.

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While the lessons from personality psychology are provocative,they have not yet changed the way most economists go abouttheir business.

Recent attacks by psychologists on conventional preferencespecifications in economics have not been productive becausethe straw men attacked - expected utility and additivelyseparable models for intertemporal choice - have long beenabandoned by economists at the frontier of knowledge.

What is needed are more focused studies that suggest specificgeneralizations of standard models that are empirically fruitfulfor a range of questions and that have empirical content.

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Both preferences and constraints should be analyzed.

Implementing the simple models presented would be a goodfirst start.

An example of how economic theory can be changed in afundamental way by learning lessons from personalitypsychology is the recent work on multidimensional screeningthat adds personality skills to traditional screening andsignaling models and produces a fundamental reformulation ofsignaling theory (Araujo, Gottlieb, and Moreira, 2007).

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Many economists and psychologists assume that preference andpersonality parameters are fixed early in life.

The evidence suggests otherwise.

Both cognitive and personality traits evolve, albeit at differentrates at different ages.

Rank-order stability of cognitive skills emerges much earlierthan rank-order stability of personality skills.

Recent research shows how cognitive and personality skills areaffected by parental investments and life experiences.

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While an assumption of complete stability is analyticallyconvenient, it is not found in the data.

Evidence of change in preferences suggests that consistent lifecycle planning may be difficult.

Agents may, or may not, know if their future preferences will belike their current preferences.

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In addition, many psychological measurement schemes assumethat the persons being assessed face common choiceenvironments.

Our analysis shows that contexts and incentives affect manifestpersonality traits (effort, for example) and may also affectself-reported traits.

This point has important lessons for the measurement andinterpretation of personality traits that have not yet made theirway into psychological or economic survey-based schemes.

It would be very informative to measure personality andcognitive traits under a broader array of different incentivearrangements than have been explored to date, and tobenchmark measurements of personality and preference traitsat common baselines and tools exist to make these adjustments

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Avenues for Future Research

Economic preference measures should be subject to the samepsychometric standards as personality measures.

These include: evidence of internal reliability, test-reteststability (over short periods), convergent validity, discriminantvalidity, and predictive validity.Subjecting economic preference measures to these standardswill increase their validity and improve their ability to predictoutcomes.At the same time, psychologists should better recognize thatthe contexts and incentives faced by agents affectmeasurements of both cognitive and personality traits.

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Economic preferences are likely multidimensional.

Time preference, for example, may have different components(for example, the inability to inhibit an impulse, the tendencynot to consider or imagine the future, comfort with ambiguity,and the like).A hierarchical view (as there is for IQ) may organize a large,currently disorganized literature and unite inconsistent findingsacross studies and low intercorrelations among measures in agiven study.In addition, recognition that certain traits may be allocateddifferently across tasks, and adjusting for this, will likelyimprove consistency of the evidence across studies.

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Econometric methods that account for measurement error andthat anchor measurements in real world behavior holdsubstantial promise in both fields.

Econometric methods can move the study of personality andits effects from purely predictive analyses to causal models.Econometric methods also hold promise in modeling theformation and evolution of traits over the life cycle.

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New studies should incorporate validated personality, IQ, andpreference measures, as well as outcome measures.

Prospective, longitudinal designs are best suited to this task.They should measure volatility of traits at a given age(depending on contexts and incentives faced by agents) as wellas the effects of experience on the evolution of personality.An open question, not fully addressed in this paper, is thesituational and cultural specificity of personality measures.More careful measurements are required to resolve this issue.The evidence presented here is consistent with stability oftraits with age but not their constancy.At a point in time, incentives and situations affect levels ofperformance, but personality is not entirely situation-specific.

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A topic not addressed in this paper but important for futurework is the relation of cognitive and personality traits to neuralsubstrates and biological factors.

Such a mapping would establish a firm basis for distinguishingamong these classes of traits, and also clarify distinctionsamong personality traits.The evidence assembled thus far suggests that the executivefunction is localized to the prefrontal cortex and its afferentand efferent connections (Miller and Cohen, 2001).Fear is localized to the amygdala (Calder, Lawrence, andYoung, 2001).Recently, the interest of neuroscientists has been extended totime preference (Glimcher, Kable, and Louie, 2007; McClure etal., 2004).

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While much remains to be discovered, the evidence presentedhere suggests that the systematic empirical and theoreticalstudy of personality is likely to be very fruitful for economics.

Personality traits are predictive of socioeconomic success.

They can be influenced by interventions and investment morereadily than IQ, at least after the early years.

A deeper understanding of personality traits promises to enricheconomic theory and to understand the sources of, andsolutions for, human inequality.

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