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This presentation is based on joint work with

Preferences and Personality: Heterogeneity, Determinants and Formation Armin Falk U Bonn, CEPR, IZA, CESifo and DIW Louis-André Gérard-Varet Conference in Public Economics Marseille, June 2012. This presentation is based on joint work with

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  1. Preferences and Personality:Heterogeneity, Determinants and FormationArmin Falk U Bonn, CEPR, IZA, CESifo and DIWLouis-André Gérard-Varet Conference in Public EconomicsMarseille, June 2012

  2. This presentation is based on joint work with Thomas Dohmen, David Huffman, Uwe Sunde, Anke Becker, Thomas Deckers, Fabian Kosse, Matthias Wibral, Bernd Weber, Jürgen Schupp and Gert G. Wagner

  3. Outline • Heterogeneity and stability risk attitudes • Determinants and formation (risk, trust, etc.) • Relation with non-cognitive skills (Big-5, LOC) and cognitive skills (IQ) • Main focus: risk attitudes (but also discounting, social preferences)

  4. Importance Heterogeneity: Most models assume representative agent Prevalence of different types in population Explaining outcomes (migration, holding stocks etc.) Determinants: If people are different, what determines these differences? Understanding determinants of heterogeneity helps explaining economic and social outcomes (e.g., gender wage gap) Understanding process of preference formation relevant for policy (social mobility and early childhood intervention) (Relation to IQ and Big-5) What do we measure, when we measure preferences, and is it similar to what psychologists measure (mapping?)

  5. Data and Measurement

  6. Data Requirements Reliable measures (experiments/validated survey measures) Representative level Combining information on preferences, personality and socio-demographic background and economic outcomes

  7. Data sources • German Socio-Economic Panel Study (SOEP) • Household panel survey, annual waves • More than 20,000 individuals (age 17+) in about 12,000 households • Representative of the population • Measures of attitudes, preferences, psychological traits • Extensive socio-demographic information • Possible to link children’s information with parents’ • Ukrainian Longitudinal Labor Market Survey (ULMS) • Set up like SOEP (n=8.000) • Pretest Panel SOEP • Own panel (three waves) constructed similar as SOEP • Including socio-demographics, personality indicators, experiments and DNA, (n=1000) • Lab experiments, students (n=400) • Combining survey and experimental measures • Experiments with young children (age 6-7) and their mothers (n=400) and age 8 (n=730)

  8. 1. Heterogeneity and stability (general population) 8

  9. Risk Measures • General risk question: • How do you see yourself: Are you generally a person who is fully prepared to take risks or do you try to avoid taking risks? Please tick a box on the scale, where the value 0 means: ‘unwilling to take risks’ and the value 10 means: ‘fully prepared to take risks’. • 11-point scale • Questions about willingness to take risks in specific contexts: • Health, car driving, financial matters, sports and leisure, career. • Hypothetical lottery: • 50/50 chance to double investment or lose half. • Possible to invest up to 100,000 Euros.

  10. Experimentally validated (DFHS, JEEA 2011) • 450 subjects, representative sample of adult population • Subjects answer question and take part in paid lottery experiment • Price list, with lottery: 300 or 0 Euros, with equal probability • Lottery measure standardizes for incentives and context • Risk question predicts switching point • Ordered Probit, Interval regressions, OLS regressions (n=450) • Socio-economic controls

  11. General Risk Question and Risk Taking Behavior

  12. Considerable heterogeneity: Distribution of Willingness to Take Risks in Germany

  13. Does heterogeneity explain outcomes? (DFHS, JEEA 2011) • Holding stocks • A one standard deviation increase in willingness to take risks (in financial matters) is associated with a 35 percent increase in the probability of holding stocks • Self-employment • A one standard deviation increase in willingness to take risks (general risk question) is associated with a 30 percent increase in the probability of being self-employed • Sorting into incentive schemes • Dohmen and Falk (2010 AER, EJ): variable pay, selection of teachers • Sorting into occupations based on earnings variance (BDFHS, LE, 2007) • Risk premium in wages: Mincer wage regression: for each point on 11-point scale, 2 percent higher log monthly wages. • Geographical mobility • Impact on the probability of moving residence between geographical regions (JDFHS, RStud 2010). • Educational choice, investment in health, traffic offenses, etc.

  14. Measurement issues/stability • Test-Retest correlation: 2 samples • Correlation of 0.62 and 0.6 after 5 weeks (same order of magnitude as for life satisfaction question in SOEP) • Caveat: mode effects (CAPI vs. phone) • Stability vs. measurement error over longer horizon (04/06) • Correlation of 0.5 • Stable over period of 1.5 to 2.5 years • Little scope for persistent changes in risk attitudes (important life events etc.) • Comparison to experimental measures (stability) • Anderson et al (2007): correlation of 0.5 • Our own experimental data: correlation of about 0.5

  15. Correlation between measurements at different time horizons

  16. 2. Determinants and Formation of Preferences 16

  17. Determinants and formation Gender, age and height Robustness and cultural differences: Ukraine vs. Germany Ukraine is culturally very different to Germany (Gächter et al. 2010) Role of parents: Intergenerational transmission Role of early life-circumstances (breastfeeding, SES) (Role of hormones (T))

  18. Gender and Risk Attitudes

  19. Women and older people less willing to take risks Willingness to take risks

  20. Age and Risk Attitudes

  21. Age and Risk Attitudes

  22. Taller people more willing to take risks Willing to take risks: Female Willing to take risks: Male

  23. Height and Risk Attitudes

  24. Height and Risk Attitudes

  25. see Appendix one standard deviation for the general risk question is about 2.4, the gender effect corresponds to a substantial decrease in willingness to take risks, about one quarter of a standard deviation

  26. Evidence from ULMS

  27. Risk attitudes and age in Ukraine (ULMS data)

  28. Risk attitudes and age in Ukraine (ULMS data)

  29. OLS estimates. *** p<0.01, ** p<0.05, * p<0.1, Standard errors in parentheses; Self-reported financial state is a cat. variable 1-7 from "far below the average" to "far above the average;personal income refers to income from any sources in the last 30 days.

  30. Determinants of Risk Attitudes in Different Contexts • Five domain-specific questions • Car driving • Health matters • Career matters • Financial matters • Sports and leisure activities • Correlation across contexts ranges from 0.46 to 0.61 • About 60 percent of the variation in individual risk attitudes is explained by one principal component • Determinants are the same across contexts.

  31. Determinants of other preferences and attitudes (all based on SOEP N>20.000 (DFHS, EJ 2008, EI 2009) 34

  32. Formation of preferences

  33. The role of parents • Are preferences, attitudes and beliefs transmitted from one generation to the next? • How precise and detailed is this transmission? • Is the transmission reinforced by assortative mating? • What is the role of the social environment in shaping preferences?

  34. DFHS, RES 2011

  35. Evidence from Ukraine

  36. Intergenerational Correlation: Risk Attitudes

  37. Specificity of the Transmission Process • Are children like their parents only in terms of a general disposition? • Or are children similar to their parents in even more detailed ways? • We use our context-specific risk questions, and individual trust questions: • Answers are highly correlated across questions. • But not perfectly: e.g., a parent may be risk averse in car driving, but not at all willing to take risks in financial matters. • We regress a child’s attitude in a given context on parents’ attitudes in all of the different contexts, simultaneously.

  38. Specificity: Risk

  39. Significant determinants of trust: (Age), Height

  40. Evidence from Ukraine

  41. Intergenerational Correlation: Trust

  42. Specificity: Trust

  43. Risk Attitudes and Trust

  44. Data Assortative mating? • Positive assortative mating in preferences reinforces strength and importance of intergenerational transmission • German Socio-Economic Panel • 7,278 couples with non-missing information on attitudes • General risk and trust measure as before

  45. Assortative Mating: Willingness to take risks

  46. Local environment plays a role as well At the level of 97 regions (Raumordnungsregionen) Significant effects (theory: Bisin/Verdier 2000) Does not reduce main effect Hypothetical families: positive correlations but much smaller than for true families

  47. Mechanisms Possible mechanisms Genetics (e.g., twin studies: Cesarini et al. 2009), Reuter (2009) (oxytocin receptor gene and trust) Unintentional transmission (imitation by children) Intentional effort of parents to shape the attitudes of the child (Bisin/Verdier 2000) Combination of all of these; may interact(see also Caspi et al. 2002, suggesting that gene expression is triggered in part by environmental conditions) ...

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