Design and Use of Psychometric Tools : Measurement and Modeling in In Search of The B-Factor

Transcription

1 Design and Use of Psychometric Tools : Measurement and Modeling in In Search of The B-Factor Joanne Yoong USC Center for Economic and Social Research Saw Swee Hock School of Public Health, NUS and NUHS London School of Hygiene and Tropical Medicine Jonathan Zinman Dartmouth College, JPAL, NBER, etc. Academic Director, U.S. Household Finance Initiative, Innovations for Poverty Action Victor Stango UC Irvine This research is funded by the Russell Sage Foundation, NIH, NUS Startup

2 What is an economic phenotype? Core cognitive, affective and behavioral components of economic behaviors (NIH) Examples : risk attitudes, loss aversion, delay discounting, cognitive control, impulsivity, anticipatory affect, numeracy, emotion regulation, sensation seeking, conscientiousness. Related to : health and economic outcomes in later life, such as educational attainment, risk-taking, job stability, health behavior (initiation and maintenance), savings, insurance, etc.

3 We Don t Think Systematically About Systematic Bias Behavioral economics = victim of its own success Proliferation of behavioral factors & tests developed and tested in different settings Models deliver similar predictions, but different welfare implications Lack of readily usable, implementable tools Need to meaningfully think about what a phenotype means & how behavioral decision making factors correlate with each other and economic decisions Are there one or more behavioral common factors that can provide the building block for a tractable, portable model of behavioral intertemporal choice?

4 Spearman s G-factor

5 Spearman s G-factor Measurement Performance across different cognitive tasks is very correlated Modeling Extract general factor of underlying intelligence IQ testing Implications Explains a large part of variation (not all) Correlates with biology /outcomes Heritable

6 Spearman s G-factor Performance across different cognitive tasks is very correlated Can use factor analysis to extract general factor of intelligence measured by IQ Is there a B-factor? Has biological correlates Related to important outcomes Heritable

7 Cross-cutting B-factor Biases in valuation EGB Anchoring Biases in preferences Time inconsistency Loss aversion Biases in expectation Overconfidence Overoptimism DECISION

8 Russell What Sage-NIH-NUS did we do? Study Development Measurement Analysis Translation Need to gather comprehensive taxonomy of behavioral measurement tools currently in use Simultaneously measure a large set of behavioral factors in decision making in large, populationrepresentative sample Also measure financial condition, health status, labor market status, and to soft pulls of respondent credit reports. Identify which factors correlate with each other and with outcomes Identify underlying common factors Develop modular questions (for multilevel factors) & tools for application

9 What do we measure? Initial 6 months scan of taxonomy of 50++ tests Risk aversion Self-control/time preferences Loss aversion and narrow bracketing Overconfidence & (Over-)optimism Limited attention/memory Anchoring Exponential growth bias Awareness/sophistication of one s biases/limitations Basic (in)numeracy General cognitive ability/intelligence Cognitive Relevant domain experience in health/finance

10 Why Online Surveys? Effectiveness Ease of programming games and adaptive tasks Straightforward randomization Easy and automatic data capture Visuals and animations Efficiency Need to conduct many tests in rapid succession Forward-looking Future of measurement and surveying

11 Risk Aversion : Barsky et al (HRS)

12 Risk Aversion : Holt-Laury Multiple Price List

13 Risk Aversion : Andreoni-Sprenger Convex Time Budget Each individual makes 24 choices allocating 100 hypothetical tokens between (weakly) smaller-sooner and larger-later amounts.

14 Other relevant information

15 Example : Decision Quality (Choi et al )

16 Example: Java - Modified Stroop

17 Survey Populations RAND American Life Panel Online survey fielded in ,500 subjects from sample of US households 25 minutes x 2 for our questions + 1 optional task Linkable to other modules With measures of risk aversion, discounting On financial status and financial literacy On other outcomes: labor market, health Singapore INSIGHT In planning Online survey to be recruited from population To be compared with ALP results

18 Early Sightings Of The b-factor present bias: $ present bias: $ present bias: c certainty prem ambig averse loss averse narrow brackets overconf level overconf precision underest underest FV: loan i- rate growth underest FV: decline NBLLN hot hand cold hand p(heads)= 10% agenhon regrets forgets task present bias: c (0.990) certainty prem (0.040) (0.390) (0.190) ambig averse (0.060) (0.860) (0.910) (0.050) loss/small- stakes risk averse (0.020) (0.760) (0.430) (0.720) (0.560) narrow brackets (0.010) (0.090) (0.580) (0.400) (0.350) (0.870) overconfident level (0.040) (0.060) (0.550) (0.070) (0.150) (0.020) (0.620) overconfident precision (0.010) (0.010) (0.060) (0.660) (0.900) (0.210) (0.590) (0.800) (0.020) underest loan interest rate (0.010) (0.030) (0.040) (0.010) (0.030) (0.030) (0.010) (0.500) (0.800) (0.350) (0.170) (0.590) (0.200) (0.330) (0.720) underest future value: growth (0.050) (0.070) (0.050) (0.280) (0.910) (0.070) (0.170) (0.070) (0.060) (0.180) (0.900) underest future value: decline (0.030) (0.020) (0.040) (0.870) (0.630) (0.320) (0.310) (0.510) (0.210) (0.100) (0.300) (0.120) NBLLN (0.020) (0.030) (0.010) (0.080) (0.150) (0.400) (0.020) (0.330) (0.840) (0.620) (0.010) (0.070) (0.010) (0.350) hot hand (0.010) (0.050) (0.020) (0.580) (0.330) (0.030) (0.760) (0.050) (0.470) (0.570) (0.180) (0.090) (0.760) cold hand (0.030) (0.020) (0.060) (0.140) (0.230) (0.280) (0.520) (0.380) (0.040) (0.250) (0.190) (0.900) (0.010) (0.050) (0.020) p(heads)=10% (0.020) (0.020) (0.040) (0.010) (0.170) (0.150) (0.370) (0.070) (0.010) (0.550) (0.880) (0.990) (0.110) (0.710) (0.550) any regrets re: inagenhon (0.010) (0.030) (0.030) (0.940) (0.220) (0.750) (0.810) (0.330) (0.930) (0.830) (0.300) (0.700) (0.700) (0.860) (0.050) (0.170) (0.200) (0.320) forgets task (0.020) (0.010) (0.020) (0.060) (0.020) (0.570) (0.010) (0.580) (0.830) (0.600) (0.440) (0.350) (0.040) (0.550) (0.320) (0.460) (0.010) (0.850) (0.590) (0.470) (0.410)

19 Challenges Assumes individual differences are key. Measurement error. Tradeoff between: measuring small set of factors well (e.g., more quantitatively) measuring larger set of factors less well (e.g., more qualitatively, relatively) Some factors: no generally accepted method for measuring at individual level.

20 Genotype Genetic makeup Economic Phenotype Observable Physical Traits B-factor? Environment

21 What are the implications? For academics Understanding the potential for a tractable portable model of choice that applies across domains e.g. health/finance/labor Availability of standard battery for surveys as well as potentially a new way For practice / policy Develop a tool(s) that could be quickly administered to understand underlying factors as well as domain-level factors Use for assessment of creditworthiness, risk preferences Use for tailoring health interventions, approaches to patient management Stay tuned for more

22 Thank you! Joanne Yoong USC Center for Economic and Social Research Saw Swee Hock School of Public Health, NUS and NUHS London School of Hygiene and Tropical Medicine