Sequential decision behavior with reference-point preferences: Theory and experimental evidence

1. Sequential decision behavior with reference-point preferences:Theory and experimental evidence - Daniel Schunk - Center for Doctoral Studies in Economicsand Sonderforschungsbereich 504University of Mannheim, Germany

2. Introduction • Why study sequential decision behavior? • Applications in labour economics, consumer economics, business management, etc. • Why a laboratory experiment? • What does existing literature say? • Heterogeneity • Early stopping • Research question: What is the relationship between individualpreferences and behaviour in sequential decision tasks?

3. Outline of talk 1 – THEORETICAL PART • The sequential decision problem • Development of 2 models  Hypotheses on the relationship between individual preferences and sequential decision behavior 2 – EMPIRICAL PART • Experimental design • Inference about behavior (preferences, sequential decisions) • Testing the hypotheses- Correlation analysis- Panel duration analysis- Alternative experimental design 3 – CONCLUSIONS

4. THEORETICALPART

5. The sequential decision problem Instructions: • Goal: Purchase a good that you value at 100 €. • Good sold at infinitely many locations, visiting a new location costs 1 €. • Price at each location is drawn from a discrete uniform distribution- lower bound: 75 €- upper bound: 150 € • You are allowed to recall previously rejected offers. Important: No losses !

6. 1 Search Behavior m – minimal price observed so farc– search cost per periodSt ={t,m}– state vector after t steps Optimal search rule: Risk-averse 95 Stopping rule: Constant, then fallingreservation price 90 85 Stop searching as soon as a price lower than or equal to € Xt is found. 80 75 Risk-seeking

7. m – minimal price observed so farc– search cost per periodF() – distribution function of prices 2 Search Behavior Reference point model: Reference point ? Stop search Higher payoff achieved  Gain F(m-c) Continue search No higher payoff achieved  Loss 1-F(m-c)

8. 2 Search Behavior Reference point model: Loss-averse 95 Stopping rule: Constant, then fallingreservation price 90 85 Stop searching as soon as a price lower than or equal to € Xt is found. 80 75 Loss-seeking

9. 1 2 1 2 We have 2 models… EU-preferences Risk aversion explains level of reservation price path RP-preferences Loss aversion explains level of reservation price path

10. EMPIRICAL PART

11. Experimental Design: Overview 2 parts of the experiment Obtained data • 1 : Lottery questions • 2 : Price search task Preferences: Risk attitude, loss attitude Sequential decision behavior

12. Use certainty equivalent method 100% x Experiment: Part 1(Risk Attitude) B € 50% ~ 50% A € Lottery I Lottery II x4 x3 x2 x1 x0 [€] Estimate risk attitudeαi(CRRA) andγi (CARA)  37% risk-neutral, 37% risk-averse, 26% risk-seeking

13. Experiment: Part 1(Loss Attitude) • Use trade-off method Estimate loss aversion indexλi x 50% ~ 100% 0 € 50% -A € Lottery I Lottery II  69% loss-averse, others loss-neutral

14. Experiment:Part 2(Price search task) Instructions: • Goal: Purchase a good that you value at 100 €. • Good sold at infinitely many locations, visiting a new location costs 1 €. • Price at each location is drawn from a discrete uniform distribution- lower bound: 75 €- upper bound: 150 € • You are allowed to recall previously rejected offers. • Play 15 payoff-relevant search games, no losses ! • Length of practice period „ad libitum“ Assume each subject i follows a single decision rule • Statistical classification algorithm assigns decision rule di • Considerable heterogeneity in sequential decision behavior

15. Testable Hypotheses

16. All results hold under CARA and CRRA specification of the utility function Results (1) • Correlation analysis:Investigate correlation between preference parameters and search parameters Loss attitude correlates, risk attitude does not correlate= Support for(H3)and(H4) • Unobserved effects panel duration analysis:Exploit …discrete time-to-event nature, and …panel nature of data in multivariate model, and explain stopping behavior with preference parameters Loss attitude has predictive power, risk attitude not= Support for(H3) Note: Relationships are particularly strong on a subgroup that is classified based on additional questions about decision behavior

17. Results (2) (c) Alternative experimental design - uses Abdellaoui-(2000) procedure for elicitation of risk attitude - confirms that risk attitude is not related to search behavior (d) Weber et al.- (2002) psychometric instrument for measuring risk attitude - measures risk attitude on different domains - risk attitude measured on the domain of gambling is related to search behavior

18. Conclusions • Considerableheterogeneity in sequential decision behavior • Loss aversion helps explain heterogeneity, risk aversion not;confirmed in different experimental designs • Many subjects set reference pointsin sequential decision tasks • Relevance of findings: • In general:Labor and consumer economics, marketing and finance(e.g.: Eckstein/V.d. Bergh, 2005; Gneezy, 2003; Zwick et al., 2003) • In the context of my research:Related to work on life-cycle decision-making and statistical classification of individual differences in dynamic choice contexts