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

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 individual preferences and behaviour in sequential decision tasks?

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

THEORETICAL PART

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 !

Search Behavior Stop searching as soon as a price lower than or equal to € X t is found. Optimal search rule: Stopping rule: Constant, then falling reservation price Risk-averse Risk-seeking 1 m – minimal price observed so far c – search cost per period S t ={t,m} – state vector after t steps

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

Search Behavior Reference point model: Loss-averse Loss-seeking Stop searching as soon as a price lower than or equal to € X t is found. Stopping rule: Constant, then falling reservation price

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

EMPIRICAL PART

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

Experiment: Part 1 (Risk Attitude) Use certainty equivalent method  37% risk-neutral, 37% risk-averse, 26% risk-seeking 50% A € Lottery ILottery II x 100% ~ [€] x0x0 x1x1 x2x2 x3x3 x4x4 Estimate risk attitude α i (CRRA) and γ i (CARA) B €

Experiment: Part 1 (Loss Attitude) Use trade-off method Estimate loss aversion index λ i  69% loss-averse, others loss-neutral 50% x -A € Lottery ILottery II 100% ~ 0 €

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. Statistical classification algorithm assigns decision rule d i Considerable heterogeneity in sequential decision behavior Play 15 payoff-relevant search games, no losses ! Length of practice period „ad libitum“  Assume each subject i follows a single decision rule

Testable Hypotheses Preference elicitation part (Part 1) Sequential decision part (Part 2) EU preferences (H1) risk aversion# search steps (H2) risk aversionrisk aversion RP preferences (H3) loss aversion# search steps (H4) loss aversionloss aversion

Results (1) All results hold under CARA and CRRA specification of the utility function (a)Correlation analysis: Investigate correlation between preference parameters and search parameters  Loss attitude correlates, risk attitude does not correlate = Support for (H3) and (H4) (b)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

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

Conclusions Considerable heterogeneity in sequential decision behavior Loss aversion helps explain heterogeneity, risk aversion not; confirmed in different experimental designs Many subjects set reference points in 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