Asymmetric search and prospect theory: choice experiment on consumer willingness to search in the gasoline retail market

Abstract

We use an internet survey conducted among a representative random sample of drivers in the State of Ohio consisting of a choice experiment designed to examine the mechanism driving asymmetric search. The internet survey affords us the opportunity to overcome endogeneity difficulties by imposing exogenous price changes on gasoline consumers to examine the decision-making process behind intended search decisions. We randomly assigned participants to one of five price treatments (either 2.5 or 5% above or below their reported expected price, or no change). We provide a simple empirical model to derive testable implications under prospect theory and use the internet survey to test them. Results indicate that among the respondents who faced prices below their expected price, only 12% chose to search, whereas 45% searched when prices were above. Further, we find results consistent with asymmetric search being driven by prospect theory. The change in consumers’ willingness to search is twice as large when prices exceed expectations by 2.5% relative to when prices exceed them by 5% suggesting that consumers derive utility of finding a good deal evaluated relative to a reference price. We show that this result is inconsistent with standard utility theory or consumers using alternative reference prices.

Keywords:

• choice experiment
• consumer price search
• loss aversion
• gasoline retail market

JEL Classification:

• D83
• D03

Acknowledgements

The authors would like to thank Peter McGee, Matt Lewis, and discussants at the 2012 International Industrial Organization Conference and 2009 Southern Economics Association Meetings for their helpful advice and suggestions.

Notes

¹ This article is the second of a series of two; in the first paper, we tested for limited attention to search costs in the gasoline retail market (see Castilla and Haab, 2013 for details).

² Such as the degree of vertical integration (Shepard, 1991; Hastings, 2004; Tappata, 2006 find it is not particularly relevant in the gasoline retail market); zone pricing, that is, if there are regulations about the way producers should set their prices in different areas (see Deck and Wilson, 2008; Verlinda, 2008); market concentration (Hastings and Gilbert, 2005); wholesale price discrimination practices (Hastings and Gilbert, 2005; Borenstein et al., 1997); and producer heterogeneity.

³ There is a search cost associated with driving to the next gas station: the gasoline spent driving, plus the time it takes to get there. In the design, we told the consumer that the next gas station was one mile down the road, but provided him/her with different amounts of information regarding the monetary value of the search cost. Consumers were randomly assigned to one of the following search cost treatments: (1) the monetary value of the gasoline spent driving for one mile considering their car’s mileage per gallon (MPG), (2) 5 minutes it would take them to get to the next gas station or (3) both. The remaining respondents are used as a baseline group and were not given an explicit cost treatment.

⁴ In order to develop a representative panel of the US population, Knowledge Networks initially utilized list-assisted Random Direct Dialing (RDD) sampling techniques based on a sample frame of the US residential landline telephone universe. However, according to the Center for Disease Control, approximately 21% of US households cannot be contacted through RDD sampling. Thus, to make it fully representative, Knowledge Networks added an Address Based Sample (ABS) frame in response to the growing number of cell-phone-only households that are outside of the RDD frame. ABS involves probability-based sampling of addresses from the US Postal Service’s Delivery Sequence File. Randomly sampled addresses are invited to join the KnowledgePanel through a series of mailings and in some cases telephone follow-up calls to nonresponders when a telephone number can be matched to the sampled address. Note that recruitment sampling is done without replacement, thus numbers already fielded do not get fielded again. All new panel members complete a separate profile survey that collects essential demographic information such as gender, age, race, income and education to create a personal member profile. This information is used to determine eligibility for specific studies, and for weighting purposes. This information is updated annually.

⁵ To reduce the effects of nonresponse and noncoverage bias, Knowledge Networks applies a post-stratification adjustment using demographic distributions from the most recent data from the Current Population Survey (CPS) and the Pew Hispanic Center Survey as benchmarks. Thus, when the sample suffers under/overrepresentation, more people are sampled from the panel to complete the survey. Further information on the post-stratification process is available upon direct request from the authors.

⁶ If indirect utility is quasi-concave in prices (which is inconsistent with quasi-concave or concave utility functions) then the trend would be