Abstract
Discrete choice experiments (DCE) are increasingly used to quantify the demand for improvements to services provided by regulated utility companies and inform price controls. This form of preference elicitation, however, often reveals a high frequency of status quo (SQ) choices. This may signal an unwillingness of respondents to evaluate the proposed trade-offs in service levels, questioning the welfare theoretic interpretation of observed choices and the validity of the approach for regulatory purposes. Using the methodology for DCE in the regulation of water and sewerage services in England and Wales, our paper contributes to the understanding of SQ choices in several novel dimensions. First, we control for the perception of the SQ and the importance of attributes in day-to-day activities. Second, we use a split sample design to vary both the description of the SQ and the survey administration mode (online vs. in-person). Third, the service attributes can both improve or deteriorate, so that the SQ is not necessarily the least-cost option. Fourth, we examine SQ choices in individual choice tasks and across all tasks so as to identify the determinants of serial SQ choices. Our results suggest that individual SQ choices mostly reflect preferences and thus represent important information for the regulator. However, serial SQ choices are mainly driven by cognitive and/or contextual factors, and these responses should be analysed as part of standard validity tests.
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Notes
Based on the assumption that consumers derive utility from characteristics of products (Lancaster 1966; Rosen 1974), a DCE simulates market transactions by constructing sets of alternative combinations of attributes (service characteristics) and requires respondents to select their most preferred alternative in a number of choice occasions. Trading-off various aspects of service provision with changes in utility bills reveal preferences for independent changes in each attribute. Originally applied in the context of transportation (Ben-Akiva and Lerman 1985), its application in recent years has encompassed marketing (e.g. Zwerina 1997), health (e.g. Ryan 1999), and the environment (e.g. Adamowicz et al. 1994).
The Water Services Regulation Authority (Ofwat) regulates price-setting among water and sewerage companies in England and Wales. The amount by which consumer bills can change over time is determined by the five-yearly Price Review process, through which Ofwat scrutinises the proposed business plans of the regulated companies. It has been established that plans should be informed by the systematic comparison of the costs and benefits of service improvements (e.g. Ofwat 2007), and explicitly take into account the preferences of customers (Ofwat 2011).
In the context of regulation in England and Wales, the widespread use of DCEs in the development of utilities’ investment plans has generated significant scrutiny of stated preference methods by all stakeholders involved in the process (including the companies themselves, the regulator, and consumer representative groups, see UKWIR 2010).
There exist a number of other empirical challenges associated with the use of DCEs. These include hypothetical bias (Diamond and Hausman 1994; List 2001), incentive compatibility of the choice format (Harrison 2007), task complexity (Swait and Adamowicz 2001), and preference ‘anomalies’ (Bateman et al. 2009; Day and Prades 2010). Importantly, evidence from controlled field experiments have provided empirical support for the DCE approach to estimate marginal WTP in terms of hypothetical bias (List et al. 2006). Further, Vossler et al. (2012) have shown that DCEs can induce truthful revelation of preferences if choices are perceived to have a chance of affecting policy. While these issues are not directly studied in the present paper, the design of our survey instrument builds on these results to minimise their potential implications.
A status quo ‘bias’ can be interpreted as a manifestation of loss aversion in a multiple good context (see Rabin 1998, for a discussion). SQ choices may then be due to implications of losses appearing larger than the gains of other goods.
Two early papers by Hartman et al. (1990, 1991) studied the propensity of respondents to stay with the SQ in a contingent valuation survey evaluating the demand for electric service reliability. While these paper document what is called a SQ ‘bias’, they do not study the determinants of these choices.
The SQ ASC, together with an additional error term (or error component), controls for the role of unobserved sources of utility for the SQ and captures the fact that the perception of the SQ may systematically differ from the experimentally specified alternatives. However, while a positive SQ ASC to rationalise observed choices provides direct evidence about the prevalence of the SQ option in DCE choices (or its market share), it is inherently difficult to assess whether it reflects preferences for the SQ or whether it is a feature of the preference elicitation method.
Figures are rounded for easier interpretation by respondents based on testing in focus groups and cognitive interviews.
In particular, this allows us to check that restricting the sample to the first block of services presented does not alter the welfare estimates.
Because we use an efficient experimental design, and have multiple observation per respondent, the sample of respondents is relatively modest. As we show below, the chosen sample size achieved the objective since all marginal WTP estimates are preciesly estimated.
This specification mirrors the error-component structure introduced by Scarpa et al. (2007) which allows the scale of the error variance to differ between the SQ option and the hypothetical alternatives. We favour the random parameter interpretation of the SQ ASC in this analysis as it provides direct evidence on preference heterogeneity for the SQ.
The model is estimated with simulated maximum likelihood, and we use 500 Halton draws to approximate the integral of the unconditional likelihood of each panel choices. Standard errors are clustered at the individual level to account for the fact that each respondent makes four different choices for each block of service.
Table 3 Discrete choice experiment: WTP-space estimation Interaction terms between these two variables were also tested but did not yield further insights.
Because of imbalances across subsamples in terms of preferences for the SQ, it may be the case that the impact of preferences for the SQ is driven by the inclusion of the online subsample. As we report in Appendix B, the main conclusions from our analysis, and in particular the importance of preferences for the SQ, are preserved if we restrict the sample to just the CAPI subsamples.
All other variables included are at the sample mean.
Note that we do not find evidence supporting the presence of non-linearities for these variables.
As for the individual SQ choices restricting the sample to CAPI respondents only does not affect our main conclusions (see Appendix C).
Again all other variables are kept at the sample mean.
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Acknowledgments
We thank Ian Bateman, Ali Chalak, Sergio Colombo, Scott Reid, Ken Willis, and participants at the EAERE conference for helpful comments on this work as well as two anonymous referees who considerably helped improve the paper. Excellent research assistance has been provided by Lawrie Harper-Simmonds. The research presented in this paper is based on a study undertaken for Thames Water Utilities Limited. The views expressed in this paper and any remaining errors are those of the authors alone.
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Lanz, B., Provins, A. Using discrete choice experiments to regulate the provision of water services: do status quo choices reflect preferences?. J Regul Econ 47, 300–324 (2015). https://doi.org/10.1007/s11149-015-9272-4
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DOI: https://doi.org/10.1007/s11149-015-9272-4
Keywords
- Cost-benefit analysis
- Regulated utilities
- Economic valuation
- Discrete choice experiments
- Individual decision making
- Status quo effect