Modelling the choice for alternative cars
1.2. Scope and modelling framework
In this section we first discuss the scope of the choice analysed in this chapter.
In a second subsection we introduce the modelling framework of discrete choice theory and in a last subsection we discuss the use of stated preference versus revealed preference data in model estimation.
1.2.1. Scope
A consumer who wants to buy a new car faces a rather extended choice set of cars that are available on the market. Different values for brand, car body, colour, comfort equipment, engine size etc. define the range of choice alternatives and the consumer has to make a decision on his preferred combination.
In this chapter we will limit our attention to the choice between technologies, both conventional and alternative. With technology we indicate the combination of fuel (including electricity), engine and power-train. This means that the
1.2. Scope and modelling framework
choice we study is the one between different vehicles that are identical in all properties except for the driving technology.3
We will limit the geographical scope of our research to the preferences by Flemish consumers. Conventional technologies that are broadly available on the Flemish car market include gasoline, diesel and LPG fuelled internal combustion engines.
For alternative technologies, a broad range of fuel, engine and power-train combinations have been discussed in the past. Arcoumanis (2000) and Interna-tional Energy Agency [IEA] (1999) focus on alternative fuels, whereas Burgwal, Dijkhuizen, Mourad, Smokers, and Winkel (2001) provide an overview of hybrid power-train technologies as well as electrical battery and fuel cell powered vehicles. Verbeiren, De Vlieger, Pelkmans, De Keyser, and Springael (2003) conduct a sustainability assessment of a varied range of conventional and alternative technologies.
To allow for the analysis of the potential of alternative technologies (which will be conducted in chapter 3) we need to define the technological scope accordingly. The scope should cover a range of (improved) conventional and innovative technologies that are sufficiently diverse in technical and economic characteristics and for which consistent data is available to allow for simulation. The selection by Verbeiren et al. (2003) meets this requirements, we decide to use it as definition of the scope of our study (see table 1.1).
We will limit the analysis in this chapter to the choice made by private consumers, and leave the technological preferences of business consumers beyond the scope of our research. Some earlier private car technology choice models include a separate sub-model for company and private car purchase (e.g. COWI A/S, 2002). There is however very limited knowledge on the size,
3In our study we assume that the choice between technologies is independent from brand, car body etc.
Table 1.1.Technological scope of the choice analysed (based on Verbeiren et al., 2003) Technology
Gasoline Gasoline hybrid Diesel
Diesel hybrid LPG
CNG CNG hybrid Hydrogen Hydrogen hybrid Hydrogen fuel cell Battery electric
composition and specific dynamics of the company car stock for Flanders.
Lacking insight in key aspects of the structure of this specific car market hampers the study of specialised issues such as preferences for alternative vehicle technologies.4
1.2.2. Discrete choice theory
Discrete choice theory provides a broad range of modelling frameworks. An extended introduction on the topic is provided in appendix A. An in depth discussion on discrete choice theory can be found in Anderson et al. (1992);
Ben-Akiva and Lerman (1985); K. Train (1986/1990); K. E. Train (2003).
The consumer who considers the purchase of a car faces a discrete choice problem. Discrete choice theory models the probability that a consumer n chooses a given alternative j in choice situation5m as a function of the random6 utility Ujmnof the alternatives, expressed as:
Ujmn =Vjmn+ejmn (1.1)
where:
• Vjmn: the deterministic part of the utility for alternative j as obtained by consumer n in choice situation m—we will in this section assume that Vjmn is linear in parameters: Vjmn = β0xjmn with β a vector of coefficients and xjmna vector of decision variables relating to consumer n and alternative j in choice situation m;
• ejmn: the stochastic part.
The consumer then chooses the alternative with the highest utility (utility maximisation).
The multinomial logit model (MNL) assumes a Gumbel distribution with variance of the stochastic utility Var(ejmn) =σ2π2/6.7This assumption results in a closed form for the choice probability of alternative j chosen by consumer n in choice situation m:
Pjmn= e
β0xjmn/σ
∑ieβ0ximn/σ (1.2)
As we can see from expression (1.2), any linear transformation of xjmn
does not affect the choice probabilities. This makes it impossible to identify
4Wuyts (2009) has conducted a survey of company car use in Flanders. His focus was mainly on issues related to labour economics, which fall well beyond the scope of our study.
5The index for choice situation m is introduced here to allow for the repeated choice character of survey data.
6Where we discuss or apply discrete choice theory we will use the terminology that is common in the literature (for instance K. E. Train, 2003). Random utility could be understood as being probabilistic in character.
7Note that throughout this and the subsequent chapter σ denotes the scale parameter of the Gumbel distribution and not the variance which is noted as Var(e). A full overview of notations and acronyms is provided in the introducing sections.
1.2. Scope and modelling framework
the value of the scale parameter σ of the stochastic part separately from the true coefficients β of the deterministic part. In estimation the utility Ujmnis scaled by a factor 1/σ which normalises the variance of the stochastic part to π2/6. The estimated coefficients ˆβ include the scale parameter σ of the stochastic utility:
ˆβ=β/σ (1.3)
Appendix A discusses how the scale parameter of two independent model estimations can be compared using the ratio of their respective coefficient estimates ˆβ.
The nested multinomial logit model (NL) extends the MNL specification by allowing for correlation in unobserved preferences (stochastic utility) for a subset of alternatives. A partition structure defined by the researcher groups the alternatives in subdivisions or nests. The more substitutable alternatives are grouped in lower nests in the tree structure. For each nest k the coefficient λk (0≤λk ≤1) is a measure for the correlation between the alternatives in nest k, with values closer to unity indicating less correlation.
The mixed logit model (ML) is a further extension to the multinomial logit specification that provides a very flexible modelling framework. It defines the utility Ujmnas:
• α a vector of fixed coefficients
• µjmn a vector of random terms with mean zero and probability distribu-tion f(µjmn), any distribution can be used (independence over j, m or n is not a necessary condition)
• xjmnand zjmnvectors of observed variables
• ejmni.i.d. Gumbel distributed with scale parameter σ normalised to unity (independent over all alternatives j, choice situations m and respondents n)
In order to better understand the potential of the mixed logit specification to account for a repeated choice situation, we rewrite the utility formula (1.4) as:
Ujmn =α0xjmn+µn0zjmn+ejmn (1.5) with µna vector of random terms with mean zero which are independent for all respondents n (but constant over choice sets m).
The error terms µnintroduce correlation between the utility Ujmnof alter-natives j of the different choice sets m faced by the same respondent. The vector zjmnmay or may not include the same variables as xjmn, this depends on the correlation pattern studied.8
8Based on the discussion of the mixed logit specification by Batley et al. (2003).
1.2.3. Stated preference and revealed preference
To analyse the choice behaviour of consumers, we need an observation data set. Roughly two approaches exist for the collection of the data set for the analysis of technology choice: revealed preference and stated preference.
The revealed preference approach uses observations of actual choices made by consumers. This approach has the major advantage that there is no doubt that the data set reflects real world behaviour.9 But it may be difficult to use these data for model estimation. Brownstone et al. (2000) indicate that a major difficulty in estimating revealed preference car choice models is the correlation in decision variables. A second problem that arises is the correct definition of the choice made: both the choice set and the choice variables have to be defined by the researcher. Finally, studying preferences for new car technologies may necessitate to assess the effect of values of characteristics that are beyond the range observed in the revealed data sample.
The stated preference approach overcomes these difficulties by using a custom designed survey to collect the choice observations. This provides the researcher with much control over the choice sets faced by each respondent:
both the number of alternatives, the variables in which they differ and the levels of these variables are controlled in the survey setup. This allows to eliminate correlation in choice variables as well as to eliminate the influence of non-observed choice alternatives or variables. The major disadvantage of the stated preference approach is that what is measured are the intentions of the consumer, without any guarantee that they correspond to real world behaviour. This may be a specific concern when the levels of presented decision variables are well outside the range experienced by the respondent in real world behaviour.
Research focusing on the choice between conventional technologies has made use of revealed preference data (some examples are De Jong, 1996;
Verboven, 1996). However, Bunch et al. (1993) argue that the current limited supply of alternative private car technologies excludes the revealed preference approach for the analysis of the choice for alternative technologies.
In this chapter we will follow the approach by Bunch et al. (1993) and conduct a survey in order to collect a stated preference data set that allows for the analysis of the choice for alternative technologies. Before we design the survey, a focus group is conducted in order to gain a better qualitative understanding of the choice process. An overview of existing experience on the stated preference approach in the analysis of alternative car technology choice allows us to optimise the survey design.
In a subsequent chapter (chapter 2) we will compare our stated preference approach to an existing revealed preference choice model for conventional technologies and discuss how both models can be integrated in order to
9Assuming no measurement bias.