Search results

Stated choice experiments can be used to estimate the parameters in discrete choice models by showing hypothetical choice situations to respondents. These attribute levels in each choice situation are determined by an underlying experimental design. Often, an orthogonal design is used, although recent studies have shown that better experimental designs exist, such as efficient designs. These designs provide more reliable parameter estimates. However, they require prior information about the parameter values, which is often not readily available. Serial efficient designs are proposed in this paper in which the design is updated during the survey. In contrast to adaptive conjoint, serial conjoint only changes the design across respondents, not within-respondent thereby avoiding endogeneity bias as much as possible. After each respondent, new parameters are estimated and used as priors for generating a new efficient design. Results using the multinomial logit model show that using such a serial design, using zero initial prior values, provides the same reliability of the parameter estimates as the best efficient design (based on the true parameters). Any possible bias can be avoided by using an orthogonal design for the first few respondents. Serial designs do not suffer from misspecification of the priors as they are continuously updated. The disadvantage is the extra implementation cost of an automated parameter estimation and design generation procedure in the survey. Also, the respondents have to be surveyed in mostly serial fashion instead of all parallel.

It has long been recognised that humans draw from a large pool of processing aids to help manage the everyday challenges of life. It is not uncommon to observe individuals adopting simplifying strategies when faced with ever increasing amounts of information to process, and especially for decisions where the chosen outcome will have a very marginal impact on their well-being. The transactions costs associated with processing all new information often exceed the benefits from such a comprehensive review. The accumulating life experiences of individuals are also often brought to bear as reference points to assist in selectively evaluating information placed in front of them. These features of human processing and cognition are not new to the broad literature on judgment and decision-making, where heuristics are offered up as deliberative analytic procedures intentionally designed to simplify choice. What is surprising is the limited recognition of heuristics that individuals use to process the attributes in stated choice experiments. In this paper we present a case for a utility-based framework within which some appealing processing strategies are embedded (without the aid of supplementary self-stated intentions), as well as models conditioned on self-stated intentions represented as single items of process advice, and illustrate the implications on willingness to pay for travel time savings of embedding each heuristic in the choice process. Given the controversy surrounding the reliability of self-stated intentions, we introduce a framework in which mixtures of process advice embedded within a belief function might be used in future empirical studies to condition choice, as a way of increasingly judging the strength of the evidence.

Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

In this paper, we analyze statistical properties of stated choice experimental designs when model attributes are functions of several design attributes. The scheduling model is taken as an example. This model is frequently used for estimating the willingness to pay (WTP) for a reduction in schedule delay early and schedule delay late. These WTP values can be used to calculate the costs of travel time variability. We apply the theoretical results to the scheduling model and design the choice experiment using measures of efficiency (S-efficiency and WTP-efficiency). In the simulation exercise, we show that the designs based on these efficiency criteria perform on average better than the designs used in the literature in terms of the WTP for travel time, schedule delay early, and schedule delay late variables. However, the gains in efficiency decrease in the number of respondents. Surprisingly, the orthogonal design performs rather well in the example we demonstrated.

Patients and health professionals often make decisions which involve a choice between discrete alternatives. This chapter reviews the econometric methods which have been developed for modelling discrete choices and their application in the health economics literature. We start by reviewing the multinomial and mixed logit models and then consider issues such as scale heterogeneity, estimation in willingness to pay space and attribute non-attendance.

Studies in psychology have long revealed that making personal choice involves multiple motivational consequences. It has only been recent, however, that the literature on neuroscience started to examine the neural underpinnings of personal choice and motivation. This chapter reviews this sparse, but emergent, body of neuroscientific literature to address possible neural correlates underlying personal choice. By conducting the review, we encourage future systematic research programs that address this topic under the new realm of “autonomy neuroscience.” The chapter especially focused on the following motivational aspects: (i) personal choice is rewarding, (ii) personal choice shapes preference, (iii) personal choice changes the perception of outcomes, and (iv) personal choice facilitates motivation and performance. The reviewed work highlighted different aspects of personal choice, but indicated some overlapping brain areas – the striatum and the ventromedial prefrontal cortex (vmPFC) – which may play a critical role in motivational processes elicited by personal choice.

Citizens are demanding better performance from governments and they are increasingly aware of the costs of poor management and corruption. In view of scarce resources and the major transformations already underway in the global economy, identification and awareness of good governance and preventing corrupt practices have become key to ensuring structural reforms and critical investments necessary for encouraging, sustaining, and enhancing economic growth and competitiveness. Political corruption severely undermines government legitimacy and weakens the development of political, economic, social, and environmental structures.

There is extensive evidence that decision-makers, faced with increasing information load, may simplify their choice by reducing the amount of information to process. One simplification, commonly referred to as attribute non-attendance (ANA), is a reduction of the number of attributes of the choice alternatives. Several previous studies have identified relationships between varying information load and ANA using self-reported measures of ANA. This chapter revisits this link, motivated by recognition in the literature that such self-reported measures are vulnerable to reporting error.

This chapter employs a recently developed modelling approach that has been shown to effectively infer ANA, the random parameters attribute non-attendance (RPANA) model. The empirical setting systematically varies the information load across respondents, on a number of dimensions.

Confirming earlier findings, ANA is accentuated by an increase in the number of attribute levels, and a decrease in the number of alternatives. Additionally, specific attributes are more likely to not be attended to as the total number of attributes increases. Willingness to pay (WTP) under inferred ANA differs notably from when ANA is self-reported. Additionally accounting for varying information load, when inferring ANA, has little impact on the WTP distribution of those that do attend. However, due to varying rates of non-attendance, the overall WTP distribution varies to a large extent.

This is the first examination of the impact of varying information load on inferred ANA that is identified with the RPANA model. The value lies in the confirmation of earlier findings despite the evolution of methodologies in the interim.