We propose a structural econometric model that incorporates altruism towards other household members into the willingness to pay for a public good. The model distinguishes preferences for public good improvements for oneself from preferences for improvements for other household members. We test for three different types of altruism - ‘pure self-interest’, ‘pure altruism’ and ‘public-good-focused non-pure

Activity travel pattern of pilgrims in a religious setting is a complex process. Extant literature on religious tourism has taken minimal efforts in addressing such complexity, which has led to a paucity of information on preferred activity participation destinations and trip chain sequences of pilgrims. So, the present research objective is two-fold. First, to examine the causal effects of socio-demographics

This paper argues that choice modelling could be a gainful approach for all social sciences, while at the same time disciplines such as sociology and political science could contribute significantly to the future development of choice modelling. So far choice modelling has mainly been applied in disciplines that investigate types of consumption choices, be it marketing to investigate preferences for

To appropriately weight dimensions of quality of life instruments for health economic evaluations, population and patient preferences need to be elicited. Two commonly used elicitation methods for this purpose are discrete choice experiments (DCE) and case 2 best-worst scaling (BWS). These methods differ in terms of their cognitive burden, which is especially relevant when eliciting preferences among

Background and objectives Additional funding will be needed to meet the growing demand for health and social care in the UK. What is the most acceptable way to raise it? Options range from taxation to mandatory insurance, voluntary insurance and user charges. We sought to analyse the preferences of the UK general public. Methods An online quantitative survey embedded within a DCE was undertaken with

We consider anchoring on visual cues in a contingent-behavior study of the effects of offshore wind power projects on beach use on the East Coast of the United States. In an internet-based survey of beachgoers, we show respondents visual simulations of wind power projects at three offshore distances and vary the order in which respondents see the visuals -- so some see near visuals first and some see

The decoy effect reveals a potential violation of the regularity assumption, which is a building block of canonical discrete choice models. This effect has been detected in various choice contexts, but the susceptibility to it and its subjective valuation have been scarcely studied before. This paper proposes, illustrates and assesses two methodologies aimed to fill this gap: systematic taste variations

An examination of model validation practices in the peer-reviewed transportation literature published between 2014 and 2018 reveals that 92% of studies reported goodness-of-fit statistics, and 64.6% reported some sort of policy-relevant inference analysis. However, only 18.1% reported validation performance measures, out of which 78% (14.2% of all studies) consisted of internal validation and 22% (4%

The emergence of the ‘gig’ economy is disrupting industries, reshaping the organisation of work and the terms and conditions under which work is carried out. In effect, the terms and conditions of ‘gig’ work mean that minimum standards, for example minimum wages, that are attached to work in advanced capitalist economies like Australia, regularly do not apply to ‘gig’ work. This study explores the

In linear-in-income logit, a case with no income effect, the expectation ofthe compensating variation and the expectation of the equivalent variation are identical and provided by the logsum formula. The statistical properties of the estimator of this measure of welfare change are investigated. Under regularity conditions, the estimator of the measure obtained from maximum likelihood estimators of

Discrete choice experiments (DCEs) are used to provide evidence for policymaking and nonmarket valuation in health. A perennial issue with the stated preference data used in DCEs is hypothetical bias; that is, hypothetical responses in experiments may differ from real-world behavior. A randomized DCE tested whether an incentive-compatible preference elicitation reduced hypothetical bias. Adult smokers

Choice modelling techniques have established themselves as a key analysis tool in health economics and have been used to understand patient and practitioner preferences across a wide variety of settings. A key interest in recent years has been the incorporation of ever more flexible levels of heterogeneity in preferences across individual decision makers, and in particular a growing interest in the

Modelling people's behaviour is a complex task, not only because of their intrinsic complexity but also because of their interaction with the environment and other individuals. The traditional format for discrete choice experiments involves the use of text and images. However, there is a growing tendency for using tools that offer a more realistic representation of complex and dynamic attributes in

Discrete choice analysis is a popular method of estimating heterogeneous customer preferences. Although model accuracy can be increased by including more choice data, this option is untenable when the obtaining of choice data from target customers is costly and time-consuming.. We thus propose a method for choice data generation for commercial products whose expected money value is a key factor in

This study focuses on the menus typically found in the marketplace (e.g., restaurants and Internet vendors), where the consumer may choose one or more from dozens of options or menu items, each at a posted price or fee. We show that modeling choices out of the typical menu leads to the “curse of dimensionality,” which transpires in two ways. First, the choice set (all possible menu selections) grows

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the estimation of statistical Discrete Choice Models based on the random utility framework. In this article, we provide new ways of dealing with large datasets

In this study, we apply the multiple discrete–continuous extreme value (MDCEV) model with triple constraints to identify the value of leisure time during weekends and long holidays. Our approach models the economic behavior of leisure trips with the triple constraints of budget, duration of weekend, and duration of holiday. The econometric model is developed to construct an estimation using the observed

Theatrical productions are supposed to be perishable good, since the tickets for a particular play cannot be inventoried and sold after a time of play. In the revenue management of a perishable good price discrimination is widely used. Since the theatre audience is heterogeneous in terms of visit purpose, ability to perceive quality, willingness-to-pay, the strategy of price discrimination is developed

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze individual choices. While the nested logit (NL) model is the classical way to address the question, this study presents multitask learning deep neural networks (MTLDNNs) as an alternative framework, and discusses its theoretical foundation, empirical performance, and behavioral intuition. We

Estimating installed-base effects for product adoption in the presence of unobserved heterogeneity is challenging since the typical solution of including fixed effects leads to inconsistent estimates in models with installed base. Narayanan and Nair (2013) highlight this problem and propose a bias correction method as a solution to the problem. This research note proposes an alternative solution: Borrowing

Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research questions. However, the research and the methodologies used are fragmented. Whilst systematic reviews on RUMs for mode choice prediction have long existed

This paper tests for the existence of loss aversion, reference dependence and diminishing sensitivity in a discrete choice experiment (DCE). A status quo alternative is introduced in a DCE of nurses' job choices and modeled as an individual-specific third alternative representing the respondent's current job. This provides a feasible method for including a status quo, which changes the reference point

In a referendum in March 2016, the British public voted by a margin of 52 per cent to 48 per cent to leave the European Union. Unfortunately, the referendum question provides little information on the sort of relationship Britons desire with the EU. The purpose of this study is to use stated choice experiments (CEs) to understand and quantify what aspects of the relationship between Britain and the

Wildlife is widely becoming an important vehicle for rural development in most third-world countries across the globe. With wildlife, as with other conservation and development policies, policymakers are usually not informed about the needs and wants of poor rural households and roll out programmes that are not tailor made to suit their desires, which often results in policy failure. We use a survey-based

Quantum probability, first developed in theoretical physics, has recently been successfully used in cognitive psychology to model data from experiments that previously resisted effective modelling by classical methods. This has led to the development of choice models based on quantum probability, which have greater flexibility than standard models due to the implementation of complex numbers through

Researchers using discrete choice experiments (DCE) are often faced with the difficult decision of selecting which are the key attributes that must be included into their analysis. Previous literature on methods for attribute selection is not particularly well documented, frequently leaving researchers with a wide choice of attributes that could lead to complex choice tasks. Moreover, selecting attributes

This paper i) compares parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV) services. Specifically, we compare the multivariate normal, the finite mixture of normals and the Dirichlet process mixture of normals (DP-MON)

Individual-specific posterior distributions are an attractive tool for disentangling the tastes for each person in the sample. However, there exists some risks and certain limitations regarding their use. This study reviews and summarizes the theoretical literature about the individual-specific posterior distributions derived from the Mixed Logit model, focusing on their properties, limitations and

The proneness of stated preference methods to different biases has increased the popularity of incentivized choice experiments (ICE). ICEs, however, are not free from challenges. One such challenge when using ICEs in market valuation is that some product alternatives may not be available to incentivize experiments. In this context, withholding information about product availability could be considered

The aim of the study is to examine the potential effect of attitudes towards physical activity on bus utility in the context of a rural area where studies have shown that the level and opportunity for physical activity are generally low. The need to analyze attitudes remains a strong motivation for the application of integrated choice and latent variable models. As such, we integrated attitudes towards

This paper proposes the use of Most Typical (MT) and Most Ideal (MI) levels when an adaptive choice-based conjoint (ACBC) survey can only obtain a small sample size n from a small population size N. This situation arises when expert decision makers are surveyed from among important small populations such as executives of large companies or political leaders, for which the expert decision maker assumption

This paper presents an agent-based model of team reasoning in a social dilemma game. Starting from the conundrum of empirically high levels of cooperation in dilemma games, contradicting traditional utility maximisation assumptions of game theory, Bacharach (1999, 2006) developed a theory of team reasoning. The idea behind team reasoning is that agents do not try to maximise their own utility but make

Combining choice modelling with social network analysis, we show how the stochastic actor-oriented model for the co-evolution of networks and behavior (SAOM) can be used as a powerful statistical framework to empirically analyze network-related choices. We discuss the underlying assumptions of SAOMs and show that they can be interpreted to represent a random utility maximization model (RUM). Network-related

In linear regressions, the ecological fallacy—the erroneous belief that aggregate-level coefficients coincide with individual-level coefficients—arises when individual outcomes depend on the group environment. This paper suggest that such “group effects” determine also the circumstances under which the ecological fallacy vanishes from basic discrete-choice models. In particular, when controlling for

Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same

In this paper, we estimate the relative value of different employment characteristics when choosing between apprenticeship and job offers. Further, we test assumptions derived from sociological rational choice theory on preference heterogeneity by individual and context characteristics. For this purpose, we analyse data from two discrete choice experiments, one focusing on the choice of an apprenticeship

Abstract One of the central topics in crime research, and one in which discrete choice modelling has been relatively recently introduced, is the study of where offenders choose to commit crime. Since the introduction of this approach in 2003, it has become relatively popular and used in over 25 published studies covering a range of crime types and study areas. However, in most of these analyses the

Abstract The huge increase in higher education coverage in many developing countries has gone hand-in-hand with an additional supply of private colleges and with the enrolment of low to middle-class students, previously excluded from a historically elitist education segment. The larger diversity of both “suppliers and consumers”, unseen a few years ago, calls for methodological approaches that recognize

Artificial Neural Networks (ANNs) are increasingly used for discrete choice analysis, being appreciated in particular for their strong predictive power. However, many choice modellers are critical – and rightfully so – about using ANNs, for the reason that they are hard to diagnose. That is, for analysts it is hard to see whether a trained (estimated) ANN has learned intuitively reasonable relationships

Abstract This paper extends discrete residential choice models by incorporating choice set formation. Most discrete residential choice models make relatively arbitrary assumptions about the choice set – the set of houses to be considered by the purchaser. In this paper we explore several formulations of endogenous choice sets in which the decision maker's selection of a choice set is based on certain

This research note presents the first study to implement a real choice experiment in a web survey. In a case study on ethical food consumption, we find statistically significant lower willingness-to-pay values for the attributes “organic production” and “fair trade” in a choice experiment involving real payments compared to a choice experiment without real payments. This holds only true for respondents

Health economists often use discrete choice experiments (DCEs) to predict behavior, as actual market data is often unavailable. Manski (1990) argues that due to the incompleteness of the hypothetical scenarios used in DCEs, substantial uncertainty surrounds stated choice. Uncertainty can be decomposed into “resolvable” and “unresolvable”; the former is expected to become resolved in actual choice,

Images, videos, and virtual reality have been widely used in the literature to portray complex attributes to survey respondents. It is reasonable to expect immersive videos will be increasingly used in the future due to their decreasing costs and potentially more accurate representation of reality. Nevertheless, the literature has not sufficiently tested their ecological validity, which can be defined

Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals and among different choices made by the same individual respectively)

•We investigate bus users' preferences for emission reduction in bus travel.•We conduct a Discrete Choice Experiment amongst Aberdeen bus users.•We make use of a natural experiment – the Aberdeen Hydrogen Bus Project – to examine the role of direct experience in preferences.•Bus users place a higher value in the reduction of local pollutants over greenhouse gas emissions.•Increasing experience using

Abstract This paper investigated the effects of including an opt-out option and the cost attribute on the elicited preference structure and response error variance in a discrete choice experiment (DCE) valuing the preferences for the delivery of dental care. The mixed logit framework was used for testing the effects of survey design features on respondents' preferences and scale. The standard practice

Abstract A method is presented which facilitates the practical construction of designs for stated choice experiments in which the choice sets are pairs of partial profiles and where, for a potentially large number of two-level attributes, the main effects and two-factor interactions are to be estimated. Although partly heuristic, the approach has a sound theoretical basis and can be used to generate

Abstract The community of choice modellers has expanded substantially over recent years, covering many disciplines and encompassing users with very different levels of econometric and computational skills. This paper presents an introduction to Apollo, a powerful new freeware package for R that aims to provide a comprehensive set of modelling tools for both new and experienced users. Apollo also incorporates

Abstract Case 2 (profile case) best–worst scaling (BWS) is a question-based survey method for measuring preferences for attribute levels. Several existing R packages help to implement the construction of Case 2 BWS questions (profiles) and the discrete choice analysis of the responses to the questions. Structuring the dataset for Case 2 BWS analysis is, however, complicated: there are several model

Destination choice models play a critical role in transportation demand analysis. However, collecting individual destination choices at a large scale is costly and time consuming. In this context, the availability of location based social media (LBSM) data gives us the opportunity to gather destination choice behavior of a large number of people in a continuous basis. In this paper, we present methods

Abstract This paper examines the effects of socioeconomic characteristics, trip characteristics and life events on outdoor leisure activities and leisure duration in the Netherlands, based on 14 554 observations from three waves of data from The Netherlands Mobility Panel (in Dutch: MobiliteitsPanel Nederland). A standard mixed logit as well as a ‘zero-leisure’ scaled model was estimated to cover interpersonal

This paper aims at explaining the choice between online and in-store shopping for typical search and experience goods (standard electronic appliances and groceries) within an artificial experimental setting assuming no privately owned cars. We present the first alternative-specific integrated choice and latent variable (ICLV) model using stated preference data in the field of shopping behavior research

This paper deals with the airline itinerary choice problem. Consider for example that a customer is searching for flights from London to New York, traveling next week on Tuesday and coming back on Saturday. This search request is then processed by a travel provider (e.g., an online travel agent) that proposes between 50 and 100 different alternatives (itineraries) to the customer. The itineraries have

Abstract This paper examines the common practice of aggregating choice alternatives within discrete choice models. We carry out a Monte Carlo study based on realistic vehicle choice data for sample sizes ranging from 500–10,000 individuals. We consider methods for aggregation proposed by McFadden (1978) and Brownstone and Li (2017) as well as the more commonly used methods of choosing a representative

Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based

Due to the surge in the amount of data that are being collected, analysts are increasingly faced with very large data sets. Estimation of sophisticated discrete choice models (such as Mixed Logit models) based on these typically large data sets can be computationally burdensome, or even infeasible. Hitherto, analysts tried to overcome these computational burdens by reverting to less computationally

Abstract Using maximum likelihood (ML) estimation for discrete choice modeling of small datasets causes two problems. The first problem is that the data may exhibit separation, in which case the ML estimates do not exist. Also, provided they exist, the ML estimates are biased. In this paper, we show how to adapt Firth's penalized likelihood estimation for use in discrete choice modeling. A powerful

Abstract The Multinomial Logit (MNL) model is popular, but a semi-parametric specification of its link/utility function has seldom been used in empirical applications. This is primarily because of the resource intensive nature of semi-parametric estimation. In this paper we propose and implement a parallel computation algorithm to estimate the semi-parametric kernel MNL model. This algorithm reduces

Abstract This paper aims at better understanding passenger valuation of subway crowding in New York City. To this end, we conducted a stated preference survey with a discrete choice experiment where New Yorkers chose an alternative from a set of two hypothetical unlabeled subway routes based on occupancy levels and other attributes. We used the collected data to estimate crowding multipliers that quantify