Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and bayes estimates of a multinomial probit model

In this paper we use Bayes estimates of a multinomial probit model with fully exible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use statedpreference data on vehicle choice from a Germany-wide survey of potential lightduty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-efficient technologies

Accounting for uncertainty in willingness to pay for environmental benefits

Previous literature on the distribution of willingness to pay has focused on its heterogeneity distribution without addressing exact interval estimation. In this paper we derive and analyze Bayesian confidence sets for quantifying uncertainty in the determination of willingness to pay for carbon dioxide abatement. We use two empirical case studies: household decisions of energy-efficient heating versus insulation, and purchase decisions of ultralow- emission vehicles. We first show that deriving credible sets using the posterior distribution of the willingness to pay is straightforward in the case of deterministic consumer heterogeneity. However, when using individual estimates, which is the case for the random parameters of the mixed logit model, it is complex to define the distribution of interest for the interval estimation problem. This latter problem is actually more involved than determining the moments of the heterogeneity distribution of the willingness to pay using frequentist econometrics. A solution that we propose is to derive and then summarize the distribution of point estimates of the individual willingness to pay under different loss functions

Estimación directa de disposición a pagar en modelos de elección discreta

Whereas the structural taste parameters of a random utility maximization model lack a straightforward interpretation, parameter ratios can be interpreted as marginal rates of substitution. In particular, a consumer's willingness-to-pay for qualitative improvements can be derived from the ratio of the marginal utility of a specific hedonic attribute and the marginal utility of income. In this paper, I propose to use an alternative normalization of scale to obtain direct inference on consumers' monetary valuation of attributes.Aunque los parámetros de un modelo de elección discreta carecen de una interpretación económica directa, la razón entre dos parámetros puede ser interpretada como una tasa marginal de sustitución. En particular, es posible obtener la disposición a pagar por mejoramientos cualitativos a partir de la razón de la utilidad marginal de un atributo hedónico y la utilidad marginal del ingreso. En este trabajo se describe una metodología alternativa de estimación de un modelo de elección discreta, basada en la normalización de la utilidad marginal del ingreso, de forma de transformar el espacio de los parámetros directamente en unidades monetarias

Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially faster than MCMC at practically no compromises in predictive accuracy. In this paper, we address two critical gaps concerning the usage and understanding of VB for MMNL. First, extant VB methods are limited to utility specifications involving only individual-specific taste parameters. Second, the finite-sample properties of VB estimators and the relative performance of VB, MCMC and maximum simulated likelihood estimation (MSLE) are not known. To address the former, this study extends several VB methods for MMNL to admit utility specifications including both fixed and random utility parameters. To address the latter, we conduct an extensive simulation-based evaluation to benchmark the extended VB methods against MCMC and MSLE in terms of estimation times, parameter recovery and predictive accuracy. The results suggest that all VB variants with the exception of the ones relying on an alternative variational lower bound constructed with the help of the modified Jensen's inequality perform as well as MCMC and MSLE at prediction and parameter recovery. In particular, VB with nonconjugate variational message passing and the delta-method (VB-NCVMP-Delta) is up to 16 times faster than MCMC and MSLE. Thus, VB-NCVMP-Delta can be an attractive alternative to MCMC and MSLE for fast, scalable and accurate estimation of MMNL models

Increasing the Influence of CO 2 Emissions Information on Car Purchase

In response to concerns related to climate change, and an attempt to encourage more sustainable behavior, individuals are often provided with information on greenhouse gas emissions (GHGs) of consumer items, such as personal vehicles. Currently in the US, information on vehicle efficiency is provided as grams of carbon dioxide (CO2) per mile. Previous research presenting CO2 as a mass and testing willingness-to-pay through Discrete Choice Experiment has found that such information can influence vehicle choice. However, other research has questioned whether how this information is presented might affect choice. That research argues that CO2 emission information generally lacks contextualization that allows for interpretation. As well, it argues that the type of contextualization may affect choices. That research though did not test willingness-to-pay and the strength of its influence is not clear. In addition, research exists that argues that using pro-social, as opposed to financial, contextualization might be more influential on people’s choices. Thus, the purpose of this paper is to build on these previous findings on how CO2 emissions are presented to determine whether changing how that information impacts vehicle choice with a Discrete Choice Experiment of vehicle choice analyzed using latent class modeling. No previous study has so robustly studied the influence that different framings might have on vehicle purchase. Five different methods of presenting CO2 information are tested in this experiment: CO2 emissions as grams per mile (current method), CO2 emissions as pounds per year (consistent imperial units), CO2 emissions as tons per year (yearly contextualization), an annual tax on CO2 (yearly financial contextualization), and CO2 as a percentage of the 2025 US EPA reduction target of 26% from 2005 levels (social goal contextualization). Results demonstrate that the current method results in lowest willingness to pay for CO2 emission reductions, while the social goal contextualization results in the highest

The climate change stage of change measure: vehicle choice experiment

ABSTRACT: Various measures have been proposed and validated to assess environmental motivation and explain peoples’ consumer behavior. However, most of the measures are rather complex, sometimes comprising dozens of items. In order to overcome the associated response burden, the goal of our research is to validate a much simpler measure of environmental motivation, namely the measure of Climate Change-Stage of Change. To do so we analyze data from a discrete choice experiment in which drivers decide to purchase a car with different levels of CO2 emissions and we also measure their environmental motivation with three alternative measures. The results show that environmental motivation assessed with Climate Change-Stage of Change explains the choices in the experiment as well as with more complex measures. Our findings have substantial implications for researchers as they may be able to assess climate-relevant motivation – a significant factor for many consumer choices – with a single question

Estimación directa de disposición a pagar en modelos de elección discreta

Whereas the structural taste parameters of a random utility maximization model lack a straightforward interpretation, parameter ratios can be interpreted as marginal rates of substitution. In particular, a consumer's willingness-to-pay for qualitative improvements can be derived from the ratio of the marginal utility of a specific hedonic attribute and the marginal utility of income. In this paper, I propose to use an alternative normalization of scale to obtain direct inference on consumers' monetary valuation of attributes.Aunque los parámetros de un modelo de elección discreta carecen de una interpretación económica directa, la razón entre dos parámetros puede ser interpretada como una tasa marginal de sustitución. En particular, es posible obtener la disposición a pagar por mejoramientos cualitativos a partir de la razón de la utilidad marginal de un atributo hedónico y la utilidad marginal del ingreso. En este trabajo se describe una metodología alternativa de estimación de un modelo de elección discreta, basada en la normalización de la utilidad marginal del ingreso, de forma de transformar el espacio de los parámetros directamente en unidades monetarias