nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2023‒09‒18
nine papers chosen by
Edoardo Marcucci, Università degli studi Roma Tre

  1. Computer vision-enriched discrete choice models, with an application to residential location choice By Sander van Cranenburgh; Francisco Garrido-Valenzuela
  2. Fear or Knowledge The Impact of Graphic Cigarette Warnings on Tobacco Product Choices By Donald S. Kenkel; Alan D. Mathios; Grace N. Phillips; Revathy Suryanarayana; Hua Wang; Sen Zeng
  3. Does courier gender matter? Exploring mode choice behaviour for E-groceries crowd-shipping in developing economies By Oleksandr Rossolov; Anastasiia Botsman; Serhii Lyfenko; Yusak O. Susilo
  4. Driver Heterogeneity in Willingness to Give Control to Conditional Automation By Muhammad Sajjad Ansar; Nael Alsaleh; Bilal Farooq
  5. Activating Change: The Role of Information and Beliefs in Social Activism By Afridi, Farzana; Basistha, Ahana; Dhillon, Amrita; Serra, Danila
  6. A Robust Method for Microforecasting and Estimation of Random Effects By Raffaella Giacomini; Sokbae Lee; Silvia Sarpietro
  7. New Passenger Vehicle Demand Elasticities: Estimates and Policy Implications By Leard, Benjamin; Wu, Yidi
  8. Nonlinear Correlated Random Effects Models with Endogeneity and Unbalanced Panels By Michael Bates; Leslie Papke; Jeffrey Wooldridge
  9. Show Me the Money! Incentives and Nudges to Shift Electric Vehicle Charge Timing By Bailey, Megan R.; Brown, David P.; Shaffer, Blake; Wolak, Frank A.

  1. By: Sander van Cranenburgh; Francisco Garrido-Valenzuela
    Abstract: Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination choices, and various safety-related choices. However, current discrete choice models cannot handle image data and thus cannot incorporate information embedded in images into their representations of choice behaviour. This gap between discrete choice models' capabilities and the real-world behaviour it seeks to model leads to incomplete and, possibly, misleading outcomes. To solve this gap, this study proposes "Computer Vision-enriched Discrete Choice Models" (CV-DCMs). CV-DCMs can handle choice tasks involving numeric attributes and images by integrating computer vision and traditional discrete choice models. Moreover, because CV-DCMs are grounded in random utility maximisation principles, they maintain the solid behavioural foundation of traditional discrete choice models. We demonstrate the proposed CV-DCM by applying it to data obtained through a novel stated choice experiment involving residential location choices. In this experiment, respondents faced choice tasks with trade-offs between commute time, monthly housing cost and street-level conditions, presented using images. As such, this research contributes to the growing body of literature in the travel behaviour field that seeks to integrate discrete choice modelling and machine learning.
    Date: 2023–08
  2. By: Donald S. Kenkel; Alan D. Mathios; Grace N. Phillips; Revathy Suryanarayana; Hua Wang; Sen Zeng
    Abstract: Requiring graphic warning labels (GWLs) on cigarette packaging has become a highly contentious unresolved legal battle. The constitutionality depends, in part, on the likely impact of GWLs on smoking decisions, and whether they generate knowledge as opposed to emotional reactions against smoking. Using an online discrete choice stated preference experiment we compare tobacco choices (cigarettes, e-cigarettes, quitting) for those presented with a GWL versus the currently existing label. We find the fraction of individuals choosing cigarettes to be lower and quitting higher for the GWL group. Our findings reveal that the differences between groups were primarily driven by the evocation of fear and disgust rather than an improvement in health knowledge related to the GWL. The discrete choice experiment also provides new evidence on how cigarette prices, e-cigarette prices, and policy-manipulable e-cigarette attributes such as e-cigarette warning labels, and flavor availability influence tobacco product choices.
    JEL: I12
    Date: 2023–08
  3. By: Oleksandr Rossolov; Anastasiia Botsman; Serhii Lyfenko; Yusak O. Susilo
    Abstract: This paper examines the mode choice behaviour of people who may act as occasional couriers to provide crowd-shipping (CS) deliveries. Given its recent increase in popularity, online grocery services have become the main market for crowd-shipping deliveries' provider. The study included a behavioural survey, PTV Visum simulations and discrete choice behaviour modelling based on random utility maximization theory. Mode choice behaviour was examined by considering the gender heterogeneity of the occasional couriers in a multimodal urban transport network. The behavioural dataset was collected in the city of Kharkiv, Ukraine, at the beginning of 2021. The results indicated that women were willing to provide CS service with 8% less remuneration than men. Women were also more likely to make 10% longer detours by car and metro than men, while male couriers were willing to implement 25% longer detours when travelling by bike or walking. Considering the integration of CS detours into the couriers' routine trip chains, women couriers were more likely to attach the CS trip to the work-shopping trip chain whilst men would use the home-home evening time trip chain. The estimated marginal probability effect indicated a higher detour time sensitivity with respect to expected profit and the relative detour costs of the couriers.
    Date: 2023–08
  4. By: Muhammad Sajjad Ansar; Nael Alsaleh; Bilal Farooq
    Abstract: The driver's willingness to give (WTG) control in conditionally automated driving is assessed in a virtual reality based driving-rig, through their choice to give away driving control and through the extent to which automated driving is adopted in a mixed-traffic environment. Within- and across-class unobserved heterogeneity and locus of control variations are taken into account. The choice of giving away control is modelled using the mixed logit (MIXL) and mixed latent class (LCML) model. The significant latent segments of the locus of control are developed into internalizers and externalizers by the latent class model (LCM) based on the taste heterogeneity identified from the MIXL model. Results suggest that drivers choose to "giveAway" control of the vehicle when greater concentration/attentiveness is required (e.g., in the nighttime) or when they are interested in performing a non-driving-related task (NDRT). In addition, it is observed that internalizers demonstrate more heterogeneity compared to externalizers in terms of WTG.
    Date: 2023–08
  5. By: Afridi, Farzana (Indian Statistical Institute); Basistha, Ahana (Indian Statistical Institute); Dhillon, Amrita (King's College London); Serra, Danila (Texas A&M University)
    Abstract: What motivates individuals to participate in social activism? Do awareness campaigns and information about others' willingness to act play a role? We conduct an online experiment within a survey of nearly 2000 Indian men, focusing on activism to combat health sector fraud during the COVID-19 pandemic. In different treatment groups, we either provide information about the social problem, correct misaligned beliefs about others' willingness to act, or both. Participants are then cross-randomized to engage in one of three forms of activism: signing a petition, making a donation to an NGO fighting for the cause, or watching a video on ways to support the cause. We also experimentally examine the impact of allowing subjects to choose between the three forms of activism. Providing information and correcting downward biased beliefs about others increases petition signing, but has no impact on donations and video viewing. Giving participants a choice of actions decreases the probability of any single action being taken up. Our comprehensive examination of the factors influencing engagement in different forms of activism within a unified framework generates insights on the motivations behind participation in collective efforts for social change.
    Keywords: activism, information, beliefs, experiment
    JEL: D73 D83 I15 P0
    Date: 2023–07
  6. By: Raffaella Giacomini; Sokbae Lee; Silvia Sarpietro
    Abstract: We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average of time series- and pooled forecasts/estimators, with individual weights that are based on time series information. We show the forecast optimality of individual weights, both in terms of minimax-regret and of mean squared forecast error. We then provide feasible weights that ensure good performance under weaker assumptions than those required by existing approaches. Unlike existing shrinkage methods, our approach borrows the strength - but avoids the tyranny - of the majority, by targeting individual (instead of group) accuracy and letting the data decide how much strength each individual should borrow. Unlike existing empirical Bayesian methods, our frequentist approach requires no distributional assumptions, and, in fact, it is particularly advantageous in the presence of features such as heavy tails that would make a fully nonparametric procedure problematic.
    Keywords: Forecast combination; Robustness
    JEL: C10 C23 C53
    Date: 2023–08–02
  7. By: Leard, Benjamin (Resources for the Future); Wu, Yidi
    Abstract: We apply a simple methodology to estimate own- and cross-price elasticities of new passenger vehicle demand based on household-level survey data. Our methodology combines own-price elasticity estimates with diversion fractions constructed from second-choice data. We obtain a set of elasticities that are relevant for policy analysis, including an aggregate market elasticity, a matrix of car and light truck elasticities, and a matrix of gasoline and electric vehicle (EV) elasticities. Our results have implications for evaluating incidence of fuel economy and greenhouse gas standards for passenger vehicles and policies for increasing EV adoption.
    Date: 2023–08–23
  8. By: Michael Bates (Department of Economics, University of California Riverside); Leslie Papke (Michigan State University); Jeffrey Wooldridge (Michigan State University)
    Abstract: We present simple procedures for estimating nonlinear panel data models in the presence of unobserved heterogeneity and possible endogeneity with respect to time-varying unobservables. We combine a correlated random effects approach with a control function approach while accounting for missing time periods for some units. We examine the performance of the approach in comparisons with standard estimators using Monte Carlo simulation. We apply the methods to estimate the effects of school spending on student pass rates on a standardized math exam. We find that a 10 percent increase in spending leads to an approximately two percentage point increase in math pass rates.
    Keywords: Control function, instrumental variables, education finance
    JEL: C33 C36 I22 I26
    Date: 2022–09
  9. By: Bailey, Megan R. (University of Calgary); Brown, David P. (University of Alberta, Department of Economics); Shaffer, Blake (University of Calgary); Wolak, Frank A. (Stanford University)
    Abstract: We use a field experiment to measure the effectiveness of financial incentives and moral suasion “nudges” to shift the timing of electric vehicle (EV) charging. We find EV owners respond strongly to financial incentives, while nudges have no statistically discernible effect. When financial incentives are removed, charge timing reverts to pre-intervention behavior, showing no evidence of habit formation and reinforcing our finding that “money matters”. Our charge price responsiveness estimate is an order of magnitude larger than typical household electricity consumption elasticities. This result highlights the greater flexibility of EV charging over other forms of residential electricity demand.
    Keywords: Electric Vehicles; Demand Response; Nudges; Experiment
    JEL: Q41 Q48 Q55 Q58 R48
    Date: 2023–08–30

This nep-dcm issue is ©2023 by Edoardo Marcucci. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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