Willingness-to-Pay Estimation with Mixed Logit Models: Some New Evidence

2005 ◽  
Vol 37 (3) ◽  
pp. 525-550 ◽  
Author(s):  
Mauricio Sillano ◽  
Juan de Dios Ortúzar
Keyword(s):  
Willingness To Pay ◽  
Stated Preference ◽  
Mixed Logit ◽  
Revealed Preference ◽  
Random Parameter ◽  
Estimation Methods ◽  
Logit Models ◽  
Choice Modelling ◽  
Mixed Logit Models ◽  
Discrete Choice Modelling

Mixed-logit models are currently the state of the art in discrete-choice modelling, and their estimation in various forms (in particular, mixing revealed-preference and stated-preference data) is becoming increasingly popular. Although the theory behind these models is fairly simple, the practical problems associated with their estimation with empirical data are still relatively unknown and certainly not solved to everybody's satisfaction. In this paper we use a stated-preference dataset—previously used to derive willingness to pay for reduction in atmospheric pollution and subjective values of time—to estimate random parameter mixed logit models with different estimation methods. We use our results to discuss in some depth the problems associated with the derivation of willingness to pay with this class of models.

scholarly journals Mixed Logit Models for Travelers’ Mode Shifting Considering Bike-Sharing

2020 ◽  
Vol 12 (5) ◽  
pp. 2081 ◽  
Author(s):  
Mao Ye ◽  
Yajing Chen ◽  
Guixin Yang ◽  
Bo Wang ◽  
Qizhou Hu
Keyword(s):  
Public Transportation ◽  
Negative Impact ◽  
Stated Preference ◽  
Mixed Logit ◽  
Revealed Preference ◽  
Logit Models ◽  
Frequency Model ◽  
Mixed Logit Models ◽  
Bike Sharing ◽  
The Impact

This study quantifies the impact of individual attributes, the built environment, and travel characteristics on the use of bike-sharing and the willingness of shifting to bike-sharing-related travel modes (bike-sharing combined with other public transportation modes such as bus and subway) under different scenarios. The data are from an RP (Revealed Preference) survey and SP (Stated Preference) survey in Nanjing, China. Three mixed logit models are established: an individual attribute–travel characteristics model, a various-factor bike-sharing usage frequency model, and a mixed scenario–transfer willingness model. It is found that age and income are negatively associated with bike-sharing usage; the transfer distance (about 1 km), owning no car, students, and enterprises are positively associated with bike-sharing usage; both weather and travel distance have a significant negative impact on mode shifting. The sesearch conclusions can provide a reference for the formulation of urban transportation policies, the daily operation scheduling, and service optimization of bike-sharing.

scholarly journals Predicting responsiveness to information: consumer acceptance of biotechnology in animal products

2020 ◽  
Vol 47 (5) ◽  
pp. 1644-1667 ◽  
Author(s):  
David L Ortega ◽  
Jayson L Lusk ◽  
Wen Lin ◽  
Vincenzina Caputo
Keyword(s):  
Willingness To Pay ◽  
Mixed Logit ◽  
Consumer Acceptance ◽  
Individual Choice ◽  
Logit Models ◽  
Choice Data ◽  
Animal Products ◽  
Mixed Logit Models ◽  
Preference Uncertainty ◽  
High Level

Abstract We propose a novel framework using individual choice data and Bayesian updating to predict which consumers are most responsive to information—namely those consumers whose pre-information choices reveal a high level of uncertainty surrounding their preferences. We apply our method to the study of consumer acceptance of genetically modified animal products, which prior research has revealed is a particularly polarising subject. Utilising conditional willingness-to-pay estimates from mixed logit models, we find that individuals with higher preference uncertainty prior to receiving information are most responsive. Implications of our results are discussed in the context of recent breakthroughs in biotechnology.

Including cost income ratio into utility function as a way of dealing with ‘exploding’ implicit prices in mixed logit models

2012 ◽  
Vol 18 (4) ◽  
pp. 370-380 ◽  
Author(s):  
Marek Giergiczny ◽  
Sviataslau Valasiuk ◽  
Mikolaj Czajkowski ◽  
Maria De Salvo ◽  
Giovanni Signorello
Keyword(s):  
Utility Function ◽  
Mixed Logit ◽  
Logit Models ◽  
Mixed Logit Models ◽  
Income Ratio ◽  
Implicit Prices

scholarly journals A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kai Lu ◽  
Alireza Khani ◽  
Baoming Han
Keyword(s):  
Open Systems ◽  
Choice Model ◽  
Mixed Logit ◽  
Random Coefficients ◽  
Logit Models ◽  
Transit Systems ◽  
Trip Purpose ◽  
Mixed Logit Models ◽  
Key Factor ◽  
Rich Data

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.

scholarly journals Fitting Mixed Logit Models by Using Maximum Simulated Likelihood

2007 ◽  
Vol 7 (3) ◽  
pp. 388-401 ◽  
Author(s):  
Arne Risa Hole
Keyword(s):  
Mixed Logit ◽  
Logit Models ◽  
Maximum Simulated Likelihood ◽  
Mixed Logit Models ◽  
Simulated Likelihood

This article describes the mixlogit Stata command for fitting mixed logit models by using maximum simulated likelihood.

Mixed logit models: accuracy and software choice

2010 ◽  
Vol 26 (1) ◽  
pp. 167-172 ◽  
Author(s):  
Jae Bong Chang ◽  
Jayson L. Lusk
Keyword(s):  
Mixed Logit ◽  
Logit Models ◽  
Mixed Logit Models

scholarly journals Masked Heterogeneity in Aggregation: Incorporating Beliefs and Geographic Information into Consumer Willingness to Pay for State Brands

2020 ◽  
Vol 52 (4) ◽  
pp. 527-544
Author(s):  
Meagan Osburn ◽  
Rodney B. Holcomb ◽  
Clinton L. Neill
Keyword(s):  
Willingness To Pay ◽  
Random Parameter ◽  
Geographic Information ◽  
Agricultural Products ◽  
Logit Models ◽  
Aggregate Model ◽  
Marginal Willingness To Pay ◽  
Disaggregate Model ◽  
Random Parameter Logit ◽  
State Region

AbstractState marketing programs for food and agricultural products are largely driven by consumers’ desires to purchase in-state products. Evaluations of state marketing programs have largely ignored consumer location and proximity to surrounding states, measures of ethnocentrism, and the presence of other geographic marketing labels. This study examines willingness to pay for own and out-of-state labels for a generic commodity, milk, within an eight-state region. The results show that an aggregate model conceals consumer heterogeneity in marginal willingness to pay values for state brands as compared to a disaggregate model, even when using random parameter logit models.

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