|
on Discrete Choice Models |
| By: | Jose Apesteguia; Miguel A. Ballester |
| Abstract: | We provide revealed preference foundations to ordered logit, for discrete and continuous decision problems. In both cases, the axiomatizations are based on a simple property that reflects the additivity of cumulative logits. |
| Keywords: | Ordered logit, cumulative logit, Revealed Preference |
| JEL: | C00 D00 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1822&r= |
| By: | Bo E. Honoré (Princeton University); Chris Muris (McMaster University); Martin Weidner (University of Oxford) |
| Abstract: | We study a dynamic ordered logit model for panel data with fixed effects. We establish the validity of a set of moment conditions that are free of the fixed effects and that can be computed using four or more periods of data. We establish sufficient conditions for these moment conditions to identify the regression coefficients, the autoregressive parameters, and the threshold parameters. The parameters can be estimated using generalized method of moments. We document the performance of this estimator using Monte Carlo simulations and an empirical illustration to self-reported health status using the British Household Panel Survey. |
| Keywords: | model |
| JEL: | C23 C25 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:pri:econom:2021-14&r= |
| By: | Xiaosheng Mu (Princeton University) |
| Abstract: | We study the outcome of a sequential choice procedure based on a potentially incomplete preference relation. A decision maker evaluates alternatives in a list and iteratively updates her choice by comparing the status quo to the next alternative. She favors the status quo whenever the two alternatives are incomparable according to her underlying preference. Developing a revealed preference approach, we characterize all choice functions that can arise from such a procedure, as well as all possible preferences that can rationalize given choices. |
| Keywords: | Choice from lists; Status quo bias; Revealed preference |
| JEL: | D11 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:pri:econom:2021-35&r= |
| By: | Nguyen, Minh-Hoang; Jones, Thomas E. |
| Abstract: | The rapidly declining biosphere integrity, representing one of the core planetary boundaries, is alarming. One of the most widely accepted measures to halt the rate of biodiversity loss is to maintain and expand protected areas that are effectively managed. However, it requires substantial finance derived from nature-based tourism, specifically visitors from urban areas. Using the Bayesian Mindsponge Framework (BMF) on 535 Vietnamese urban residents, the current study examined how their biodiversity loss perceptions can affect their willingness to pay for the entrance fee and conservation in protected areas. We found that perceived environmental degradation, loss of economic growth, loss of nature-based recreation opportunity, and loss of knowledge as consequences of biodiversity loss has indirect effects on paying willingness through the mediation of the attitude towards conservation. Especially, the perceived knowledge loss also has a direct positive influence on the willingness to pay for the entrance fee and conservation. In contrast, perceived loss of health is negatively associated with the attitude towards conservation. Based on these findings, we suggest that building an eco-surplus culture among urban residents can be a promising way to generate more finance from nature-based tourism for conservation in protected areas and ease the domestic government’s and international organizations’ funding allocations problems. |
| Date: | 2022–01–04 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:txzr3&r= |
| By: | Passmore, Reid; Watkins, Kari E.; Guensler, Randall |
| Abstract: | Many cities are focused on increasing bicycle use through development of infrastructure such as bicycle lanes and multi-use paths. Traditionally, travel demand models (TDMs) are used to evaluate the demand for (and impact of) proposed transportation projects. However, the vast majority of TDMs cannot be used to evaluate the impact of bicycle projects. Improved TDMs are needed to help estimate the impacts of new bicycle projects on cycling activity and prioritize the construction of the most beneficial bicycle projects with limited transportation department resources. To more accurately model bicycle travel, preference-based route assignments are needed. Researchers at the Georgia Institute of Technology created a semi-automated process for developing an all-streets network to be used in TDM applications. The researchers combined detailed roadway characteristic information from three different transportation networks in GIS shapefile format and used BikewaySim, Georgia Tech’s newly developed shortest-path calculator for cycling trips, to compare shortest-path routing on the newly created all-streets network versus the simplified TDM network for a 12-square-mile study area in Atlanta. View the NCST Project Webpage |
| Keywords: | Engineering, Bikeways, Routes and routing, Shortest path algorithms, Travel time |
| Date: | 2022–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt8np1r2zx&r= |
| By: | Kevin Remmy |
| Abstract: | This paper develops a structural model of endogenous product attribute choice in the presence of indirect network effects to study electric vehicle (EV) subsidies. Using data on the German EV market from 2012-2018, I find that a support scheme increased EV sales by 98% but led to strong range distortions. When designing subsidies, these distortions create a trade-off between optimizing different policy objectives. Large purchase subsidies maximize EV sales whereas large charging station subsidies maximize consumer and total surplus. The results suggest that policymakers should carefully weigh the benefits of increasing EV sales against the distortions this causes. |
| Keywords: | network externalities, product attribute choice, elctric vehicles, subsidies |
| JEL: | D12 D62 H23 L62 Q55 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2022_335&r= |
| By: | Pantelis P. Analytis; Francesco Cerigioni; Alexandros Gelastopoulos; Hrvoje Stojic |
| Abstract: | People's behavior is informed and influenced by other people's choices. In many online technologies, for instance, aggregate information about the choices of other individuals is encoded in the form of rankings. Such rankings, in turn, have a direct impact on people's future choices. What are the long-term dynamics of these rankings, and do the dynamics depend on specific assumptions about people's behavior? In this paper, we propose a general framework for modeling the dynamics in settings where information about peoples' past choices is recorded as a ranking and influences future choices. We find a general condition for convergence, show that it is satisfied by many important models in economics and beyond, and characterize the possible limits in terms of the choice probabilities. |
| Keywords: | ranking, sequential choice, social learning, social influence, inattention, herding |
| JEL: | D01 D11 D83 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1819&r= |