nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2018‒07‒16
three papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Is there really a difference between “contingent valuation” and “choice experiments”? By Patrick Lloyd-Smith; Ewa Zawojska; Wiktor L. Adamowicz
  2. Valuing Non-Market Benefits of Participatory Sport Events Using Willingness to Travel: Payment Card vs Random Selection with Mitigation of Hypothetical Bias By John C. Whitehead; Pamela Wicker
  3. Structural Labour Supply Models and Microsimulation By Aaberge, Rolf; Colombino, Ugo

  1. By: Patrick Lloyd-Smith (Department of Agricultural and Resource Economics, University of Saskatchewan); Ewa Zawojska (Faculty of Economic Sciences, University of Warsaw); Wiktor L. Adamowicz (Department of Resource Economics and Environmental Sociology)
    Abstract: “Contingent valuation” (“CV”) and “choice experiments” (“CE”) are generally introduced as two separate stated preference methods to estimate welfare measures, and a large literature investigates their convergent validity. We first review the literature comparing “CV” and “CE”, and show that these comparisons typically differ in (1) the number of options presented per value elicitation task, (2) the number of tasks given to a single respondent, (3) the framing of tasks, (4) the set (and order) of attributes characterizing options in tasks, (5) sizes of “CV” and “CE” samples, (6) econometric models used for data analysis, and (7) the format of information presented. Despite the wide variety of applications, we argue that the main (and perhaps only) difference between “CV” and “CE” is the presentation of information in elicitation tasks: as text in “CV” and as a table in “CE”. We then assess the effect of presentation of information in an induced-value experiment. We find that participants perform equally well in “CV” and “CE” tasks in terms of making payoff-maximizing choices based on the induced values, but “CV” tasks take substantially more time to answer. A significant difference between payoff-maximizing choices in “CV” and “CE” is observed when only answers to the first elicitation task are considered. This latter finding is particularly important in light of recommendations for stated preference research that suggest that valuation studies should use only one task for eliciting preferences.
    Keywords: Stated preference, Contingent valuation, Choice experiment, Experimental economics
    JEL: Q51 D6 H4 C91 M31
    Date: 2018
  2. By: John C. Whitehead; Pamela Wicker
    Abstract: This study estimates the monetary value of participation in a cycling event using a willingness to travel question. The empirical analysis is based on three years of data (2014 2016) from a post-race survey (n=976). Respondents were asked for their likelihood of revisiting the event in the following year contingent on different additional driving distances. Return visitation is higher in the randomly selected question than in the payment card format. The random selection format also produces larger willingness to pay estimates. The combination and joint estimation of stated and revealed preference data allows identifying the magnitude of hypothetical bias. Key Words: Contingent behavior method; intention to revisit; sport participation; travel cost; willingness to pay
    Date: 2018
  3. By: Aaberge, Rolf (Statistics Norway); Colombino, Ugo (University of Turin)
    Abstract: The purpose of the paper is to provide a discussion of the various approaches for accounting for labour supply responses in microsimulation models. The paper focuses attention on two methodologies for modelling labour supply: the discrete choice model and the random utility – random opportunities model. The paper then describes approaches to utilising these models for policy simulation in terms of producing and interpreting simulation outcomes, outlining an extensive literature of policy analyses utilising these approach. Labour supply models are not only central for analyzing behavioural labour supply responses but also for identifying optimal tax-benefit systems, given some of the challenges of the theoretical approach. Combining labour supply results with individual and social welfare functions enables the social evaluation of policy simulations. Combining welfare functions and labour supply functions, the paper discusses how to model socially optimal income taxation.
    Keywords: behavioural microsimulation, labour supply, discrete choice, tax reforms
    JEL: C50 D10 D31 H21 H24 H31 J20
    Date: 2018–05

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