nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2019‒03‒18
four papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Mixture models for consumers' preferences in healthcare By S. Capecchi; M. Meleddu; M. Pulina; G. Solinas
  2. When Does Real Become Consequential in Non-hypothetical Choice Experiments? By Daniel E. Chavez; Marco A. Palma; Rodolfo M. Nayga Jr.
  3. Generalised Random Categorisation Rules By Matthew Ryan
  4. Towards Renewable Electricity in Europe: An Empirical Analysis of the Determinants of Renewable Electricity Development in the European Union By Ciara?n Mac Domhnaill; L. (Lisa B.) Ryan

  1. By: S. Capecchi; M. Meleddu; M. Pulina; G. Solinas
    Abstract: This paper aims to explain preferences behaviour by a sample of Sardinia residents with respect to combined choice of attributes related to cardiology services. The rating of proposed cards, containing a combination of several attributes to qualify the services, are examined in terms of intrinsic components and main drivers to determine the ordinal choice - location, screening mode, cost, waiting time for the visit and subjects' covariates. The topic is relevant in telemedicine as experienced in Sardinia, a region with a mobility and a socio-economic disadvantage. The innovative approach allows for effective visual support to interpret and compare results and it is useful also to predict the respondents' profile with respect to their individual characteristics. Empirical evidence supports policy interventions and suggests the usefulness of the implemented statistical procedure.
    Keywords: E-health Preferences;Discrete modelling;Decision drivers;CUB models
    Date: 2019
  2. By: Daniel E. Chavez (University of Kentucky, Department of Marketing and Supply Chain); Marco A. Palma (Texas A&M University, Department of Agricultural Economics); Rodolfo M. Nayga Jr. (University of Arkansas, Department of Agricultural Economics and Agribusiness)
    Abstract: The proneness of stated preference methods to hypothetical bias has increased the popularity of incentivized studies, in particular the use of real choice experiments (RCE). Challenges of RCE include the lack of engagement with the choice task by some subjects, and that some of the product alternatives may not be available in order to incentivize all the choices. This issue brings to question whether the proportion of available products influences the results of the RCE. Would the subjects' engagement change? Using an induced value choice experiment with a profit maximization optimal strategy for agents, we varied the number of potentially binding alternatives in four treatments. Our results suggest that incentives matter, as the percentage of optimal choices was lowest in the hypothetical treatment. Interestingly, however, we do not find statistically significant differences in the number of optimal choices between the incentivized treatments, regardless of the number of potentially binding alternatives used in our treatments. This suggests that practitioners could conduct incentivized RCE without the need to have all the product alternatives be made available in the study. Furthermore, we explore the interaction of incentives with subjects' numerical ability and individual reflective state. Both are also shown to influence how incentives impact performance, shedding some light on what individual characteristics to look for when conducting valuation research.
    Keywords: Choice Experiments, Eye Tracking, Hypothetical bias, Induced values.
    JEL: C91 C18
  3. By: Matthew Ryan (School of Economics, Auckland University of Technology)
    Abstract: Aguiar's (2017) random categorisation rule (RCR) describes random choice behaviour as the maximisation of a linear preference order over the intersection of a random consideration set with the set of available options. A key axiom in Aguiar's (2017) characterisation of the RCR is an acyclicity condition on a revealed preference relation derived from the random choice function. We show that this condition may be substantially weakened - to asymmetry of the revealed preference relation - without jeopardising the essence of the RCR representation. In our generalisation of the RCR, preferences may be ill-behaved on subsets of alternatives that are never considered together. While these pathologies in preference are masked by the decision-maker's selective attention to an particular choice problem, they may still be revealed by data across di§erent choice problems. Finally, we show that the generalised model remains within the random utility class.
    Date: 2019–02
  4. By: Ciara?n Mac Domhnaill; L. (Lisa B.) Ryan
    Abstract: The twenty-first century must see a decarbonisation of electricity production to mitigate the flow of greenhouse gas emissions into the atmosphere. This paper presents an econometric analysis of the factors that motivate the use of renewable energy in electricity production using panel data from EU Member States during the period 2000-2015. The research extends the literature in this area in several ways. Firstly, the econometric analysis is focused on the electricity sector rather than on the overall primary energy supply, which also includes the diverse heating and transport sectors. In addition, an alternative public policy variable is proposed using the tax and levy component of electricity bills. Furthermore, an alternative econometric approach is employed using a hybrid mixed effects estimator. The results of this analysis are found to be broadly as expected, with mixed fossil fuel price effects; electricity grid interconnection and higher levels of greenhouse gas emissions both motivate the development of renewable electricity. Policy implications are that policy support for fossil fuels should be ceased; electricity grid interconnections should be developed between countries; and furthermore, levies on retail electricity prices to fund RE support schemes are effective at promoting renewable electricity.
    Keywords: Renewable electricity policy; Energy economics; Climate policy; Hybrid mixed effects econometric model
    JEL: Q21 Q4 Q41 Q42 Q58
    Date: 2018–12

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