nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2019‒12‒02
six papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Charged up? Preferences for Electric Vehicle Charging and Implications for Charging Infrastructure Planning By Wolff, Stefanie; Madlener, Reinhard
  2. Joint analysis of the discount factor and payoff parameters in dynamic discrete choice games By Komarova, Tatiana; Sanches, Fábio Adriano; Silva Junior, Daniel; Srisuma, Sorawoot
  3. Inference in Models of Discrete Choice with Social Interactions Using Network Data By Michael P. Leung
  4. Exploring in-depth joint pro-environmental behaviors: a multivariate ordered probit approach By Olivier Beaumais; Apolline Niérat
  5. Consumer Preference and Willingness to Pay for Meat derived from Chicken fed on Insect-based feed in Kenya By Harriet, Mawia; Mburu, John; Irungu, Patrick; Diiro, Gracious; Tanga, Chrysantus Mbi; Subramanian, S.; Fiaboe, K.K.M.; Loon, Joop J.A. van; Dicke, M.; Ekesi, S.
  6. Drivers of New Light-Duty Vehicle Fleet Fuel Economy in Saudi Arabia By Rubal Dua; Tamara Sheldon

  1. By: Wolff, Stefanie (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN)); Madlener, Reinhard (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))
    Abstract: This study assesses respondents’ preferences for privately-used passenger electric vehicle (EV) charging with respect to the six attributes: (1) place of charging; (2) charging duration (full charge); (3) charging technology; (4) waiting time for charging spot to become available; (5) share of renewables in the electricity mix used for vehicle charging; and (6) total cost for the whole bundle of attributes per month. Due to the low number of current EV users in Germany, investigating consumers’ EV charging infrastructure preferences and their willingness to pay (WTP) for it based on real usage data is challenging. In addition, the results would not be directly transferable to the development of sound business cases since the sample size is too small. Therefore, we gathered data through a Discrete Choice Experiment (DCE) conducted in Germany (N = 4,101). Our DCE measures the preferences for certain attributes of EV charging infrastructures indirectly by confronting participants with hypothetical choice bundles. We analyze the data using conditional logit models, including fixed effects at the participant level, in order to gain actionable insights into the expected charging behavior of current and future EV drivers. We predict tendencies of consumer behavior and show that locational and time attributes are highly appreciated. Respondents are willing to pay, on average, around 22 €/month more for charging at home rather than at work and 46.26 €/month more for charging at home rather than on the roadside. For a reduction in charging time from 8 h to 7 h, respondents are willing to pay around 8 €/month; whereas from 8 h to 10 min, respondents are willing to pay around 70 €/month for all monthly charging processes. We also find WTP of five specific consumer categories (environmentalists, EV owners, EV experts, at-home charger, and home owners). Our results could be useful for charging point operators.
    Keywords: Electric mobility charging behavior; Discrete choice experiment; Econometric modeling; Willingness to pay
    JEL: C25 D12 M38 Q58 R40
    Date: 2019–03–01
  2. By: Komarova, Tatiana; Sanches, Fábio Adriano; Silva Junior, Daniel; Srisuma, Sorawoot
    Abstract: Most empirical and theoretical econometric studies of dynamic discrete choice models assume the discount factor to be known. We show the knowledge of the discount factor is not necessary to identify parts, or all, of the payoff function. We show the discount factor can be generically identifed jointly with the payoff parameters. It is known the payoff function cannot nonparametrically identified without any a priori restrictions. Our identification of the discount factor is robust to any normalization choice on the payoff parameters. In IO applications normalizations are usually made on switching costs, such as entry costs and scrap values. We also show that switching costs can be nonparametrically identified, in closed-form, independently of the discount factor and other parts of the payoff function. Our identification strategies are constructive. They lead to easy to compute estimands that are global solutions. We illustrate with a Monte Carlo study and the dataset from Ryan (2012).
    Keywords: discount factor; dynamic discrete choice problem; identification; estimation; switching costs
    JEL: C14 C25 C51
    Date: 2018–11–01
  3. By: Michael P. Leung
    Abstract: This paper studies inference in models of discrete choice with social interactions when the data consists of a single large network. We provide theoretical justification for the use of spatial and network HAC variance estimators in applied work, the latter constructed by using network path distance in place of spatial distance. Toward this end, we prove new central limit theorems for network moments in a large class of social interactions models. The results are applicable to discrete games on networks and dynamic models where social interactions enter through lagged dependent variables. We illustrate our results in an empirical application and simulation study.
    Date: 2019–11
  4. By: Olivier Beaumais (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique); Apolline Niérat (CREAM - Centre de Recherche en Economie Appliquée à la Mondialisation - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - IRIHS - Institut de Recherche Interdisciplinaire Homme et Société - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université)
    Abstract: As commitment levels to pro-environmental activities are usually coded as ordered categorical variables, we argue that the multivariate ordered probit model is an appropriate tool to account for the effect of common observable and unobservable variables on joint pro-environmental behaviors. However, exploring in-depth joint pro-environmental behaviors using the multivariate ordered probit model requires not only to assess whether some variables are found to be significant, but also to calculate joint probabilities, conditional probabilities and partial effects on these quantities. As an illustration, we explore the joint commitment levels of households to recycling of materials in France. We show that beyond the estimation of the multivariate ordered probit model, much can be learned from the calculation of the aforementioned quantities. (JEL C35, Q53)
    Keywords: Joint pro-environmental behavior,multivariate ordered probit model,waste recycling
    Date: 2019–11–13
  5. By: Harriet, Mawia; Mburu, John; Irungu, Patrick; Diiro, Gracious; Tanga, Chrysantus Mbi; Subramanian, S.; Fiaboe, K.K.M.; Loon, Joop J.A. van; Dicke, M.; Ekesi, S.
    Keywords: Demand and Price Analysis, Livestock Production/Industries
    Date: 2019–09
  6. By: Rubal Dua; Tamara Sheldon (King Abdullah Petroleum Studies and Research Center)
    Abstract: This paper investigates the drivers of recent improvements in Saudi Arabia’s new light-duty vehicle fleet fuel economy. A vehicle choice model is estimated using aggregate and disaggregate new vehicle purchase data. The estimates are used to simulate counterfactual policy scenarios.
    Keywords: Automobile Transport, Fuel Efficient Vehicles, Fuel Price, Gasoline Consumption, Gasoline Demand
    Date: 2019–04–30

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