nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2020‒03‒23
nine papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Towards Sustainable Energy Consumption Electricity Demand Flexibility and Household Fuel Choice By Daniel, Aemiro Melkamu
  2. Consequentiality, elicitation formats, and the willingness-to-pay for green electricity: Evidence from Germany By Andor, Mark Andreas; Frondel, Manuel; Horvath, Marco
  3. Consumer Willingness to Pay for Locally Produced Hard Cider By Jensen, Kimberly; Hughes, David; DeLong, Karen; Wright, Hannah; Menard, Jamey; MacKenzie, Gill
  4. Joint Estimation of Discrete Choice Model and Arrival Rate with Unobserved Stock-out Events By Hongzhang Shao; Anton J. Kleywegt
  5. Identification of Random Coefficient Latent Utility Models By Roy Allen; John Rehbeck
  6. Producer Perceptions and Willingness to Adopt Adaptive Multi-Paddock Grazing By Clifford, McKenna; McKendree, Melissa G.S.
  7. How do U.S. Visa Policies Affect Unauthorized Immigration? By Brian K. Kovak; Rebecca Lessem
  8. Analyzing and Identifying Peer-to-Peer Housing Loan Default Risk Factors by a Logit Modelling By Mo, Lijia
  9. Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares By Jean-Pierre H. Dubé; Ali Hortaçsu; Joonhwi Joo

  1. By: Daniel, Aemiro Melkamu (Department of Economics, Umeå University)
    Abstract: Paper [I] investigates household heterogeneity in valuing electricity contract attributes that include various load controls and information sharing to induce demand flexibility. Using a stated preference choice experiment conducted with Swedish households, this paper shows that, although a large proportion of households asks for substantial compensation, some households are willing to share their electricity consumption information and require relatively lower compensation to allow load controls. In addition, this paper finds that some households that are willing to provide flexibility by accepting load controls at a relatively low compensation ask for sizeable compensation to share their electricity consumption information, and vice versa. From the perspective of the contract providers, these findings suggest that information-optional contracts can generate more customers than contracts that bundle households’ consumption information with various load controls. Paper [II] uses a flexible model to accommodate heterogeneous decision rules in analysing data obtained from a discrete choice experiment aimed at eliciting Swedish households’ willingness to accept compensation for restrictions on household electricity and heating use during peak hours. The model combines behavioural processes based on random utility maximization with an elimination-by-aspects strategy, where the latter involves a two-stage decision process. In the first stage, respondents are allowed to eliminate from their choice set alternatives that contain an unacceptable level, in this case restrictions on the use of heating and electricity. In the second stage, respondents choose between the remaining alternatives in a rational utility maximizing manner. Our results show that about half of the respondents choose according to an elimination-by-aspects strategy, and considering elimination-by-aspects behaviour leads to a downward shift in elicited willingness-to-accept. Paper [III] tests the effect of a pro-environmental framing on households’ stated willingness to accept restrictions on their electricity use. We use a split-sample choice experiment and ask respondents to choose between their current electricity contract and hypothetical contracts featuring various load controls and monetary compensation. Our results indicate that the pro-environmental framing has little impact on the respondents’ choices. We observe a significant framing effect on choices and marginal willingness-to-accept for only a few contract attributes. The results further suggest that there is no significant framing effect among households that are already engaged in pro-environmental activities. Paper [IV] explores the socio-demographic and housing characteristics that affect household fuel choice and fuel use decisions in urban Ethiopia. The results indicate that, whereas households with a female head are more likely to combine traditional solid (firewood and charcoal) and modern (electricity) fuels for different uses, households with less-educated heads, many family members, and poor living conditions (fewer rooms) tend to use traditional solid biomass fuels. We find that households with an individual electricity meter are significantly less likely to use charcoal. Further, the results show the satiation effect from the increasing use of a fuel by households is relatively higher for firewood and lower for electricity.
    Keywords: Choice experiment; demand flexibility; electricity contract; fuel choice; fuel stacking; household heterogeneity; load control; pro-environmental framing; willingness-to-accept
    JEL: C25 C99 D01 D12 Q42 Q48 Q51
    Date: 2020–03–10
  2. By: Andor, Mark Andreas; Frondel, Manuel; Horvath, Marco
    Abstract: Based on hypothetical responses originating from a large-scale survey among about 6,000 German households, this study investigates the discrepancy in willingness-to-pay (WTP) estimates for green electricity across single-binary-choice and open-ended valuation formats. Recognizing that respondents self-select into two groups distinguished by their belief in their answers' consequences for policy making, we employ a switching regression model that accounts for the potential endogeneity of respondents' belief in consequences and, hence, biases from sample selectivity. Contrasting with the received literature, we find WTP bids that tend to be higher among those respondents who obtained questions in the openended format, rather than single-binary-choice questions. This difference substantially shrinks, however, when focusing on individuals who perceive the survey as politically consequential.
    Keywords: Elicitation format,contingent valuation,consequentialism
    JEL: D03 D12 Q48 Q50 H41
    Date: 2020
  3. By: Jensen, Kimberly; Hughes, David; DeLong, Karen; Wright, Hannah; Menard, Jamey; MacKenzie, Gill
    Keywords: Marketing
  4. By: Hongzhang Shao; Anton J. Kleywegt
    Abstract: This paper studies the joint estimation problem of a discrete choice model and the arrival rate of potential customers when unobserved stock-out events occur. In this paper, we generalize [Anupindi et al., 1998] and [Conlon and Mortimer, 2013] in the sense that (1) we work with generic choice models, (2) we allow arbitrary numbers of products and stock-out events, and (3) we consider the existence of the null alternative, and estimates the overall arrival rate of potential customers. In addition, we point out that the modeling in [Conlon and Mortimer, 2013] is problematic, and present the correct formulation.
    Date: 2020–03
  5. By: Roy Allen; John Rehbeck
    Abstract: This paper provides nonparametric identification results for random coefficient distributions in perturbed utility models. We cover discrete and continuous choice models. We establish identification using variation in mean quantities, and the results apply when an analyst observes aggregate demands but not whether goods are chosen together. We require exclusion restrictions and independence between random slope coefficients and random intercepts. We do not require regressors to have large supports or parametric assumptions.
    Date: 2020–02
  6. By: Clifford, McKenna; McKendree, Melissa G.S.
    Keywords: Farm Management, Agribusiness, Marketing, Resource /Energy Economics and Policy, Environmental Economics and Policy
  7. By: Brian K. Kovak; Rebecca Lessem
    Abstract: We examine how increasing the number of visas available to potential migrants would affect unauthorized immigration from Mexico to the U.S. Current U.S. policy bans people who are deported from receiving legal status for a period of time. This policy aims to serve as an additional deterrent to unauthorized immigration, but may be ineffective given that most potential Mexican migrants have an extremely low probability of ever being able to legally move to the U.S. We develop a dynamic discrete location choice model, which we estimate using data from the Mexican Migration Project, and consider various counterfactual policies that vary the intensity of enforcement and access to work visas. We find that legal entry bans for deported individuals are ineffective at current rates of legal immigration, but that increased legalization rates would amplify the deterrent effects of deportation. We also show that a temporary work visa program would yield similar deterrent effects as an increase in permanent legalization without resulting in very large increases in the total stock of migrants residing in the U.S. These findings have important implications for structuring future immigration reforms.
    JEL: F22 J61
    Date: 2020–02
  8. By: Mo, Lijia
    Keywords: Farm Management, Research Methods/ Statistical Methods
  9. By: Jean-Pierre H. Dubé; Ali Hortaçsu; Joonhwi Joo
    Abstract: Although typically overlooked, many purchase datasets exhibit a high incidence of products with zero sales. We propose a new estimator for the Random-Coefficients Logit demand system for purchase datasets with zero-valued market shares. The identification of the demand parameters is based on a pairwise-differencing approach that constructs moment conditions based on differences in demand between pairs of products. The corresponding estimator corrects non-parametrically for the potential selection of the incidence of zeros on unobserved aspects of demand. The estimator also corrects for the potential endogeneity of marketing variables both in demand and in the selection propensities. Monte Carlo simulations show that our proposed estimator provides reliable small-sample inference both with and without selection-on- unobservables. In an empirical case study, the proposed estimator not only generates different demand estimates than approaches that ignore selection in the incidence of zero shares, it also generates better out-of-sample fit of observed retail contribution margins.
    JEL: D12 L00 L66 L81 M3
    Date: 2020–02

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