nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2021‒05‒17
seven papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Nonparametric welfare analysis for discrete choice: levels and differences of individual and social welfare By Bart Capéau; Liebrecht De Sadeleer; Sebastiaan Maes; André Decoster
  2. Identification of Dynamic Discrete-Continuous Choice Models, with an Application to Consumption-Savings-Retirement By Levy, Matthew; Schiraldi, Pasquale
  3. Effect of Socio – Behavioural Design of Conversational Agents on Customer Responses: A Review By Joffi Thomas; Priya Premi
  4. A BLP Demand Model of Product-Level Market Shares with Complementarity By Wang, Ao
  5. Objective Bayesian meta-analysis based on generalized multivariate random effects model By Bodnar, Olha; Bodnar, Taras
  6. Are your tax problems an opportunity not to pay taxes? Evidence from a randomized survey experiment By Blesse, Sebastian
  7. Efficient Peer Effects Estimators with Random Group Effects By Guido M. Kuersteiner; Ingmar R. Prucha; Ying Zeng

  1. By: Bart Capéau; Liebrecht De Sadeleer; Sebastiaan Maes; André Decoster
    Abstract: Empirical welfare analyses often impose stringent parametric assumptions on individuals’ preferences and neglect unobserved preference heterogeneity. In this paper, we develop a framework to conduct individual and social welfare analysis for discrete choice that does not suffer from these drawbacks. We first adapt the broad class of individual welfare measures introduced by Fleurbaey (2009) to settings where individual choice is discrete. Allowing for unrestricted, unobserved preference heterogeneity, these measures become random variables. We then show that the distribution of these objects can be derived from choice probabilities, which can be estimated nonparametrically from cross-sectional data. In addition, we derive nonparametric results for the joint distribution of welfare and welfare differences, as well as for social welfare. The former is an important tool in determining whether those who benefit from a price change belong disproportionately to those who were initially well-off. An empirical application illustrates the methods.
    Keywords: discrete choice, nonparametric welfare analysis, individual welfare, social welfare, money metric utility, compensating variation, equivalent variation
    Date: 2020
  2. By: Levy, Matthew; Schiraldi, Pasquale
    Abstract: This paper studies the non-parametric identification of the discount factor and utility function in the class of dynamic discrete-continuous choice (DDCC) models. In contrast to the discrete-only model we show the discount factor is identified. Our results further highlight why Euler equation estimation approaches that ignore agents' discrete choices are inconsistent. We estimate utility and discount factors for a consumption- savings-retirement choice problem using the Panel Study of Income Dynamics (PSID). We show that the relative risk aversion parameter and the intertemporal elasticity of substitution are separately identified, and that the latter varies across agents due to the wealth-dependence of the surplus from the discrete choice. This surplus also implies that the value function may be locally convex in wealth, and we find that a simulated Universal Basic Income (UBI) policy counterintuitively benefits wealthier working households more than poorer ones due to this effect.
    Date: 2021–01
  3. By: Joffi Thomas (Indian Institute of Management Kozhikode); Priya Premi (Indian Institute of Management Kozhikode)
    Abstract: The purpose of this paper is to review the literature examining the effect of socio-behavioural design of conversational agents on customer responses. Research modelling effect of socio behavioural design on customer responses were synthesised based on their use in different stages in the consumption cycle and in different industry contexts. The effects of anthropomorphic design categorised as (a) communication design cues and (b) agent related design cues were examined on consumer responses classified into four categories, namelycognitive, affective, socio/relational and behavioural. The design cues had mixed effects on customer responses in different industry contexts and in different use contexts. For instance, anthropomorphic design cues of CA have positive influence on customer responses across industries (ecommerce, movie & entertainment, online gaming) whereas it negatively affect customer responses in banking & finance and e-healthcare industry in purchase/consumption stage, particularly. Anthropomorphic design of CA evoke positive customer responses across various use contexts such (product recommendation, customer assistant, pedagogical agent, advertising), however, leads to negative customer responses while using CA as data collection tool, in pre purchase stage, specifically. We also discussed key mediators and moderators used in modelling effect of CA and its design on customer responses across industries to provide an explanation of varying customer responses to CA design. The potential of CA to increase business and customer value calls for further research in different use and industry contexts to explore customer evaluation mechanisms in the adoption of CA. This emerging area, early stage synthesis of extant research generates insights for future research.
    Keywords: conversational agent, customer responses, use context, industry context, literature review
    Date: 2021–03
  4. By: Wang, Ao (University of Warwick)
    Abstract: Applied researchers most often estimate the demand for dierentiated products assuming that at most one item can be purchased. Yet simultaneous multiple purchases are pervasive. Ignoring the interdependence among multiple purchases can lead to erroneous counterfactuals, in particular, because complementarities are ruled out. I consider the identification and estimation of a random coefficient discrete choice model of bundles, namely sets of products, when only product-level market shares are available. This last feature arises when only aggregate purchases of products, as opposed to individual purchases of bundles, are available, a very common phenomenon in practice. Following the classical approach with aggregate data, I consider a two-step method. First, using a novel inversion result in which demand can exhibit Hicksian complementarity, I recover the mean utilities of products from product-level market shares. Second, to infer the structural parameters from the mean utilities while dealing with price endogeneity, I use instrumental variables. I propose a practically useful GMM estimator whose implementation is straightforward, essentially as a standard BLP estimator. Finally, I estimate the demand for Ready-To-Eat (RTE) cereals and milk in the US. The demand estimates suggest that RTE cereals and milk are overall complementary and the synergy in consumption crucially depends on their characteristics. Ignoring such complementarities results in misleading counterfactuals.
    Date: 2021
  5. By: Bodnar, Olha (Örebro University School of Business); Bodnar, Taras (Örebro University School of Business)
    Abstract: Objective Bayesian inference procedures are derived for the parameters of the multivariate random effects model generalized to elliptically contoured distributions. The posterior for the overall mean vector and the between-study covariance matrix is deduced by assigning two noninformative priors to the model parameter, namely the Berger and Bernardo reference prior and the Jeffreys prior, whose analytical expressions are obtained under weak distributional assumptions. It is shown that the only condition needed for the posterior to be proper is that the sample size is larger than the dimension of the data-generating model, independently of the class of elliptically contoured distributions used in the definition of the generalized multivariate random effects model. The theoretical findings of the paper are applied to real data consisting of ten studies about the effectiveness of hypertension treatment for reducing blood pressure where the treatment effects on both the systolic blood pressure and diastolic blood pressure are investigated.
    Keywords: Multivariate random-effects model; Jeffreys prior; reference prior; propriety; elliptically contoured distribution; multivariate meta-analysis
    JEL: C11 C13 C15 C16
    Date: 2021–05–07
  6. By: Blesse, Sebastian
    Abstract: Taxpayers often view tax rules and filing processes as complicated. In this paper I study whether the perceived tax uncertainty among peers leads to a reduction of voluntary tax compliance. I find strong supportive evidence for this hypothesis using a survey experiment for a large representative sample of the German population. Providing randomized information that others are uncertain about how to file their taxable income decreases individual tax morale. This suggests that subjects use negative peer signals as an excuse in order to opt-out of tax compliance. Studying related heterogeneous treatment effects, I find that both older and left-wing subjects are more responsive to tax uncertainty of others. I also show persistent treatment effects among very honest taxpayers in a follow-up survey.
    Keywords: Tax Complexity,Taxpayer Uncertainty,Tax Morale,Survey Experiments
    JEL: H26 Z13 K42 C9
    Date: 2021
  7. By: Guido M. Kuersteiner; Ingmar R. Prucha; Ying Zeng
    Abstract: We study linear peer effects models where peers interact in groups, individual's outcomes are linear in the group mean outcome and characteristics, and group effects are random. Our specification is motivated by the moment conditions imposed in Graham 2008. We show that these moment conditions can be cast in terms of a linear random group effects model and lead to a class of GMM estimators that are generally identified as long as there is sufficient variation in group size. We also show that our class of GMM estimators contains a Quasi Maximum Likelihood estimator (QMLE) for the random group effects model, as well as the Wald estimator of Graham 2008 and the within estimator of Lee 2007 as special cases. Our identification results extend insights in Graham 2008 that show how assumptions about random group effects as well as variation in group size can be used to overcome the reflection problem in identifying peer effects. Our QMLE and GMM estimators can easily be augmented with additional covariates and are valid in situations with a large but finite number of different group sizes. Because our estimators are general moment based procedures, using instruments other than binary group indicators in estimation is straight forward. Monte-Carlo simulations show that the bias of the QMLE estimator decreases with the number of groups and the variation in group size, and increases with group size. We also prove the consistency and asymptotic normality of the estimator under reasonable assumptions.
    Date: 2021–05

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