
on Discrete Choice Models 
By:  Lata Gangadharan (Monash University, Department of Economics); Philip J. Grossman (Monash University, Department of Economics); Nina Xue (Monash University, Department of Economics) 
Abstract:  We introduce a novel experimental procedure to measure altruistic giving along a spectrum, from warm glow to pure altruism, by eliciting willingness to pay to increase the likelihood that a donation is received by a recipient. Whereas previous methods identify pure warmglow motives, our approach directly measures altruistic preferences and is validated by a survey measure developed by Carpenter (2021). Participants who identify in the survey as altruistic givers are more likely to pay to increase the probability that the donation is implemented and pay more on average than those who identified as mainly motivated by warm glow. 
Keywords:  warm glow, altruism, donation, charitable giving, experiment 
JEL:  H4 
Date:  2023–02 
URL:  http://d.repec.org/n?u=RePEc:mos:moswps:202304&r=dcm 
By:  Haruki Kono; Kota Saito; Alec Sandroni 
Abstract:  The random utility model is one of the most fundamental models in discrete choice analysis in economics. Although Falmagne (1978) obtained an axiomatization of the random utility model, his characterization requires strong observability of choices, i.e., that the frequency of choices must be observed from all subsets of the set of alternatives. Little is known, however, about the axiomatization when a dataset is incomplete, i.e., the frequencies on some choice sets are not observable. In fact, it is known that in some cases, obtaining a tight characterization is NP hard. On the other hand, datasets in reality almost always violate the requirements on observability assumed by Falmagne (1978). We consider an incomplete dataset in which we do not observe frequencies of some alternatives: for all other alternatives, we observe frequencies. For such a dataset, we obtain a finite system of linear inequalities that is necessary and sufficient for the dataset to be rationalized by a random utility model. Moreover, the necessary and sufficient condition is tight in the sense that none of the inequalities is implied by the other inequalities, and dropping any one of the inequalities makes the condition not sufficient. 
Date:  2023–02 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2302.03913&r=dcm 
By:  Maximilian Sp\"ath 
Abstract:  The Becker DeGroot Marshak method is widely used to elicit the valuation that an individual assigns to an object. Theoretically, the secondprice structure of the method gives individuals the incentive to state their true valuation. Yet, the elicitation methods empirical accuracy is subject to debate. With this paper, I provide a clear verification of the qualitative accuracy of the method. Participants of an incentivized laboratory experiment can sell a virtual object. The value of the object is publicly known and experimentally varied in a betweensubjects design. Replicating previous findings on the low quantitative accuracy, I observe a very small share of individuals placing a payoffoptimal stated valuation. However, the analysis shows that the stated valuation increases with the value of the object. This result shows the qualitative accuracy of the BDM method and suggests that the method can be applied in comparative studies. 
Date:  2023–02 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2302.04055&r=dcm 
By:  Alessandra Migliore; Cristina RossiLamastra; Chiara Tagliaro 
Abstract:  The Covid19 pandemic has forced most workers to work from home (WFH). At a first glance, this seems not a big change for academics, who, even in normal time, are used to performing their research activities autonomously and to balancing oncampus and offcampus locations. Instead, exactly for their flexible habits it is interesting to study where academics have worked during the Covid19 pandemic and which factors relate to their location choices. This paper addresses these issues by relying on survey data from a sample of 7, 865 Italian tenured academics. First, cluster analysis unveils four main location choices of Italian academics during the Covid19 pandemic depending on the frequency of access to home, university or other spaces, namely Homecentric, Universitycentric, Between home and university and Multilocated. Second, multinomial probit models reveal a nuanced picture of the factors associated to the belonging to each cluster. Decisions over location choice depend, mostly, on workrelated factors (i.e., discipline); then on spacerelated factors (i.e., satisfaction towards campus workspace characteristics and the need of a laboratory); finally, on, liferelated factors (i.e., living with school children or a partner) and other factors (i.e., commuting times and gender). However, each of the four location patterns depend on different determinants. The results offer university and practicewide implications anticipating future changes in how work in academia is spatially organized. 
Keywords:  COVID19; Knowledgework; Location Choice; University 
JEL:  R3 
Date:  2022–01–01 
URL:  http://d.repec.org/n?u=RePEc:arz:wpaper:2022_139&r=dcm 
By:  Nail Kashaev; Charles Gauthier; Victor H. Aguiar 
Abstract:  We analyze consumer demand behavior using Dynamic Random Utility Model (DRUM). Under DRUM, a consumer draws a utility function from a stochastic utility process in each period and maximizes this utility subject to her budget constraint. DRUM allows unrestricted time correlation and crosssection heterogeneity in preferences. We fully characterize DRUM for a panel data of consumer choices and budgets. DRUM is linked to a finite mixture of deterministic behavior represented as the Kronecker product of static rationalizable behavior. We provide a generalization of the WeylMinkowski theorem that uses this link and enables conversion of the characterizations of the static Random Utility Model (RUM) of McFaddenRichter (1990) to its dynamic form. DRUM is more flexible than Afriat's (1967) framework for time series and more informative than RUM. We show the feasibility of the statistical test of DRUM in a Monte Carlo study. 
Date:  2023–02 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2302.04417&r=dcm 
By:  Kurz, Konstantin; Bock, Carolin; Knodt, Michèle; Stöckl, Anna 
Date:  2022 
URL:  http://d.repec.org/n?u=RePEc:dar:wpaper:136773&r=dcm 
By:  Prasad, Vignesh; Koert, Dorothea; StockHomburg, Ruth; Peters, Jan; Chalvatzaki, Georgia 
Date:  2022 
URL:  http://d.repec.org/n?u=RePEc:dar:wpaper:136149&r=dcm 
By:  Mohammed Abdellaoui (HEC Paris  Ecole des Hautes Etudes Commerciales, GREGHEC  Groupement de Recherche et d'Etudes en Gestion  HEC Paris  Ecole des Hautes Etudes Commerciales  CNRS  Centre National de la Recherche Scientifique); Horst Zank (University of Manchester [Manchester]) 
Abstract:  Foundations are provided for rankdependent preferences within the popular twostage framework of AnscombeAumann, in which risk and ambiguity feature as distinct sources of uncertainty. We advance the study of attitudes towards ambiguity without imposing expected utility for risk. As a result, in our general model, ambiguity attitude can be captured by nonadditive subjective probabilities as under Choquet expected utility or by a specific utility for ambiguity as in recursive expected utility or, if required, by both. The key property for preferences builds on (discrete) rates of substitution which are standardly applied in economics. By demanding consistency for these rates of substitution across events and within or across sources of uncertainty, we obtain a model that nests popular theories for risk and ambiguity. This way, new possibilities for theoretical and empirical analyses of these theories emerge. 
Keywords:  Ambiguity, Recursive Expected Utility, Risk, Substitution Consistency, Sourcedependence, Source and Rankdependent Utility 
Date:  2022 
URL:  http://d.repec.org/n?u=RePEc:hal:journl:hal03924295&r=dcm 
By:  Javier Alejo (IECONUniversidad de la República); Antonio F. Galvao (Michigan State University); Julián MartinezIriarte (UC Santa Cruz); Gabriel MontesRojas (Universidad de Buenos Aires/CONICET) 
Abstract:  This paper develops a semiparametric procedure for estimation of unconditional quantile partial effects using quantile regression coefficients. The main result is based on the fact that, for continuous covariates, unconditional quantile effects are a weighted average of conditional ones at particular quantile levels that depend on the covariates. We propose a twostep estimator for the unconditional effects where in the first step one estimates a structural quantile regression model, and in the second step a nonparametric regression is applied to the first step coefficients. We establish the asymptotic properties of the estimator, say consistency and asymptotic normality. Monte Carlo simulations show numerical evidence that the estimator has very good finite sample performance and is robust to the selection of bandwidth and kernel. To illustrate the proposed method, we study the canonical application of the Engel’s curve, i.e. food expenditures as a share of income. 
Keywords:  Quantile regression, unconditional quantile regression, nonparametric regression 
JEL:  C14 C21 
Date:  2023–02 
URL:  http://d.repec.org/n?u=RePEc:aoz:wpaper:217&r=dcm 