
on Discrete Choice Models 
By:  Manhique, Henrique; Wätzold, Frank 
Abstract:  The use of stated preference surveys for the valuation of environmental goods in developing countries has to take into account that there is substantial public distrust towards the institutions providing the environmental goods under valuation. Thus, high protest responses and low value estimates may indicate rejection or protest against the institutional setting of the survey design, rather than the dislike or low welfare effects of these goods. In this context, we investigate the effects of institutional trust on value estimates by examining the performance of three different institutions – government, conservation NGO, and farmers – in a case study aimed at eliciting preferences for conserving different types of biodiversity within orchards in the Cape Floristic Region – a biodiversity hotspot in South Africa threatened by the expansion and intensification of agriculture. We find that institutional trust has an effect on preferences and willingnesstopay, with farmers leading to the highest level of trust and value estimates, followed rather closely by a conservation NGO and, with some distance, by the government with the lowest trust level and value estimates. In terms of preferences for biodiversity conservation, our results show that respondents prefer measures to conserve endangered and endemic species over measures primarily aimed at providing the ecosystem services of pollination and pest control. 
Keywords:  Biodiversity Hotspot; Institutional distrust; Ecosystem services; Economics; Endangered species; Payment vehicle; Western Cape 
JEL:  C5 C9 Q1 Q2 Q51 Q57 
Date:  2023–09–30 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:118750&r=dcm 
By:  Shakeeb Khan; Tatiana Komarova; Denis Nekipelov 
Abstract:  In this paper we reconsider the notion of optimality in estimation of partially identified models. We illustrate the general problem in the context of a semiparametric binary choice model with discrete covariates as an example of a model which is partially identified as shown in, e.g. Bierens and Hartog (1988). A set estimator for the regression coefficients in the model can be constructed by implementing the Maximum Score procedure proposed by Manski (1975). For many designs this procedure converges to the identified set for these parameters, and so in one sense is optimal. But as shown in Komarova (2013) for other cases the Maximum Score objective function gives an outer region of the identified set. This motivates alternative methods that are optimal in one sense that they converge to the identified region in all designs, and we propose and compare such procedures. One is a Hodges type estimator combining the Maximum Score estimator with existing procedures. A second is a two step estimator using a Maximum Score type objective function in the second step. Lastly we propose a new random set quantile estimator, motivated by definitions introduced in Molchanov (2006). Extensions of these ideas for the cross sectional model to static and dynamic discrete panel data models are also provided. 
Date:  2023–10 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2310.02414&r=dcm 
By:  Pauline Vorjohann (Department of Economics, University of Exeter) 
Abstract:  I derive a theoretical model of choice bracketing from two behavioral axioms in an expected utility framework. The first behavioral axiom establishes a direct link between narrow bracketing and correlation neglect. The second behavioral axiom identifies the reference point as the place where broad and narrow preferences are connected. In my model, the narrow bracketer is characterized by an inability to process changes from the reference point in different dimensions simultaneously. As a result, her tradeoffs between dimensions are distorted. While she disregards interactions between actual outcomes, she appreciates these interactions mistakenly with respect to the reference point. 
Keywords:  narrow bracketing, correlation neglect, reference dependence, axiomatic foundation 
JEL:  D3 D11 D91 
Date:  2023–09–05 
URL:  http://d.repec.org/n?u=RePEc:exe:wpaper:2309&r=dcm 
By:  Brice Romuald Gueyap Kounga 
Abstract:  This paper studies the identification and estimation of a semiparametric binary network model in which the unobserved social characteristic is endogenous, that is, the unobserved individual characteristic influences both the binary outcome of interest and how links are formed within the network. The exact functional form of the latent social characteristic is not known. The proposed estimators are obtained based on matching pairs of agents whose network formation distributions are the same. The consistency and the asymptotic distribution of the estimators are proposed. The finite sample properties of the proposed estimators in a MonteCarlo simulation are assessed. We conclude this study with an empirical application. 
Date:  2023–10 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2310.07151&r=dcm 
By:  Zhaonan Qu; Alfred Galichon; Johan Ugander 
Abstract:  For a broad class of choice and ranking models based on Luce's choice axiom, including the BradleyTerryLuce and PlackettLuce models, we show that the associated maximum likelihood estimation problems are equivalent to a classic matrix balancing problem with target row and column sums. This perspective opens doors between two seemingly unrelated research areas, and allows us to unify existing algorithms in the choice modeling literature as special instances or analogs of Sinkhorn's celebrated algorithm for matrix balancing. We draw inspirations from these connections and resolve important open problems on the study of Sinkhorn's algorithm. We first prove the global linear convergence of Sinkhorn's algorithm for nonnegative matrices whenever finite solutions to the matrix balancing problem exist. We characterize this global rate of convergence in terms of the algebraic connectivity of the bipartite graph constructed from data. Next, we also derive the sharp asymptotic rate of linear convergence, which generalizes a classic result of Knight (2008), but with a more explicit analysis that exploits an intrinsic orthogonality structure. To our knowledge, these are the first quantitative linear convergence results for Sinkhorn's algorithm for general nonnegative matrices and positive marginals. The connections we establish in this paper between matrix balancing and choice modeling could help motivate further transmission of ideas and interesting results in both directions. 
Date:  2023–09 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2310.00260&r=dcm 
By:  Toru Kitagawa; Sokbae Lee; Chen Qiu 
Abstract:  We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finitesample analysis on mean square regret, a decision criterion advocated by Kitagawa, Lee, and Qiu (2022). We find that optimal rules are always fractional, irrespective of the width of the identified set and precision of its estimate. The optimal treatment fraction is a simple logistic transformation of the commonly used tstatistic multiplied by a factor calculated by a simple constrained optimization. This treatment fraction gets closer to 0.5 as the width of the identified set becomes wider, implying the decision maker becomes more cautious against the adversarial Nature. 
Date:  2023–10 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2310.06242&r=dcm 