nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2021‒01‒25
seven papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Drivers of intentions and drivers of actions: willingness toparticipate versus actual participation in fire management inSardinia, Italy By Giovanni B. Concu; Claudio Detotto; Marco Vannini
  2. An experimental analysis of German farmers' decisions to buy or rent farmland By Buchholz, Matthias; Danne, Michael; Mußhoff, Oliver
  3. I’d Like to Move It! Consumption Rivalry in the EV Public Charging Market: Demand Estimation with Deterministic Choice Set Variation By Soetevent, Adriaan R.
  4. Simple and Credible Value-Added Estimation Using Centralized School Assignment By Joshua Angrist; Peter Hull; Parag A. Pathak; Christopher R. Walters
  5. Partial Identification in Nonseparable Binary Response Models with Endogenous Regressors By Jiaying Gu; Thomas M. Russell
  6. Robust Estimation of Probit Models with Endogeneity By Andrea A. Naghi; Máté Váradi; Mikhail Zhelonkin
  7. Well-formed decompositions of Generalized Additive Independence models By Michel Grabisch; Christophe Labreuche; Mustapha Ridaoui

  1. By: Giovanni B. Concu (Università di Sassari); Claudio Detotto (Università di Corsica); Marco Vannini (Università du Sassari)
    Abstract: Changing wildfire regimes coupled with budget cuts are spurring increased involvement of communities and citizens in fire management programs. Policy making faces the task of understanding citizens’ willingness to participate and mobilizing will into actions. As there is no reason to expect that the same factors affect willingness to participate and actual participation in the same direction, policy making would require information both on citizens’ preferences over management programs and on drivers and barriers to adoption. In this paper we compare data on preferences from a latent class Discrete Choice Experiment (DCE) with data on adoption of fire prevention and mitigation measures. The objective is to test if the same factors explain actual participation and willingness to participate in fire management programs. Results suggest that sufficient information for policy design cannot be gained exclusively from the DCE or the analysis of actual behavioural data as the sets of explanatory factors do not entirely overlap. However, two variables – knowledge of fire prescriptions and community’s capacity – can be used to influence both the adoption of prevention and mitigation measures and citizens’ willingness to participate in fire management. Policy makers can directly control these factors to nudge the public towards greater involvement in fire prevention and mitigation.
    Keywords: Citizens’ participation; Willingness to participate; Drivers of preparedness; Latent class discrete choice
    Date: 2021–01
  2. By: Buchholz, Matthias; Danne, Michael; Mußhoff, Oliver
    Abstract: Farmland is an essential agricultural production factor that farmers can choose to either buy or rent. In this paper, we apply a discrete choice experiment to analyse German farmers' individual buying and rental decisions for farmland. Our results reveal that farmers have a higher willingness to buy than to rent farmland. Covariates such as farmers' risk attitude affect the decisions in the discrete choice experiment while no effect was observable for individual expectations about future farmland prices. Direct payments considerably raise farmers' willingness to buy and rent farmland. Farmers' decisions deviate substantially from normative predictions from the present value model.
    Keywords: Agricultural Land Market,Farmland,Rent-or-Buy Decision,Discrete Choice Experiment,Present Value Model
    JEL: C93 D90 Q10
    Date: 2020
  3. By: Soetevent, Adriaan R.
    Abstract: Consumption rivalry generates variation in the choice sets decision-makers face. Not taking into account such variation may generate biased demand estimates. It remains unclear how this impacts estimation accuracy because researchers often lack information on temporal variation in product availability. This paper uses information on the exact set of available alternatives at the time of choosing to formulate time-variant deterministic constraints. In an application to the market for public charging infrastructure for electric vehicles, I show that incorporating this information significantly improves the out-of-sample forecasting accuracy of individual choice and hence the aggregate demand estimates for local charging facilities.
    Keywords: Discrete choice,Preference estimation,Consumption rivalry,Electric Vehicles
    JEL: H23 H42 H54 Q41 Q48
    Date: 2021
  4. By: Joshua Angrist; Peter Hull; Parag A. Pathak; Christopher R. Walters
    Abstract: Many large urban school districts match students to schools using algorithms that incorporate an element of random assignment. We introduce two simple empirical strategies to harness this randomization for value-added models (VAMs) measuring the causal effects of individual schools. The first estimator controls for the probability of being offered admission to different schools, treating the take-up decision as independent of potential outcomes. Randomness in school assignments is used to test this key conditional independence assumption. The second estimator uses randomness in offers to generate instrumental variables (IVs) for school enrollment. This procedure uses a low-dimensional model of school quality mediators to solve the under-identification challenge arising from the fact that some schools are under-subscribed. Both approaches relax the assumptions of conventional value-added models while obviating the need for elaborate nonlinear estimators. In applications to data from Denver and New York City, we find that models controlling for both assignment risk and lagged achievement yield highly reliable VAM estimates. Estimates from models with fewer controls and older lagged score controls are improved markedly by IV.
    JEL: C11 C21 C26 C52 I21 I28 J24
    Date: 2020–12
  5. By: Jiaying Gu; Thomas M. Russell
    Abstract: This paper considers (partial) identification of a variety of parameters, including counterfactual choice probabilities, in a general class of binary response models with possibly endogenous regressors. Importantly, our framework allows for nonseparable index functions with multi-dimensional latent variables, and does not require parametric distributional assumptions. We demonstrate how various functional form, independence, and monotonicity assumptions can be imposed as constraints in our optimization procedure to tighten the identified set, and we show how these assumptions have meaningful interpretations in terms of restrictions on latent types. In the special case when the index function is linear in the latent variables, we leverage results in computational geometry to provide a tractable means of constructing the sharp set of constraints for our optimization problems. Finally, we apply our method to study the effects of health insurance on the decision to seek medical treatment.
    Date: 2021–01
  6. By: Andrea A. Naghi (Erasmus University Rotterdam); Máté Váradi (Erasmus University Rotterdam); Mikhail Zhelonkin (Erasmus University Rotterdam)
    Abstract: Probit models with endogenous regressors are commonly used models in economics and other social sciences. Yet, the robustness properties of parametric estimators in these models have not been formally studied. In this paper, we derive the influence functions of the endogenous probit model’s classical estimators (the maximum likelihood and the two-step estimator) and prove their non-robustness to small but harmful deviations from distributional assumptions. We propose a procedure to obtain a robust alternative estimator, prove its asymptotic normality and provide its asymptotic variance. A simple robust test for endogeneity is also constructed. We compare the performance of the robust and classical estimators in Monte Carlo simulations with different types of contamination scenarios. The use of our estimator is illustrated in several empirical applications.
    Keywords: Binary outcomes, Probit model, Endogenous variable, Instrumental variable, Robust Estimation
    JEL: C26 C13 C18
    Date: 2021–01–14
  7. By: Michel Grabisch (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics); Christophe Labreuche (Thales Research and Technology [Palaiseau] - THALES); Mustapha Ridaoui (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics)
    Abstract: Generalized Additive Independence (GAI) models permit to represent interacting variables in decision making. A fundamental problem is that the expression of a GAI model is not unique as it has several equivalent different decompositions involving multivariate terms. Considering for simplicity 2-additive GAI models (i.e., with multivariate terms of at most 2 variables), the paper examines the different questions (definition, monotonicity, interpretation, etc.) around the decomposition of a 2-additive GAI model and proposes as a basis the notion of well-formed decomposition. We show that the presence of a bi-variate term in a well-formed decomposition implies that the variables are dependent in a preferential sense. Restricting to the case of discrete variables, and based on a previous result showing the existence of a monotone decomposition, we give a practical procedure to obtain a monotone and well-formed decomposition and give an explicit expression of it in a particular case.
    Keywords: decomposition,decision making,multichoice game,Generalized Additive Independence
    Date: 2020

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