Discrete Choice Models 2025-07-21

National defence is a quintessential public good which generates welfare but does not have a market price to be used for its valuation. This study uses non-market valuation, namely a discrete choice experiment, to enhance the understanding of how individuals' perceptions of threat influence their valuation of defense policy instruments. The analysis employs an integrated choice and latent variable model in which perception of foreign, domestic and economic threat are included as latent variables and interacted with estimates of marginal willingness to pay (WTP) for the different policy instruments. Results show that perceptions of foreign threat are associated with higher WTP for defence policy instruments. Effect sizes are substantial with WTP varying by a factor of 2 to 4 between respondents with the weakest and strongest threat perceptions. In contrast, a strong perception of domestic threats correlates with low or even zero WTP for defence policy instruments. No clear effect can be found for perceived economic threat. The validity of the assessment of threat perceptions is supported by the analysis of systematic variation of threat perception with demographic characteristics. The policy-relevance of this link between psychological antedecents and valuation of national defence is discussed. Tobias Börger Tim Lohse Jürgen Meyerhoff Salmai Qari choice experimen, threat perceptions, defence policy, willingness to pay, integrated choice, latent variable model 2025 This study examines how changes in travel time affects participants’ intention to revisit a sport event and how willingness to travel (WTT) questions and resulting willingness to pay (WTP) estimates differ depending on the question format. The analysis relied on post-race online survey data of participants of a running event in the United States (n=592). WTT questions were assessed with payment card (multiple cost levels) and dichotomous choice formats (single cost level). Hypothetical travel cost increase was framed as additional travel time rather than travel distance. Results reveal that respondents are less likely to participate as travel time rises, while higher-income respondents are more likely to return. The payment card question format generates greater travel cost sensitivity than the dichotomous choice format, while yielding higher WTP estimates. The study introduced travel time as a valid payment vehicle and offered evidence of how different question formats affect WTT and WTP. Key Words: Intention to revisit; Monetary valuation; Sport event; Sport tourism; Travel cost; Willingness to pay John C. Whitehead Pamela Wicker 2025 We provide identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: known heterogeneity, initially unknown heterogeneity that may be revealed over time, and transitory uncertainty. We consider a common environment where the researcher only has access to a short panel on choices and realized outcomes. We establish identification of the outcome equation parameters and the distribution of the unobservables, under the standard assumption that unknown heterogeneity and uncertainty are normally distributed. We also show that, absent known heterogeneity, the model is identified without making any distributional assumption. We then derive the asymptotic properties of a sieve MLE estimator for the model parameters, and devise a tractable profile likelihood based estimation procedure. Our estimator exhibits good finite-sample properties. Finally, we illustrate our approach with an application to ability learning in the context of occupational choice. Our results point to substantial ability learning based on realized wages. Bunting, Jackson Diegert, Paul Maurel, Arnaud occupational choice, identification, heterogeneity, learning 2025-06 Voll, Kyra Höcker, Martin Christian Bachtal, Yassien Nico Pfnür, Andreas Schlereth, Christian 2025 Objective: To explore the impact of online food shopping in France on the selection of products purchased and its potential impact on shoppers' dietary balance. Design: A qualitative study involving in-depth semi-structured individual interviews. The interviews were recorded, transcribed verbatim and analysed through a reflexive thematic analysis approach. Setting: France. Participants: Thirty-four male and female respondents aged between 21 and 61 years old, residing in various regions of France, including urban, suburban and rural areas, with diverse profiles in terms of gender, age, location and number of children under 18. Results: Five key themes were identified as influencing decision-making with regard to the products purchased, namely 'less choice, especially for fresh produce', 'sense of security in buying the same products', 'convenience of online shopping through time-saving and product recommendation lists', 'avoiding unplanned purchases' and 'less fresh produce purchased, sometimes replaced by more processed items'. In turn, all of these factors potentially have an impact on the diet of online shoppers. Conclusions: With grocery e-commerce penetration expected to double in the next 5 years, the study underscores the consequences of online shopping on consumers' dietary balance. The findings have practical implications for online food retailers, inciting them to develop solutions that would encourage e-grocery shoppers to buy more fresh produce and sample a more varied diet. Additionally, they highlight the importance of monitoring the influence of technology on the consumer buying process, particularly with regard to food. Olivier Droulers Sophie Lacoste-Badie Balanced diet, Consumer decision-making, Food consumption, Online grocery shopping 2025 We investigate how strongly the local environment beyond the family can contribute to understanding the formation of children’s economic preferences. Building on precise geolocation data for around 6000 children, we use fixed effects, spatial autoregressive models and Kriging to capture the relation between the local environment and children’s preferences. The spatial models explain a considerable part of so far unexplained variation in preferences. Moreover, the “spatial stability†of preferences exceeds the village level. Our results highlight the importance of the local environment for the formation of children’s preferences, which we quantify to be as large as that of parental preferences. Shyamal Chowdhury Manuela Puente Beccar Hannah Schildberg-Hörisch Sebastian O. Schneider Matthias Sutter skill formation, spatial models, kriging, local environment, patience, risk attitudes, prosociality, experiments with children, Bangladesh 2025-07 We study response behavior in surveys and propose a method to identify and improve the informativeness of survey evidence. First, we develop a choice model of survey response behavior under the assumption that responses imperfectly reveal respondents' characteristics due to limited self-knowledge, inattention, or lack of engagement. Respondents receive individual-specific signals about their characteristics and choose their responses accordingly. We identify the conditions under which this process leads to biased inference from survey evidence and demonstrate how focusing on respondents with high signal precision mitigates bias. Importantly, we show that a respondent's signal precision can be inferred from observed response patterns. Second, based on these insights, we develop a consistent and unbiased estimator for a respondent's signal precision. Third, we provide experimental and survey evidence concerning the performance of the model and estimator. We experimentally test the model's key predictions in a context where the researcher knows the true characteristics. The data confirm both the model's predictions and the estimator's validity. Using a large survey, we show how our estimator can be used to improve survey evidence. Our estimator significantly increases the explanatory power of self-assessments and their association with behavior, and performs well relative to alternative methods proposed in the literature. Armin Falk Luca Henkel Thomas Neuber Philipp Strack survey research, rational inattention, online experiment, non-cognitive skills, preferences 2025 This paper introduces a novel approach to simulation smoothing for nonlinear and non-Gaussian state space models. It allows for computing smoothed estimates of the states and nonlinear functions of the states, as well as visualizing the joint smoothing distribution. The approach combines extremum estimation with simulated data from the model to estimate the conditional distributions in the backward smoothing decomposition. The method is generally applicable and can be paired with various estimators of conditional distributions. Several applications to nonlinear models are presented for illustration. An empirical application based on a stochastic volatility model with stable errors highlights the flexibility of the approach. Karim Moussa 2025-05-16 Hwang, Roland Engineering, Social and Behavioral Sciences 2025-07-01 This paper develops a semiparametric estimation method that jointly identifies the probability weighting and utility functions implicit in option prices. Our econometric method avoids direct specification of the objective conditional return distributions, which are instead obtained by transforming the options’ implied risk-neutral distributions according to the posited rank-dependent utility model. We nonparametrically estimate the probability weighting function using the kernel density of suitable utility-adjusted probability integral transforms. The parameters of the utility function are estimated by maximizing the resulting profile likelihood. We establish the asymptotic properties of our estimation procedure, and demonstrate its good finite sample performance in Monte Carlo simulations. Empirical results based on S&P 500 index option prices and returns over the period 1996–2023 reveal the relevance of probability weighting, in particular at the monthly horizon where the weighting function is inverse-S shaped, which is robust to various specifications of the utility function. H. Peter Boswijk Jeroen Dalderop Roger J. A. Laeven Niels Marijnen Semiparametric inference; Probability weighting function; Profile likelihood; Kernel estimation; Options 2025-03-21 Eating organic food is a key health issue… but at what cost? While market gardening in Africa, particularly in urban areas, has helped improve the dietary diversity of city dwellers, its development has been accompanied by the untimely use of uncontrolled pesticides, inducing a significant health risk to both producers and consumers. The actions taken by governments to control the use of pesticides mainly target producers -and have proven to be ineffective (Cissé et al., 2003). The purpose of this article is to explore the demand side by identifying the attributes of food products (vegetables), particularly their organic nature, which guide consumers' choice on the market. Wadjamsse Beaudelaire Djezou Atsé Eric Noel Aba Martine Audibert Prix hédonique, Pesticide, Attributs du légume, Préférences, Consommateur, Côte d'ivoire 2025-02-07 We introduce a new class of location-scale models for dynamic functional data in arbitrary but fixed dimensions, where the location and scale functional parameters can evolve over time. A key feature of the parameter dynamics in these models is its observation-driven nature, where the one-step-ahead evolution is fully determined conditional on past observations, yet remains stochastic unconditionally. We estimate the model using a likelihood-based approach designed for sparsely observed data and establish the consistency and asymptotic normality of the underlying static parameters that govern the location-scale dynamics. The choice of objective function and the construction of the dynamics together shield the time-varying location and scale parameters from the potentially distorting effects of influential observations. Simulations reveal that our method can recover the unobserved location-scale dynamics from sparse data, even in the presence of model mis-specification and substantial outliers. We apply our framework to examine the intraday volatility dynamics of Pfizer stock returns during the COVID-19 pandemic, and PM2.5 concentrations measured by low-cost sensors across Europe. The proposed model exhibits robust performance in capturing dynamics for both datasets despite the presence of many large shocks. Yicong Lin André Lucas time variation, location-scale, functional score-driven dynamics, sparse data, outlier robustness 2025-04-17 This paper presents a class of higher-order diagnostic classification models (HO–DCMs) capable of capturing complex, nonlinear hierarchical relationships among attributes. Building on and extending prior work, we adopt a nominal response model framework in item response theory and leverage standard maximum likelihood estimation (MLE). In parallel, we demonstrate that sequential HO–DCMs can likewise be implemented within an MLE framework. Furthermore, we introduce a novel chunk-based approach for representing attribute hierarchies, wherein attributes are organized into cognitively coherent subgraphs (chunks) nested within a continuous general ability continuum. The performance of the models is validated through simulation studies evaluating parameter recovery, classification accuracy, and null rejection rates of goodness-of-fit measures. An empirical demonstration showcases how the proposed framework can be applied in practice, highlighting its advantages in model flexibility, interpretability, and the additional diagnostic insights it affords. Lee, Minho Suh, Yon Soo 2025-06-17 Many panel data methods, while allowing for general dependence between covariates and time-invariant agent-specific heterogeneity, place strong a priori restrictions on feedback: how past outcomes, covariates, and heterogeneity map into future covariate levels. Ruling out feedback entirely, as often occurs in practice, is unattractive in many dynamic economic settings. We provide a general characterization of all feedback and heterogeneity robust (FHR) moment conditions for nonlinear panel data models and present constructive methods to derive feasible moment-based estimators for specific models. We also use our moment characterization to compute semiparametric efficiency bounds, allowing for a quantification of the information loss associated with accommodating feedback, as well as providing insight into how to construct estimators with good efficiency properties in practice. Our results apply both to the finite dimensional parameter indexing the parametric part of the model as well as to estimands that involve averages over the distribution of unobserved heterogeneity. We illustrate our methods by providing a complete characterization of all FHR moment functions in the multi-spell mixed proportional hazards model. We compute efficient moment functions for both model parameters and average effects in this setting. Stéphane Bonhomme Kevin Dano Bryan S. Graham 2025-06 Gender gaps in labor market outcomes persist in South Asia. An open question is whether supply- or demand-side constraints play a larger role. This paper investigates this using matched data from three sources in Lahore, Pakistan: representative samples of jobseekers and employers, administrative data from a job matching platform, and an incentivized binary choice experiment. Employers’ gender restrictions are a larger constraint on women’s job opportunities than supply-side decisions. This demand-side gap in the quantity of job opportunities closes as education levels increase and jobs become more “white-collar.” Gentile, Elisabetta Kohli, Nikita Subramanian, Nivedhitha Tirmazee, Zunia Vyborny, Kate 2025-06-10