Journal of Choice Modelling最新文献

We propose a modeling framework that uses only cross-sectional data to disentangle labor supply and demand choices simultaneously. This modeling framework extends the labor-market analytical toolkits to adapt to environments where data are limited, flexibility in working hours is lacking, or structural changes are present, as is the case in most emerging and low-income countries. We showcase our model by using the 2011 China Household Finance Survey to decipher labor market choices in urban China. We find that the main discrepancies in labor supply between males and females are driven by the number and age of children, the lower utility of working rather than fewer job opportunities for females, and larger impacts of education and work experience on females’ job opportunities. Household wealth in the form of ‘cash inflow’ incentivizes individuals not to work, while wealth in the form of ‘stock’ induces higher utility to work for both males and females. The interpretation of empirical findings hinges on particular assumptions that might be disputed.

In this article we propose two-step generalized method of moment (GMM) procedure for a Spatial Binary Probit Model. In particular, we propose a series of two-step estimators based on different choices of the weighting matrix for the moments conditions in the first step, and different estimators for the variance–covariance matrix of the estimated coefficients. In the context of a Monte Carlo experiment, we compare the properties of these estimators, a linearized version of the one-step GMM and the recursive importance sampler (RIS). Our findings reveal that there are benefits related both to the choice of the weight matrix for the moment conditions and in adopting a two-step procedure.

Subjective value has long been measured using binary choice experiments, yet responses like willingness-to-pay prices can be an effective and efficient way to assess individual differences risk preferences and value. Tony Marley’s work illustrated that dynamic, stochastic models permit meaningful inferences about cognition from process-level data on paradigms beyond binary choice, yet many of these models remain difficult to use because their likelihoods must be approximated from simulation. In this paper, we develop and test an approach that uses deep neural networks to estimate the parameters of otherwise-intractable behavioral models. Once trained, these networks allow for accurate and instantaneous parameter estimation. We compare different network architectures and show that they accurately recover true risk preferences related to utility, response caution, anchoring, and non-decision processes. To illustrate the usefulness of the approach, it was then applied to estimate model parameters for a large, demographically representative sample of U.S. participants who completed a 20-question pricing task — an estimation task that is not feasible with previous methods. The results illustrate the utility of machine-learning approaches for fitting cognitive and economic models, providing efficient methods for quantifying meaningful differences in risk preferences from sparse data.

Forecasting the future impact of climate change on migration is difficult, for many reasons, including the interactive and dynamic nature of many decisions and the heterogeneity of behavior. One popular solution, agent-based models (ABM) cope well with dynamics and heterogeneity, but often lack rigorous foundations in terms of individual behavior. Moreover, given limited exposure to actual climate change, it can be a challenge to build adequate behavioral models of migration choice based on historical data. To tackle this issue, we build an ABM of future migration using a bespoke choice experiment (CE) designed to examine intention to migrate among farmers living in the Vietnamese Mekong Delta (VMD). In the CE, respondents are asked to make migration choices for scenarios constructed using six attributes: drought intensity, flood frequency, income gain from migration, migration networks, neighbors' choice, and crop choice restriction. The simulation runs to 2050 and is based on two scenarios of future global emissions of greenhouse gases—Representative Concentration Pathway (RCP) 4.5 and RCP8.5. The results suggest potentially high levels of migration as a result of climate change and the particular importance of positive feedback from pre-existing migration and neighbor's choices. The results also suggest that crop-restriction regulations have a significant impact on migration for coastal provinces of VMD. Finally, we find that migration drivers vary significantly across provinces, which suggests the policymakers point to targeted action for each province. In summary, the study demonstrates how integrating CE into ABM can foster the predictive modeling of climate-induced migration.

Two-attribute-two-alternative stated choice experiments are widely used to infer the Value-of-Travel-Time (VTT) distribution. Two-attribute-two-alternative stated choice experiments have the advantage that their data can be analysed using nonparametric models, which allow for the inference of the VTT distribution without having to impose assumptions on its shape. However, a software package that enables researchers to estimate nonparametric models promptly is currently lacking. As a result, nonparametric models are underused. This paper aims to fill this software void. It presents NP4VTT, a Python package that enables researchers to estimate and compare nonparametric models in a fast and convenient way. It comprises five nonparametric models for estimating the VTT distribution from data coming from two-attribute-two-alternative stated choice experiments. We illustrate the use of NP4VTT by applying it to the Norwegian 2009 VTT data. We hope this software package will help researchers studying the VTT make more informed decisions concerning the shape of the VTT distribution and encourages the use and development of nonparametric models for choice behaviour analyses.

Point allocation experiments are widely used in the social sciences. In these experiments, survey respondents distribute a fixed total number of points across a fixed number of alternatives. This paper reviews the different perspectives in the literature about what respondents do when they distribute points across options. We find three main alternative interpretations in the literature, each having different implications for empirical work. We connect these interpretations to models of utility maximization that account for point and budget constraints and investigate the role of budget constraints in more detail. We show how these constraints impact the regression specifications for point allocation experiments that are commonly used in the literature. We also show how a formulation of a taste for variety as entropy that had been previously used to analyse market shares can fruitfully be applied to choice behaviour in point allocation experiments.

Motivated by the National Bioengineered Food Disclosure Standard (NBFDS), which requires companies to label bioengineered food products, this paper examines the choice effects of using a symbol approved by the standard relative to using text to disclose that a food product has bioengineered contents. Choice effects were determined using a randomized group design that assigned respondents to one-of-two labeled choice experiment groups. One group selected products that used the symbol disclosure and the other group selected products using text disclosure. Besides a label, the price was the only attribute displayed to respondents during the choice experiment and varied at three levels. The same price levels were used for all labels, and prices were balanced within a label, and balanced and orthogonal across labels. This randomized design using a discrete choice experiment allows for the identification of group effects, which in this paper are the choice effects associated with the form of bioengineering disclosure.

Studies investigating preferences for country-of-origin labeling (COOL) often overemphasize this attribute, which risks inflating estimated market value. We address this issue by studying consumer preferences for Florida versus Mexico tomatoes in a shopping environment that allows freedom to notice or ignore COOL when making decisions. A significant portion of subjects failed to notice COOL in the study, despite expressing a preference for COOL and a habit of looking at COOL when shopping. We find a significant difference in preferences between subjects who noticed COOL and subjects who did not, which points to a potential mismatch between research results and real-world behavior.

The inclusion of attitudinal indicator variables within discrete choice models is now largely common practice. Typically, this involves the estimation of multiple indicator multiple cause (MIMIC) type models which are used to construct latent attitudinal variables that are then employed as independent variables within standard discrete choice models. Such models, collectively termed hybrid choice models (HCM) assume a particular causal relationship between the indicator variables, latent construct, and choice. In effect, the underlying assumption of such a model system is that latent variables of interest exist independent of the indicator variables used to measure them, and that the survey items used are reflective in nature insofar as responses to such questions reflect the underlying constructs. In this paper, we describe an alternative form of attitude measure, known as formative measures, where the items themselves are used to create the latent variable rather than the other way around. In addition to making a distinction between formative and reflective attitudinal measures, the paper seeks to describe how the HCM can be adapted to model different types of attitude question formats. Further the paper seeks to act as a catalyst for choice modellers to think more about the quality and validity of attitudinal items capture in survey questionnaires, by placing more emphasis on proper scale development techniques.

We present a discrete choice modeling framework with heterogeneous decision rules accounting for non-trading behavior. The proposed approach builds upon the state-of-the-art probabilistic finite mixture models and tackles non-trading behavior while accounting for inertia effects and serial correlation in the SP data, and contextual effects on the probability of an individual employing a specific decision rule. The framework involves three subpopulations of decision-makers, referred to respectively as pure utility-maximizers, utility-maximizers with strong preference for one alternative, and non-traders non-utility-maximizers employing a non-trading heuristic. The second subpopulation is expected to exhibit non-trading behavior, despite making trade-offs consistent with utility maximization. Our goal is to disentangle the two types of manifested non-trading behavior. We assume that the manifestation of non-trading behavior – by otherwise utility-maximizing individuals – may be driven by important context variables. In order to accommodate this assumption in the modeling framework, we define and add a relative advantage (RA) component in the class-membership model. Finally, we apply the framework to a Swiss stated preferences (SP) mode choice case study, and demonstrate the impact of accounting for non-trading behavior on the value of time estimates.