The structural Quantal Response Equilibrium (QRE) generalizes the Nash equilibrium by augmenting payoffs with random elements that are not removed in some limit. This approach has been widely used both as a theoretical framework to study comparative statics of games and as an econometric framework to analyze experimental and field data. The framework of structural QRE is flexible: it can be applied to arbitrary finite games and incorporate very general error structures. Restrictions on the error structure are needed, however, to place testable restrictions on the data (Haile et al., 2004). This paper proposes a reduced-form approach, based on quantal response functions that replace the best-response functions underlying the Nash equilibrium. We define a regular QRE as a fixed point of quantal response functions that satisfies four axioms: continuity, interiority, responsiveness, and monotonicity. We show that these conditions are not vacuous and demonstrate with an example that they imply economically sensible restrictions on data consistent with laboratory observations. The reduced-form approach allows for a richer set of regular quantal response functions, which has proven useful for estimation purposes.

Ecological inference is a statistical technique used to infer individual behavior from aggregate data. A particularly relevant instance of ecological inference involves the estimation of the inner cells of a set of R × C related contingency tables when only their aggregate margins are known. This problem spans multiple disciplines, including quantitative history, epidemiology, political science, marketing, and sociology. This paper proposes new models for solving the problem using the latent structure theory, and presents the ecolRxC package, an R implementation of this methodology. This article exemplifies, explains, and statistically documents the new extensions and, using real inner cell election data, shows how the new models in ecolRxC lead to significantly more accurate solutions than ecol and VTR, two Stata routines suggested within this framework. ecolRxC also holds its own against ei.MD.bayes and nslphom, the two algorithms currently identified in the literature as the most accurate to solve this problem. ecolRxC records accuracies as good as those reported for ei.MD.bayes and nslphom. Besides, from a theoretical perspective, ecolRxC stands up for modeling a causal theory of political behavior to build its algorithm. This distinguishes it from other procedures proposed from different frameworks (such as ei.MD.bayes and nslphom) which model expected behaviors, instead of modeling how voters make choices based on their underlying preferences as ecolRxC does.

When analyzing data, researchers are often less interested in the parameters of statistical models than in functions of these parameters such as predicted values. Here we show that Bayesian simulation with Markov-Chain Monte Carlo tools makes it easy to compute these quantities of interest with their uncertainty. We illustrate how to produce customary and relatively new quantities of interest such as variable importance ranking, posterior predictive data, difficult marginal effects, and model comparison statistics to allow researchers to report more informative results.

In the absence of a complete voting record, decision records are an important data source to analyze committee decision-making in various institutions. Despite the ubiquity of decision records, we know surprisingly little about how to analyze them. This paper highlights the costs in terms of bias, inefficiency, or inestimable effects when using decision instead of voting records and introduces a Bayesian structural model for the analysis of decision-record data. I construct an exact likelihood function that can be tailored to many institutional contexts, discuss identification, and present a Gibbs sampler on the data-augmented posterior density. I illustrate the application of the model using data from US state supreme court abortion decisions and UN Security Council deployment decisions.

Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.

When separation is a problem in binary dependent variable models, many researchers use Firth's penalized maximum likelihood in order to obtain finite estimates (Firth, 1993; Zorn, 2005; Rainey, 2016). In this paper, I show that this approach can lead to inferences in the opposite direction of the separation when the number of observations are sufficiently large and both the dependent and independent variables are rare events. As large datasets with rare events are frequently used in political science, such as dyadic data measuring interstate relations, a lack of awareness of this problem may lead to inferential issues. Simulations and an empirical illustration show that the use of independent “weakly-informative” prior distributions centered at zero, for example, the Cauchy prior suggested by Gelman et al. (2008), can avoid this issue. More generally, the results caution researchers to be aware of how the choice of prior interacts with the structure of their data, when estimating models in the presence of separation.

Empirical applications of the spatial theory of elections typically rely on the discrete choice framework to arrive at probabilistic voting models. Whereas in the classic model voter choice is solely a function of spatial proximity, neo-Downsian models also incorporate voter-specific nonpolicy attributes, which are represented by sociodemographic characteristics. One prominent line of such probabilistic models, Schofield’s Valence Model, additionally includes party valences into voter utility functions. The model rests on the estimated party intercepts to measure the valence advantages empirically. The party intercepts are ordered based on their values, and then this valence ranking is used further to predict equilibrium locations. The paper demonstrates that this measurement strategy does not provide unique results in fully specified models due to central properties of discrete choice models and the specific nature of party intercepts in these models. Drawing on a simple example based on mass election surveys from Germany, it is shown that the valence ranking, the crucial factor to investigate how valence differences affect the nature of spatial competition, is highly sensitive to arbitrary coding decisions. As a consequence, it is impossible to represent valence with the constants and to infer something substantial from the resulting valence ranking.

We introduce a model that extends the standard vote choice model to encompass text. In our model, votes and speech are generated from a common set of underlying preference parameters. We estimate the parameters with a sparse Gaussian copula factor model that estimates the number of latent dimensions, is robust to outliers, and accounts for zero inflation in the data. To illustrate its workings, we apply our estimator to roll call votes and floor speech from recent sessions of the US Senate. We uncover two stable dimensions: one ideological and the other reflecting to Senators’ leadership roles. We then show how the method can leverage common speech in order to impute missing data, recovering reliable preference estimates for rank-and-file Senators given only leadership votes.

Discrete choice models estimated over a large household database, show the impacts of demographics, household behavior, health status, obesity issues and prices on household servings of fruits and vegetables. These impacts are ranked from the most to least effects on daily servings. A major result is the importance of obesity and calorie issues relative to other major demand drivers.

This paper applies a discrete choice version of the household production framework to assess parents’ ex ante willingness to pay to reduce their child’s victimization from bullying at school. Willingness to pay is estimated using a bivariate probit model and a unique panel of 595 families from the NICHD Study of Early Child Care and Youth Development for 2000 to 2003. Empirical results find a statistically significant positive association between an elementary school child’s bully victimization and parents’ choice to change their child’s school in the subsequent sample period. Parents’ annual willingness to pay for reduced child bully victimization averages $130 and ranges from $54 for parents whose child was not bullied to $633 for parents whose child was bullied. Given current literature estimates of U.S. bullying prevalence and the cost and effectiveness of currently available anti-bullying programs, parental willingness to pay estimates suggest that U.S. households’ net annual return on investments in elementary school bullying prevention programs could be substantial.

Revitalization of state brands is deemed important to several constituencies. Stated preference with choice experiment methods were used to elicit consumer preferences for two locally grown products: spinach, which has had a well-publicized food safety incidence, and carrots, which have had no such incidence in recent history. A full factorial design was used to implement the choice experiment, with each commodity having four identical attributes varying at different levels. Findings reveal that consumers are willing to pay a premium for locally grown spinach marked with the Arizona Grown label over locally grown spinach that was not labeled. This premium was higher than the premium that would be paid for state-branded carrots. This difference highlights consumers’ perceptions of “locally grown” as an indicator of safety in their food supply. Findings have important implications with respect to providing consumer value and point to differentiated positioning strategies for state-branded produce.

Decreased care or offsetting behavior by potential victims can reduce or reverse benefits provided by some safety policies. We explore reasons for offsetting behavior associated with food safety policies using a survey of a nationally representative sample of almost 3,000 consumers. Results reveal that positive food safety information can change consumers’ risk perceptions and attitudes, causing them to be less vigilant and to consume more of relatively unsafe foods. This behavioral anomaly plausibly explains ongoing incidences of food poisoning after a meat processing facility implements a pathogen-reduction hazard analysis of critical control points (PR/HACCP).

The growing popularity of marine recreational fishing has created considerable interest in artificial marine habitat development to maintain and enhance coastal fishery stocks. This paper provides a comparative evaluation of travel cost methods to estimate recreational use benefits for new habitat site planning. Theoretical concerns about price and quality effects of substitute sites, corner solutions in site choice, and econometric estimation are considered. Results from a case study indicate that benefit estimates are influenced by the way these concerns are addressed, but relatively simple single site models can provide defensible estimates. Practical limitations on data collection and model estimation are also considered.

This paper examines the opinions of Spanish older people regarding the ideal living situation in later life – living in their own home, co-residing in a relative's home or institutionalisation – differentiating between two hypothetical situations: healthy ageing and frailty. Data are drawn from the Instituto de Mayores y Servicios Sociales (Institute of Older People and Social Services; IMSERSO) survey Encuesta de Mayores 2010 (Older People Survey 2010), comprising 2,535 individuals aged 65 and over living in private dwellings. The results confirm that residential preferences vary depending on expected health conditions. Remaining in one's own home is preferred when older people foresee a healthy old age, whilst co-residence at a relative's home turns into the favoured solution if older people have to face some physical or cognitive limitation. The particularities of the Spanish context regarding family-oriented values about care responsibilities and the structural deficiency in the provision of formal support, in addition to other socio-demographic, psychological and attitudinal aspects, were explanatory factors of the lower desirability for ageing at home in the case of frailty. The findings question the uniform image of ‘ageing in place’ as a preference, inviting reflections on the need to distinguish between later-life stages and national contexts.