Hedonic analysis of Wisconsin Beef Improvement Association Bull Sale and Development Program data revealed buyer preferences for calving ease, growth, production weight, and carcass merit traits. Attributes like calving ease direct Expected Progeny Differences (EPD), average daily gain, birth to yearling gain EPD, and rib-eye area consistently ranked higher and significantly influenced the bull’s sale price. Further analysis using a 2-Class Finite Mixture Model indicated distinct groups of buyers in the region who prioritized measured bull attributes and others who did not, confirming heterogeneity in buyer preferences.

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T ≤ 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one.

In political science, data with heterogeneous units are used in many studies, such as those involving legislative proposals in different policy areas, electoral choices by different types of voters, and government formation in varying party systems. To disentangle decision-making mechanisms by units, traditional discrete choice models focus exclusively on the conditional mean and ignore the heterogeneous effects within a population. This paper proposes a conditional binary quantile model that goes beyond this limitation to analyze discrete response data with varying alternative-specific features. This model offers an in-depth understanding of the relationship between the explanatory and response variables. Compared to conditional mean-based models, the conditional binary quantile model relies on weak distributional assumptions and is more robust to distributional misspecification. The model also relaxes the assumption of the independence of irrelevant alternatives, which is often violated in practice. The method is applied to a range of political studies to show the heterogeneous effects of explanatory variables across the conditional distribution. Substantive interpretations from counterfactual scenarios are used to illustrate how the conditional binary quantile model captures unobserved heterogeneity, which extant models fail to do. The results point to the risk of averaging out the heterogeneous effects across units by conditional mean-based models.

Off-highway vehicle (OHV) use is a rapidly growing outdoor activity that results in a host of environmental and management problems. Federal agencies have been directed to develop travel management plans to improve recreation experiences, reduce social conflicts, and diminish environmental impacts of OHVs. We examine the effect of land access restrictions on the welfare of OHV enthusiasts in Utah using Murdock's unobserved heterogeneity random utility model (Murdock 2006). Our models indicate that changing access to public lands from fully “open” to “limited” results in relatively small welfare losses, but that prohibiting access results in much larger welfare losses.