One problem with describing election vote shares using ordinary least squares (OLS) is that it ignores the possible presence of spatial error correlation, whereby the errors are correlated in a systematic manner over space. This omission can bias OLS standard errors. We examine the 2004 presidential county vote outcome using OLS and a spatial error model (SEM) that accounts for spatial autocorrelation in the error structure. We find that spatial error correlation is present, that the SEM is superior to OLS for making inferences, and that several factors deemed important to the 2004 election outcome are not significant once the spatial error autocorrelation is taken into account.

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The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, the authors formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.

There is a large literature estimating the effect of economic freedom on economic growth or income levels. Most studies examine the relationship between economic freedom and growth or income levels for countries, while a few examine the relationship for U.S. states. Absent in the state‐level literature is consideration of the presence of spatial spillovers affecting the freedom‐income relationship. Neglecting to account for spatial autocorrelation can bias estimation results and therefore inferences drawn. We find evidence of a spatial pattern in real per‐capita gross state product (GSP) that affects nonspatial estimates of the freedom‐income relationship. Taking into account the direct and indirect effects of economic freedom on real per‐capita GSP, we find a 10% increase in economic freedom is associated with a 5% increase in real per‐capita GSP. (JEL E02, O47, R11)

This article addresses the economic effects of mental disabilities by analyzing contemporary data in the context of micro- and macroeconomic thought and relevant statistical literature. Within the parameters of these conceptual and statistical reference points, the authors seek to discern current trends in the direct, indirect, and opportunity costs posed by mental disabilities, not only to the individuals who suffer from them, but also to their families, to employers, and to society as a whole. The authors also discuss uncertainties that inhere in available data concerning both the prevalence of these conditions and the related costs of treatment, as well as the complexity of drawing correlations among variables with respect to these costs and the difficulty of identifying a meaningful measure of the economic consequences that attend mental disabilities. Copyright © 2017 John Wiley & Sons, Ltd.