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This paper analyzes the influence of alternative voting technologies on electoral outcomes in multi-party systems. Using data from a field experiment conducted during the 2005 legislative election in Argentina, we examine the role of information effects associated with alternative voting devices on the support for the competing parties. We find that differences in the type of information displayed and how it was presented across devices favored some parties to the detriment of others. The impact of voting technologies was found to be larger than in two-party systems, and could lead to changes in election results. We conclude that authorities in countries moving to adopt new voting systems should carefully take the potential partisan advantages induced by different technologies into account when evaluating their implementation.

Drawing inferences about individual behavior from aggregate ecological data has been a persistent problem in electoral and behavioral studies, in spite of important methodological advances. In a recent article Anselin and Tam Cho (2002) provided Monte Carlo evidence that King's Ecological Inference (EI) solution will produce biased estimates in the presence of extreme spatial heterogeneity. In this article we provide further empirical evidence that supports their findings and shows that in the presence of spatial effects the residuals of Goodman's naïve model exhibit the same spatial structure that King's local B B i estimates. Solving for extreme spatial heterogeneity, it is argued here, requires controlling the omitted variable bias expressed in the spatial structure of much ecological data. In this article we propose a Geographically Weighted Regression approach (GWR) for solving problems of spatial aggregation bias and spatial autocorrelation that affect all known methods of ecological inference. The estimation process is theoretically intuitive and computationally simple, showing that a wellspecified GWR approach to Goodman and King's Ecological Inference methods may result in unbiased and consistent local estimates of ecological data that exhibit extreme spatial heterogeneity.

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