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Scholars have recently begun to connect political phenomena with geographic proximity, noting that in addition to one's personal characteristics, individuals are strongly affected by their social context. We push this literature further by examining how institutions such as state borders mediate and condition the effects of geographic proximity. Our findings expand our understanding of geography by demonstrating that the geographic landscape has interesting facets beyond proximity and distance. Rather, geography is the product of political relationships that intersect in particular places.

Campaigns and political parties are faced with the immensely important practical challenge of financing their efforts. Raising money is instrumental to all other aims. In recent years, this task has been complicated by the need to enlist ever greater numbers of contributors to raise ever larger sums of money. At the same time, fundraising burdens are eased a bit because contributors flock together. That is, campaign contributing is a spatially dependent phenomenon, associated with affluence and the presence of networks. Accordingly, geospatial tools provide a helpful method for understanding and predicting where contributions can be most successfully mined.

We examine the limits of ecological inference methods by focusing on the case of split‐ticket voting. Burden and Kimball (1998) report that, by using the King estimation procedure for inferring individual‐level behavior from aggregate data, they are the first to produce accurate estimates of split‐ticket voting rates in congressional districts. However, a closer examination of their data reveals that a satisfactory analysis of this problem is more complex than may initially appear. We show that the estimation technique is highly suspect in general and especially unhelpful with their particular data.

Research on Asian-American elected officials is scarce, but Asian-American officeholders are not. The National Asian Pacific American Political Almanac (Lai and Nakanishi 2001) lists hundreds of Asian Americans who hold elected offices in national, state, and local governments, in addition to a number of appointed officials and judges. Furthermore, increasing numbers of Asian-American political candidates run for national and state-level offices (Cho 2000a; Lien forthcoming), and there have been periodic AsianAmerican political movements (Wei 1993). Nevertheless, Asian-American political leadership and Asian-Americans' roles in campaigns have often been overlooked in the discussion of minority politics, in part due to the community's relatively young and largely foreign-born population (Brackman and Erie 1995).

To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, the minimum discrepancy between the distributions of these covariates in the control and treatment groups. Our formulation captures the key elements of the Rubin causal model and translates nicely into a discrete optimization framework.

It has become a popular pastime for political pundits and scholars alike to predict the winner of the U.S. presidential election. Although forecasting has now quite a history, we argue that the closeness of recent presidential elections and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the Electoral College outcome and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into a dynamic programming algorithm to determine the probability that a candidate will win an election.