If voters had the opportunity to choose the characteristics of their representative institutions directly, how would they do so? Voters in several states selected the base of their state legislative apportionment through the initiative process prior to the reapportionment revolution of the 1960s, which provides a unique opportunity to answer this question. This study examines 13 such initiatives in four states between 1924 and 1962. Four factors are hypothesized to influence vote choice on these initiatives: urban-rural conflict, partisanship, race, and economic self-interest. Through regression analysis of the county-level vote in these elections I find that economic self-interest consistently influences voter support for apportionment initiatives while these other factors influence it only occasionally. This finding suggests that distributive politics drive voters' evaluation of representative institutions and that the influence of other political factors depends on the historical and local context of an initiative.

This article compares three approaches to handling missing data at the state level under three distinct conditions. Using Monte Carlo simulation experiments, I compare the results from a linear model using listwise deletion (LD), Markov Chain Monte Carlo with the Gibbs sampler algorithm (MCMC), and multiple imputation by chained equations (MICE) as approaches to dealing with different severity levels of missing data: missing completely at random (MCAR), missing at random (MAR), and nonignorable missingness (NI). I compare the results from each of these approaches under each condition for missing data to the results from the fully observed dataset. I conclude that the MICE algorithm performs best under most missing data conditions, MCMC provides the most stable parameter estimates across the missing data conditions (but often produced estimates that were moderately biased), and LD performs worst under most missing data conditions. I conclude with recommendations for handling missing data in state-level analysis.

Term limits advocates argued that the reform would revitalize the electorate and spur increased voter participation. They suggested two main mechanisms by which term limits would do this: (1) term limits would increase the number of open-seat races, which tend to be more competitive, and the increased competition would inspire more voting, and (2) the removal of entrenched incumbents would inspire more trust and confidence in government, thereby encouraging voter enthusiasm in a more diffuse way, increasing turnout for all races. I evaluate these hypotheses empirically using data from California state legislative races from 1976 to 2004; incorporating a variety of methods, including pooled cross-sectional time-series regression. I find evidence that state legislative term limits not only fail to achieve the reformers' goals, but they, in fact, decrease voter turnout.

In this article, I revisit a widely used measure of legislative professionalism that I developed over a decade ago (Squire 1992a). I argue that professionalism has different implications for legislators than for legislatures and that the concept is distinct from careerism. I then discuss the mechanics of compiling the measure, its reliability and validity, and potential criticism of the measure. Finally, I provide scores on the measure for 1979, 1986, 1996, and 2003, as well as scores for 1979 and 2003 for a revised measure that is theoretically appropriate for use in dynamic analyses.