Objectives. Economics, partisanship, and demographics have all been identified as linked to support for environmental protection. The principal objective of this study is to extend the extant literature by using a larger data set and a variety of methods. Methods. We use variety of statistical methods to test measures of party strength, demographics, and economics against county-level data from 29 environmental initiative elections in 13 states. Results. Democratic partisanship is the most consistent predictor of aggregate support for environmental measures. This trend holds through pooled, individual-level, and ecological inference analysis. Median family income and income squared are consistently significant, as is education. Conclusion. Based on these data, we reach three general conclusions. First, while several variables are consistently significant, party strength is the most consistent predictor of pro-environmental voting across states and initiatives. Second, our analyses suggest that limiting analyses to data from a single state or region may have important implications for statistical inferences. Lastly, a preliminary analysis using methods of ecological inference suggests that the aggregate results are robust to ecological problems.A number of recent studies have examined environmental voting behavior by using county-level election returns (cf. Kahn and Matsusaka, 1997;Press, 2003). Most of these studies use a single state or region to test their arguments, thus limiting the general applicability of the extant literature. We expand on the extant literature in three ways: first, we use a national sample of 29 initiatives from 13 states. Second, we demonstrate that using a limited data set can have serious inferential implications. Finally, we show that although using aggregate-level data exposes researchers to the potential for an ecological fallacy, new techniques and a careful examination of the data provide additional confidence in aggregate-level results. These findings generally show consistent aggregate relationships across analyses and suggest that aggregate empirical results are essential to making general theoretical claims. We begin our analysis with a review of the literature, followed by n Direct correspondence to Mirya Holman, 2808 Lexington St., Durham, NC 27707 hmirya.holman.@cgu.edui. Mirya Holman will share all data and coding information with those who wish to replicate this study.