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The Republican Party has historically held a strong financial advantage over the Democrats in Congressional elections. The National Republican Congressional Committee (NRCC) generally has a large resource advantage over the Democratic Congressional Campaign Committee (DCCC) in Congressional elections, allowing the Republicans to outcontribute the Democrats in most Congressional districts. Further, the NRCC also led the DCCC in developing a centralized organization for managing Congressional campaigns. Thus, many scholars of political party activity in US Congressional elections believe that in the 1970s and 1980s the NRCC was the more effective campaigning force in terms of both total resources and efficiency (targeting close races for contributions). Although there is no question the Republicans were more effective fundraisers than the Democrats, empirical evidence reveals it was the DCCC that was better at targeting close races for contributions in the late 1970s and the 1980s. In the 1990s, NRCC efficiency improved relative to the DCCC, with both committees now operating as efficient campaign organizations.

Mixed logit (MXL) is a general discrete choice model thus far unexamined in the study of multicandidate and multiparty elections. Mixed logit assumes that the unobserved portions of utility are a mixture of an IID extreme value term and another multivariate distribution selected by the researcher. This general specification allows MXL to avoid imposing the independence of irrelevant alternatives (IIA) property on the choice probabilities. Further, MXL is a flexible tool for examining heterogeneity in voter behavior through random-coefficients specifications. MXL is a more general discrete choice model than multinomial probit (MNP) in several respects, and can be applied to a wider variety of questions about voting behavior than MNP. An empirical example using data from the 1987 British General Election demonstrates the utility of MXL in the study of multicandidate and multiparty elections.

Do individuals believe that an election victory by their favored candidate will improve their personal economic well-being? Previous work has either adopted an approach that is not well suited to determining this relationship, or ignored this question to focus on perceptions of macroeconomic conditions. In this paper we adopt a new approach that allows us to determine the relationship individuals perceive between elections & personal economic welfare, examining the relationship between vote choice, the election outcome, & post-election expectations for personal economic well-being. We find that economic individualism plays an important role in shaping the relationship individuals perceive between election outcomes & their personal economic well-being. Individuals who reject economic individualism do perceive a relationship, with those viewing an election outcome as favorable more optimistic in their expectations for personal economic well-being than those who view the election outcome as unfavorable. Conversely, election outcomes do not influence the expectations of economic individualists. 5 Tables, 48 References. [Copyright 2005 Elsevier Ltd.]

AbstractThe U.S. Environmental Protection Agency (EPA) uses health risk assessment to help inform its decisions in setting national ambient air quality standards (NAAQS). EPA's standard approach is to make epidemiologically‐based risk estimates based on a single statistical model selected from the scientific literature, called the "core" model. The uncertainty presented for "core" risk estimates reflects only the statistical uncertainty associated with that one model's concentration‐response function parameter estimate(s). However, epidemiologically‐based risk estimates are also subject to "model uncertainty," which is a lack of knowledge about which of many plausible model specifications and data sets best reflects the true relationship between health and ambient pollutant concentrations. In 2002, a National Academies of Sciences (NAS) committee recommended that model uncertainty be integrated into EPA's standard risk analysis approach. This article discusses how model uncertainty can be taken into account with an integrated uncertainty analysis (IUA) of health risk estimates. It provides an illustrative numerical example based on risk of premature death from respiratory mortality due to long‐term exposures to ambient ozone, which is a health risk considered in the 2015 ozone NAAQS decision. This example demonstrates that use of IUA to quantitatively incorporate key model uncertainties into risk estimates produces a substantially altered understanding of the potential public health gain of a NAAQS policy decision, and that IUA can also produce more helpful insights to guide that decision, such as evidence of decreasing incremental health gains from progressive tightening of a NAAQS.