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In a recent article Paldam & Skott (1995) provide a theoretical explanation for an important empirical phenomenon in democratic countries: incumbent governments tend to lose votes. In this paper, I show that Paldam & Skott's theoretical explanation for this "cost of ruling" is potentially much stronger than they recognize. Specifically, when generalized in a straightforward way, their model explains not only the cost of ruling itself, but also a second well-established empirical fact: that the longer an incumbent government has been in power, the more votes it loses. Further, this generalization of the model produces two additional empirical hypotheses that have not yet been tested in the empirical literature. 5 Figures, 9 References. Adapted from the source document.

This article aims to establish empirically whether changes in the aggregate policy preferences of voters in western democracies relate systematically to national economic performance. Results from a time-series, cross-sectional regression analysis of data on aggregate policy preferences from fourteen western democracies (1956-1989) support a hypothesis originally suggested, for the American case, by Durr (1993): when the economy expands aggregate policy preferences move left, but when the economy contracts aggregate policy preferences move right. This finding sustains the normatively appealing conclusion that change in aggregate policy preference reflects the measured response of many individuals to changes in their political environment. 3 Tables, 1 Figure, 49 References. Adapted from the source document.

AbstractThe crosswise model is an increasingly popular survey technique to elicit candid answers from respondents on sensitive questions. Recent studies, however, point out that in the presence of inattentive respondents, the conventional estimator of the prevalence of a sensitive attribute is biased toward 0.5. To remedy this problem, we propose a simple design-based bias correction using an anchor question that has a sensitive item with known prevalence. We demonstrate that we can easily estimate and correct for the bias arising from inattentive respondents without measuring individual-level attentiveness. We also offer several useful extensions of our estimator, including a sensitivity analysis for the conventional estimator, a strategy for weighting, a framework for multivariate regressions in which a latent sensitive trait is used as an outcome or a predictor, and tools for power analysis and parameter selection. Our method can be easily implemented through our open-source software cWise.

We argue that the kind of political information voters should possess varies contextually in response to relevant political processes. Focusing on the partisan organization of legislatures, we derive hypotheses for what the typical American should know about politics at the national and state level and test these hypotheses in two studies. The first documents a dramatic change in American political knowledge at the national level in response to polarization—the replacement of individually oriented information with partisan information. While voters' ability to identify the candidates running to represent them in Congress has been cut in half, their ability to rank order the parties ideologically has nearly doubled. The second study provides evidence that voters are better able to identify the majority party in their state legislature where partisan control of the legislative agenda and roll‐call voting is stronger. We conclude by discussing the implications of our findings.

AbstractSocial scientists use the concept of interactions to study effect dependency. In the causal inference literature, interaction terms may be used in two distinct type of analysis. The first type of analysis focuses on causal interactions, where the analyst is interested in whether two treatments have differing effects when both are administered. The second type of analysis focuses on effect modification, where the analyst investigates whether the effect of a single treatment varies across levels of a baseline covariate. While both forms of interaction analysis are typically conducted using the same type of statistical model, the identification assumptions for these two types of analysis are very different. In this paper, we clarify the difference between these two types of interaction analysis. We demonstrate that this distinction is mostly ignored in the political science literature. We conclude with a review of several applications where we show that the form of the interaction is critical to proper interpretation of empirical results.