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To what extent did the extensive flooding caused by Hurricane Katrina affect voter participation in the 2006 mayoral election? This article uses voting record data from 20 election cycles, GIS-coded flood-depth data, and census data to examine the voting behavior of registered voters in New Orleans before and after Hurricane Katrina. We use a variety of statistical techniques, primarily propensity score matching methods, to examine how flooding affected mayoral turnout. We find that flooding decreased participation, but registered voters who experienced more than 6 ft of flooding were more likely to participate in the election than those who experienced less flooding. This finding confirms that increasing the cost of voting decreases turnout and suggests several mechanisms motivating an expressive component of voting behavior. Our results indicate there is a complex relationship between participation and the costs and benefits of turnout. Our findings about the characteristics of the voters who participated in the mayoral election provide insights into the scope of change for the political landscape of New Orleans.

Absentee voting is becoming more prevalent throughout the United States. Although there has been some research focused on who votes by absentee ballot, little research has considered another important question about absentee voting: which absentee ballots are counted and which are not? Research in the wake of the 2000 presidential election has studied the problem of uncounted ballots for precinct voters but not for absentee voters. Using data from Los Angeles County - nation's largest and most diverse voting jurisdiction - for the November 2002 general election, we test a series of hypotheses that certain types of voters have a higher likelihood that their ballots will be counted. We find that uniform service personnel, overseas civilians, voters who request non-English ballots and permanent absentee voters have a much lower likelihood of returning their ballot, and once returned, a lower likelihood that their ballots will be counted compared with the general absentee voting population. We also find that there is little partisan effect as to which voters are more likely to return their ballots or have their ballots counted. We conclude our paper with a discussion of the implications of our research for the current debates about absentee voting. [Copyright 2008 Elsevier Ltd.]

In 2020, Alaskans voted to adopt a nonpartisan top-4 primary followed by a ranked-choice general election. Proposals for "final four" and "final five" election systems are being considered in other states, as well as ranked-choice voting. The initial use of Alaska's procedure in 2022 serves as a test case for examining whether such reforms may help moderate candidates avoid being "primaried." In 2022, incumbent Alaska Senator Lisa Murkowski held her seat against a Trump-endorsed Republican, Kelly Tshibaka. We use data from the 2022 election in Alaska, along with a mixed-mode survey of Alaskan voters before the general election, to test hypotheses about how voters behave in these kinds of elections, finding: (1) the moderate Republican candidate, Murkowski, likely would have lost a closed partisan primary; (2) some Democrats and independents favored the moderate Republican over the candidate of their own party, and the new rules allowed them to support her at all stages of the election, along with others who voted for her to stop the more conservative Republican candidate; and (3) that Alaskan voters are largely favorable toward the new rules, but that certain kinds of populist voters are likely to both support Trump and oppose the rules.

As with any high fashion, the beauty and horror of 'big data' is in the eye of the beholder. The question that prompted the present symposium-'Are formal theory, causal inference, and big data contradictory trends in political science?'-is representative of the concerns that big data has raised in political science. Indeed, this is representative of discussions underway in every area of social science about how big data interacts with existing modes of inquiry as well as its potential benefits (Lazer et al. 2009; Varian 2014) and potential pitfalls (Boyd and Crawford 2012; Lazer et al. 2014). A review of these discussions does not yield any consensus on even what is meant by the term 'big data.' For us, the concept is broad and simultaneously captures several ideas. Taken together, these new types of and approaches to data are enabling new forms of data-intensive political science, some of which in isolation appear to challenge established models of inquiry in political science and science more generally. We argue, however, that none of this means that big data is fundamentally incompatible with formal theory, causal inference, or social science research methods in general. To the contrary, big data already is interacting with formal theoretic and causal inference approaches in ways that are not only consistent with these approaches but that also enhance them by enabling us to answer new questions. Perhaps more important, social science is beginning to shape the world of big data. Much of big data is social data-that is, data about the interactions of people: how they communicate, how they form relationships, how they come into conflict, and how they shape their future interactions through political and economic institutions. It is the responsibility of social scientists to assume their central place in the world of big data, to shape the questions we ask of big data, and to characterize what does and does not make for a convincing answer. In the discussion that follows, we describe examples in political science in which big data helps us to (1) design better experiments, (2) make better comparisons between more precise populations of interest, and (3) observe theoretically relevant social and political behavior that previously was difficult to detect. Adapted from the source document.

Investigations of American politics have increasingly turned to analyses of political networks to understand public opinion, voting behavior, the diffusion of policy ideas, bill sponsorship in the legislature, interest group coalitions and influence, party factions, institutional development, and other empirical phenomena. While the association between political networks and political behavior is well established, clear causal inferences are often difficult to make. This article consists of five independent essays that address practical problems in making causal inferences from studies of political networks. They consider egocentric studies of national probability samples, sociocentric studies of political communities, measurement error in elite surveys, field experiments on networks, and triangulating on causal processes.

AbstractGrowing attitudinal and affective differences across party lines and increasing social polarization are often attributed to the strengthening of partisanship as a social identity. Scholars have paid less attention to personal preferences as a contributor to these phenomena. Our focus is on how citizens' policy beliefs—their operational ideologies—are associated with their views of partisan groups. We examine our perspective with two studies. In the first, we find that the attribution of ideologically extreme political views to an individual's peer significantly reduces interest in interpersonal interaction but find limited evidence that partisan group membership alone induces social polarization. In the second, we show that citizens' policy views are strongly associated with their perceptions of their own partisan group as well as their counterpartisans. Together, our results have important implications for understanding the consequences of increased polarization and partisan antipathy in contemporary politics.