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This paper studies the effects of term duration on legislative behavior using field experiments that occur in the Arkansas, Illinois, and Texas Senates in the United States. After mandatory changes in senate district boundaries, state senators are randomly assigned to serve either two-year or four-year terms, providing a rare opportunity to study legislative behavior experimentally. Despite important differences across states, when considered together, the results show that senators serving two years abstain more often, introduce fewer bills, and do not seem to be more responsive to their constituents than senators serving four years. In addition, senators serving shorter terms raise and spend significantly more money, although in those states where funds can be raised continuously during the legislative term, the differences arise only when the election is imminent.

We study how representation works in a context where accountability to voters is restricted because of term limits and accountability to parties is limited because of party weakness. Analyzing all Brazilian mayoral elections between 1996 and 2012 using a regression discontinuity design, we show that becoming the incumbent party results in large subsequent electoral losses. We theorize that the presence of term limits, combined with political parties to which politicians are only weakly attached, affects the incentives and behavior of individual politicians in such a way that their parties' suffer systematic losses. A descriptive analysis of an original dataset on the career paths of Brazilian mayors suggests that our assumptions are an accurate description of Brazil's political context, and we find support for three central empirical implications of our theoretical explanation. Moreover, based on an analysis of additional data from Mexico, Peru, Chile, Costa Rica, and Colombia, we show that the negative effects found in Brazil also exist in other democracies.

Political scientists often attempt to exploit natural experiments to estimate causal effects. We explore how variation in geography can be exploited as a natural experiment and review several assumptions under which geographic natural experiments yield valid causal estimates. In particular, we focus on cases where a geographic or administrative boundary splits units into treated and control areas. The different identification assumptions we consider suggest testable implications, which we use to establish their plausibility. Our methods are illustrated with an original study of whether ballot initiatives increase turnout in Wisconsin and Ohio, which illustrates the strengths and weaknesses of causal inferences based on geographic natural experiments.

Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural experiment due to its relatively weak assumptions. We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas occurs in an as-if random fashion. We show how this design is equivalent to a standard RD with two running variables, but we also clarify several methodological differences that arise in geographical contexts. We also offer a method for estimation of geographically located treatment effects that can also be used to validate the identification assumptions using observable pretreatment characteristics. We illustrate our methodological framework with a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries.

Natural experiments help to overcome some of the obstacles researchers face when making causal inferences in the social sciences. However, even when natural interventions are randomly assigned, some of the treatment–control comparisons made available by natural experiments may not be valid. We offer a framework for clarifying the issues involved, which are subtle and often overlooked. We illustrate our framework by examining four different natural experiments used in the literature. In each case, random assignment of the intervention is not sufficient to provide an unbiased estimate of the causal effect. Additional assumptions are required that are problematic. For some examples, we propose alternative research designs that avoid these conceptual difficulties.

AbstractThe widespread availability of voter files has improved the study of participation in American politics, but the lack of comprehensive data on nonregistrants creates difficult inferential issues. Most notably, observational studies that examine turnout rates among registrants often implicitly condition on registration, a posttreatment variable that can induce bias if the treatment of interest also affects the likelihood of registration. We introduce a sensitivity analysis to assess the potential bias induced by this problem, which we call differential registration bias. Our approach is most helpful for studies that estimate turnout among registrants using posttreatment registration data, but it is also valuable for studies that estimate turnout among the voting‐eligible population using secondary sources. We illustrate our approach with two studies of voting eligibility effects on subsequent turnout among young voters. In both cases, eligibility appears to decrease turnout, but these effects are found to be highly sensitive to differential registration bias.

In this Element, which continues our discussion in Foundations, the authors provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. The focus is on extensions to the canonical sharp RD setup that we discussed in Foundations. The discussion covers (i) the local randomization framework for RD analysis, (ii) the fuzzy RD design where compliance with treatment is imperfect, (iii) RD designs with discrete scores, and (iv) and multi-dimensional RD designs.

AbstractWe study how parties balance the benefits of disciplined programmatic campaigning with the electoral appeal of charismatic but potentially unfaithful candidates. We incorporate the well‐known collective action problem arising from candidates' inability to fully internalize the fruits of programmatic brand building. Although parties may strategically use promotions to induce brand building efforts, we show that the party may be unable to commit to such a promotion scheme when the electoral returns to candidate charisma are high. We further demonstrate how electoral volatility and parties' ingroup loyalties shape their commitment to reward brand building. Volatility increases the focus on candidate charisma and decreases programmatic campaigning, but only among parties with weak group attachments. Parties with loyal partisans place emphasis on both candidate charisma and programmatic messaging. Empirical analyses of cross‐national data and quantitative and qualitative case studies in Brazil, Austria, and Spain are consistent with our predictions.

Legislative redistricting alters the political and electoral context for some voters but not others, thus offering a potentially promising research design to study many questions of interest in political science. We apply this design to study the effect that descriptive representation has on co-ethnic political engagement, focusing on Hispanic participation following California's 2000 redistricting cycle. We show that when redistrictors draw legislative boundaries in California's 1990, 2000, and 2010 apportionment cycles, they systematically sorthigher-participating Hispanic voters into majority-Hispanic (MH) jurisdictions represented by co-ethnic candidates, biasing subsequent comparisons of Hispanic participation across districts. Similar sorting occurs during redistricting in Florida and Texas, though here the pattern is reversed, with less-participating Hispanic voters redistricted to MH districts. Our study highlights important heterogeneity in redistricting largely unknown or underappreciated in previous research. Ignoring this selection problem could significantly bias estimates of the effect of Hispanic representation, either positively or negatively. After we correct for these biases using a hierarchical genetic matching algorithm, we find that, in California, being moved to a district with an Hispanic incumbent has little impact on Hispanic participation in our data.