In this paper we prove theoretically and demonstrate empirically that all existing measures of incumbency advantage in the congressional elections literature are biased or inconsistent. We then provide an unbiased estimator based on a very simple linear regression model. We apply this new method to congressional elections since 1900, providing the first evidence of a positive incumbency advantage in the first half of the century.

We derive a unified statistical method with which one can produce substantially improved definitions and estimates of almost any feature of two-party electoral systems that can be defined based on district vote shares. Our single method enables one to calculate more efficient estimates, with more trustworthy assessments of their uncertainty, than each of the separate multifarious existing measures of partisan bias, electoral responsiveness, seats-votes curves, expected or predicted vote in each district in a legislature, the probability that a given party will win the seat in each district, the proportion of incumbents or others who will lose their seats, the proportion of women or minority candidates to be elected, the incumbency advantage and other causal effects, the likely effects on the electoral system and district votes of proposed electoral reforms such as term limitations, campaign spending limits, and drawing majority-minority districts, and numerous others. To illustrate, we estimate the partisan bias and electoral responsiveness of the U.S. House of Representatives since 1900 and evaluate the fairness of competing redistricting plans for the 1992 Ohio state legislature.

The regression-discontinuity (RD) design has experienced a resurgence of interest in many fields, including political science. Following David Lee’s pioneering work on the incumbency advantage in U.S. House elections, many scholars have begun to apply RD designs to popular elections. Under certain assumptions, the RD design yields causal estimates that approach the gold standard of randomized experiments. There are good reasons to believe, however, that outcomes of narrowly decided U.S. Congressional elections are anything but random. Consistent with this notion, we demonstrate that bare winners and bare losers in U.S. House elections exhibit dramatic differences on key pretreatment covariates. Bare winners have substantially more financial resources than candidates who just lose and are far more likely to belong to the party that won the previous election in the district. Congressional Quarterly’s pre-election race ratings predict the outcomes of even extremely close elections with a high degree of accuracy. Close House elections are so predictable that it is impossible to achieve covariate balance between matched treated and control observations. We show that post-election recounts do not change election results often enough to account for the large imbalances in pre-election covariates that we observe. We conclude that bare winners and bare losers in U.S. House elections are not exchangeable ex ante. Therefore, the RD design is not valid in the case of U.S. House elections,

discussions.

Since 1914, incumbent U.S. senators running for reelection have won almost 80 % of the time. We investigate why incumbents win so often, and how changes in electoral design would affect the value of senatorial seat. We allow for three potential explanations for the incumbency advantage: selection, tenure, and challenger quality, which are separately identified using histories of election outcomes following an open seat election. We specify a dynamic model of voter behavior that allows for these three effects, and structurally estimate the parameters of the model using U.S. Senate data. We find that tenure effects

We investigate why incumbents win disproportionately often. To do so, we structurally estimate the parameters of a dynamic model of voter behavior using U.S. Senate data. Our model specifies three potential reasons for the incumbency advantage: selection, tenure and challenger quality. Each of these separate e#ects is identified from data on histories of election outcomes. We estimate the parameters of the model using the method of maximum likelihood. We find that tenure e#ects are negative or small. The incumbency advantage is due to the average quality of incumbents being higher and to incumbents facing weaker challengers than candidates running for open seats.

Political dynasties have long been present in democracies, raising concerns that this inequality in the distribution of political power may reflect imperfections in democratic representation. However, the persistence of political elites may simply reflect differences in ability or political vocation across families and not their entrenchment in power. We show that political dynasties in the Congress of the United States do not merely reflect permanent differences in family characteristics. On the contrary, using two instrumental variable techniques we find that political power is self-perpetuating: legislators who hold power for longer become more likely to have relatives entering Congress in the future. Thus, in politics, power begets power.

We study whether the incumbent status of previously elected parties and politicians translates into an electoral or incumbency advantage in the U.S. Senate, using a regression discontinuity (RD) design that compares states where the Democratic Party barely won a Senate election to states where the Democratic party barely lost. Since the Senate has only one hundred seats up for election every six years, the number of close races is small and standard RD estimation techniques are ill-suited for our problem. We develop a randomization inference framework that is appropriate for our small sample size, and show that the results obtained with our approach can be markedly different from results based on standard methods. Our framework is general and applicable to any RD design where small sample sizes constrain researchers ’ ability to make inferences, and is motivated by a recent strand of the literature that advocates interpreting RD designs as local randomized experiments. Our approach has two steps. The first is to select a window around the cutoff where a randomization-type condition is assumed to hold. Researchers can choose this window based on substantive knowledge

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. Anatural experiment is a study in which the assignment of treatments to subjects is haphazard and possibly random. Such experiments have become increasingly prominent in recent years, and they have been used by scholars in a wide variety of fields to help make causal inferences, including political participation (Krasno and Green 2008; Lassen 2005),