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Below results based on the criteria 'spatial voting'
Total number of records returned: 10
Rational Expectations Coordinating Voting in American Presidential and House Elections
Mebane, Walter R.
generalized extreme value model
Monte Carlo integration
I define a probabilistic model of individuals' presidential-year vote choices for President and for the House of Representatives in which there is a coordinating (Bayesian Nash) equilibrium among voters based on rational expectations each voter has about the election outcomes. I estimate the model using data from the six American National Election Study Pre-/Post-Election Surveys of years 1976--1996. The coordinating model passes a variety of tests, including a test against a majoritarian model in which there is rational ticket splitting but no coordination. The results give strong individual-level support to Alesina and Rosenthal's theory that voters balance institutions in order to moderate policy. The estimates describe vote choices that strongly emphasize the presidential candidates. I also find that a voter who says economic conditions have improved puts more weight on a discrepancy between the voter's ideal point and government policy with a Democratic President than on a discrepancy of the same size with a Republican President.
Aggregate Voting Data and Implied Spatial Voting
Herron, Michael C.
The paper draws attention to the micro--foundations of aggregate voting data by introducing the concept of an implied spatial voting model. The adjective ``implied'' refers to the fact that this paper's spatial theory primitives, which describe how individual--level preferences are distributed across and within voting districts, are implied by or derived from aggregate voting data. The key idea proposed here is that, given an observed distribution of aggregate voting data, it is possible to derive features of an individual--level, spatial voting model capable of generating the observed data. Thus, an implied spatial voting model is an inverse image of an observed, aggregate vote share distribution. We provide numerical examples of how spatial voting models can be implied by aggregate voting data and we then analyze aggregate data and National Election Study survey data from the 1980, 1984, and 1988 presidential elections. And, to demonstrate that implied spatial voting models can be calculated from aggregate data alone, we consider presidential elections 1928--1960 and the Chicago mayoral elections of 1983 and 1987. This paper's focus on the micro--foundations of aggregate data highlights the limitations inherent in aggregate data analyses. In particular, the paper discusses identification problems, in part a consequence of the lack of scale and location invariance in preference orderings and in part a consequence of the lack of individual--level information in aggregate data, that affect movement between individual--level theories like spatial voting theory and aggregate voting data.
Estimating voter preference distributions from individual-level voting data (with application to split-ticket voting
Lewis, Jeffrey B.
split ticket voting
ideal point estimation
spatial voting models
In the last decade a great deal of progress has been made in estimating spatial models of legislative roll-call voting. There are now several well-known and effective methods of estimating the ideal points of legislators from their roll-call votes. Similar progress has not been made in the empirical modeling of the distribution of preferences in the electorate. Progress has been slower, not because the question is less important, but because of limitations of data and a lack of tractable methods. In this paper, I present a method for inferring the distribution of voter ideal points on a single dimension from individual-level voting returns on ballot propositions. The statistical model and estimation technique draw heavily on the psychometric literature on test taking and, in particular, on the work of Bock and Aitkin (1981}. The method yields semi-parametric estimates of the distribution of voters along an unobserved spatial dimension. The model is applied to data from the 1992 general election in Los Angeles County. I present the distribution of voter ideal points of each of 17 Congressional districts. Finally, I consider the issue of split-ticket voting estimating for two Congressional districts the distribution of voters that split their tickets and of those that did not.
The Spatial Theory of Voting and the Presidential Election of 1824
Jenkins, Jeffery A.
Sala, Brian R.
spatial voting theory
One recent analysis claims that in at least five p residential contests since the end of World War II a relatively minor vote shift in a small number of states would have produced Electoral College deadlock, leading to a House election for president (Longley and Peirce 1996). A presidential contest in the House would raise fundamental questions from agency theory - do members "shirk" the collective preferences of their constituent-principals on highly salient votes and, if so, what explains the choices they do make? Can vote choices be rationalized in a theory of ideological voting, or are legislators highly susceptible to interest-group pressures and enticements? We apply a spatial-theoretic model of voting to the House balloting for president in 1825 in order to test competing hypotheses about how MCs would likely vote in a presidential ballot. We find that a sincere voting model based on ideal points for MCs and candidates derived from Nominate scores closely matches the choices made by MCs in 1825.
The Political Entropy of Vote Choice: An Empirical Test of Uncertainty Reduction
Voting Under Uncertainty
Proximity Spatial Voting Model
Recent literature in voting theory has developed the idea that individual voting preferences are probabilistic rather than strictly deterministic. This work builds upon spatial voting models (Enelow and Hinich 1981, Ferejohn and Fiorina 1974, Davis, DeGroot and Hinich 1972, Farquharson 1969) by introducing probabilistic uncertainty into the calculus of voting decision on an individual level. Some suggest that the voting decision can be modeled with traditional probabilistic tools of uncertainty (Coughlin 1990, Coughlin and Nitzen 1981). Entropy is a measure of uncertainty that originated in statistical thermodynamics. Essentially, entropy indicates the amount of uncertainty in probability distributions (Soofi 1992), or it can be thought of as signifying a lack of human knowledge about some random event (Denbigh and Denbigh, 1985). Entropy in statistics developed with Kolmogorov (1959), Kinchin (1957), and Shannon (1948), but has rarely been applied to social science problems. Exceptions include Darcy and Aigner's (1980) use of entropy to analyze categorical survey responses in political science, and economic applications by Theil (1967) and Theil and Fiebig (1984). I examine voters' uncertainty as they assess candidates, and measure policy positions. I then test whether or not these voters minimize the cost of voting (specifically the cost of information) by determining a maximum entropy selection. Except for the inclusion of entropy terms, this approach is similar to others in the recent literature. In this paper I develop a measure to aggregate evaluation of issue uncertainty and corresponding vote choice where the uncertainty parameterization is derived from an entropy calculation on a set of salient election issues. The primary advantage of this approach is that it requires very few assumptions about the nature of the data. Using 1994 American National Election Study survey data from the Center for Political Studies, I test the hypothesis that the ``Contract with America'' reduced voter uncertainty about the issue positions of Republican House candidates. The entropic model suggests that voters used the written and explicit Republican agenda as a means of reducing issue uncertainty without substantially increasing time spent evaluating candidate positions.
Coordinating Voting in American Presidential and House Elections
Mebane, Walter R.
pivotal voter theorem
I describe and estimate a probabilistic voting model designed to test whether individuals' votes for President and for the House of Representatives are coordinated with respect to two cutpoints on a single spatial dimension, in the way that Alesina and Rosenthal's pivotal voter theorem suggests they should be. In my model the cutpoints are random variables about which each individual has a subjective probability distribution. Each person's probabilistic coordinating voting behavior occurs relative to the cutpoints' expected values under the distribution. The model implements the idea the pattern of coordination depends on an individual's evaluation of the economy. The economic bias in the coordinating pattern implies that voters punish a Democratic President for success in improving the economy. The economically successful Democratic President can avoid losses only if the voters who rate the economy as having improved also believe that the policy position of the Democratic party has shifted to the right.
Spatial Voting Theory and Counterfactual Inference: John C. Breckenridge and the Presidential Election of 1860
Jenkins, Jeffery A.
spatial voting theory
One important catalyst for the onset of the Civil War was the presidential election of Abraham Lincoln in 1860. Lincoln, competing against three other candidates, won election with the smallest percentage of the popular vote in American history. Given the circumstances, a slightly different electoral slate might have engineered his defeat. We examine this possibility by focusing on the candidacy of John C. Breckinridge, the final entrant into the race. Historians disagree over the rationale behind Breckinridge's candidacy. Some argue that it was a desperate effort to defeat Lincoln; others suggest that it was designed to insure Lincoln's victory. Using election counterfactuals and applying spatial voting theory, we examine these arguments. Our evidence suggests that Breckinridge had no reasonable chance to win. Support for Breckinridge's candidacy was only reasonable if the intention were to elect Lincoln.
The Statistical Analysis of Roll Call Data
spatial voting model
item response theory
roll call voting
We develop a Bayesian procedure for estimation and inference for spatial models of roll call voting. Our appraoch is extremely flexible, applicable to any legislative setting, irrespective of size, the extremism of legislative voting histories, or the number of roll calls available for analysis. Our model is easily extended to let other sources of information inform the analysis of roll call data, such as the number and nature of the underlying dimensions, the presence of party whipping, the determinants of legislator preferences, or the evolution of the legislative agenda; this is especially helpful since it is gernally inappropriate to use estimates of extant methods (usually generated under assumptions of sincere voting) to test models embodying alternative assumptions (e.g., log-rolling). A Bayesian approach also provides a coherent framework for estimation and inference with roll call data that eludes extant methods; moreover, via Bayesian simulation methods, it is straightforward to generate uncertainty assessments or hypothesis tests concerning any auxiliary quantity of interest or to formally compare models. In a series of examples we show how our method is easily extended to accommodate theoretically interesting modesl of legislative behavior. Our goal is to move roll call analysis away from pure measurement or description towards a tool for testing substantive theories of legislative behavior.
Designing Tests of the Supreme Court and the Separation of Powers
Sala, Brian R.
Spriggs II, James F.
spatial voting theory
While "rational choice" models of Supreme Court decision making have enhanced our appreciation for the separation of powers built into the Madisonian Constitutional design, convincing empirical support for a separation-of-powers (SOP) constraint on justices' behavior has been elusive. We apply a standard spatial voting model to identify circumstances in which "Attitudinalist" and SOP predictions about justices' behavior diverge. Our reconsideration of the theory indicates that prior efforts to test quantitatively the two models have been biased by having included cases for which the two models' predictions do not differ. While our more focused test offers a fairer test of the SOP constraint, the results strongly reject the SOP model. Nonetheless, our analysis provides leverage on this issue by: (1) delineating and executing necessary research design protocols for crafting a critical test of the SOP model; and (2) rejecting the two exogenously fixed alternative SOP model and suggesting avenues for future research.
Operationalizing and Testing Spatial Theories of Voting
Quinn, Kevin M.
Martin, Andrew D.
Spatial models of voting behavior provide the foundation for a substantial number of theoretical results. Nonetheless, empirical work involving the spatial model faces a number of potential difficulties. First, measures of the latent voter and candidate issue positions must be obtained. Second, evaluating the fit of competing statistical models of voter choice is often more complicated than previously realized. In this paper, we discuss precisely these issues. We argue that confirmatory factor analysis applied to mass-level issue preference questions is an attractive means of measuring voter ideal points. We also show how party issue positions can be recovered using a variation of this strategy. We go on to discuss the problems of assessing the fit of competing statistical models (multinomial logit vs. multinomial probit) and competing explanations (those based on spatial theory vs. those derived from other theories of voting such as sociological theories). We demonstrate how the Bayesian perspective not only provides computational advantages in the case of fitting the multinomial probit model, but also how it facilitates both types of comparison mentioned above. Results from the Netherlands and Denmark suggest that even when the computational cost of multinomial probit is disregarded, the decision whether to use multinomial probit (MNP) or multinomial logit (MNL) is not clear-cut.