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Below results based on the criteria 'Perot'
Total number of records returned: 3
Economic Performance, Job Insecurity, and Electoral Choice
The mass political economy literature concentrates on egocentric and sociotropic evaluations of short-term economic performance. Scant attention is paid to other economic concerns people may have. In a neo-liberal economic climate characterized by a downsized labor market and the retrenchment of government welfare entitlements, one such widely-publicized concern is job insecurity. We show that job insecurity is a novel form of discontent that is independent of the retrospective evaluations of short-term performance that are the stuff of the mainstream mass political economy literature. At the same time, the political effects of job insecurity are distinctive. In a multinomial probit model of electoral choice in the 1996 U.S. presidential election, job insecurity is associated with support for the third-party candidate, Ross Perot, but, contrary to conventional wisdom, has no implications for turnout. Traditional retrospective evaluations of economic performance explain the major-party vote and abstention.
Economics, Issues and the Perot Candidacy: Voter Choice in the 1992 Presidential Election
Alvarez, R. Michael
Theory: Theories of presidential elections (economic voting and spatial issue and ideology models), combined with the popular explanation of "angry voting", are used to account for voter choice in the 1992 Presidential Election. Hypotheses: Voter choice in this three-candidate race is a function of economic perceptions, issue and ideological positions of voters and candidates, or ``voter anger.'' Methods: Multinomial probit analysis of 1992 National Election Studies data including individual-specific and alternative-specific variables. Simulations based on counterfactual scenarios of ideological positions of the candidates and of voter perceptions of the economy. Results: The economy was the dominant factor in accounting for voter decisions in 1992, and Clinton, not Perot, was the beneficiary of economic discontent. While issues (mainly abortion) and ideology did play some role, Clinton was not perceived by the electorate as a ``New Democrat.'' We find little support for the hypothesis of ``angry voting.'' Last, Perot took more votes from Bush than from Clinton.
Getting the Mean Right is a Good Thing: Generalized Additive Models
This is a substantial revision of the paper submitted as beck96. A shorter version of this paper is under consideration at a political science journal of note. Theory: Social scientists almost always use statistical models positing the dependent variable as a linear function of X, despite suspicions that the social and political world is not so parsimonious. Generalized additive models (GAMs) permit each independent variable to be modelled non-parametrically while requiring that the independent variables combine additively, striking a sensible balance between the flexibility of non-parametric techniques and the ease of interpretation and familiarity of linear regression. GAMs thus offer social scientists a practical methodology for improving on the extant practice of ``linearity by default''. Method: We present the statistical concepts and tools underlying GAMs (e.g., scatterplot smoothing, non-parametrics more generally, and accompanying graphical methods), and summarize issues pertaining to estimation, inference, and the statistical properties of GAMs. Monte Carlo experiments assess the validity of tests of linearity accompanying GAMs. Re-analysis of published work in American politics, comparative politics, and international relations demonstrates the usefulness of GAMs in social science settings. Results: Our re-analyses of published work show that GAMs can extract substantive mileage beyond that yielded by linear regression, offering novel insights, particularly in terms of modelling interactions. The Monte Carlo experiments show there is little danger of GAMs spuriously finding non-linear structures. All data analysis, Monte Carlo experiments, and statistical graphs were generated using S-PLUS, Version 3.3. The routines and data are available at ftp://weber.uscd.edu/pub/nbeck/gam.