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Below results based on the criteria 'split-population'
Total number of records returned: 4
Signals, Models, and Congressional Overrides of the Supreme Court
event history models
split-population duration models
Sparked by interest in game-theoretic representations of the separation of powers, empirical work examining congressional overrides of Supreme Court statutory decisions has burgeoned in recent years. Much of this work has been hampered, however, by the relative rarity of such events; as has long been noted, congressional attention to the Court is limited, and most Court decisions represent the last word on statutory interpretation. With this fact foremost in our minds, we examine empirically a number of theories regarding such reversals. We apply a split-population duration model to the survival of Supreme Court statutory interpretation decisions. This approach allows us to separate the factors which lead to the event itself (i.e., the presence or absence of an override in a particular case) from those which influence the timing of the event. We find that case-specific factors relating to the salience of a case are an important influence in the incidence of overrides, while Congress- and Court-specific political influences dominate the timing at which those overrides occur. By separating the incidence and timing of overrides, our results yield a more accurate and nuanced understanding of this aspect of the separation of powers system.
Modeling Heterogeneity in Duration Models
Box-Steffensmeier, Janet M.
As increasing numbers of political scientists have turned to event history models to analyze duration data, there has been growing awareness of the issue of heterogeneity: instances in which subpopulations in the data vary in ways not captured by the systematic components of standard duration models. We discuss the general issue of heterogeneity, and offer techniques for dealing with it under various conditions. One special case of heterogeneity arises when the population under study consists of one or more subpopulations which will never experience the event of interest. Split-population, or "cure" models, account for this heterogeneity by permitting separate analysis of the determinants of whether an event will occur and the timing of that event, using mixture distributions. We use the split-population model to reveal additional insights into the strategies of political action committees' allocation decisions, and compare split-population and standard duration models of Congressional responses to Supreme Court decisions. We then go on to explore the general issue of heterogeneity in survival data by considering two broad classes of models for dealing with the lack of independence among failure times: variance correction models and "frailty" (or random effects) duration models. The former address heterogeneity by adjusting the variance matrix of the estimates to allow for correct inference in the presence of that heterogeneity, while the latter approach treats heterogeneity as an unobservable, random, multiplicative factor acting on the baseline hazard function. Both types of models allow us to deal with heterogeneity that results, for example, from correlation at multiple levels of data, or from repeated events within units of analysis. We illustrate these models using data on international conflicts. In sum, we explore the issue of heterogeneity in event history models from a variety of perspectives, using a host of examples from contemporary political science. Our techniques and findings will therefore be of substantial interest to both political methodologists and others engaged in empirical work across a range of subfields.
Authoritarian Reversals and Democratic Consolidation
transitions to democracy
cure rate models
I investigate the determinants and the process of authoritarian reversals and democratic consolidation. I employ a new empirical model that allows me to distinguish between two central dynamics: the likelihood that a democracy consolidates, and the timing of authoritarian reversals in democracies that are not consolidated. I demonstrate that existing democracies are a mixture of transitional and consolidated democracies rather than a single population. This approach leads to new insights into the causes of democratic consolidation that cannot be obtained with existing techniques. I find that the level of economic development, type of executive, and authoritarian past determine whether a democracy consolidates, but have no effect on the timing of reversals. That risk is only associated with economic recessions. I also find that the existing studies greatly underestimate the risk of early reversals while they simultaneously overestimate the risk of late reversals, and that a large number of existing democracies are in fact consolidated.
A Split Population Model for Middle-Category Inflation in Ordered Survey Responses
ordered dependent Variables
Recent research find that, for social desirability reasons, uninformed individuals disproportionately give ``neither agree nor disagree'' type responses to survey attitude questions, even when a ``don't know'' option is available (Sturgis et al. 2010). Such ``face-saving don't knows'' inflate the indifference (i.e. middle) categories of ordered attitude variables with non-ordered responses. When this inflation occurs within one's dependent variable, estimates from ordered probit/logit models are biased and inefficient. This poster develops a set of mixture models (the middle-inflated ordered probit with and without correlated errors) that estimate and account for the presence of ``face-saving'' responses in middle-categories of ordered survey response variables, and applies these models to (1) simulated data and (2) a commonly studied survey question measuring support for EU-membership among EU-candidate countries. Findings suggest that, when middle-category inflation is present in one's ordered dependent variable, the estimates obtained from middle-category mixture models are less biased than---and in some cases substantively distinct from---the estimates obtained from ``naive'' ordered probit models.