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Below results based on the criteria 'survival'
Total number of records returned: 11
Public Opinion Shocks and Government Termination
Martin, Lanny W.
Abstract. The ability of a government to remain in power depends partially upon its vulnerability to unexpected changes occurring in the outside political environment. In this paper, I examine the relationship between government termination and changes in the electoral expectations of political parties in the legislature, as reflected by shifts in popular support for the government. I find that the decision to terminate the government is related in complex ways to changes in public opinion. Governments are more likely to collapse as certain members of the incumbent coalition expect to gain more ministerial portfolios, and in cases of minority government, when the opposition expects to gain more legislative seats. Further, I show that these effects increase with the approach of regularly-scheduled elections.
The Government Agenda in Parliamentary Democracies
Martin, Lanny W.
In this paper, I examine the effects of party ideology and legislative institutions on the organization of the government agenda in parliamentary democracies. Analyzing data on the timing and policy content of over eight hundred government bills from the 1980s for four European democracies, I show that cabinets schedule bills earlier on the legislative agenda the greater their saliency to the prime minister and to those ministers responsible for their formulation and implementation. Moreover, I show that cabinets tend to delay bills that impose ideological compromise on cabinet members or that create conflict between the cabinet and parties in the opposition, particularly in periods of minority government. I find that all of these effects are greater at the beginning of a government’s tenure and decline by varying degrees over time.
Strategic Position-Taking and the Timing of Voting Decisions in Congress
Box-Steffensmeier, Janet M.
Arnold, Laura W.
Voting behavior is intimately linked with many of the most prominent questions of concern to students of legislatures, including the strength of legislative parties and factions, the parameters of individual decision making, and the nature of representation (Collie 1985). One critical element of voting in legislatures is the timing of various choices legislators make. The study of strategic position taking and the timing of voting decisions is important for three major reasons: it adds information about the context and sequence of decision making; the analysis more closely approximates members' strategic considerations; and finally, in contrast to most of the literature on legislative roll call voting, the process is examined rather than strictly the result. Yet, despite the importance of position taking and timing, no one has examined comprehensively this crucial aspect of timing. Research on the timing of voting decisions provides insight into theoretical questions regarding the strategic behavior of legislators, institutional constraints on member behavior, and strategies of interest group influence. The project examines the vote to ratify the North American Free Trade Agreement, which has been called ". . . the most important vote on Capital Hill since the Berlin Wall came down" (Frenzel 1994, 3).
Covariate Functional Form in Cox Models
In most event history models, the effect of a covariate on the hazard is assumed to have a log-linear functional form. For continuous covariates, this assumption is often violated as the effect is highly nonlinear. Assuming a log-linear functional form when the nonlinear form applies causes specification errors leading to erroneous statistical conclusions. Scholars can, instead of ignoring the presence of nonlinear effects, test for such nonlinearity and incorporate it into the model. I review methods to test for and model nonlinear functional forms for covariates in the Cox model. Testing for such nonlinear effects is important since such nonlinearity can appear as nonproportional hazards, but time varying terms will not correct the misspecification. I investigate the consequences of nonlinear function forms using data on international conflicts from 1950-1985. I demonstrate that the conclusions drawn from this data depend on fitting the correct functional form for the covariates.
Estimating Interdependent Duration Models with an Application to Government Formation and Survival
Seemingly Unrelated Regressions
Simultaneous Equation Models
This paper is part of a larger project in which we develop methods for estimating the causal effects of variables on (1) the duration of bargaining processes, broadly defined, and (2) the survival of bargained outcomes when both are jointly determined. There are many potential applications in political science including, but not limited to, the duration of war and survival of cease-fire agreements, coalition formation and government survival, and negotiations over and enforcement of international agreements. Our primary claim is that, in most cases, it is inappropriate to estimate the effects of variables on these two durations -- the bargaining and the outcome -- in isolation. Our argument is motivated by game theoretic models that show bargaining duration is correlated with the survival of bargained outcomes because players incorporate their beliefs about the survival of bargained outcomes into their decision-making at the bargaining stage. To address this problem, we develop, and examine the properties of two maximum likelihood estimators -- a seemingly unrelated regresssions (SUR) estimator and a limited information maximum likelihood (LIML) estimator. We use both estimators to analyze the duration of government formation and survival in a sample of European parliamentary democracies over the period 1945 to 1998. We conclude that estimated effects based on single equation models of either government formation or survival, the predominant method of analysis in the existing literature, are likely biased because they fail to capture significant indirect effects generated by strategic and other forms of interdependence that link the two durations.
Diffusion or Confusion? Modeling Policy Diffusion with Discrete Event History Data
No abstract provided.
A Monte Carlo Analysis for Recurrent Events Data
Box-Steffensmeier, Janet M.
De Boef, Suzanna
Scholars have long known that multiple events data, which occur when subjects experience more than one event, cause a problem when analyzed without taking into consideration the correlation among the events. In particular there has not been a solution about the best way to model the common occurrence of repeated events, where the subject experiences the same type of event more than once. Many event history model variations based on the Cox proportional hazards model have been proposed for the analysis of repeated events and it is well known that these models give different results (Clayton 1994; Lin 1994; Gao and Zhou 1997; Klein and Moeschberger 1997; Therneau and Hamilton 1997; Wei and Glidden 1997; Box-Steffensmeier and Zorn 1999; Hosmer and Lemeshow 1999; Kelly and Lim 2000). Our paper focuses on the two main alternatives for modeling repeated events data, variance corrected and frailty (also referred to as random effects) approaches, and examines the consequences these different choices have for understanding the interrelationship between dynamic processes in multivariate models, which will be useful across disciplines. Within political science, the statistical work resulting from this project will help resolve some important theoretical and policy debates about political dynamics, such as the liberal peace, by commenting on the reliability of the different modeling strategies used to test those theories and applying those models. Specifically, the results of the project will help assess whether one of the two primary approaches is better able to account for within-subject correlation. We evaluate the various modeling strategies using Monte Carlo evidence to determine whether and under what conditions alternative modeling strategies for repeated events are appropriate. The question as to the best modeling strategy for repeated events data is an important one. Our understanding of political processes, as in all studies, depends on the quality of the inferences we can draw from our models. There is currently little guidance about which approach or model is appropriate and so, not surprisingly, we see analysts unsure of the best way to analyze their data. Given the dramatic substantive differences that result from using the different models and approaches, this is a problem that will be of interest across research communities.
Stochastic Dependence in Competing Risks
Gordon, Sanford C.
Monte Carlo simulation
Markov Chain Monte Carlo
The term "Competing Risks" describes duration models in which spells may terminate via multiple outcomes: The term of a cabinet, for example, may end with or without an election; wars persist until the loss or victory of the aggressor. Analysts typically assume stochastic independence among risks, the duration modeling equivalent of independence of irrelevant alternatives. However, many political examples violate this assumption. I review competing risks as a latent variables approach. After discussing methods for modeling dependence that place restrictions on the nature of association, I introduce a parametric generalized dependent risks model in which inter-risk correlation may be estimated and its significance tested. The method employs risk-specific random effects drawn from a multivariate normal distribution. Estimation is conducted using numerical methods and/or Bayesian simulation. Monte Carlo simulation reveals desirable large sample properties of the estimator. Finally, I examine two applications using data on cabinet survival and legislative position taking.
A Frailty Model of Negatively Dependent Competing Risks
Gordon, Sanford C.
"Competing Risks" is a term used to describe duration models in which an individual spell may terminate via more than one outcome. Numerous applications in political science exist: For example, the term of a cabinet may end either with or without an election; criminal investigations may terminate either with prosecution or abandonment of a case; wars persist until the loss or victory of the aggressor state. Analysts typically assume stochastic independence among risks. However, many political examples are characterized by negative risk dependence: A high hazard rate for termination via one risk implies a low rate for termination via another. Ignoring this dependence can potentially bias inference. This paper suggests a class of bivariate (i.e. two risk), negatively dependent competing risks models. Negative risk dependence enters through an individual-specific random effect that simultaneously increases the hazard rate for one risk while decreasing the hazard for the second. Monte Carlo simulation reveals this specification to be superior to a naive model in which risks are assumed independent. Finally, I examine an application of the negative dependence model using Strom's (1985) and King et. al.'s (1990) data on cabinet survival.
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.
The Past is Ever-Present: The Dynamic Nature of Intrastate Conflict
Civil wars pose a grave challenge to international stability as they tend to recur frequently over time. Nevertheless, existing theory treats civil wars as independent events. I reconceptualize civil war as a dynamic process, which creates a new statistical challenge â€“ modeling multi-stage processes through a series of transitions within a longitudinal process. To overcome this problem, I introduce a multi-state event history model, which models the entire civil war process as a series of successive stages in which previous outcomes shape subsequent events, and apply it to a dataset of all civil wars from 1950-2004. The results provide strong evidence that previous outcomes exert both a direct, and indirect effect on subsequent transitions, revealing the conditional nature of factors frequently associated with war and peace.