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Below results based on the criteria 'diffusion'
Total number of records returned: 7
Potential Ambiguities in a Directed Dyad Approach to State Policy Emulation
In this paper I discuss circumstances under which the dyadic model of policy diffusion can produce misleading estimates in favor of policy emulation. These circumstances arise in the context of state pain management policy, and correspond generally to policies that states are uniformly expanding. When this happens, dyadic models of policy diffusion conflate policy emulation and policy adoption: since early adopters are policy leaders, later adopters will appear to emulate them, even if they are merely stragglers acting on their own accord. I demonstrate the possibility of this ambiguity analytically and through Monte Carlo simulation. Both start with the assumption that the data are generated according to a standard, monadic model of policy adoption and then converted to a dyadic model, which can incorrectly produce evidence of emulation. I propose a simple modification of the dyadic emulation model --- conditioning on the opportunity to emulate --- and show that it is much less likely to produce inaccurate findings. I then return to the study of pain management policy and find substantial differences between the dyadic emulation model and the conditional emulation model.
The Spatial Probit Model of Interdependent Binary Outcomes: Estimation, Interpretation, and Presentation
Bayesian Gibbs-Sampler Estimator
Recursive Importance-Sampling Estimator
We have argued and shown elsewhere the ubiquity and prominence of spatial interdependence in political science research and noted that much previous practice has neglected this interdependence or treated it solely as nuisance to the serious detriment of sound inference. Previously, we considered only linear-regression models of spatial and/or spatio-temporal interdependence. In this paper, we turn to binary-outcome models. We start by stressing the ubiquity and centrality of interdependence in binary outcomes of interest to political and social scientists and note that, again, this interdependence has been ignored in most contexts where it likely arises and that, in the few contexts where it has been acknowledged, the endogeneity of the spatial lag has not be recognized. Next, we explain some of the severe challenges for empirical analysis posed by spatial interdependence in binary-outcome models, and then we follow recent advances in the spatial-econometric literature to suggest Bayesian or recursive-importance-sampling (RIS) approaches for tackling estimation. In brief and in general, the estimation complications arise because among the RHS variables is an endogenous weighted spatial-lag of the unobserved latent outcome, y*, in the other units; Bayesian or RIS techniques facilitate the complicated nested optimization exercise that follows from that fact. We also advance that literature by showing how to calculate estimated spatial effects (as opposed to parameter estimates) in such models, how to construct confidence regions for those (adopting a simulation strategy for the purpose), and how to present such estimates effectively.
Empirical Modeling Strategies for Spatial Interdependence: Omitted-Variable vs. Simultaneity Biases
Spatial Lag Models
Omitted Variable Bias
Scholars recognize that time-series-cross-section data typically correlate across time and space, yet they tend to model temporal dependence directly while addressing spatial interdependence solely as nuisance to be “corrected” (FGLS) or to which to be “robust” (PCSE). We demonstrate that directly modeling spatial interdependence is methodologically superior, offering efficiency gains and generally helping avoid biased estimates even of “non-spatial” effects. We first specify empirical models representing two modern approaches to comparative and international political economy: (context-conditional) open-economy comparative political-economy (i.e., common stimuli, varying responses) and international political-economy, which implies interdependence (plus closed-economy and orthogonal-open-economy predecessors). Then we evaluate four estimators—non-spatial OLS, spatial OLS, spatial 2SLS-IV, and spatial ML—for analyzing such models in spatially interdependent data. Non-spatial OLS suffers from potentially severe omitted-variable bias, tending to inflate estimates of common-stimuli effects especially. Spatial OLS, which specifies interdependence directly via spatial lags, dramatically improves estimates but suffers a simultaneity bias, which can be appreciable under strong interdependence. Spatial 2SLS-IV, which instruments for spatial lags of dependent variables with spatial lags of independent variables, yields unbiased and reasonably efficient estimates of both common-stimuli and diffusion effects, when its conditions hold: large samples and fully exogenous instruments. A tradeoff thus arises in practice between biased-but-efficient spatial OLS and consistent- (or, at least, less-biased-) but-inefficient spatial 2SLS-IV. Spatial ML produces good estimates of non-spatial effects under all conditions but is computationally demanding and tends to underestimate the strength of interdependence, appreciably so in small-N samples and when the true diffusion-strength is modest. We also explore the standard-error estimates from these four procedures, finding sizable inaccuracies by each estimator under differing conditions, and PCSE’s do not necessarily reduce these inaccuracies. By an accuracy-of-reported-standard-errors criterion, 2SLS-IV seems to dominate. Finally, we explore the spatial 2SLS-IV estimator under varying patterns of interdependence and endogeneity, finding that its estimates of diffusion strength suffer only when a condition we call cross-spatial endogeneity, wherein dependent variables (y’s) in some units cause explanatory variables (x’s) in others, prevails.
States as Policy Laboratories: Experimenting with the Children's Health Insurance Program
For more than a decade, scholars of policy diffusion across the states have relied on state-year event history analyses. Such work has been limited by: (1) focusing mainly on neighbor-to-neighbor diffusion paths, rather than other similarities across states; (2) neglecting the role of the success or failures of policies in their diffusion; (3) studying singular specific policy adoptions rather than the choice among policy variants; and (4) setting aside questions about how diffusion mechanisms vary across different policies and different political processes. This paper proposes the alternative approach of dyad-year event history analysis, commonly used in international relations, and applies it to the study of policy diffusion in Children's Health Insurance Program from 1998-2001. This approach uncovers strong evidence of the emulation of states with similar political, demographic, and budgetary characteristics, and those with successful policies. Moreover, the diffusion mechanisms differ substantially across different policy types and political processes.
Diffusion or Confusion? Modeling Policy Diffusion with Discrete Event History Data
No abstract provided.
The Diffusion of Democracy, 1946-1994
Ward, Michael D.
Lofdahl, Corey L.
Cohen, Jordin S.
Brown, David S.
Gleditsch, Kristian S.
Shin, Michael E.
exploratory spatial data analysis
measures of democracy
Research to date on democratization neglects the interconnections between temporal and spatial components that influence this process. This article presents research that reveals the relationship between the temporal and spatial aspects of democratic diffusion in the world-system since 1946. We provide strong and consistent evidence of temporal cascading of democratic and autocratic trends as well as strong spatial association (or autocorrelation) of authority structures. The analysis uses an exploratory data approach in a longitudinal framework to understand global and regional trends in democratization. Our work also reveals discrete changes in regimes that run counter to the dominant aggregate trends of democratic waves or sequences. We demonstrate how the ebb and flow of democracy varies between the world's regions. We conclude that further modeling of the process of regime change from autocracy to democracy as well as reversals should start from a "domain-specific" position that disaggregates the globe into its regional mosaics.
WhentheSTARs Align:What IOs AreMoreConducivetoDemocratization
The scholars of democracy have long noted the tendency of democratic states to cluster in time and space. While most theoretical explanations of this phenomenon posit causal mechanisms related to spatial interdependence (e.g. diffusion, socialization), very few studies have conducted adequate empirical tests of these theories. This methodological oversight is due both to the scarcity of available statistical techniques that allow for testing these types of effects, as well as to the methodological sophistication of the existing techniques. Yet the value of empirical inferences is largely dependent on correct model specification. I develop several hypotheses linking state democracy level to membership in international organizations (IOs) that vary in scope, institutional capacity, and centralization. I test these hypotheses using several alternative approaches that allow to correct or explicitly model spatial and temporal dependence. I start with more common approaches, such as the use of a lagged dependent variable, fixed effects, and panel corrected standard errors, and then re-estimate the results using a multi-parametric spatio-temporal autocorrelation model (m-STAR). In this final model, I test my hypotheses using overlapping IO memberships in different types of IOs, as well as geographic contiguity as the spatial weights. I argue that while the lagged dependent variable, fixed effects, and panel-corrected standard errors show more desirable qualities than a naÃ¯ve model, the m-STAR provides for the most adequate testing, from both a methodological and a theoretical perspective. Unlike the former three techniques that treat spatial and temporal dependence as a nuisance, the M-STAR allows for explicit modeling and estimation of contemporaneous spatial effects. Its ability to estimate spatial effects occurring within the same time-period as the unit-level effects makes this model particularly useful at evaluating the hypotheses posited in this paper, as well as such phenomena as diffusion and socialization more broadly.