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Below results based on the criteria 'bounds'
Total number of records returned: 4
Polarization and Political Violence
extreme bounds analysis
We explore the implications of a new notion of inequality --- polarization --- for the incidence and level of political violence. A society is said to be polarized when its members can be classified into different clusters, with each cluster being similar in terms of the attributes of its members (intra--group homogeneity) but with different clusters having members with dissimilar attributes (inter--group heterogeneity). The notion of polarization provides an important conceptual breakthrough in understanding inequality in societies because a society may be facing a decrease (increase) in inequality while at the same time experiencing an increase (decrease) in polarization. We conduct empirical analysis on a large sample of countries to demonstrate the positive link between polarization and political violence. In contrast, traditional measures of inequality perform poorly with the introduction of polarization in the model specification. Additionally, we conduct global sensitivity analysis to explore the robustness of the polarization measure to reasonable changes in the conditioning information set.
Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analyses of a Field Experiment on Democratic Deliberations
Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measurement error on causal estimation. The proposed nonparametric identification analysis avoids arbitrary modeling decisions and formally characterizes the roles of additional assumptions. We show the serious consequences of differential misclassification and offer a new sensitivity analysis that allows researchers to evaluate the robustness of their conclusions. Our methods are motivated by a field experiment on democratic deliberations, in which one set of estimates potentially suffers from differential misclassification. We show that an analysis ignoring differential measurement error may considerably overestimate the causal effects. This finding contrasts with the case of classical measurement error which always yields attenuation bias.
Non-Parametric Treatment Effect Estimation Strategy for Missing Treatment Data
Classic Treatment Effect
What should scholars do when faced with missing treatment data in randomized experiments or observational studies? Rather than, for example, assuming the treatment data is missing at random, Molinari (2010) introduces a non-parametric approach for computing bounds on treatment effects when there are missing treatment data. I review the Molinari approach and then use it to address an important question in international relations: is it true that ``issue linkage'' (the simultaneous negotiation of multiple issues for joint settlement) helps states conclude otherwise unattainable negotiated agreements?
Bounds for Logistic Regression Coefficients with Nonignorable Missing Outcomes
I develop a new method to estimate logistic regression coefficients when there is nonignorable missingness or measurement error in the outcome variable. The estimator finds the set of all coefficient vectors that could be obtained under any assumption about the missing outcomes.