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When defined in terms of social identity and affect toward copartisans and opposing partisans, the polarization of the American electorate has dramatically increased. We document the scope and consequences of affective polarization of partisans using implicit, explicit, and behavioral indicators. Our evidence demonstrates that hostile feelings for the opposing party are ingrained or automatic in voters' minds, and that affective polarization based on party is just as strong as polarization based on race. We further show that party cues exert powerful effects on nonpolitical judgments and behaviors. Partisans discriminate against opposing partisans, doing so to a degree that exceeds discrimination based on race. We note that the willingness of partisans to display open animus for opposing partisans can be attributed to the absence of norms governing the expression of negative sentiment and that increased partisan affect provides an incentive for elites to engage in confrontation rather than cooperation.

The ability to monitor state behavior has become a critical tool of international governance. Systematic monitoring allows for the creation of numerical indicators that can be used to rank, compare, and essentially censure states. This article argues that the ability to disseminate such numerical indicators widely and instantly constitutes an exercise of social power, with the potential to change important policy outputs. It explores this argument in the context of the United States' efforts to combat trafficking in persons and find evidence that monitoring has important effects: Countries are more likely to criminalize human trafficking when they are included in the U.S. annual Trafficking in Persons Report, and countries that are placed on a \"watch list\" are also more likely to criminalize. These findings have broad implications for international governance and the exercise of soft power in the global information age.

According to indicators of political repression currently used by scholars, human rights practices have not improved over the past 35 years, despite the spread of human rights norms, better monitoring, and the increasing prevalence of electoral democracy. I argue that this empirical pattern is not an indication of stagnating human rights practices. Instead, it reflects a systematic change in the way monitors, like Amnesty International and the U.S. State Department, encounter and interpret information about abuses. The standard of accountability used to assess state behaviors becomes more stringent as monitors look harder for abuse, look in more places for abuse, and classify more acts as abuse. In this article, I present a new, theoretically informed measurement model, which generates unbiased estimates of repression using existing data. I then show that respect for human rights has improved over time and that the relationship between human rights respect and ratification of the UN Convention Against Torture is positive, which contradicts findings from existing research.

Recently, there has been a proliferation of measures responding to demands for accountability and transparency. Using the example of media rankings of law schools, this article argues that the methodological concept of reactivity-the idea that people change their behavior in reaction to being evaluated, observed, or measured- offers a useful lens for disclosing how these measures effect change. A framework is proposed for investigating the consequences, both intended and unintended, of public measures. The article first identifies two mechanisms, self-fulfilling prophecy and commensuration, that induce reactivity and then distinguishes patterns of effects produced by reactivity. This approach demonstrates how these increasingly fateful public measures change expectations and permeate institutions, suggesting why it is important for scholars to investigate the impact of these measures more systematically. Adapted from the source document.

Transparency is often viewed as crucial to government accountability, but its measurement remains elusive. This concept encompasses many dimensions, which have distinct effects. In this article, we focus on a specific dimension of transparency: governments' collection and dissemination of aggregate data. We construct a measure of this aspect of transparency, using an item response model that treats transparency as a latent predictor of the reporting of data to the World Bank's World Development Indicators. The resultant index covers 125 countries from 1980 to 2010. Unlike some alternatives (e.g., Freedom House), our measure—the HRV index—is based on objective criteria rather than subjective expert judgments. Unlike newspaper circulation numbers, HRV reflects the dissemination of credible content—in that it has survived the World Bank's quality control assessment. In a validation exercise, we find that our measure outperforms newspaper circulation as a predictor of Law and Order and Bureaucratic Quality as measured by the ICRG, particularly in autocracies. It performs as well as newspaper circulation in predicting corruption. These findings suggest that data dissemination is a distinct, and politically relevant, form of transparency.

Street-level organizations are pivotal players in the making of public policy. The importance of these organizations is reflected in new public management strategies that aim to influence how street-level organizations work, in part, by \"steering\" discretionary practices through performance-based incentives. The underlying assumptions are that if performance indicators provide the equivalent of a bottom line and incentives (or penalties) are attached to them, one can leave it to street-level organizations to determine how best to do policy work. This article directly challenges the premise that how policy work is done does not matter so long as performance benchmarks are met. It brings a street-level perspective to bear on a growing debate that questions both the effectiveness and the political implications of new public management (NPM) strategies. I argue that these strategies are based, in part, on flawed assumptions about how street-level discretion interacts with performance incentives and how these strategies relate to policy politics. In this article, I elaborate an analytic framework for understanding the street-level logic of choice and constraint under new managerialism and then turn to the case of welfare reform to examine how new managerialism and discretion interacted in the everyday life of a major urban welfare agency. The analysis reveals that street-level practitioners do not just respond to performance incentives; they use their discretion to adjust to them, producing informal practices that are substantively different from—and more diverse than—what policymakers or managers tend to recognize. A street-level view illuminates dimensions of welfare reform's apparent \"success\" that performance metrics do not capture, arguably obscuring the very transparency they are ostensibly designed to provide. A better understanding of how street-level organizations do policy work reveals NPM's limitations and provides a foundation for developing alternatives to it.

We adopt an interactionist logic to study the determinants of risk taking by chief executive officers (CEOs). We introduce the concept of \"capability cues\"— contextual signals that decision makers might reasonably interpret as indicators of their current level of overall ability— arguing that positive cues will induce boldness, while negative cues will induce timidity. Then, drawing from prior theory about how narcissists react to stimuli, we hypothesize that highly narcissistic CEOs will be relatively unresponsive to objective indicators of their performance; in contrast, highly narcissistic CEOs will be exceptionally emboldened by social praise (in the forms of media praise and media awards). We test our theory in two distinct studies, one of risky outlays by CEOs of publicly owned U.S. companies from 1992 to 2006, and a second of acquisition premiums paid by CEOs of a sample of U.S. acquiring firms, 2001-2008. Our analyses show that capability cues generally influence executive risk taking, but highly narcissistic CEOs are much less responsive to recent objective performance than their less narcissistic peers; in contrast, highly narcissistic CEOs are especially bolstered by social praise.

Social scientists rely on surveys to explain political behavior. From consistent overreporting of voter turnout, it is evident that responses on survey items may be unreliable and lead scholars to incorrectly estimate the correlates of participation. Leveraging developments in technology and improvements in public records, we conduct the first-ever fifty-state vote validation. We parse overreporting due to response bias from overreporting due to inaccurate respondents. We find that nonvoters who are politically engaged and equipped with politically relevant resources consistently misreport that they voted. This finding cannot be explained by faulty registration records, which we measure with new indicators of election administration quality. Respondents are found to misreport only on survey items associated with socially desirable outcomes, which we find by validating items beyond voting, like race and party. We show that studies of representation and participation based on survey reports dramatically misestimate the differences between voters and nonvoters.

Indexes have gained favor over the past decade as a tool to measure social vulnerability to hazards. Numerous index designs have been put forward, yet we still know very little about their reliability. This research investigates the methods of social vulnerability index construction, examining decisions related to indicator selection, scale of analysis, measurement error, data transformation, normalization, and weighting. Each of these stages is imbued with uncertainty due to choices made by the index developer. The study applies Monte Carlo-based uncertainty analysis to assess and visualize uncertainty for a hierarchical social vulnerability index. Confidence limits are computed for the index rankings, leading to a finding of a high magnitude of uncertainty. The performance of the index compared to alternative configurations is strong in some places but statistically biased in about a third of the census tracts. The variability of index rankings is also assessed, indicating that index precision decreases with increasing vulnerability. Uncertainty analysis provides a useful, yet largely unapplied stage of index production that highlights places where the model is most reliable. If applied to the creation of social vulnerability indexes, output metrics can be produced with a greater degree of precision, transparency, and credibility.

Much of the recent research on civil war treats explanations rooted in political and economic grievances with considerable suspicion and claims that there is little empirical evidence of any relationship between ethnicity or inequality and political violence. We argue that common indicators used in previous research, such as the ethno-linguistic franctionalization (ELF) and the Gini coefficient for income dispersion, fail to capture fundamental aspects of political exclusion and economic inequality that can motivate conflict. Drawing on insights from group-level research, we develop new countrylevel indices that directly reflect inequalities among ethnic groups, including political discrimination and wealth differentials along ethnic lines. Our analysis reveals that these theoretically informed country profiles are much better predictors of civil war onset than conventional inequality indicators, even when we control for a number of alternative factors potentially related to grievances or opportunities for conflict.