Suchergebnisse

The migration of political asylum seekers into the United States has long been a salient political topic; however, social scientists have yet to examine this process in its entirety and in the context of political changes since September 11, 2001. Previous research shows that humanitarian and strategic interests are important for decisions made by asylum officers but that research overlooks the decisions made by immigration judges. Here we examine decisions made by both asylum officers and immigration judges using data from a global set of countries, from 1999 to 2004. We find that the waning importance of human rights is more pronounced for asylum officers than for immigration judges after the attack on the World Trade Center. We also find that language heritage, specifically for asylum seekers from English-, Spanish-, and Arabic-speaking countries, substantially affects acceptance rates made by both decision-makers between the two time periods of our study.

Military spending data measure key international relations concepts such as balancing, arms races, the distribution of power, and the severity of military burdens. Unfortunately, missing values and measurement error threaten the validity of existing findings. Addressing this challenge, we introduce the Global Military Spending Dataset (GMSD). GMSD collates new and existing expenditure variables from a comprehensive collection of sources, expands data coverage, and employs a latent variable model to estimate missing values and quantify measurement error. We validate the data and present new findings. First, correlations between economic surplus and military spending are currently higher than at any point in the last two-hundred years. Second, updating DiGiuseppe and Poast's (2018) analysis, we find larger substantive effects. Specifically, we find that the (negative) effect of a democratic ally on military spending is three times larger, and the (positive) effect of an increase in GDP is five times larger than previously estimated.

How do elected representatives respond to the needs of immigrant constituents? We report the results of a field experiment on U.S. state legislators in which the nativity, likelihood of voting, and race/ethnicity of a hypothetical constituent are independently manipulated. The experimental design allows us to contribute new insights by isolating the various elements that may impede the connection between immigrants and elected representatives. Moreover, we explore racial/ethnic identities beyond black and white by including Latino and Asian aliases. Contrary to expectations, nativity and voting status do not affect response rates. Instead, legislator behavior appears to be driven by racial/ethnic bias. Whites benefit from the highest response rate, while blacks, Hispanics, and Asians all receive lower rates, respectively. This bias follows a partisan logic. The low response rate for Hispanic constituents comes primarily from Republican legislators, whereas Asians experience bias from representatives of both parties. We argue that this difference may result from Hispanic identity sending a stronger signal about partisan affiliation, or from a prejudicial view of Asians as outsiders. In this last interpretation, rather than the model minority, Asians become the excluded minority.

AbstractSeveral prominent human rights treaties seek to minimize violations during emergencies by authorizing states to "derogate"—that is, to suspend certain civil and political liberties—in response to crises. The drafters of these treaties envisioned that international restrictions on derogations, together with international notification and monitoring mechanisms, would limit rights suspensions during emergencies. This article analyzes the behavior of derogating countries using new global data sets of derogations and states of emergency from 1976 to 2007. We argue that derogations are a rational response to domestic political uncertainty. They enable governments facing serious threats to buy time and legal breathing space from voters, courts, and interest groups to confront crises while signaling to these audiences that rights deviations are temporary and lawful. Our findings have implications for studies of treaty design and flexibility mechanisms, and compliance with international human rights agreements.

Scholarship suggests the profits from conquest have decreased over time. Given this, why were some states faster to abandon profit-motivated conquest, and why are some still seeking wealth from territorial control? We argue that land-rent dependence influences a regime's economic preference for territory. The more a state depends on rents extracted from land (i.e., the more land-oriented the economy), the greater its willingness to invest in securing control of territory. We develop a novel measure of land orientation, with 200 years of data, to evaluate the linkages between land orientation and military competition over territory. Across 160 regression models, we find robust evidence that land orientation predicts territorial competition. These results hold in both democracies and autocracies. The global reduction in land-oriented states offers a plausible explanation for the decline in the number of large-scale territorial conquests. Our findings also explain why some states retain strong economic motivations for conquest.

AbstractIn authoritarian regimes, repression encourages private actors to censor not only themselves, but also other private actors—a behavior we call "regime-induced private censorship." We present the results of a correspondence experiment conducted in Russia that investigates the censorship behavior of private media firms. We find that such firms censor third-party advertisements that include anti-regime language, calls for political or non-political collective action, or both. Our results demonstrate the significance of other types of censorship besides state censorship in an important authoritarian regime and contribute to the rapidly growing literature on authoritarian information control.

Abstract
Most cross-national human rights datasets rely on human coding to produce yearly, country-level indicators of state human rights practices. Hand-coding the documents that contain the information on which these scores are based is tedious and time-consuming, but has been viewed as necessary given the complexity and detail of the information contained in the text. However, advances in automated text analysis have the potential to streamline this process without sacrificing accuracy. In this research note, we take the first step in creating this streamlined process by employing a supervised machine learning automated coding method that extracts specific allegations of physical integrity rights violations from the original text of country reports on human rights. This method produces a dataset including 163,512 unique abuse allegations in 196 countries between 1999 and 2016. This dataset and method will assist researchers of physical integrity rights abuse because it will allow them to produce allegation-level human rights measures that have previously not existed and provide a jumping-off point for future projects aimed at using supervised machine learning to create global human rights metrics.