There is an ongoing debate about whether human rights standards have changed over the last 30 years. The evidence for or against this shift relies upon indicators created by human coders reading the texts of human rights reports. To help resolve this debate, we suggest translating the question of changing standards into a supervised learning problem. From this perspective, the application of consistent standards over time implies a time-constant mapping from the textual features in reports to the human coded scores. Alternatively, if the meaning of abuses have evolved over time, then the same textual features will be labeled with different numerical scores at distinct times. Of course, while the mapping from natural language to numerical human rights score is a highly complicated function, we show that these two distinct data generation processes imply divergent overall patterns of accuracy when we train a wide variety of algorithms on older versus newer sets of observations to learn how to automatically label texts with scores. Our results are consistent with the expectation that standards of human rights have changed over time.

Automated text analysis allows researchers to analyze large quantities of text. Yet, comparative researchers are presented with a big challenge: across countries people speak different languages. To address this issue, some analysts have suggested using Google Translate to convert all texts into English before starting the analysis (Lucas et al. 2015). But in doing so, do we get lost in translation? This paper evaluates the usefulness of machine translation for bag-of-words models—such as topic models. We use the europarl dataset and compare term-document matrices (TDMs) as well as topic model results from gold standard translated text and machine-translated text. We evaluate results at both the document and the corpus level. We first find TDMs for both text corpora to be highly similar, with minor differences across languages. What is more, we find considerable overlap in the set of features generated from human-translated and machine-translated texts. With regard to LDA topic models, we find topical prevalence and topical content to be highly similar with again only small differences across languages. We conclude that Google Translate is a useful tool for comparative researchers when using bag-of-words text models.

Does treatment mode matter in studies of the effects of candidate race or ethnicity on voting decisions? The assumption implicit in most such work is that such treatment mode differences are either small and/or theoretically well understood, so that the choice of how to signal the race of a candidate is largely one of convenience. But this assumption remains untested. Using a nationally representative sample of white voting-age citizens and a modified conjoint design, we evaluate whether signaling candidate ethnicity with ethnic labels and names results in different effects than signaling candidate ethnicity with ethnically identifiable photos and names. Our results provide strong evidence that treatment-mode effects are substantively large and statistically significant. Further, these treatment-mode effects are not consistent with extant theoretical accounts. These results highlight the need for additional theoretical and empirical work on race/ethnicity treatment-mode effects.

The Comparative Manifesto Project (CMP) provides the only time series of estimated party policy positions in political science and has been extensively used in a wide variety of applications. Recent work (e.g., Benoit, Laver, and Mikhaylov 2009; Klingemann et al. 2006) focuses on nonsystematic sources of error in these estimates that arise from the text generation process. Our concern here, by contrast, is with error that arises during the text coding process since nearly all manifestos are coded only once by a single coder. First, we discuss reliability and misclassification in the context of hand-coded content analysis methods. Second, we report results of a coding experiment that used trained human coders to code sample manifestos provided by the CMP, allowing us to estimate the reliability of both coders and coding categories. Third, we compare our test codings to the published CMP “gold standard” codings of the test documents to assess accuracy and produce empirical estimates of a misclassification matrix for each coding category. Finally, we demonstrate the effect of coding misclassification on the CMP's most widely used index, its left-right scale. Our findings indicate that misclassification is a serious and systemic problem with the current CMP data set and coding process, suggesting the CMP scheme should be significantly simplified to address reliability issues.

The accuracy of U.S. Social Security Administration (SSA) demographic and financial forecasts is crucial for the solvency of its Trust Funds, other government programs, industry decision-making, and the evidence base of many scholarly articles. Because SSA makes public insufficient replication information and uses antiquated statistical forecasting methods, no external group has ever been able to produce fully independent forecasts or evaluations of policy proposals to change the system. Yet, no systematic evaluation of SSA forecasts has ever been published by SSA or anyone else—until a companion paper to this one. We show that SSA's forecasting errors were approximately unbiased until about 2000, but then began to grow quickly, with increasingly overconfident uncertainty intervals. Moreover, the errors are largely in the same direction, making the Trust Funds look healthier than they are. We extend and then explain these findings with evidence from a large number of interviews with participants at every level of the forecasting and policy processes. We show that SSA's forecasting procedures meet all the conditions the modern social-psychology and statistical literatures demonstrate make bias likely. When those conditions mixed with potent new political forces trying to change Social Security, SSA's actuaries hunkered down, trying hard to insulate their forecasts from strong political pressures. Unfortunately, this led the actuaries into not incorporating the fact that retirees began living longer lives and drawing benefits longer than predicted. We show that fewer than 10% of their scorings of major policy proposals were statistically different from random noise as estimated from their policy forecasting error. We also show that the solution to this problem involves SSA or Congress implementing in government two of the central projects of political science over the last quarter century: (1) transparency in data and methods and (2) replacing with formal statistical models large numbers of ad hoc qualitative decisions too complex for unaided humans to make optimally.

Electoral competitiveness is a key explanatory construct across a broad swath of phenomena, finding application in diverse areas related to political incentives and behavior. Despite its frequent theoretical use, no valid measure of electoral competitiveness exists that applies across different electoral and party systems. We argue that one particular type of electoral competitiveness'electoral risk'can be estimated across institutional contexts and matters most for incumbent behavior. We propose, estimate, and make available a cross-nationally applicable measure for elections in 22 developed democracies between 1960 and 2011. Unlike extant alternatives, our measure captures vote volatility and is constructed at the party (not system) level, exogenous to most policy predictors, and congruent with the perceptions and incentives of policy-makers.

This article investigates inconsistency and invalid statistical inference that often characterize dynamic panel analysis in international political economy. These econometric concerns are tied to Nickell bias and cross-sectional dependence. First, we discuss how to avoid Nickell bias in dynamic panels. Second, we put forward factor-augmented dynamic panel regression as a means for addressing cross-sectional dependence. As a specific application, we use our methods for an analysis of the impact of terrorism on economic growth. Different terrorism variables are shown to have no influence on economic growth for five regional samples when Nickell bias and cross-dependence are taken into account. Our finding about terrorism and growth is contrary to the extant literature.

Questions about racial or ethnic group identity feature centrally in many social science theories, but detailed data on ethnic composition are often difficult to obtain, out of date, or otherwise unavailable. The proliferation of publicly available geocoded person names provides one potential source of such data'if researchers can effectively link names and group identity. This article examines that linkage and presents a methodology for estimating local ethnic or racial composition using the relationship between group membership and person names. Common approaches for linking names and identity groups perform poorly when estimating group proportions. I have developed a new method for estimating racial or ethnic composition from names which requires no classification of individual names. This method provides more accurate estimates than the standard approach and works in any context where person names contain information about group membership. Illustrations from two very different contexts are provided: the United States and the Republic of Kenya.

Electoral rules have the potential to affect the size and composition of the voting public. Yet scholars disagree over whether requiring voters to register well in advance of Election Day reduces turnout. We present a new approach, using web searches for “voter registration” to measure interest in registering, both before and after registration deadlines for the 2012 U.S. presidential election. Many Americans sought information on “voter registration” even after the deadline in their state had passed. Combining web search data with evidence on the timing of registration for 80 million Americans, we model the relationship between search and registration. Extrapolating this relationship to the post-deadline period, we estimate that an additional 3–4 million Americans would have registered in time to vote, if deadlines had been extended to Election Day. We test our approach by predicting out of sample and with historical data. Web search data provide new opportunities to measure and study information-seeking behavior.