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AbstractScholars often use language to proxy ethnic identity in studies of conflict and separatism. This conflation of language and ethnicity is misleading: language can cut across ethnic divides and itself has a strong link to identity and social mobility. Language can therefore influence political preferences independently of ethnicity. Results from an original survey of two post-Soviet regions support these claims. Statistical analyses demonstrate that individuals fluent in a peripheral lingua franca are more likely to support separatism than those who are not, while individuals fluent in the language of the central state are less likely to support separatist outcomes. Moreover, linguistic fluency shows a stronger relationship with support for separatism than ethnic identification. These results provide strong evidence that scholars should disaggregate language and ethnic identity in their analyses: language can be more salient for political preferences than ethnicity, and the most salient languages may not even be ethnic.

Expert-coded datasets provide scholars with otherwise unavailable data on important concepts. However, expert coders vary in their reliability and scale perception, potentially resulting in substantial measurement error. These concerns are acute in expert coding of key concepts for peace research. Here I examine (1) the implications of these concerns for applied statistical analyses, and (2) the degree to which different modeling strategies ameliorate them. Specifically, I simulate expert-coded country-year data with different forms of error and then regress civil conflict onset on these data, using five different modeling strategies. Three of these strategies involve regressing conflict onset on point estimate aggregations of the simulated data: the mean and median over expert codings, and the posterior median from a latent variable model. The remaining two strategies incorporate measurement error from the latent variable model into the regression process by using multiple imputation and a structural equation model. Analyses indicate that expert-coded data are relatively robust: across simulations, almost all modeling strategies yield regression results roughly in line with the assumed true relationship between the expert-coded concept and outcome. However, the introduction of measurement error to expert-coded data generally results in attenuation of the estimated relationship between the concept and conflict onset. The level of attenuation varies across modeling strategies: a structural equation model is the most consistently robust estimation technique, while the median over expert codings and multiple imputation are the least robust.

The disintegration of the Soviet Union is an essential case for the study of ethnic politics and identity-based mobilization. However, analyses in this article demonstrate that commonly used measures of ethnic diversity and politically relevant group concentration show little consistent relationship with events of ethnic mobilization in Soviet regions during the period 1987-1992. In contrast, the proportion of a regional population that did not speak a metropolitan language has a consistently strong negative relationship with mobilization across these regions. In line with recent work on identity politics, I argue that a lack of proficiency in a metropolitan language marks nonspeakers as outsiders and hinders their social mobility. Regions with many of these individuals thus have a relatively high potential for identity-based mobilization. These findings provide further impetus for looking beyond ethnic groups in measuring identity-based cleavages, and indicate that language can play an important role in political outcomes aside from proxying ethnicity.

The government of the Chuvash Republic, an ethno-federal region of the Russian Federation, used a targeted and symbolic language policy in an attempt to stabilize the position of the republic's titular language while avoiding conflict with local Russophones and the Russian federal government. The resulting policy allowed the republic's government to frame the existence of an autonomous Chuvash republic – as well as the local elite's form of governance – as being essential to the preservation of the Chuvash language and thus the Chuvash people. In this way, it used language politics to strengthen its position vis-à-vis both local constituents and the Russian federal government. However, the limited nature of the government's program has made its gains tenuous in the face of continuing Russian political and cultural recentralization.

The government of the Chuvash Republic, an ethno-federal region of the Russian Federation, used a targeted and symbolic language policy in an attempt to stabilize the position of the republic's titular language while avoiding conflict with local Russophones and the Russian federal government. The resulting policy allowed the republic's government to frame the existence of an autonomous Chubash republic-as well as the local elite's form of governance-as being essential to the preservation of the Chuvash language and thus the Chuvash people. In this way, it used language politics to strengthen its position vis-à-vis both local constituents and the Russian federal government. However, the limited nature of the government's program has made its gains tenuous in the face of continuing Russian political and cultural recentralization. (Nationalities Papers)

AbstractModels for converting expert-coded data to estimates of latent concepts assume different data-generating processes (DGPs). In this paper, we simulate ecologically valid data according to different assumptions, and examine the degree to which common methods for aggregating expert-coded data (1) recover true values and (2) construct appropriate coverage intervals. We find that the mean and both hierarchical Aldrich–McKelvey (A–M) scaling and hierarchical item-response theory (IRT) models perform similarly when expert error is low; the hierarchical latent variable models (A-M and IRT) outperform the mean when expert error is high. Hierarchical A–M and IRT models generally perform similarly, although IRT models are often more likely to include true values within their coverage intervals. The median and non-hierarchical latent variable models perform poorly under most assumed DGPs.

Data sets quantifying phenomena of social-scientific interest often use multiple experts to code latent concepts. While it remains standard practice to report the average score across experts, experts likely vary in both their expertise and their interpretation of question scales. As a result, the mean may be an inaccurate statistic. Item-response theory (IRT) models provide an intuitive method for taking these forms of expert disagreement into account when aggregating ordinal ratings produced by experts, but they have rarely been applied to cross-national expert-coded panel data. We investigate the utility of IRT models for aggregating expert-coded data by comparing the performance of various IRT models to the standard practice of reporting average expert codes, using both data from the V-Dem data set and ecologically motivated simulated data. We find that IRT approaches outperform simple averages when experts vary in reliability and exhibit differential item functioning (DIF). IRT models are also generally robust even in the absence of simulated DIF or varying expert reliability. Our findings suggest that producers of cross-national data sets should adopt IRT techniques to aggregate expert-coded data measuring latent concepts.