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Measuring Politically-relevant Identity, With and Without Groups

Quantitative scholarship on civil conflict still largely relies upon the ethnic group as the foundation for measures of politically-relevant diversity and, in particular, identitybased political inclusion. However, ethnicity remains notoriously difficult to measure: even cutting-edge analyses are subject to the issues of intraand inter-ethnic variation in identity salience that plagued earlier work. Here I propose a new way to measure identitybased exclusion. Specifically, I use latent variable models to combine data from both the Ethnic Power Relations Project, which uses the demographic size of politically-relevant ethnic groups to operationalize inclusion; and the Varieties of Democracy Project, which measures overall identity-based inclusion without directly accounting for demographic group size. The latent variable models combine insights from both measurement approaches, ameliorating concerns about using either strategy in isolation. In addition to providing cross-nationally cohesive data on identity-based exclusion for future work, these models provide a framework for scholars to build their own theoretically-driven models of politically-relevant diversity and inclusion. A wide body of literature links the exclusion of politically-relevant ethnic groups to important social-scientific outcomes, particularly civil conflict (Cederman, Wimmer & Min 2010, Cederman, Gleditsch & Buhaug 2013, Cederman, Hug, Schädel & Wucherpfennig 2015). In this tradition, scholars first enumerate the universe of politically-relevant ethnic groups present at a given level of analysis, then determine the degree and form in which these groups are excluded or included. However, as Kanchan Chandra (2006, 2012) has detailed, traditional conceptualizations of ethnic identity yield contradictory criteria for membership in an ethnic group, and belie significant intraand inter-ethnic variation in the attributes that make some identities more salient than others. Exclusion itself may vary substantially within and across groups, often in correlation with the salience of identity. Measures of exclusion based on ethnic group demographics may therefore have difficulty accounting for the actual level of identity-based exclusion present at any given level of analysis. An alternative approach to measuring identity-based exclusion involves coding its overall prevalence within a society, without first enumerating the groups it concerns. Such an approach avoids making assumptions about the specific social identities relevant to identity-based inclusion, as well as the degree to which members of these identity groups face similar levels of inclusion. By avoiding these assumptions, such data may 1) better capture the extent of exclusion within and across groups and 2) be more sensitive to changes on these metrices. However, such an approach has its own disadvantages. First, the demographic strength of cohesive groups is important in determining whether or not exclusion leads to outcomes of interest. By not explicitly incorporating group demographics into the measurement process, this approach cannot account for this important element of identity politics. Second, the absence of clear anchoring on ethnic demographics exacerbates concerns about cross-national comparability in the coding of this phenomenon. In this paper, I use Bayesian latent variable modeling techniques to combine these two approaches, leveraging the strengths of both to provide longitudinally and cross-nationally comparable measures of identity-based exclusion. To do so, I first conceptually and empirically illustrate the advantages and disadvantages of both existing approaches, using the current gold standards in the literature: Ethnic Power Relations (EPR) and Varieties of Democracy (V–Dem). The EPR Project measures the demographic size of relevant identity groups in a territory, and explicitly focuses on ethnic groups. In contrast, the V– Dem data collection process does not involve enumerating groups. Instead, expert coders use Likert scales to report the general level of identity-based inclusion; these data are then aggregated using a Bayesian Ordinal Item Response Theory model (Pemstein et al. 2018). As expected, I find that V–Dem and EPR data strongly correlate and generally reveal similar cross-national and within-country trends in identity-based inclusion. However, the analyses also reveal the V–Dem data provide nuanced information about change