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Conceptualizing and Measuring Ethnic Politics: An Institutional Complement to Demographic, Behavioral, and Cognitive Approaches

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Abstract

The influence of “ethnic politics” has been demonstrated in a range of empirical studies of economic growth, violence, and public goods provision. While others have raised concerns about the measurement of ethnic variables in these works, we seek to situate such discussions within a more thoroughgoing conceptual analysis. Specifically, we argue that four conceptual approaches—demographic, cognitive, behavioral, and institutional—have been used to develop theories in which the mechanism that relates causes to outcomes is ethnic political competition. Within this literature, we believe that institutional approaches have been relatively under-appreciated, and we attempt to address that imbalance. We begin by critically reviewing the three main ways in which ethnic variables have been specified and operationalized, delineating the assumptions and trade-offs underlying their use. Next, we describe an institutional approach to the study of ethnic politics, which focuses on the rules and procedures for differentiating ethnic categories. We propose some new indices based on this latter approach that might be developed and used in future research. Subsequently, we analyze the relationship between each of these approaches and patterns of ethnic political competition in a set of six country cases, highlighting their strengths and weaknesses, as well as theoretical links between them.

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Notes

  1. Calculated according to the formula \( \left[ {{\text{ELF}} = {1} - \sum\nolimits_{{i = 1}}^n {s_i^2} } \right] \), where s i indicates the population share represented by each group, i = (1,…, n).

  2. It is important to note here that while the measure of cross-cuttingness is primarily based on counts of ethnic groups and we therefore classify it within the demographic family of approaches, in so far as it also includes data on income differences between groups and geographic concentration, the index builds on and could also be potentially grouped with the hybrid approaches that we will discuss later in this article.

  3. Habyarimana et al. (2009: 156) provide a useful delineation of the mechanisms by which ethnic demography has been hypothesized to lead to reduced provision of a range of different public goods. It is notable that two of the three families of mechanisms that they identify—preferences and technology—are closely linked to competition between ethnic groups. For example, the absence of other-regarding or shared preferences over outcomes as well as the process is hypothesized to generate competition between members of different ethnic groups. A similar competitive dynamic can be seen to underlie the technology mechanism whereby individuals from different ethnic groups are hypothesized to be less likely to function, understand or think they understand, engage and track down each other.

  4. Of course, such approaches are also used to study the individual-level causes and consequences of ethnic identification, but this article focuses on aggregate or macrolevel relations.

  5. For example, there is considerable variation on questions about national identity depending on whether they are structured in terms of eliciting “closeness,” “belonging,” or “pride.” In general, far more respondents claimed that they felt “close” to their country (International Social Survey 1995) as compared to “belonging” to it (European Values Study 1990). In Hungary, for instance, 96 % said they felt close to their nation (ISS 1995) but only 63 % felt that they belonged to it (EVS 1990). Even within the same survey, for example, the European Values Study of 1990, there are remarkable differences in the proportion of people who said they were proud of their nationality and those that indicated a strong sense of identification with their country. In the USA, 98 % of people said they were proud to be American, but only 58 % said they felt a sense of belonging to the USA. The difference is even more conspicuous for a country such as Latvia where 92 % of respondents indicate pride in their country but only 15 % felt that they belonged to it. It is far from clear which of these is the best measure of national identity and depending on which one you use, countries line up very differently.

  6. For example, General Inquirer, Diction 5.0, VBPro, Yoshikoder and Wordstat.

  7. The first measure is an extension of the Montalvo and Reynal-Querol (2005) and Esteban and Ray (1994) polarization indices, discussed in the section on “Demographic approaches,” and captures the extent to which economic resources, measured by the household assets of respondents, and social resources, measured by respondents’ years of education are clustered by ethnic group. Secondly, she introduces a two-group measure called HI (Horizontal Inequality), which ranges from 0 (perfect equality in resources between groups) to 1 (one group has all assets/education).

  8. We seek to identify particular cleavages and to code censuses in earlier decades, and therefore, we could not simply use Morning’s data for our purposes.

  9. In order to determine if institutions codified ethnicity, we attempted, wherever possible, to obtain actual primary documents. As a second step, we tried to attain authoritative secondary sources based on primary analyses of relevant documents and data and/or by contacting foreign nationals, diplomatic representatives, and scholarly authorities.

  10. While religious and linguistic categories are largely self-explanatory, we classify race categories as those ethnic categories explicitly described in terms of physical characteristics (for example, color) and/or referred to as “race” by given state institutions; caste categories as those linked to a codified caste system, recognized from religious scripture or those referred to as “caste” by state institutions; indigenous categories as those groups referred to as “indigenous,” “original inhabitant,” or “natives,” by institutions, except when the group(s) are also commonly linked to one of the other categories already described (for example, “Natives” are considered a race group in South Africa), and ethnic/other is a residual category used when state institutions refer to specific ethnic or “tribal” groups that could not be classified in one of the abovementioned categories (for instance, an ethnic group that is not distinguished by use of a single language). For each category, we employed standardized sourcebooks to identify any evidence that the second largest group constituted at least 1 % of the population at any moment in time and, if not, we ignored that category for the purposes of our analysis. In other words, if more than 99 % of a country belonged to a particular religious faith, we did not investigate and do not report data on institutionalized ethnicity in terms of religion because it is not a potential boundary between substantially large groups of citizens.

  11. While the question of the “agency” of the adivsasis in the Maoist insurgency is a deeply contested one (Nigam 2010); this is a conflict that is at the very least fought in the name of the indigenous people and the combatants are drawn overwhelmingly from indigenous groups.

  12. Even though there were physiological stereotypes associated with the Hutus and Tutsis, the task of distinguishing individuals from the two communities was complicated by the high rates of intermarriage. The practice of marking the bearer’s ethnic origin on official identity documents greatly facilitated the conduct of the genocide by making it “easy to identify Tutsi” (Longman 2001: 355). Longman writes that “Since every Rwandan was required to carry an identity card, people who guarded barricades demanded that everyone show their cards before being allowed to pass. Those with “Tutsi” marked on their cards were generally killed on the spot” (Longman 2001: 355).

  13. The MAR dataset also identifies a “moderate” risk for rebellion against the state by the Amazonian Indians.

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  1. Department of Politics, Princeton University, Princeton, NJ, USA

    Evan S. Lieberman

  2. Department of Government, Harvard University, Cambridge, MA, USA

    Prerna Singh

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Lieberman, E.S., Singh, P. Conceptualizing and Measuring Ethnic Politics: An Institutional Complement to Demographic, Behavioral, and Cognitive Approaches. St Comp Int Dev 47, 255–286 (2012). https://doi.org/10.1007/s12116-012-9100-0

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