Introducing the AMAR (All Minorities at Risk) Data

Birnir, Jóhanna Kristín; Laitin, David D.; Wilkenfeld, Jonathan; Waguespack, David M.; Hultquist, Agatha Skierkowski; Gurr, Ted Robert

doi:10.1177/0022002717719974

Cited by 27 publications

(17 citation statements)

References 31 publications

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“…It is calibrated using the Transborder Ethnic Kin data set . The condition of an 12) homogeneous central state (HOMOG) is calibrated indirectly on basis of different data sources (Birnir et al, 2018;CIA, 2017;Levinson, 1998).…”

Section: Case Selection and Calibrationmentioning

confidence: 99%

Ethnic Conflict Regulation Through Territorial Autonomy – A Configurational Analysis of Success and Failure

Schulte¹

2018

Preprint

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Territorial autonomies (TA) are increasingly implemented as tools forregulating ethnic conflicts. Since there are successful as well as failed cases,the ongoing debate about a general conflict-solving potential is not a veryfruitful one. The article turns towards the analysis of various factors affectingthe success and failure of conflict regulating autonomies. It is argued thatautonomy consolidation requires specific conditions which support theprocess of mutual recognition between ethnic groups. Two causal models aredeveloped and tested on a global data set on all conflict regulatingautonomies using a two-step Qualitative Comparative Analysis (QCA). It isfound that a combination of low horizontal inequalities, a high scope ofautonomy, inclusive institutions and international support lead to autonomysuccess whereas persistent inequalities, a low scope of transferredcompetencies and exclusive institutions result in a return to violent strategies.One important implication of these findings is that favorable conditions forpost-conflict institutional engineering can, at least to some extent, be created.

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Section: Case Selection and Calibrationmentioning

confidence: 99%

Ethnic Conflict Regulation Through Territorial Autonomy – A Configurational Analysis of Success and Failure

Schulte¹

2018

Preprint

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“…Fourth, other types of systematic data should be explored and developed. In particular, efforts that rely on systematic coding of qualitative reports or assessment by independent experts, such as the Ethnic Power Relations (EPR), Minorities at Risk (MAR), and All Minorities at Risk (AMAR) databases (Birnir et al 2018;Cederman et al 2010;Gurr 1993), may be informative. Such sources have strengths and weaknesses, but in situations where major data gaps are due to the political challenges outlined above, such sources also can be invaluable.…”

Section: Resultsmentioning

confidence: 99%

Correction to: Horizontal Inequality and Data Challenges

Canelas

Gisselquist

2019

Soc Indic Res

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Inequality and social exclusion receive considerable contemporary policy attention. In the field of international development, inequality-both vertical (between individuals and households) and horizontal (between groups)-is a core concern in the 2030 Agenda for Sustainable Development. Despite considerable attention to horizontal inequality in both research and policy, there are notable gaps and weaknesses in our empirical knowledge about how it manifests within and across countries and over time. This has implications not only for the rigour with which we can build and test theories in this area, but also for informing policy, monitoring trends, and evaluating the impact of interventions. This article probes what more can be learned from existing survey and census data to address empirical gaps. It argues that key methodological, conceptual, and-in particular-political issues pose persistent challenges for such survey and census data on topics relating to 'ethnicity' broadly defined. These challenges imply not only real limits in the so-called data revolution for sustainable development, but also risks to 'evidence-based' policy making in this area when it relies too heavily on quantitative data. This article serves also as the introductory and framing paper for this special issue.

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“…For many questions, such as for analyses of the onset of separatist mobilization or transitions from nonviolent to violent mobilization, our data must be supplemented with additional variables and merged, depending on the researchers’ preferences, with country-level or group-level data (such as EPR [Vogt et al 2015] or All Minorities at Risk (AMAR) [Birnir et al 2015, 2018]). In this respect, it is important to note that no extant group-level data set provides information on all SDM groups.…”

Section: Uses Of the Sdm Datamentioning

confidence: 99%

SDM: A New Data Set on Self-determination Movements with an Application to the Reputational Theory of Conflict

Sambanis

Germann

Schädel

2017

Journal of Conflict Resolution

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This article presents a new data set on self-determination movements (SDMs) with universal coverage for the period from 1945 to 2012. The data set corrects the selection bias that characterizes previous efforts to code SDMs and significantly expands coverage relative to the extant literature. For a random sample of cases, we add information on state–movement interactions and several attributes of SDM groups. The data can be used to study the causes of SDMs, the escalation of self-determination (SD) conflicts over time, and several other theoretical arguments concerning separatist conflict that have previously been tested with incomplete or inferior data. We demonstrate the usefulness of the new data set by revisiting Barbara Walter’s influential argument that governments will not accommodate SD challengers if they face several potential future challengers down the road because they want to build a reputation for strength. We do not find support for Walter’s reputational theory of separatist conflict.

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Introducing the AMAR (All Minorities at Risk) Data

Cited by 27 publications

References 31 publications

Ethnic Conflict Regulation Through Territorial Autonomy – A Configurational Analysis of Success and Failure

Ethnic Conflict Regulation Through Territorial Autonomy – A Configurational Analysis of Success and Failure

Correction to: Horizontal Inequality and Data Challenges

SDM: A New Data Set on Self-determination Movements with an Application to the Reputational Theory of Conflict

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