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Cross-Cuttingness, Cleavage Structures and Civil War Onset

Published online by Cambridge University Press:  28 September 2010

Joel Sawat Selway
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Abstract

This article seeks to further our understanding of how social structure affects the onset of civil war. Existing studies to date have been inconclusive, focusing only on single-cleavage characteristics of social structure, such as ethnic or religious fractionalization. This study argues that models that do not take into account the relationship between cleavages (or cleavage structure) are biased and thus reach faulty conclusions. With the focus on the cleavages of ethnicity and religion, the effects of two characteristics of cleavage structure on civil war onset (cross-cuttingness and cross-fragmentation) are defined and tested. A new index of ethno-religious cross-cuttingness (ERC), derived from national public opinion surveys, reveals that ERC is a significant determinant of civil war onset when interacted with ethnic fractionalization.

Type
Research Article
Information
British Journal of Political Science , Volume 41 , Issue 1 , January 2011 , pp. 111 - 138
Copyright
Copyright © Cambridge University Press 2010

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References

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23 All the previously cited cleavages have been or can be further dissected: ethnicity into race and language, religion into sects and denominations, and geography into region, rural/urban residence and centre/periphery.

24 I explicitly separate the two cleavages as distinct, while recognizing that much recent work on ethnicity incorporates religion into a broader definition of ethnicity. See: Varshney, A., Ethnic Conflict and Civic Life (New Haven, Conn.: Yale University Press, 2002)Google Scholar; Chandra, K., Why Ethnic Parties Succeed (Cambridge: Cambridge University Press, 2004)CrossRefGoogle Scholar; Posner, Daniel N., ‘Measuring Ethnic Fractionalization in Africa’, American Journal of Political Science, 48 (2004), 849–863CrossRefGoogle Scholar. This decision comes with trade-offs, most important of which is that in countries where ethnicity and religion seem virtually inseparable, such as Malaysia, we cannot know whether a measure of cross-cuttingness is meaningful. More broadly, we can categorize this in the long-standing challenge of measuring the salience of cleavages in society.

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27 There is a growing sociological literature, under the rubric of the ‘religious economy’, which argues that switching religions (especially denominations within religions) is more common than many think. In my dataset, for most of the countries that appeared in more than one survey over time, there appeared to be minimal changes in the scores. On the whole, this supports my assertion that religious and ethnic identity are difficult, though not impossible, to change, especially when they are the bases for conflict.

28 A group is a category of identity/membership within a single cleavage, such as Christian and Muslim within the religion cleavage.

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41 Collier and Hoeffler, ‘Greed and Grievance in Civil War’. Annett also offers a measure of social fractionalization, which captures fractionalization on both the religious and ethnic cleavages - defined as one half times the value of ethnic fractionalization plus one half times the value of religious fractionalization. To date, I have not seen this measure used in a study of civil war. See Annett, Anthony, ‘Social Fractionalization, Political Instability, and the Size of Government’, IMF Staff Papers, 48 (2001)Google Scholar.

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43 This term is my own. Past references to this same characteristic/phenomenon include cross-cutting cleavages, cross-cutting circles, criss-cross pattern of cleavage structure and intersection of cleavages. The antonym (reinforcing) has also been referred to as overlapping cleavages, consolidation of cleavages, superimposition of lines of differentiation and correspondence of cleavages.

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55 In between the individual and cleavage levels of analysis is the group-level dynamic. Cross-fragmentation, furthermore, dissolves in-groups and diminishes group pressures. To motivate measures of cross-cuttingness, I thus pose the question: ‘In what way should groups on a cleavage x be distributed amongst groups on a second cleavage y to maximize cross-cuttingess?’ This question motivates at least two additional measures – cross-proportionalization and cross-bipolarization – and indeed is able to motivate our first two measures also. Indices for these measures have not yet been computed, but are forthcoming. See Selway, ‘Cross-cutting Cleavages’.

56 Blau and Schwartz, Crosscutting Social Circles.

57 Lipset, Political Man.

58 Rae and Taylor, The Analysis of Political Cleavages.

59 Since the statistics literature is unclear on which measure is superior, I opt for the more widely used Cramer’s V. See: Agresti, Alan, Categorical Data Analysis (New York: Wiley-Interscience, 2002)CrossRefGoogle Scholar.

60 O stands for the observed frequencies in a simple cross-tabulation of the religious and ethnic cleavages; E stands for expected frequencies, calculated by multiplying the two marginal frequencies from the original cross-tabulation; i refers to cells in the contingency table.

61 However, neither heterogeneity nor sub-group fractionalization are sensitive to group identity on both cleavages, and thus do not capture cross-cuttingness. A simple example illustrates why sub-group fractionalization is not a measure of cross-cuttingness. Consider Society A with its two ethnic groups, black and white, each composing 50 per cent of the population, and its two religions, Christian and Muslim, each also composing 50 per cent of the population. The sub-group fractionalization score for Society A is 0.5. In a second society, B, we could have a society with the same sub-group fractionalization score, 0.5, but with a huge difference in the structure of its cleavages. Specifically, in Society B, there is just one ethnic group split 50–50 between religions. We can thus see where subgroup fractionalization fails to capture the concept of cross-cuttingness: it is not sensitive to the identities of the groups that compose the subgroups. Selway identifies this as a cross-cuttingness axiom. See Selway, ‘Cross-cutting Cleavages’.

62 Collier and Hoeffler imply that this relationship should be interactive also. See Collier, and Hoeffler, , ‘Greed and Grievance in Civil War’, p. 570Google Scholar.

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67 Takashi Inoguchi et al., AsiaBarometer Survey Data (2003 or 2004) [computer file]. AsiaBarometer Project (http://www.asiabarometer.org/) [producer and distributor], Accessed December 2006. AsiaBarometer is a registered trademark of Professor Takashi Inoguchi, Chuo University, Japan, Director of the AsiaBarometer Project, Room 2137, Building Two, Chuo University, Korakuen Campus, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan.

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70 Appendix available at author’s website, http://www.umich.edu/~jselway.

71 See author’s website http://www.umich.edu/~jselway.

72 The raw data, including tables from these analyses, will eventually be available at the author’s website http://www.umich.edu/~jselway.

73 This comparison was necessarily restricted to African countries, and thus the Afrobarometer and World Values Survey, since Posner’s index concentrates on Africa. The results are nevertheless encouraging. See: Posner, ‘Measuring Ethnic Fractionalization in Africa’. Full analysis is available in the appendix at http://www.umich.edu/~jselway.

74 There may be additional problems of representativeness in terms of the self-selection bias inherent in the types of surveys I am using. While this may exclude countries that have recently experienced civil war, the data go back far enough to diminish this problem.

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77 See Beck, Nathaniel, Katz, Jonathan N. and Tucker, Richard, ‘Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable’, American Journal of Political Science, 42 (1998), 1260–1288CrossRefGoogle Scholar. I also experiment with two alternative ways to control for time dependence. First, I use a lagged variable for the occurrence of war as Fearon and Laitin do (see Fearon and Laitin, ‘Ethnicity, Insurgency, and Civil War’). In addition, I follow Hegre et al.’s method of using a variable that assumes that the effect of a previous conflict is decaying over time according to the formula exp{(-peace years)/X}, where ‘peace years’ is the number of years since a country experienced a civil war (see Hegre and Sambanis, ‘Sensitivity Analysis of Empirical Results on Civil War Onset’. The value X determines the rate of decay. I follow Toset et al. and set X to 4, implying that the risk of civil war is halved approximately every 3 years (see Toset, H. P. W., Gleditsch, N. P. and Hegre, H., ‘Shared Rivers and Interstate Conflict’, Political Geography, 19 (2000), 971–996CrossRefGoogle Scholar. This decaying variable takes on values close to 1 immediately after the end of a conflict and approaches 0 over time. The major results are not appreciably affected by these various approaches.

78 Gleditsch, N. P., Wallensteen, P., Eriksson, M., Sollenberg, M. and Strand, H., ‘Armed Conflict 1946–2001: A New Dataset’, Journal of Peace Research, 39 (2002), 615–637CrossRefGoogle Scholar. Sometimes referred to as the ‘Armed Conflict Data’, the PRIO data I use were restricted to conflicts with at least 1,000 battle deaths by Urdal. I thank him for his generous sharing of data (see Urdal, ‘A Clash of Generations?’).

79 Appendix available at http://www.umich.edu/~jselway.

80 Collier, and Hoeffler, , ‘Greed and Grievance in Civil War’, Oxford Economic Papers, 56 (2004)CrossRefGoogle Scholar.

81 Collier, and Hoeffler, , ‘Greed and Grievance in Civil War’. World Bank Policy Research Working Paper, No. 2355Google Scholar.

82 The exact definition is a country with territory holding at least 10,000 people and separated from the land area containing the capital city either by land or by 100 kilometres of water. These were coded as ‘non-contiguous’ (see Fearon and Laitin, ‘Ethnicity, Insurgency, and Civil War’).

83 I also include a measure of democracy based on the Polity IV index, which ranges from +10, most democratic, to −10, least democratic (Democracy). All these variables are taken from Fearon and Laitin. I thank them for their generous sharing of data (see Fearon and Laitin, ‘Ethnicity, Insurgency, and Civil War’).

84 Coefficients on each of the single-dimension characteristics were also statistically insignificant, as were various combinations of pairs of these variables. For lack of space, these results are not displayed.

85 In addition, I ran models using all the other indices of civil war used in this article and all other social structure variables. The conclusion is that F&L’s results on Oil and Income are not robust.

86 I note that I also find mixed results for ethnic and religious bipolarization measures, for which Montalvo and Reynal-Querol show strong empirical support (see Montalvo and Reynal-Querol, ‘Ethnic Polarization, Potential Conflict, and Civil Wars’).

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88 All excluded figures will be made available on the author’s website.

89 Full results are available on the author’s website at http://www.umich.edu/~jselway.

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91 See http://globalpolicy.gmu.edu/pitf/

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94 For a more in-depth discussion on the important differences amongst these different datasets, see Sambanis, Nicholas, ‘What Is Civil War? Conceptual and Empirical Complexities of an Operational Definition’, Journal of Conflict Resolution, 48 (2004), 814–858CrossRefGoogle Scholar. These additional data are perhaps best seen as additional robustness tests.

95 Sambanis, Nicholas, ‘Do Ethnic and Nonethnic Civil Wars Have the Same Causes? A Theoretical and Empirical Inquiry (Part 1)’, Journal of Conflict Resolution, 45 (2001), 259–282CrossRefGoogle Scholar.

96 All results are available in the appendix at http://www.umich.edu/~jselway.

97 Indeed, the author is currently compiling such data. A more comprehensive project to map the geographic location of ethnic groups is currently being directed by Lars-Erik Cederman and various co-authors. See: Cederman, Rød and Weidmann, ‘Geo-Referencing of Ethnic Groups’.