education level. All of the DHS surveys used in this analysis are nationally representative. Table A2 provides a complete list of the surveys (countries and survey years) used in this analysis to generate the HI variables.

# **Socioeconomic Horizontal Inequalities**

The DHS data provide a rich set of large, representative surveys with nearly identical questionnaires, hence presenting a golden opportunity for generating objective measures of inequalities across identity groups. However, a challenge is that the DHS surveys generally lack information on income or consumption expenditures. I overcome the absence of such data by using the information collected on respondent and household characteristics. More specifically, I use two different indicators of socioeconomic welfare to calculate the inequality measures and evaluate the hypotheses: a household asset index, and a variable counting the years of education for each respondent.

First, I construct a household asset index, generated on the basis of the following variables from the DHS surveys: v119–v125 (dummies for whether or not each household has electricity, a radio, a television, a refrigerator, a bicycle, a motorcycle and/or a car). My second indicator, schooling inequality, is based on the variable v133 (years of education completed).

I measure horizontal inequalities in household assets and educational level using three different group identifiers from the DHS surveys: ethnicity (v131), religion (v130), and region of residence (v101). First I calculate the socioeconomic divisions (HIs) between the two largest ethnic groups in each country. Second I do the same for the two largest religious groups.<sup>13</sup> Finally, I examine horizontal inequality as ratios of welfare scores between the region in which the capital is located and the rest of the country.<sup>14</sup> This measure is calculated on the basis of the formula introduced by Østby (2005a): where _M_ is the maximum number of household assets; _A1_ refers to mean asset score of group 1 (e.g. the capital region) and _A2_ is the corresponding mean score of group 2 (e.g. the rest of the country). This provides a continuous variable potentially ranging from 0 (the lowest level of asset inequality between capital region and the rest of the country) to 1 (the highest level of such inequality). The measure of educational inequality is generated along the same lines. For countries where multiple surveys have been undertaken, I use linear interpolation to estimate inequalities for the intervening years. Otherwise I copy the results from the survey closest in time to the subsequent years, within the period 1986–2003 in order to increase the sample to a more manageable size. This could imply a problem of endogeneity (i.e. HI could result from former conflict instead of vice versa), and it would of course be preferable to have annual data on group inequalities for each country. However, as stated earlier, group inequalities tend to be quite stable over time, which is also evident from the data for some of the countries which have had several surveys during the period 1986–2003. Figures 1 and 2 depict the level of inequality in

> 13 The group sizes are based on weighted measures of ethnic and religious groups (see Rutstein & Rojas (2003) for details on DHS weighing procedures)

> 14 The capital is coded based on the _CIA World Factbook_ , various editions.

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