less) or the end of transition period defined by the lack of stable political institutions (denoted by a standardised authority score). Following Hegre et al. (2001), I then code Proximity of Regime Transition as 2^(-years since regime transition/X). I chose 1 as the value of X, which assumes that the impact of a regime transition on the probability of domestic armed conflict is initially high and then reduced at a constant rate with a half-life of one year.

# **Electoral System**

A term measuring the inclusiveness of electoral systems is constructed on the basis of data from Golder’s (2005) dataset _Democratic Electoral Systems Around the World_ , 1946–2000. I basically adopt the idea presented in Reynal-Querol (2002a,b), constructing an ordinal variable ranging four dummies with respect to political inclusiveness. However, I base my measure on Golder’s definition of the electoral system followed in the assembly and type of the executive: The variable takes the value ‘0’ if the system is not free (i.e. if it has a POLITY score of -6 or less or is coded as an autocracy by Golder (2005), ‘1’ if it has a majoritarian system, ‘2’ if it has a mixed system, and ‘3’ if it has a proportional system. As noted by Reynal-Querol (2002a: 45), the election of a president is by definition by majority rule, hence what can make a difference is the voting rule followed in the assembly. Hence, presidential systems which use a proportional or mixed voting rule in the election of the assembly are coded as ‘2’ in my variable for inclusive electoral system. Otherwise, they are coded as ‘1’. Since the codings of electoral and institutional systems are from the end of each year, the variable is lagged one year in order to determine causality. There are about 15% missing observations. Most of these are due to the fact that the voting data only go to 2000 (or 2001 when lagged). In order to test whether the level of political inclusiveness influences the relationship between horizontal inequality and civil conflict, I also include an interaction term multiplying the political inclusiveness and regional HIs.

# **Political Exclusion of Minorities**

Drawing on the _Discrimination Dataset_ of the Minorities at Risk (MAR) Project (Davenport, 2003), I include a term for political exclusion of minority groups in a given year. I use the variable POLDIS, which is originally coded for each minority group as an ordinal variable ranging from 0 (no discrimination) to 4 (exclusion/repressive policy: public policies substantially restrict the group’s political participation by comparison with other groups). Aggregating this information to the country level I multiply the discrimination index with the population share of the minority discriminated against. In case of several minorities the sum of all population weighted discrimination indices is taken.<sup>16</sup> For my sample, the new continuous variable ranges from 0 (e.g. Tanzania, various years) to 3.56 (Rwanda, 1994). I also include interaction terms multiplying the political exclusion and regional HIs.

> 16 See Neumayer (2003) for a similar application of the MAR data with regard to economic group discrimination at the countrylevel.

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