# **_Key Independent Variables: Natural Resources and Public Spending_**

**Oil revenue:** to operationalize oil revenue, we follow Ross (2012) and use his oil-gas value per capita<sup>15</sup> . The data measures the value of oil and gas production per capita, multiplying the amount of oil gas production in a country with current oil and gas prices.<sup>16</sup>

**General government spending:** we use general government final consumption expenditure relative to GDP (WDI). This includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes expenditure on national defense and security, but excludes government military expenditures.

**Military spending:** the Correlates of War Project (COW) provides information on total military expenditure that we scale by total population.<sup>17</sup>

**Welfare spending:** this includes total health, social security and education expenditures. Total health expenditure (public and private) is based on the WDI, complemented with data from the IMF and Kugler et al. (2000). Social security expenditure includes public pensions and unemployment benefits. We use for this variable the data provided by Kugler et al. (2000) and Burgoon (2006). The education spending variable is the total public expenditure (current and capital) on education expressed as a percentage of GDP in a given year. We use education spending from the IMF to complement the WDI data. Following Burgoon (2006) and Taydas and Peksen (2012), we create a welfare spending variable by combining the health, social security, and education spending indicators to see the impact of aggregate social spending and oil on conflict.

Missing observations in the welfare spending variable, especially in developing countries, is an important issue that may pose difficulties in interpreting results.<sup>18</sup> To address this issue and for the purpose of comparison and replication, we use the same approach as in Taydas and Peksen (2012) and use Stata’s ICE multiple imputation procedure. The procedure specifically imputes

> 15 The data is available online at http://dvn.iq.harvard.edu/dvn/dv/mlross (accessed on March 14th, 2013). Previous work shows that even when looking at a wider range of natural resources, it is hydrocarbons that affect civil war onset (Fearon, 2005; de Soysa and Neumeyer, 2008).

> 16 This data has advantages over other measures that scale oil production to GDP or exports, both of which can be driven down by risk of civil war. More importantly, it gives a sense of the size of the oil wealth available to the government for spending. We use both logged and non-logged oil variables in our analyses. For models using nonlogged oil variable, one may suspect that results are driven by a handful of outliers. Yet, this is not the case for our results. Excluding outliers that have more than 10,000 dollars oil-gas value per capita from the models did not change the main results. In Tables 1 and 2, we report models using the non-logged oil variable without excluding outliers. In the Appendix, we show results using the logged oil variable, including replications of the results of Ross (2012).

> 17 We prefer scaling military spending by total population because the GDP data from sources other than the COW are not in the same metric. For comparison, in our summary Table 1 we scale military spending with GDP data from the Penn World Table 7.1.

> 18 Education spending, for instance, has significantly more missing values than health and social security spending.

11