Breaking the Waves? Education, Youth Bulges, and Armed Conflict

# _3.2 Control variables_

_Level of development_ is interchangeably proxied by infant mortality rate (IMR) collected from the _World Population Prospects_ (UN, 1999) and a logtransformed measurement for PPP adjusted GDP per capita collected from the Penn World Tables (Heston et al., 2002). The Polity IV data (Marshall & Jaggers, 2000) are used to measure regime type, and the variable ranges from -10 (most autocratic) to 10 (most democratic). _Total population_ size is drawn from the _World Population Prospects_ (UN, 1999) and is log-transformed.

# _3.3 Combined dataset and model_

The above datasets were matched by country and year. In order to do so, the IIASA data, which covers 5-year intervals, was expanded by creating records for the intermediate years. This was done by applying the data for year x to years x-2 through x+2 (e.g. the value for 1975 was assigned to the years 1973 through 1977). As a result, the education variables as well as the age structure variables change step-wise every five years. This approach was chosen over interpolation for reasons of simplicity and because there was no compelling theoretical argument for any particular form of interpolation curve.

The combined dataset covers 124 countries/territories for the years 1970-2000.<sup>4</sup> The key variables used in the analysis are: onset of conflict, years since last conflict, infant mortality rate (IMR), Polity index, dependency ratio, (natural logarithm of) total population, age composition, % of males aged 20-24 years with secondary schooling or higher. Among these variables, the number of missing values is 312 out of a total number of 3,434 observations. All are missing due to lack of information on the Polity index, and for the purpose of this analysis missing values on Polity are set to the sample average (‘0’) adding a dummy variable controlling for imputed values.

The analysis takes the form of a logistic regression, with the onset of an armed internal conflict (defined above) as the dependent variable. All subsequent observations of the same conflict are omitted and we also apply a control variable for time in peace, counting the number of years in peace since the previous conflict. The variable counts time in peace going all the way back to 1945 which is the first year covered by the PRIO/Uppsala conflict data.

The inclusion of a variable measuring the duration in years of the current spell of peace means this approach can be interpreted as a survival analysis (c.f. Singer and Willett, 2003; see Beck et al., 1998 and Raknerud & Hegre, 1997 for applications in the study of armed conflict). While data points within the same peace spell are not originally independent, they are _conditionally_ independent given the inclusion of the 'time since last conflict' variable. Statistical dependence _across_ multiple peace spells experienced by the same country is more difficult to

> 4 The datasets differed in their handling of country partitions and unifications for the period studied, in particular the demographic and education data were not available for historical states. These were reconciled manually. For historical periods of partitioned states (e.g. the Soviet Union, Yugoslavia), education and population measures were constructed based on data from the successor states for which such information was available (the education dataset lacks information for several former Soviet as well as Former Yugoslavian Republics). For states that have unified (e.g. Germany, Yemen, Vietnam), these variables have been constructed by distributing age- and education-specific populations on the separate historical territories based on historical estimates of total population size, assuming that age structure and educational attainment levels were the same.

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