Breaking the Waves? Education, Youth Bulges, and Armed Conflict all later models, Measure 1 is used.

Infant Mortality Rates used as the development measure is clearly statistically significant, while a measure of GDP per capita in the same model is insignificant (not shown here). Replacing the development measure with the secondary male educational attainment term reinstates the effect (not shown here), underscoring the high correlation between secondary education and other development measures.

**Table 1 Youth bulges, education and internal armed conflict**

|**Explanatory Variables**|**Model 1**<br>Measure 1|**Model 2**<br>Measure 2<sup>‡</sup>|**Model 3**<br>With<br>Education|**Model 4**<br>With GDP|**Model 5**<br>% secondary,<br>all|
|---|---|---|---|---|---|
|Young males β<br>Z|2.058<br>(0.72)|0.311<br>(0.66)|1.254<br>(0.38)|1.468<br>(0.44)|1.047<br>(0.31)|
|Secondary attainment, males|||-0.439<br>(-0.52)|-1.531<sup>**</sup><br>(-2.02)||
|Secondary attainment, all|||||-1.703<sup>**</sup><br>(-2.21)|
|Infant mortality rate|0.010<sup>***</sup><br>(3.25)|0.011<sup>***</sup><br>(3.57)|0.009<sup>**</sup><br>(2.21)|||
|GDP per capita, ppp (ln)||||-0.017<br>(-0.09)|0.033<br>(0.16)|
|Total population (ln)|0.274<sup>***</sup><br>(3.53)|0.275<sup>***</sup><br>(3.52)|0.275<sup>***</sup><br>(3.54)|-0.276<sup>***</sup><br>(3.45)|-0.273<sup>***</sup><br>(3.42)|
|Regime type|0.023<br>(1.16)|0.023<br>(1.15)|0.023<br>(1.15)|0.012<br>(0.63)|0.014<br>(0.73)|
|Regime type squared|-0.012<sup>**</sup><br>(-2.52)|-0.013<sup>***</sup><br>(-2.71)|-0.012<sup>**</sup><br>(-2.55)|-0.011<sup>**</sup><br>(-2.39)|-0.011<sup>**</sup><br>(-2.42)|
|Missing regime data|-0.457<br>(-0.84)|-0.482<br>(-0.90)|-0.461<br>(-0.85)|-0.379<br>(-0.70)|-0.393<br>(-0.73)|
|Time since conflict|-0.071<sup>***</sup><br>(-3.17)|-0.072<sup>***</sup><br>(-3.20)|-0.072<sup>***</sup><br>(-3.18)|-0.077<sup>***</sup><br>(-3.46)|-0.075<sup>***</sup><br>(-3.38)|
|Constant|-3.58<sup>***</sup><br>(-3.48)|-3.43<sup>***</sup><br>(-3.81)|-2.99<sup>*</sup><br>(-1.95)|-1.85<br>(-1.30)|-1.69<br>(-1.18)|
|N<br>Log likelihood<br>PseudoR<sup>2</sup><br>|2,989<br>-329.67<br>0.097<br>|2,989<br>-329.71<br>0.097<br>|2,989<br>-329.53<br>0.097|2,989<br>-331.91<br>0.091|2,989<br>-331.46<br>0.092|

* p<0.10, **p<0.05, ***p<0.01. Robust z statistics in parentheses.

‡ The reason for the very different parameter estimates between measures 1 and 2 is the different scales.

# _4.2 With education_

In a next step, the secondary schooling measure defined above was added to the original model as an independent predictor (Model 3). This in itself does not improve the model fit or show a significant effect. However, the education variable is highly correlated with several of the other predictors, in particular the proxy for general development, IMR. Moreover, the aim of the study is to uncover interaction effects (especially with age structure) as much as direct effects of education. When running male secondary attainment in a model using GDP per capita, and not IMR, as development measure (Model 4), male secondary attainment is clearly statistically significant. This is consistent with Thyne’s (2006) findings. This underscores that there is more to ‘development’ than income or state 14