Urdal & Hoelscher: Urban Youth Bulges and Social Disorder for militant organizations (Lia, 2005: 145–146). Similar grievances promoting educated youth to engage in political violence have been anecdotally reported in Kenya (Kahl 1998:103), Thailand (Xenos 2004:28), and Sri Lanka (Braungart 1984:14–15).

H4: The lower the level of education, the greater the effect of large urban youth bulges on urban social disorder.

H5: The stronger the increases in youth with higher education, the greater the effect of large urban youth bulges on urban social disorder.

# **2.3 Gender imbalance**

Gender selection in migration can lead to gender imbalances in urban youth populations. In many countries, rural to urban migration has traditionally been dominated by young male labor migrants, often engaged in seasonal, circular migration. It has been suggested that the male selectivity in migration may be due to greater returns to migrating for males. The urban to rural wage gap is generally greater for males than for females, and as a result the observable gains to migrating to cities are greater for males (Agesa & Agesa, 1999). At the same time, high fertility levels have traditionally contributed to limit mobility for women (Brokerhoff & Eu, 1993). However, in more recent decades there has been a greater recognition of the ‘feminisation of migration’ (Castles & Miller, 1998), and in some developing countries the propensity to migrate is now greater for young women than for young men. Some scholars have argued that male surpluses create competition for marriage and jobs between young unattached males, increasing the risks for instability and violence (Mesquida & Wiener, 1996; Hudson & den Boer, 2004).

H6: The greater the number of young urban males over young urban females, the greater the effect of large urban youth bulges on urban social disorder.

# **3. Research design and model**

The study covers 55 major cities, 23 in Sub-Saharan Africa and 32 in Central and East Asia, in 49 different countries for the 1960-2006 period. The temporal-spatial domain is determined by a new dataset on urban social disorder compiled from electronic news reports in the ‘Keesing’s Record of World Events’ (KRWE). The data collection builds on a similar project developed for the State Failure Task Force (Marshall, 2001). All electronic searches were done manually by human coders, using a specific search algorithm containing terms associated with political violence and disturbance (see Urdal, 2008b). Each returned report was then screened individually to determine relevance. For all events that were deemed relevant, the following information was coded: name of country and city; type of event; day, month and year for the beginning and the end of an event; actor(s) and target(s); number of participants and number of deaths; location of event. Type of event was chosen from a pre-defined list of 12 categories, while no pre-defined categories were developed for actors and targets. Given the sometimes limited information provided by the news reports, a set of coding rules were developed to determine whether an event should be included in the dataset. First, it was established whether the event took place within the bounds of the city in question. Events were generally included if the location was 6