This seminar provides a new methodology to predict armed conflict by using newspaper text. Through machine learning, vast quantities of newspaper text are reduced to interpretable topics. These topics are then used in panel regressions to predict the onset of conflict. We propose the use of the within-country variation of these topics to predict the timing of conflict. This allows us to avoid the tendency of predicting conflict only in countries where it occurred before. We show that the within-country variation of topics is a good predictor of conflict and becomes particularly useful when risk in previously peaceful countries arises. Two aspects seem to be responsible for these features. Topics provide depth because they consist of changing, long lists of terms which makes them able to capture the changing context of conflict. At the same time topics provide width because they are summaries of the full text, including stabilizing factors.

Hannes Mueller joined the IAE in September 2008, after completing the PhD in Economics at the London School of Economics. His research interests are Political Economy and Development Economics – broadly defined. In his past research he has, for example, studied the impact of the Northern Irish conflict on housing prices, the interaction between bureaucratic and political institutions and the role of intrinsic motivation in not-for-profit organizations. Hannes is currently researching, amongst other things, the adoption of primary elections in political parties, the use of factors (labor and capital) in the military, the role of group identity in terrorism.