Abstract

The purpose of this chapter is to identify the determinants of displacement behavior based on various push and pull factors at the village level. The study concentrates on changes in village population during three years of civil conflict (1999–2002) in Aceh, Indonesia. The empirical analysis is based on a unique dataset from two census rounds of the Indonesian Village Potential Census (PODES). It uses data on around 5,200 Acehnese villages and relates village-level population change to conflict variables, geographic patterns and traditional socioeconomic determinants of migration. By applying quantile regressions, the push (outflow) factors and the pull (inflow) determinants of migration can also be distinguished. We identify the following factors as the main determinants of the Aceh migration pattern in this period: First, conflict clashes induced large rea r ra ngements of t he popu lat ion bet ween v illages in h ighly a f fe c ted districts, as well as strong village emigration from the geographically remote regions in Central Aceh towards the less conflict-affected coastal industrial areas. Besides conflict factors, an (ongoing) rural– urban migration process, driven by socioeconomic factors, has taken place during the conflict period. Second, there is also evidence that security considerations, such as the presence of police in a village or neighborhood, were emigration-reducing (or, immigration-inducing). Third, although the presence of ethnic-Javanese has not been a primary cause of conflict incidence, their intimidation by the rebel movement has led to a significant outflow, primarily from conflict-affected villages in Central Aceh. These results reveal that, beside a conflict-induced fear of violence, population movements in Aceh have also been an outcome of traditional migration determinants.KeywordsQuantile RegressionUrban MigrationForced DisplacementPull FactorCivil ConflictThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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