Roads, riches, and residuals: a multilevel analysis of subnational determinants of violent conflict in Syria (2011-2019)

Abstract

This paper investigates the subnational determinants of violent conflict in Syria from 2011 to 2019. District-level, annual counts of total estimated fatalities, total conflict events, state-based violence, non-state violence, and one-sided violence are combined with two structural indices, (1) infrastructure (roads, population density, urban/built up land cover) and (2) land cover (croplands, open shrublands, barren/sparsely vegetated, savannas, cropland natural vegetation mosaics and grasslands), each derived via Principal Component Analysis (PCA).

Three governorate-level human development indices (income, education, health) are residualised on the other two to isolate unique effects. Mixed effects negative binomial models with governorate random intercepts, and a constant-only zero-inflation term for non-state violence, estimate how infrastructure, land cover, and human development imbalances predict conflict intensity, controlling for year fixed effects.

Spatial clustering is assessed via Moran’s I and Local Indicators of Spatial Association (LISA), confirming clustering of violence and identifying district-level hot spots. Results indicate that higher road density, urban cover, and population concentration are associated with significantly increased state-based and one-sided violence, while higher-than-expected education attainment may influence non-state violence against civilians. By modelling district-level conflict with governorate-level heterogeneity, this analysis demonstrates how disaggregated, multilevel models combined with spatial autocorrelation diagnostics can uncover the structural drivers of violence and highlight the trade-off between within- and between-unit inference when human development data is available only at coarser scales.