We propose an empirical method for improving food assistance scoring and targeting, which minimizes under-coverage and leakage of food and cash assistance programs. The empirical strategy relies on a joint econometric estimation of food insecurity and economic vulnerability indicators at the household level, using data-driven instead of predetermined quantiles. We applied the method to recent micro data on Syrian refugees in Lebanon, to explore how regional and community-based aggregates can improve the targeting effectiveness of aid programs, notably food aid by the World Food Program in Lebanon. Our results confirm that using regional aggregates are useful for augmenting the Balanced Poverty Accuracy Criterion, and our method performs much better than the current policy in terms of targeting effectiveness and accuracy for economically vulnerable households.