This paper provides the first systematic analysis of migration to, within, and from Libya. The data used in the analysis are from the Displacement Tracking Matrix data set of the International Organization for Migration. The analysis uses this unique source of data, combining several techniques to analyze various dimensions of migration in Libya. First, the paper provides a detailed description of the demographic characteristics and national composition of the migrant populations in Libya. Next, it discusses the determinants of migration flow within Libya. The findings show that migration in Libya can be characterized as forced migration, because conflict intensity is the main determinant of the decision to relocate across provinces. Finally, the paper describes the direction, composition, and evolution of international migration flows passing through Libya and identifies the mechanisms of location selection by migrants within Libya by identifying hotspots and cluster provinces.
Download paper (PDF)
Causal inference often neglects the presence of interference. This takes place when treatment exposure of one unit also affects other units connected through physical, social or economic interactions in a network structure. Extensive work has been done to assess the role played by spillover effects in policy evaluations, but most of the literature focuses on randomized experiments under cluster interference.
This paper presents a methodology to draw causal inference in a non-experimental setting subject to network interference. Specifically, we develop a generalized propensity score-based estimator to estimate both direct and spillover effects of a continuous treatment.
Spillover effects are defined by the exposure to the network treatment, that is, a summary of the treatment received by connected units.
Our estimator also allows to consider asymmetric network connections characterized by heterogeneous intensities. To showcase this methodology, we investigate whether and how spillover effects shape the optimal level of policy interventions in agricultural markets. Our results show that, in this context, neglecting interference may underestimates the degree of policy effectiveness.
Download paper (PDF)