Reproducibility package for Climate Immobility Traps: A Household-Level Test

The complex relationship between climate shocks, migration, and adaptation hampers a rigorous understanding of the multiple channels that drive the heterogeneous mobility responses of farm households exposed to climate stress. To unpack this heterogeneity, we couple a causal machine learning approach, tailored to a conceptual framework bridging the New Economics of Labor Migration and the poverty traps literature, with longitudinal multi-topic household data from Nigeria. The estimated conditional average treatment effects suggest that some key variables—pre-shock asset levels, in situ adaptive capacity, and cumulative shock exposure—drive not just the magnitude but even the sign of the impact of agriculture-relevant weather anomalies on the (im)mobility outcomes of farming households. While local adaptation acts as a substitute for migration, the amplifying role played by liquidity constraints and repeated shock exposure is consistent with the existence of climate-induced immobility traps.