Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences
This addresses a critical issue in economics for researchers and policymakers dealing with land management and climate policy, but it appears incremental as it builds on quasi-experimental designs.
The paper tackles the problem of identifying causal effects in cross-sectional data with unobservable heterogeneity by proposing a spatial first differences (SFD) method, which yields new estimates for the effects of soil and climate on long-run agricultural productivities, though no concrete numbers are provided.
We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifying assumptions of SFD are similar in mathematical structure and plausibility to other quasi-experimental designs. We use SFD to obtain new estimates for the effects of time-invariant geographic factors, soil and climate, on long-run agricultural productivities --- relationships crucial for economic decisions, such as land management and climate policy, but notoriously confounded by unobservables.