Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences

Hannah Druckenmiller and Solomon Hsiang

No 25177, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We develop a simple cross-sectional research design to identify causal effects that is robust to unobservable heterogeneity. When many observational 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. This approach is conceptually similar to first differencing approaches in time-series or panel models, except the index for time is replaced with an index for locations in space. The SFD design identifies plausibly causal effects, even when no instruments are available, so long as local changes in the treatment and unobservable confounders are not systematically correlated between immediately adjacent neighbors. We demonstrate the SFD approach by recovering new cross-sectional estimates for the effects of time-invariant geographic factors, soil and climate, on long-run average crop productivities across US counties — relationships that are notoriously confounded by unobservables but crucial for guiding economic decisions, such as land management and climate policy.

JEL-codes: C21 I26 Q15 Q51 Q54 (search for similar items in EconPapers)
Date: 2018-10
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ure
Note: DEV ED EEE EFG EH IO ITI LS PE POL
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Citations: View citations in EconPapers (25)

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