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Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences

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  • Hannah Druckenmiller
  • Solomon Hsiang

Abstract

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.

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  • Hannah Druckenmiller & Solomon Hsiang, 2018. "Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences," Papers 1810.07216, arXiv.org, revised Aug 2019.
    • Handle: RePEc:arx:papers:1810.07216
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    Cited by:

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    3. Diane Alexander & Hannes Schwandt, 2022. "The Impact of Car Pollution on Infant and Child Health: Evidence from Emissions Cheating," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 2872-2910.
    4. Edwards, Ryan Barclay, 2024. "Spillovers from agricultural processing," SocArXiv uvjef, Center for Open Science.
    5. Marein, Brian, 2022. "Colonial Roads and Regional Inequality," Journal of Urban Economics, Elsevier, vol. 131(C).

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