Spatial Interdependence and Instrumental Variable Models

Timm Betz, Scott J. Cook, Florian M. Hollenbach*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity - spatial interdependence - is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments: rainfall, natural disasters, economic shocks, and regionally- or globally-weighted averages. Because the biases due to spatial interdependence and predictor endogeneity can offset, addressing only one can increase the bias relative to ordinary least squares. We demonstrate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a general estimation strategy - S-2SLS - that accounts for both outcome interdependence and predictor endogeneity, thereby recovering consistent estimates of predictor effects.
Original languageEnglish
JournalPolitical Science Research and Methods
Issue number4
Pages (from-to)646-661
Number of pages16
Publication statusPublished - Oct 2020
Externally publishedYes

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