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Spatial interdependence and instrumental variable models

Published online by Cambridge University Press:  30 January 2019

Timm Betz
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA
Scott J. Cook
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA
Florian M. Hollenbach*
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA
*
*Corresponding author. Email: fhollenbach@tamu.edu

Abstract

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.

Type
Original Article
Copyright
Copyright © The European Political Science Association, 2019

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