This article reveals a significant issue with standard instrumental variable (IV) methods. Contrary to expectations, even randomly assigned instruments can produce biased estimates if spatial interdependence is ignored.

Data & Methods

Researchers demonstrate this problem analytically and through extensive Monte Carlo simulations covering various cases.

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The key findings show that:

* Ignoring spatial interdependence leads to asymptotic bias in IV estimates.

* This bias worsens when instruments themselves are spatially correlated, a common occurrence with rainfall or regional averages.

Why It Matters

Researchers caution against standard IV approaches for spatial data. Addressing only one type of bias (spatial dependence or predictor endogeneity) can be counterproductive if the other is present; sometimes it increases error relative to simpler ordinary least squares models.

The proposed solution provides a robust approach.