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This paper examines the identification power of instrumental variables (IVs)for average treatment effect (ATE) in partially identified models. We decomposethe ATE identification gains into components of contributions driven by IVrelevancy, IV strength, direction and degree of treatment endogeneity, andmatching via exogenous covariates. Our decomposition is demonstrated withgraphical illustrations, simulation studies and an empirical example ofchildbearing and women's labour supply. Our analysis offers insights forunderstanding the complex role of IVs in ATE identification and for selectingIVs in practical policy designs. Simulations also suggest potential uses of ouranalysis for detecting irrelevant instruments.

Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects