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Identifies supercompliers in job training, highlighting their value in policy evaluation.
In an instrumental variable framework, we define supercompliers as the subpopulation whose treatment take-up positively responds to eligibility and whose outcome positively responds to take-up. Supercompliers are the only subpopulation to benefit from treatment eligibility and, hence, are important for policy evaluation. We propose conditions for characterizing supercompliers, and show how estimation and inference can be conducted with instrumental variable regression. In two job training experiments, we demonstrate our machinery’s utility, particularly in incorporating social welfare weights into marginal value of public funds analyses. • Supercompliers take up treatment when eligible and benefit from treatment. • Supercomplier characteristics are identified by a Wald-type estimand. • Estimation and inference use standard IV regression. • Supercomplier profiles enable welfare-weighted MVPF calculations. • Two job training experiments illustrate the method’s utility.
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Comey et al. (2026) studied this question.
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