This paper studies identification of the marginal treatment effect (MTE) when a binary treatment variable is misclassified. We show under standard assumptions that the MTE is identified as the derivative of the conditional expectation of the observed outcome given the true propensity score, which is partially identified. We characterize the identified set for this propensity score, and then for the MTE. We use our MTE bounds to derive bounds on other commonly used parameters in the literature. We show that our bounds are tighter than the existing bounds for the local average treatment effect.We illustrate the practical relevance of our derived bounds through some numerical and empirical results.
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Contingent valuation methods are used to identify observed and unobserved preferences of goods and services. We apply these methods to compute willingness to pay (WTP) for a product conditional on having purchased another offered product. We provide a derivation for own-price and compensated cross-price elasticities whose results suggest a pricing strategy considering all offered goods simultaneously. Therefore, we solve the social planner's problem maximizing a weighted function of producer's revenues and consumer's utility for the set of optimal prices. We show an application to collegiate sports, but these methods can be extended in a straightforward fashion to other goods.
This paper provides partial identification results for the marginal treatment effect (M T E) when the binary treatment variable is potentially misreported and the instrumental variable is discrete. Identification results are derived under different sets of nonparametric assumptions. The identification results are illustrated in identifying the marginal treatment effects of food stamps on health.
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