Correcting for Misclassified Binary Regressors Using Instrumental Variables

Haider, Steven J.; Stephens, Melvin

doi:10.3386/w27797

Cited by 3 publications

(1 citation statement)

References 15 publications

(31 reference statements)

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“…This constraint is known as the monotonicity condition is the literature on misclassification, and is different from the monotonicity restriction imposed on the treatment selection in model (2.1).9 Note however that in a recent paper,Haider and Stephens Jr. (2020) show that this assumption is invalid in routine empirical settings. In Subsection E.1, we discuss how one can allow the false positive/negative rates to depend on z.…”

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confidence: 99%

Marginal Treatment Effects with Misclassified Treatment

Acerenza¹,

Ban²,

Kédagni³

2021

Preprint

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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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confidence: 99%

Marginal Treatment Effects with Misclassified Treatment

Acerenza¹,

Ban²,

Kédagni³

2021

Preprint

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Identifying program benefits when participation is misreported

Tommasi,

Zhang

2024

J of Applied Econometrics

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SummaryIn cases of noncompliance with an assigned treatment, estimates of causal effects typically rely on instrumental variables (IV). However, when participation is also misreported, the IV estimand may become a nonconvex combination of local average treatment effects that fails to satisfy even a minimal condition for being causal. The aim of our paper is to generalize the MR‐LATE approach. This is an alternative IV estimand that is more robust in cases of noncompliance and nondifferential misclassification of the treatment variable. Our generalization is threefold: First, we incorporate discrete and multiple‐discrete instrument(s); second, we consider the use of instrument(s) under a weaker, partial monotonicity condition; third, we provide a general inferential procedure. Under relatively stringent assumptions, MR‐LATE is either identical to the IV estimand or less biased than the naïve IV estimand. Under less stringent assumptions, the MR‐LATE estimand can identify the sign of the IV estimand. We conclude with the use of a dedicated Stata command, ivreg2m, to assess the return on education in the United Kingdom.

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Purchases over the SNAP benefit cycle: Evidence from supermarket panel data

Harris‐Lagoudakis,

Wich

2024

Economic Inquiry

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This paper investigates the effect of Supplemental Nutrition Assistance Program (SNAP) benefit disbursement on intramonthly household level purchases made from a supermarket retailer. We find that spending, the likelihood of shopping, the bulk expenditure share and the national brand expenditure share increase by $2, 1.5, 2, and 0.6% points, respectively, on the day that SNAP benefits are disbursed. We also compare and contrast estimates that use variation in the indicator for benefit receipt to estimates that utilize variation in the probability of SNAP benefit receipt. We find substantial differences between the two approaches for the outcome of spending.

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Correcting for Misclassified Binary Regressors Using Instrumental Variables

Cited by 3 publications

References 15 publications

Marginal Treatment Effects with Misclassified Treatment

Marginal Treatment Effects with Misclassified Treatment

Identifying program benefits when participation is misreported

Purchases over the SNAP benefit cycle: Evidence from supermarket panel data

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