Non-classical measurement error in instrumental variables estimation: An application to the medical care costs of obesity.
Health Econ
; 2024 Jul 18.
Article
in En
| MEDLINE
| ID: mdl-39030850
ABSTRACT
Estimates of the impact of body mass index and obesity on health and labor market outcomes often use instrumental variables estimation (IV) to mitigate bias due to endogeneity. When these studies rely on survey data that include self- or proxy-reported height and weight, there is non-classical measurement error due to the tendency of individuals to under-report their own weight. Mean reverting errors in weight do not cause IV to be asymptotically biased per se, but may result in bias if instruments are correlated with additive error in weight. We demonstrate the conditions under which IV is biased when there is non-classical measurement error and derive bounds for this bias conditional on instrument strength and the severity of mean-reverting error. We show that improvements in instrument relevance alone cannot eliminate IV bias, but reducing the correlation between weight and reporting error mitigates the bias. A solution we consider is regression calibration (RC) of endogenous variables with external validation data. In simulations, we find IV estimation paired with RC can produce consistent estimates when correctly specified. Even when RC fails to match the covariance structure of reporting error, there is still a reduction in asymptotic bias.
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Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Health Econ
Journal subject:
SERVICOS DE SAUDE
Year:
2024
Document type:
Article
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