Systematic self-report bias in health data: impact on estimating cross-sectional and treatment effects

  • Sebastian Bauhoff


This paper examines the effect of systematic self-report bias, the non-random deviation between the self-reported and true values of the same measure. This bias may be constant or variable, and can mislead empirical analyses based on descriptive statistics, program evaluation and instrumental variables estimation. I illustrate these issues with data on self-reported and measured overweight/obesity status, and BMI, height and weight z-scores of public school students in California from 2004 to 2006. I find that the prevalence of overweight/obesity is 2.4–7.6% points lower in self-reported data relative to measured data in the cross-section. A school nutrition policy changed the bias differentially in the treatment and control groups so that program evaluations could find spurious positive or null impacts of the intervention. Potential channels for this effect include improved information and stigma.


Measurement error Program evaluation Instrumental variables Survey data Obesity 

JEL classification

I10 C10 



I am grateful to Alberto Abadie, Eliana Carranza, David Cutler, Caroline Hoxby, Holger Kern, James O’Malley, Thomas McGuire, Manoj Mohanan, Joseph Newhouse, Alan Zaslavsky and an anonymous referee for helpful suggestions; and to Kiku Annon, Jerry Bailey and Julie Williams for assistance with the PFT and CHKS data.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Health Care PolicyHarvard UniversityBostonUSA

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