Systematic Self-Report Bias in Health Data: Impact on Estimating Cross-Sectional and Treatment Effects

Health Services and Outcomes Research Methodology, February 2011

Posted: 20 Mar 2011

Date Written: February 9, 2011

Abstract

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.

Keywords: Measurement error, Program evaluation, Instrumental variables, Survey data, Obesity

JEL Classification: I10, C10

Suggested Citation

Bauhoff, Sebastian, Systematic Self-Report Bias in Health Data: Impact on Estimating Cross-Sectional and Treatment Effects (February 9, 2011). Health Services and Outcomes Research Methodology, February 2011. Available at SSRN: https://ssrn.com/abstract=1790567

Sebastian Bauhoff (Contact Author)

Center for Global Development ( email )

2055 L Street NW
Washington, DC DC 20009
United States

HOME PAGE: http://scholar.harvard.edu/bauhoff/

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