Measurement error and its impact on estimates of income dynamics

This paper examines whether reported income generates biases for studies on economic mobility and poverty dynamics. Using a linear measurement error model capturing mean-reverting measurement error, this study finds that substantial classical measurement error exists in reported data, leading to a bias toward zero in the estimate of income dynamics. Time-invariant non-classical measurement error and unobserved heterogeneity offset the effect of classical measurement error. This study also identifies the standard deviation of the measurement error, which is estimated to be about 70% of that of the equation error in the income model, suggesting that random measurement error is substantial.

Automated summary

This paper examines the potential biases in studies on economic mobility and poverty dynamics caused by reported income. By using a linear measurement error model, the study finds substantial classical measurement error in reported data, resulting in a bias towards zero in income dynamics estimation. However, time-invariant non-classical measurement error and unobserved heterogeneity offset the impact of classical measurement error. The study also identifies that the standard deviation of the measurement error is around 70% of the equation error in the income model, indicating the significant presence of random measurement error.