Correlated non-classical measurement errors, ‘second best’ policy inference and the inverse size-productivity relationship in agriculture

Abstract

We show analytically and empirically that non-classical measurement errors in the two key variables in a hypothesized relationship can bias the estimated relationship between them in any direction. Furthermore, if these measurement errors are correlated, correcting for either one alone can aggravate bias in the parameter estimate of interest relative to ignoring mismeasurement in both variables, a ‘second best’ result with implications for a broad class of economic phenomena of policy interest. We illustrate these results empirically by demonstrating the implications of mismeasured agricultural output and plot size for the long-debated (inverse) relationship between size and productivity.