Measurement error mechanisms matter: Agricultural intensification with farmer misperceptions and misreporting

This paper was named AAEA's Best Paper at their 2022 Annual Meeting.

The mechanism(s) that generate measurement error matter for inference. Survey measurement error is typically thought to represent simple misreporting correctable through improved measurement. But errors might also or alternatively reflect respondent misperceptions that materially affect the respondent decisions under study. We show analytically that these alternate data generating processes imply different appropriate regression specifications and have distinct effects on the bias in parameter estimates. We introduce a simple empirical technique to generate unbiased estimates under more general conditions and to apportion measurement error between misreporting and misperceptions in measurement error when one has both self‐reported and objectively measured observations of the same explanatory variable. We then apply these techniques to the longstanding question of agricultural intensification: Do farmers increase input application rates per unit area as the size of the plots they cultivate decreases? Using nationally representative data from four sub‐Saharan African countries, we find evidence that measurement error in plot size reflects a mixture of farmer misreporting and misperceptions. The results matter for inference around the intensification hypothesis and call into question whether more objective, precise measures are always preferable when estimating behavioral parameters.