Attenuating measurement errors in agricultural productivity analysis by combining objective and self-reported survey data

This paper exploits unique survey data from Mali to validate an alternative approach to estimate the relationship between crop yields and inputs. The estimation relies on predicted objective crop yields that stem from a machine learning model trained on a random subsample of surveyed plots, for which crop cutting and self-reported sorghum yield estimates are both available. The analysis demonstrates that it is possible to predict sorghum yields with attenuated non-classical measurement error, resulting in a less-biased assessment of the relationship between yields and agricultural inputs. The external validity of the findings based on the data from a sub-national survey experiment is verified using the data from a nationally representative agricultural survey. The discussion expands on the implications of the findings for the design of future surveys where objective data collection could be limited to a subsample to save costs, with the intention to apply the suggested machine learning approach.