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

• 成果类型: Article
• 署名作者: Djima, Ismael Yacoubou; Kilic, Talip
• 署名单位: Paris School of Economics; The World Bank
• 刊物名称: JOURNAL OF DEVELOPMENT ECONOMICS
• ISSN/ISSBN: 0304-3878
• DOI: 10.1016/j.jdeveco.2023.103249
• 发表日期: 2024
• 关键词: Agricultural development; Smallholder farming; Agricultural inputs; crop yields; measurement error; Crop cutting; Machine Learning; Household surveys; Mali; Sub-Saharan Africa

摘要

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 subnational 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.