Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification

Summary

This article examines the impact that misreporting adoption status has on the identification and estimation of causal effects on productivity. By comparing measurement error-ridden, self-reported adoption data with measurement-error-free, DNA-fingerprinted adoption data, we investigate the extent to which such errors bias the causal effects of adoption on productivity. Taking DNA-fingerprinted adoption data as a benchmark, we find 25% “false negatives” and 10% “false positives” in farmers’ responses. The results of this article underscore the crucial role that correct measurement of adoption plays in designing policy interventions that address the constraints to technology adoption in agriculture.