Agricultural technology adoption and household welfare: Measurement and evidence. (August 2019)

• Record Type: Journal Article
• Authors: Wossen, Tesfamicheal; Alene, Arega; Abdoulaye, Tahirou; Feleke, Shiferaw; Manyong, Victor
• Is Part Of: Food policy. Volume 87(2019)
• Publication Date: 2019-08
• Subjects: Adoption -- Bias -- DNA -- Misclassification -- Nigeria -- Welfare; O330 -- C510 -- C180 -- I32; Food supply -- Periodicals; Food security -- Periodicals; Food -- Quality -- Periodicals; Food Supply -- Periodicals; Alimentation -- Périodiques; Electronic journals; 338.1905
• DOI: 10.1016/j.foodpol.2019.101742
• Languages: English
• ISSNs: 0306-9192

Abstract

Highlights: Self-reported and DNA-fingerprinted adoption status compared. Substantial misclassification in self-reported adoption status. Misclassification leads to biased welfare estimates. Improved monitoring of the diffusion process of improved varieties is crucial. Abstract: Previous studies on the adoption and impacts of improved crop varieties have relied on self-reported adoption status of the surveyed households. However, in the presence of weak variety maintenance and poorly functioning seed certification system, measurement errors in self-reported adoption status can be considerable. This paper investigates how such measurement errors can lead to biased welfare estimates. Using DNA-fingerprinting based varietal identification as a benchmark, we find that misclassification in self-reported adoption status is considerable, with significant false negative and positive response rates. We empirically show that such measurement errors lead to welfare estimates that are biased towards zero and substantially understate the poverty reduction effects of adoption. While the empirical evidence suggests attenuation bias, our theoretical exposition and simulations demonstrate that upward bias and sign reversal effects are also possible. The results point to the need for improved monitoring of the diffusion process of improved varieties through innovative adoption data collection approaches to generate robust evidence for prioritizing and justifying investments in agricultural research and extension.