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Improving Smallholder Agriculture via Video-Based Group Extension (WP-23-06)

Tushi Baul, Dean Karlan, Kentaro Toyama, and Kathryn Vasilaky

Providing technical advice at scale poses operational challenges, particularly with respect to managing a sufficiently large staff. Technology may help, but risks reducing efficacy given reduced customization and human interaction. The researchers tested a video added onto standard human-provided extension services promoting a climate-smart practice, System Rice Intensification in India. Using frequentist statistical methods, they find large but imprecisely estimated treatment effects: The 95% confidence interval is 10kgs to 500kgs and 717Rps to 9650Rps for output and profits, respectively. However, the researchers’ data are not normally distributed: Specifically, key outcomes have fat tails. A Bayesian hierarchical model finds smaller but more precise treatment effects: analogous 95% intervals from -8kgs to 70kgs and -193Rps to 1380Rps. They also test two messaging sub-treatments designed to address commonly cited constraints to adoption: labor needs and self-efficacy. A frequentist analysis shows no added gains, while the Bayesian shows an added benefit when delivered in tandem.

Tushi Baul, Senior Researcher, Facebook

Dean Karlan, Professor of Economics and Finance, Frederic Esser Nemmers Chair, and IPR Associate, Northwestern University

Kentaro Toyama, W K Kellogg Professor of Community Information and Professor of Information, University of Michigan

Kathryn Vasilaky, Assistant Professor of Economics, Orfalea College of Business, Cal Poly

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