Improving smallholder agriculture via video-based group extension
Bibliographic Data
| ID | 11890992 |
|---|---|
| Authors | Tushi Baul (0000-0002-2316-3932), Dean Karlan (0000-0003-0164-1398, Abdul Latif Jameel Poverty Action Lab, corresponding author), Kentaro Toyama (0000-0002-9128-2255, University of Michigan), Kathryn Vasilaky (0000-0002-9778-4379, Columbia University) |
| Year | 2024 |
| Volume | 169 |
| Pages | 103267-103267 |
| Publication date | 2024-02-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Development Economics (JOURNAL) |
| Journal identifiers | ISSN: 0304-3878 • E-ISSN: 1872-6089 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.jdeveco.2024.103267 |
| OpenAlex | W4391918146 |
| Language | EN |
| Citations received | 9 |
| References cited | 70 |
Providing agricultural advice at scale poses operational challenges. Technology may help if repeating content reinforces learning for recipients and thus improves adoption, but risks reducing efficacy given limited customization and human interaction. We tested videos shared with female farmers in India as a supplement to standard human-provided extension services promoting a climate-smart practice, System Rice Intensification. The average treatment effects are large but imprecise because of non-normally distributed outcomes, specifically fat right tails. Weighted quantile regressions show that the imprecision in estimating an average treatment effect comes from farmers with output or yields in the upper quantiles. Both quantile regressions of the 25% and 50% quantiles and a Bayesian hierarchical model (robust to several priors) reveal positive treatment effects, and two subtreatments, one that reinforces information on labor costs from adoption and a second that presents role models to motivate adoption, lead to even higher estimated treatment effects on output
Agriculture · Bayesian probability · Business · Econometrics · Economics · Geography · Personalization · Prior probability · Quantile · Quantile regression · Scale (ratio · Statistics · Agricultural Innovations and Practices · Agricultural risk and resilience · Climate change impacts on agriculture · Computer Science · Mathematics · Marketing
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| Unique citing works | 9 |
|---|---|
| Citations per year | 4,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |