Department of Agricultural and Resource Economics
University of California at Berkeley
207 Giannini Hall
Berkeley, CA 94720-3310
NBER Program Affiliations:
NBER Affiliation: Research Associate
Institutional Affiliation: University of California at Berkeley
Information about this author at RePEc
NBER Working Papers and Publications
|January 2020||Factor Market Failures and the Adoption of Irrigation in Rwanda|
with Maria Jones, Florence Kondylis, John Loeser: w26698
We examine constraints to adoption of new technologies in the context of hillside irrigation schemes in Rwanda. We leverage a plot-level spatial regression discontinuity design to produce 3 key results. First, irrigation enables dry season horticultural production, which boosts on-farm cash profits by 70%. Second, adoption is constrained: access to irrigation causes farmers to substitute labor and inputs away from their other plots. Eliminating this substitution would increase adoption by at least 21%. Third, this substitution is largest for smaller households and wealthier households. This result can be explained by labor market failures in a standard agricultural household model.
|August 2018||Can Network Theory-based Targeting Increase Technology Adoption?|
with Lori Beaman, Ariel BenYishay, Ahmed Mushfiq Mobarak: w24912
In order to induce farmers to adopt a productive new agricultural technology, we apply simple and complex contagion diffusion models on rich social network data from 200 villages in Malawi to identify seed farmers to target and train on the new technology. A randomized controlled trial compares these theory-driven network targeting approaches to simpler strategies that either rely on a government extension worker or an easily measurable proxy for the social network (geographic distance between households) to identify seed farmers. Our results indicate that technology diffusion is characterized by a complex contagion learning environment in which most farmers need to learn from multiple people before they adopt themselves. Network theory based targeting can out-perform traditional approa...
|June 2017||Split-Sample Strategies for Avoiding False Discoveries|
with Michael L. Anderson: w23544
Preanalysis plans (PAPs) have become an important tool for limiting false discoveries in field experiments. We evaluate the properties of an alternate approach which splits the data into two samples: An exploratory sample and a confirmation sample. When hypotheses are homogeneous, we describe an improved split-sample approach that achieves 90% of the rejections of the optimal PAP without requiring preregistration or constraints on specification search in the exploratory sample. When hypotheses are heterogeneous in priors or intrinsic interest, we find that a hybrid approach which prespecifies hypotheses with high weights and priors and uses a split-sample approach to test additional hypotheses can have power gains over any pure PAP. We assess this approach using the community-driven develo...