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Project Citation: 

Beaman, Lori, BenYishay, Ariel, Magruder, Jeremy, and Mobarak, Ahmed Mushfiq. Data and Code for: Can Network Theory-based Targeting Increase Technology Adoption? Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-05-24.

Project Description

Summary:  View help for Summary Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion.  Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low.
Funding Sources:  View help for Funding Sources CEGA/JPAL Agricultural Technology Adoption Initiative (ATAI) ; International Initiative for Impact Evaluation (3ie)

Scope of Project

Subject Terms:  View help for Subject Terms Social Learning; Agricultural Technology Adoption; Complex Contagion; Malawi
JEL Classification:  View help for JEL Classification
      O13 Economic Development: Agriculture; Natural Resources; Energy; Environment; Other Primary Products
Geographic Coverage:  View help for Geographic Coverage Malawi
Time Period(s):  View help for Time Period(s) 2011 – 2013
Collection Date(s):  View help for Collection Date(s) 2011 – 2013
Universe:  View help for Universe Adult farmers living in 200 villages (approximately 5,600 households) in 3 Malawian districts with largely semi-arid climates (Machinga, Mwanza, and Nkhotakota).
Data Type(s):  View help for Data Type(s) experimental data; survey data


Sampling:  View help for Sampling The study used a clustered randomization at the village level.  Randomization was stratified by district, and implemented using a re-randomization procedure which checked balance on three village-level covariates: percent of village using compost at baseline; percent village using fertilizer at baseline, and percent of village using pit planting at baseline.
Collection Mode(s):  View help for Collection Mode(s) face-to-face interview; on-site questionnaire
Unit(s) of Observation:  View help for Unit(s) of Observation Household
Geographic Unit:  View help for Geographic Unit Village

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