Data and Code for: Can Network Theory-based Targeting Increase Technology Adoption?

Principal Investigator(s): View help for Principal Investigator(s) Lori Beaman, Northwestern University; Ariel BenYishay, College of William and Mary; Jeremy Magruder, University of California-Berkeley; Ahmed Mushfiq Mobarak, Yale University

Version: View help for Version V1

Name	File Type	Size	Last Modified
code			03/08/2021 02:32:PM
data			03/08/2021 02:38:PM
output			01/13/2021 06:22:PM
ReadMe.pdf	application/pdf	171.2 KB	03/08/2021 09:30:AM

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. https://doi.org/10.3886/E130605V1

Project Description

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: CEGA/JPAL Agricultural Technology Adoption Initiative (ATAI) ; International Initiative for Impact Evaluation (3ie)

Scope of Project

Subject Terms: Social Learning; Agricultural Technology Adoption; Complex Contagion; Malawi

JEL Classification:
O13 Economic Development: Agriculture; Natural Resources; Energy; Environment; Other Primary Products

Geographic Coverage: Malawi

Time Period(s): 2011 – 2013

Collection Date(s): 2011 – 2013

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): experimental data; survey data

Methodology

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): face-to-face interview; on-site questionnaire

Unit(s) of Observation: Household

Geographic Unit: Village

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Published Versions

V1 [2021-05-24]

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