Adoption of agricultural technologies in the developing world: A meta-analysis dataset of the empirical literature

• DOI: 10.1016/j.dib.2021.107384
• Publication Date: 2021-09-20T05:18:49Z
• Authors: Sacha Ruzzante; Amy Bilton
• Keywords: Developing world; 2. Zero hunger; Science (General); Computer applications to medicine. Medical informatics; 05 social sciences; R858-859.7; 1. No poverty; Natural Resource Management; Q1-390; Adoption; Improved varieties; 0502 economics and business; Agricultural technology; Probit; Data Article

The meta-analysis dataset presented is a convenience sample from 218 separate studies of agricultural technology adoption in Africa, Asia, and Latin America. Each study uses survey data to estimate a form of multiple regression of adoption of a technology (dependent variable) with a diverse array of predictor variables. Fifteen predictor variable categories are included in this dataset: Age, education, gender, household size, farming experience, land size, soil fertility, land slope, distance to inputs/outputs, access to credit, land tenure, livestock ownership, non-farm income, access to extension, and organization membership. Data have been cleaned and transformed to common units. A total of 384 statistical models are recorded, with a total of 2875 effect size estimates.