Can differences in individual learning explain patterns of technology adoption? Evidence on heterogeneous learning patterns and hybrid rice adoption in Bihar, India

•We use field experiments in India to understand how farmers process information.•Most farmers simplify judgments by using only a portion of the information set.•Nearly 40% of farmers rely on first impressions to make subsequent judgments.•More than 36% of farmers use only the most recent piece of i...

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Bibliographic Details
Published in:World development 2019-03, Vol.115, p.178-189
Main Authors: Gars, Jared, Ward, Patrick S.
Format: Article
Language:English
Subjects:
Access control
Access to credit
Adoption of innovations
Bayesian analysis
Bias
Data processing
Experimental economics
Experimentation
Experiments
Farmers
Heterogeneity
Heuristic
Hybrids
Impressions
India
Information processing
Intelligence
Learning
Learning heuristics
Learning processes
Machine learning
New technology
Observational learning
Oryza
Problem solving
Research methodology
Rice
Rural areas
Rural communities
Technology adoption
Technology utilization
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Summary:•We use field experiments in India to understand how farmers process information.•Most farmers simplify judgments by using only a portion of the information set.•Nearly 40% of farmers rely on first impressions to make subsequent judgments.•More than 36% of farmers use only the most recent piece of information.•Farmers who simplify judgments are less likely to cultivate rice hybrids. Much empirical research that has shown that an individual’s decision to adopt a new technology is the result of learning – both in personal experimentation as well as observing the experimentation of others. Yet even casual observation would suggest significant heterogeneity learning processes, manifesting itself in widely varying patterns of adoption over space and time. In this paper we explore this heterogeneity in the context of early adoption of hybrid rice in rural India. Using specially-designed experiments conducted as part of a primary survey in the field, we are able to identify which of four broad learning heuristics most accurately reflects individuals’ information processing strategies. Linking these learning heuristics with observed use of rice hybrids, we demonstrate that pure Bayesian learning is well suited for the tinkering and marginal adjustments that would be required to learn about a technology like hybrid rice, but is also more cognitively taxing, requiring a longer memory and more complex updating processes. Consequently, only about 25 percent of the farmers in our sample can be characterized as pure Bayesian learners. Present-biased learning and relying on first impressions will likely hinder adoption of a technology like hybrid rice, even after controlling for access to credit and a rudimentary proxy for intelligence.
ISSN:0305-750X
1873-5991
DOI:10.1016/j.worlddev.2018.11.014