
Contents
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16.1 Introduction 16.1 Introduction
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16.2 Design and Implementation Issues 16.2 Design and Implementation Issues
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16.2.1 Identification 16.2.1 Identification
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16.2.2 Measurement 16.2.2 Measurement
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16.2.3 Network Sampling 16.2.3 Network Sampling
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16.2.4 Inference 16.2.4 Inference
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16.2.5 Other Implementation Considerations 16.2.5 Other Implementation Considerations
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16.2.6 Endogeneity 16.2.6 Endogeneity
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16.3 Social Learning and Diffusion 16.3 Social Learning and Diffusion
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16.3.1 Testing Theories of Social Learning 16.3.1 Testing Theories of Social Learning
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16.3.2 Social Learning and Technology Adoption 16.3.2 Social Learning and Technology Adoption
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16.3.2.1 Health and Agricultural Technology Adoption with Peer Data 16.3.2.1 Health and Agricultural Technology Adoption with Peer Data
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16.3.2.2 Technology Adoption with Full Networks Data 16.3.2.2 Technology Adoption with Full Networks Data
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16.3.2.3 Strategic Information Diffusion 16.3.2.3 Strategic Information Diffusion
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16.3.3 Information Extraction 16.3.3 Information Extraction
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16.4 Other-Regarding Preferences and Social Networks 16.4 Other-Regarding Preferences and Social Networks
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16.5 Public Commitments, Peer Monitoring, and Enforcement 16.5 Public Commitments, Peer Monitoring, and Enforcement
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16.6 Risk-Sharing 16.6 Risk-Sharing
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16.7 Network Formation and Change 16.7 Network Formation and Change
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16.8 Conclusion and Open Issues 16.8 Conclusion and Open Issues
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References References
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16 Field Experiments, Social Networks, and Development
Get accessEmily Breza, Assistant Professor, Finance and Economics, Columbia Business School
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Published:02 June 2016
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Extract
Introduction
The study of social networks has become an important part of the economics tool kit. Modeling agents as independent actors misses the countless ways in which actions are shaped by the traits, beliefs, and decisions of their peers. Similarly, in order to measure general equilibrium effects of a policy intervention, it is crucial to understand not only how a beneficiary is affected directly, but how an intervention spills over to others in the agent’s community.
Measuring spillovers and peer effects is notoriously difficult, especially using observational data (Manski, 1993). Social relationships are often endogenous, and connected individuals are likely to have similar characteristics and experience common shocks. Thus, it should be no surprise that following the rise of experimental methods in microeconomics, many researchers have also begun to use experiments to tackle questions involving social networks and to revisit insights from foundational non-experimental papers.1
The way in which networks have been studied using experiments has evolved as the literature has matured. One of the simplest ways to measure spillovers is to use the spatial variation induced from randomization to measure geographical spillovers. Another common technique is to record baseline membership in groups such as kinship, caste, ethnicity, classroom, or borrowing groups and to again use variation in treatment status or group-level treatment intensity to identify social effects. A more recent, but growing subset of the literature has begun to move away from mutually exclusive and symmetric group memberships towards incorporating more complex network structures and even global network (i.e., graph- or community-level) characteristics. Usage of network data allows researchers to ask three key questions. First, how does the embedding of individuals in a social network shape economic outcomes? Second, what is the effect of the community-level network structure on those same outcomes? Finally, how are networks themselves molded by external forces?
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