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Social Learning, Social Influence, and Projection Bias: A Caution on Inferences Based on Proxy Reporting of Peer Behavior

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  • Heidi Hogset
  • Christopher B. Barrett

Abstract

This study explores the consequences of conflating social learning and social influence concepts and of the widespread use of proxy-reported behavioral data for accurate understanding of learning from others. Our empirical analysis suggests that proxy reporting is more accurate for new innovations, about which social learning is more plausible, than for mature technologies. Furthermore, proxy-reporting errors are correlated with respondent attributes, suggesting projection bias. Self- and proxy-reported variables generate different regression results, raising questions about inferences based on error-prone, proxy-reported peer behaviors. Self-reported peer behavior consistently exhibits statistically insignificant effects on network members' adoption behavior, suggesting an absence of social effects. (c) 2010 by The University of Chicago. All rights reserved.

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  • Heidi Hogset & Christopher B. Barrett, 2010. "Social Learning, Social Influence, and Projection Bias: A Caution on Inferences Based on Proxy Reporting of Peer Behavior," Economic Development and Cultural Change, University of Chicago Press, vol. 58(3), pages 563-589, April.
    • Handle: RePEc:ucp:ecdecc:v:58:y:2010:i:3:p:563-589
    DOI: 10.1086/650424
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    6. Muange, Elijah N. & Schwarze, Stefan & Qaim, Matin, 2014. "Social networks and farmer exposure to improved crop varieties in Tanzania," GlobalFood Discussion Papers 183635, Georg-August-Universitaet Goettingen, GlobalFood, Department of Agricultural Economics and Rural Development.
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    8. Shikuku, K.M., 2018. "Information exchange links, knowledge exposure, and adoption of agricultural technologies in Northern Uganda," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275974, International Association of Agricultural Economists.
    9. Kondylis, Florence & Mueller, Valerie, 2012. "Seeing is Believing? Evidence from a Demonstration Plot Experiment in Mozambique:," MSSP working papers 1, International Food Policy Research Institute (IFPRI).
    10. Shikuku, Kelvin Mashisia, 2019. "Information exchange links, knowledge exposure, and adoption of agricultural technologies in northern Uganda," World Development, Elsevier, vol. 115(C), pages 94-106.
    11. Mbugua, M. & Nzuma, J. & Muange, E. & Njuguna, M. & Jaeckering, L., 2018. "Social Networks and Household Dietary Diversity, Evidence from Smallholder Farmers in Kenya," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277341, International Association of Agricultural Economists.
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    14. David Sedik & Fujin Yi & Richard T. Gudaj, 2020. "Implications of Chinese Farmers in the Russian Far East," American Journal of Economics and Sociology, Wiley Blackwell, vol. 79(5), pages 1615-1622, November.
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