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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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    8. D'Souza, Anna & Tandon, Sharad, 2015. "Using Household and Intrahousehold Data To Assess Food Insecurity: Evidence from Bangladesh," Economic Research Report 262207, United States Department of Agriculture, Economic Research Service.
    9. Xi Chen, 2014. "Gift-Giving and Network Structure in Rural China: Utilizing Long-Term Spontaneous Gift Records," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-14, August.
    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. Nathalie Lazaric & Fabrice Guel & Jean Belin & Vanessa Oltra & Sébastien Lavaud & Ali Douai, 2020. "Determinants of sustainable consumption in France: the importance of social influence and environmental values," Journal of Evolutionary Economics, Springer, vol. 30(5), pages 1337-1366, November.
    12. 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.
    13. 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.
    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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    17. 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).

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