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Social network effects on mobile money adoption in Uganda

Author

Listed:
  • Murendo, Conrad
  • Wollni, Meike
  • de Brauw, Alan
  • Mugabi, Nicholas

Abstract

Social networks play a vital role in generating social learning and information exchange that can drive the diffusion of new financial innovations. This is particularly relevant for developing countries where education, extension and financial information services are underprovided. This article identifies the effect of social networks on the adoption of mobile money by households in Uganda. Using data from a household survey, conditional logistic regression is estimated controlling for correlated effects and other information sources. Results show that mobile money adoption is positively influenced by the size of social network members exchanging information, and the effect is more pronounced for non-poor households. The structure of social network however has no effect. The findings show that information exchange through social networks is crucial for adoption of mobile money. Mobile money adoption is likely to be enhanced if promotion programs reach more social networks.

Suggested Citation

  • Murendo, Conrad & Wollni, Meike & de Brauw, Alan & Mugabi, Nicholas, 2015. "Social network effects on mobile money adoption in Uganda," 2015 Conference, August 9-14, 2015, Milan, Italy 212514, International Association of Agricultural Economists.
  • Handle: RePEc:ags:iaae15:212514
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    1. Fisher, Monica & Kandiwa, Vongai, 2014. "Can agricultural input subsidies reduce the gender gap in modern maize adoption? Evidence from Malawi," Food Policy, Elsevier, vol. 45(C), pages 101-111.
    2. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
    3. William Jack & Tavneet Suri, 2014. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution," American Economic Review, American Economic Association, vol. 104(1), pages 183-223, January.
    4. L├Ąpple, Doris & Kelley, Hugh, 2013. "Understanding the uptake of organic farming: Accounting for heterogeneities among Irish farmers," Ecological Economics, Elsevier, vol. 88(C), pages 11-19.
    5. Yingying Dong & Arthur Lewbel & Thomas Tao Yang, 2012. "Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 789, Boston College Department of Economics, revised 15 May 2012.
    6. ., 2014. "Mobile technology in the modern era," Chapters,in: Mobile Telecommunications Networks, chapter 2, pages 26-47 Edward Elgar Publishing.
    7. Brock, William A. & Durlauf, Steven N., 2007. "Identification of binary choice models with social interactions," Journal of Econometrics, Elsevier, vol. 140(1), pages 52-75, September.
    8. Kirui, Oliver K. & Okello, Julius Juma & Nyikal, Rose Adhiambo, 2012. "Determinants of Use and Intensity of Use of Mobile Phone-based Money Transfer Services in Smallholder Agriculture: Case of Kenya," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 125739, International Association of Agricultural Economists.
    9. Katleen Van den Broeck & Stefan Dercon, 2011. "Information Flows and Social Externalities in a Tanzanian Banana Growing Village," Journal of Development Studies, Taylor & Francis Journals, vol. 47(2), pages 231-252.
    10. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    11. William Jack & Adam Ray & Tavneet Suri, 2013. "Transaction Networks: Evidence from Mobile Money in Kenya," American Economic Review, American Economic Association, vol. 103(3), pages 356-361, May.
    12. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Viewpoint: Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics, Canadian Economics Association, vol. 45(3), pages 809-829, August.
    13. Ira Matuschke & Matin Qaim, 2009. "The impact of social networks on hybrid seed adoption in India," Agricultural Economics, International Association of Agricultural Economists, vol. 40(5), pages 493-505, September.
    14. Wollni, Meike & Andersson, Camilla, 2014. "Spatial patterns of organic agriculture adoption: Evidence from Honduras," Ecological Economics, Elsevier, vol. 97(C), pages 120-128.
    15. William Greene, 2004. "The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 98-119, June.
    16. Lenis Saweda O. Liverpool-Tasie & Alex Winter-Nelson, 2012. "Social Learning and Farm Technology in Ethiopia: Impacts by Technology, Network Type, and Poverty Status," Journal of Development Studies, Taylor & Francis Journals, vol. 48(10), pages 1505-1521, October.
    17. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    18. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, July - De.
    19. Wydick, Bruce & Karp Hayes, Harmony & Hilliker Kempf, Sarah, 2011. "Social Networks, Neighborhood Effects, and Credit Access: Evidence from Rural Guatemala," World Development, Elsevier, vol. 39(6), pages 974-982, June.
    20. Mark Granovetter, 2005. "The Impact of Social Structure on Economic Outcomes," Journal of Economic Perspectives, American Economic Association, vol. 19(1), pages 33-50, Winter.
    21. David McKenzie, 2005. "Measuring inequality with asset indicators," Journal of Population Economics, Springer;European Society for Population Economics, vol. 18(2), pages 229-260, June.
    22. Magnus Lindelow, 2006. "Sometimes more equal than others: how health inequalities depend on the choice of welfare indicator," Health Economics, John Wiley & Sons, Ltd., vol. 15(3), pages 263-279.
    23. Okten, Cagla & Osili, Una Okonkwo, 2004. "Social Networks and Credit Access in Indonesia," World Development, Elsevier, vol. 32(7), pages 1225-1246, July.
    24. Sahn, David E. & Stifel, David C., 2000. "Poverty Comparisons Over Time and Across Countries in Africa," World Development, Elsevier, vol. 28(12), pages 2123-2155, December.
    25. Awudu Abdulai & Wallace Huffman, 2014. "The Adoption and Impact of Soil and Water Conservation Technology: An Endogenous Switching Regression Application," Land Economics, University of Wisconsin Press, vol. 90(1), pages 26-43.
    26. Drouard, Joeffrey, 2011. "Costs or gross benefits? - What mainly drives cross-sectional variance in Internet adoption," Information Economics and Policy, Elsevier, vol. 23(1), pages 127-140, March.
    27. Goldfarb, Avi & Prince, Jeff, 2008. "Internet adoption and usage patterns are different: Implications for the digital divide," Information Economics and Policy, Elsevier, vol. 20(1), pages 2-15, March.
    28. Timothy J. Richards & Stephen F. Hamilton & William J. Allender, 2014. "Social Networks and New Product Choice," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(2), pages 489-516.
    29. Annemie Maertens & Christopher B. Barrett, 2013. "Measuring Social Networks' Effects on Agricultural Technology Adoption," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(2), pages 353-359.
    30. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
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    Cited by:

    1. repec:gam:jsusta:v:11:y:2019:i:3:p:568-:d:199876 is not listed on IDEAS
    2. Kazushi Takahashi & Yukichi Mano & Keijiro Otsuka, 2018. "Spillovers as a Driver to Reduce Ex-post Inequality Generated by Randomized Experiments: Evidence from an Agricultural Training Intervention," Working Papers 174, JICA Research Institute.
    3. Gupta, I. & Veettil, P.C. & Speelman, S., 2018. "Caste, Technology and Social Networks," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277048, International Association of Agricultural Economists.

    More about this item

    Keywords

    Consumer/Household Economics; Research and Development/Tech Change/Emerging Technologies;

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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