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Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda

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Abstract
A large literature describes how local risk sharing networks can help individuals smooth consumption in the face of idiosyncratic economic shocks. However, when an entire community faces a large covariate shock, and when the transaction costs of transfers are high, these risk sharing networks are likely to be less effective. In this paper, we document how a new technology – mobile phones – reduces transaction costs and enables Rwandans to share risk quickly over long distances. We examine a comprehensive database of person-to-person transfers of mobile airtime and find that individuals send this rudimentary form of “mobile money” to friends and family affected by natural disasters. Using the Lake Kivu earthquake of 2008 to identify the effect of a large covariate shock on interpersonal transfers, we estimate that a current-day earthquake would result in the transfer of between $22,000 and $30,000 to individuals living near the epicenter. We further show that the pattern of transfers is most consistent with a model of reciprocal risk sharing, where transfers are determined by past reciprocity and geographical proximity, rather than one of pure charity or altruism, in which transfers would be expected to be increasing in the wealth of the sender and decreasing in the wealth of the recipient.

Suggested Citation

  • Joshua Evan Blumenstock & Nathan Eagle & Marcel Fafchamps, 2011. "Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda," Working Papers 11-25, NET Institute, revised Sep 2011.
    • Handle: RePEc:net:wpaper:1125
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    Cited by:

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    2. Beck, Thorsten & Cull, Robert, 2013. "Banking in Africa," Policy Research Working Paper Series 6684, The World Bank.
    3. Cull, Robert & Gine, Xavier & Harten, Sven & Heitmann, Soren & Rusu, Anca Bogdana, 2018. "Agent banking in a highly under-developed financial sector: Evidence from Democratic Republic of Congo," World Development, Elsevier, vol. 107(C), pages 54-74.
    4. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    5. Xin Lu & David J. Wrathall & Pål Roe Sundsøy & Md. Nadiruzzaman & Erik Wetter & Asif Iqbal & Taimur Qureshi & Andrew J. Tatem & Geoffrey S. Canright & Kenth Engø-Monsen & Linus Bengtsson, 2016. "Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen," Climatic Change, Springer, vol. 138(3), pages 505-519, October.
    6. Jennifer Helgeson & Simon Dietz & Stefan Hochrainer-Stigler, 2012. "Vulnerability to weather disasters: the choice of coping strategies in rural Uganda," GRI Working Papers 91, Grantham Research Institute on Climate Change and the Environment.

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    JEL classification:

    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
    • O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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