Background
Across East Africa, individuals across every demographic are rapidly adopting “Mobile Money” payment services; paying bills, transferring funds, as well as withdrawing and depositing cash. Since the early 2000s, Uganda, Tanzania, Zambia and Kenya in particular have been experiencing wide adoption of these tools. In Kenya, Vodafone’s M-Pesa cultivates a network of independent agents who offer banking services to their local communities. These agents are then able to visit independent businesses contracted by Vodafone where they can exchange e-money for cash and balance their books. Fees are extracted based upon the size of each transaction, allowing agents, contractors, and Vodafone to profit from the exchange. Similar models are being adopted by companies across the region. Currently, peer-to-peer cash flows in Kenya account for a significant portion of the Kenyan economy: roughly 25.6 billion US dollars. Roughly 70% of these transactions are occurring via mobile phone.[1]
In rural areas, mobile money is a medium for financial inclusion. Lack of infrastructure, regulation, and institutional support, which in this analysis are considered transaction costs[2], inhibit and disincentivize participation in the formal economy. Mobile money allows these isolated communities to access cash more readily and save their earnings. In 2014, 76% of Kenyans above the age of 15 living in rural areas used mobile phones to make digital payments.[3] However, only 8% of money transfers between individuals and firms occur over mobile phone.
What are the sources of these trends? Why is mobile money and the financial services it provides being adopted so quickly and across such a wide section of the population? Why is mobile money so frequently used in Kenya for peer-to-peer transactions, but not for payments to firms? Why is mobile money outpacing use of traditional financial institutions?
For the last few weeks, Hitachi Center Fellow Dr. Jonathan Greenacre and I have been discussing these questions at length. From our meetings and preliminary research, we have found that while a number of multinational corporations, international organizations, and academics have conducted comprehensive population surveys at the national level, none have accounted for social ties within individual communities. We believe that a formal social network analysis of a specified Kenyan community group and their money use would provide insight into the questions listed above. This social network analysis will supplement Dr. Greenacre’s broader study of bottom-up mobile money regulation within Kenya.
Hypothesis
Following my conversations with Dr. Greenacre and Associate Director of Research and Doctoral Research Fellow for Innovation and Change, Ravi Shankar Chaturvedi, I hypothesize that mobile money popularity is in part due to the way it compliments traditional community structure. In addition to institutional mistrust and the transaction costs associated with the use of formal financial institutions, the financial services offered by companies like M-Pesa allow individuals to easily transfer money between friends and relatives; people they trust. Furthermore, agents providing banking services can be held accountable because they themselves are members of the community.
These agents will likely be central nodes within the network. High-density cliques and clusters may form within family groups, which may, in turn, be subsets of larger factions determined by social group. Agents will serve as primary brokers between these clusters and factions. This analysis will illustrate communal social ties, how they influence the use of mobile money services, and how those services are used.
Methodology
There are several relevant datasets available through the World Bank Global Financial Inclusion Database as well as the Harvard Dataverse. While the former offers primarily country-wide metadata, it is possible that Dr. Greenacre’s substantial connections within the World Bank may be able to yield individual household data. Alternatively, FSD Kenya’s survey of 1600 Kenyan households conducted by Dr. Tavneet Suri of MIT Sloan over a 5 year period looks more promising.[4] Finally, as a contingency, Dr. Guy Stuart, Executive Director of Microfinance Opportunities (MFO) and Harvard Innovation Program Fellow, has shared data from his work with FSD Zambia with me.[5] This includes individual household spending diaries from underserved Zambian families over several years. Whether Kenya or Zambia, the structure of this analysis will not be affected. MTN Mobile Money in Zambia uses the same model as M-Pesa in Kenya; the two formed a partnership to provide service to 19 African countries in 2015.[6]
The simplest way to represent tie-strength between community members is through the absence or presence of a mobile transfer between individuals in the past year. This will yield a binary directed dataset, however, it may prove too broad. Depending on the available data, I may use frequency of transaction as an indicator of tie strength, differentiating transaction frequency by week, month, and year. Alternatively, significance of transaction may be used as a measure of tie strength: goods purchase vs. lending emergency funds. If neither is a valid measure of tie strength, I may employ alternative tie strength measurement models.[7] [8]
This network may be complemented or combined with 2-mode attribute datasets to further depict community spending patterns, namely transactions with businesses and financial institutions. These datasets will be calculated for two separate years, which will vary depending on the chosen dataset (e.g. Dr. Suri’s surveys: 2011 & 2014). This will show changes in mobile money use over time.
Limitations
The most pressing limitation on this analysis is the large size of the datasets I am using. First, a key question I will need to answer is how I will narrow down my analysis to a select number of households within a given community, ensuring that the chosen subset is representative of the larger whole. Second, I will need to widdle down the existing attribute data, choosing only the most relevant attributes to my analysis.
I am taking the second module of the course.
I am taking the second module of the course.
[1] https://sites.tufts.edu/ibgc/files/2016/04/IFMM-Case-Study.pdf Supplemented by private conversations with Mr. Chaturvedi
[2] Posited by Dr. Jonathan Greenacre, Hitachi Center Faculty Fellow
[4] https://dataverse.harvard.edu/dataverse/mobilemoney Dr. Greenacre has contacted her for additional information on the study
[6] https://www.nation.co.ke/business/eMoney/Safaricom-MTN-partnership-Africa/1625712-2694198-yc82wo/index.html
1 comment:
This has all kinds of interesting possibilities, but what is missing is an overarching, high-level research question that would help you clarify the value of doing SNA as opposed to (or in addition to) other types of analysis. Further, if your goal is to enhance existing work by "accounting for social ties within individual communities," how will you do this by using money transfers as the main network "flow." Or will the money flow be used in addition to existing studies of the relationship or kinship networks? All this needs to be thought through carefully in order to make the case for SNA. We'll discuss this in class.
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