Women, Social Capital, and Financial Inclusion: Linking Customer Data with Ethnographic Perspectives

By IMTFI Researcher Sibel Kusimba−American University, Gabriel Kunyu−Independent Researcher, and Dave Mark−CTO, M-Changa

December 2015, research team with one of our participants in the IMTFI project
Photo Credit: Chap Kusimba 

Financially including women has become a priority among development and finance experts. Women are less likely to be financially included. However, it has been widely observed that when included they are more likely to produce substantial economic gains for their households. It follows then that any good financial inclusion strategy must include women (GPFI 2015). Women face barriers to inclusion due to combination of various factors such as lack of literacy, access to mobile phones or banks, and time constraints among others. What does finance mean to unbanked women? For some time now, advisers to the industry have been suggesting flexible bank hours, mobile agents, and phone interfaces in multiple languages to address these realities (GPFI 2015; El-Zoghbi 2016; Murray 2016). In this context, IMTFI’s approach to use an ethnographic perspective to understand practices of money and finance around the world can help build models for women’s finance that connect to their existing practices (Dalinghaus 2015).

M-Changa platform:
fundraising for a wedding 

Our research seeks to understand the effect of gender on networks across differences in social class, income, and rural/urban settings. In this post we focus on a customer dataset from the fundraising platform M-Changa in Kenya, which provides interesting clues. M-Changa collects money via mobile money, EFT or Paypal into a unique account and is used by originators to fundraise money for medical needs, funerals, school fees and weddings. The company provides transparency and its activities are directed towards ensuring both trust and transparency which include posting and making public on their website hospital and school bills and funeral certificates. Since its launch in 2012, M-Changa has managed over 6000 fundraisers.

A customer data analysis by FSD Kenya categorized M-Changa fundraising events into five types based on the success of the fundraiser. Among these, one cluster was distinctly successful in fundraising events and was able to raise a large amount of money over a relatively short period of time from the largest number of contributors. In this cluster the originators were 45% female – even though only 20% of all fundraiser originators in the dataset as a whole are female.  What can account for the great success of women in using M-Changa fundraising?

The M-Changa dataset finds a compliment in the findings of ethnographic study that we undertook in 2012 and 2014 focusing on the social networks of primarily farming people in western Kenya. Supported by IMTFI, the study recorded examples of informal finance groups based on friends, family, co-workers, and neighbors, and drew the pathways of money sending connecting family members. We found that money circulated among close relatives, especially siblings, who were often connected to mothers and mother’s relatives. In these networks, women tended to be central nodes in the many pathways of money sending and receiving to other network members.

Furthermore, emotional connections and powerful social norms around reciprocity and obligation often seemed to drive remittances to women in Western Kenya. For instance, consider the case of Emmanuel, an unmarried 22 year-old caretaker at a private primary school. Emmanuel was raised by his maternal grandmother Wilbroda because he was born out of wedlock. His mother eventually married elsewhere and he has eight half siblings. He dropped out of school after the eighth grade due to financial reasons.

Emmanuel (Photo Credit: Gabriel Kunyu)

Emmanuel sends money to Wilbroda every month before she even needs to ask him. In the case of his mother, however, he normally waits for her to call, which she often does at the end of each month. Emmanuel explained that normally, if the amount he sends his mother is less than her minimum expectation (say 200 shillings (US $2)), she will not call back to give thanks but instead go silent, implying she was not satisfied with the amount. He says she will sometimes call with a false excuse of checking on him, but at the end of the call inquire if he has something to send her. In May 2016, Emmanuel’s mother called and requested assistance, barely two weeks after Emmanuel had sent her 300 shillings (US $3). As a way of encouraging Emmanuel, she also called her brother − Emmanuel’s maternal uncle − who in turn called Emmanuel and persuaded him to send her money, explaining that she needed it for buying fertilizer. Because of his uncle’s call, Emmanuel said he broke into his savings and sent her 1000 shillings (US $10). Emmanuel never sends money to his father, who took little interest in him growing up and refused to pay his school fees. His remittances to his mother rely on nudges from his maternal uncle and his own sense of obligation. His grandmother is clearly his financial priority.

The M-Changa dataset, like the Western Kenya study, shows a similar advantage for women in collecting resources, as nodes and hubs of social networks. It is all the more intriguing that M-Changa women are not rural farmers, but primarily college-educated, salaried, and technology-savvy Nairobi women. Further ethnographic work with M-Changa’s clientele will seek to tease out more of the sources of fundraising skill for its affluent, urban female users. Are emotional bonds or gendered social norms around obligation to women the common factor such that these urban women leverage close ties of family? Do they have broad networks reflecting diverse social circles, in which they perhaps cultivate more or closer friendships than men? How far do these urban-centered networks extend to relatives in rural areas? Following questions like these through a thick data understanding (Wang 2013) of users − taking into account well-elaborated customer data and ethnographic studies simultaneously − can reveal otherwise overlooked insights into the ways in which women may be financially included based on their existing financial strengths.

Sources Cited 

Digital Financial Solutions to Advance Women’s Economic Participation. GPFI (Global Partnership for Financial Inclusion), November 2015. 

Dalinghaus, Ursula. 2015. Going to Where the Women are: Insights from the Making Finance Work for Women Summit in Berlin, Germany. http://blog.imtfi.uci.edu/2016/01/going-to-where-women-are-insights-from.html

El-Zoghbi, Mayada. 2016. What Excludes Women from Formal Finance in the Arab States? http://www.cgap.org/blog/what-excludes-women-formal-finance-arab-states

Murray, Inez. 2016. Catalyzing Women’s Financial Inclusion: The Role of Data. http://www.cgap.org/blog/catalyzing-women%E2%80%99s-financial-inclusion-role-data.

Wang, Tricia. 2013. Big Data needs Thick Data. http://ethnographymatters.net/blog/2013/05/13/big-data-needs-thick-data/.