Landscaping Study: Digital Signals & Access to Finance in Kenya

Pulse Lab New York

Project Description

In emerging markets, eight out of ten small businesses cannot access the loans they need to grow. USAID’s Development Credit Authority (DCA) helps small businesses to access capital. The goal of this collaboration between UN Global Pulse and USAID is to explore how big data could support the work of USAID’s Development Credit Authority. Kenya has become an established tech leader in Africa in recent years – generating greater volumes of digital data as a result. The goal of this study is to explore what new sources of digital data, and methods for analysis, could be helpful in answering the question: “What barriers to accessing loans do small businesses in Kenya face?” Accordingly, the final report (below) paints a picture of the big data landscape in Kenya, shows preliminary findings, and lays the groundwork for further investigation.

The landscaping study was conducted in three steps:

  1. Background research on the digital media landscape in Kenya
  2. Developing a taxonomy (key words) related to loans, as commonly used in Kenya, through surveys and field research
  3. Using a variety of tools to analyze trends in social media and online search

The study began by looking at baseline data for ICT and social media usage rates in Kenya. By interviewing a sample of DCA’s clients – farmers from the region around Nakuru and Nairobi – Global Pulse created a taxonomy of colloquial language used in relation to accessing loans and finance. Global Pulse’s analysts then used this taxonomy to mine available data from Twitter and Google search trends, to understand online conversations and information-seeking behavior. It’s clear that in Kenya’s current digital landscape, the quantity of relevant social media data restricts its utility. For example, the study found that in July 2013 there was an average of only about 10 tweets related to “loans” on Twitter, per day. The relatively small numbers of relevant digital signals available indicate that in the short-term, social data can likely only provide supplementary insight about barriers to finance in Kenya. For instance, online analysis could be useful for revealing early themes or trends, to inform the topics of focus groups to validate. Despite the relatively small numbers of contextually relevant tweets available in 2013, there is an emergence of a Kenya-specific Twitter culture. In particular, Twitter is being used to seek, access and share information about loans, especially mobile loans. The introduction of a savings and small loans service called M-Shwari to M-Pesa customers in Kenya has already sent ripples through Kenyan social media.

The study also conducted some initial tests with Google to understand finance related information-seeking behavior in Kenya. Events that are broadly shared (i.e. the beginning of a school semester or elections) are clearly visible in the data. This could mean that other systemic, or anomalous, events will be visible as well. However, the analytical tools made available by Google for Kenya data are limited – currently it is only possible to know the relative volume of searches, rather than access real numbers. In addition to this analysis, the study looked at the digital footprint of the Development Credit Authority, its clients and priority sectors, and offers a menu of what other relevant sources of digital data might exist in Kenya, and how the data could potentially be accessed.

Access the full study here.


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