in the 21st Century

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164 Kenya’s Financial Transformation in the 21st Century separately for different strata of the population – by gender, location, and age – and compare them over time. The drivers behind this expansion in access were on both the supply side, especially the explosion in the number of M-PESA agents, and the demand side, as network effects increased the value of the service and hence the willingness of individuals to adopt it. 7 Growth in the probability of using M-PESA We first estimate the coefficients μ̂ 2009 and μ̂ 2013 , and use these to predict the probability of M-PESA use for individuals in each year, ρ̂i,2009 and ρ̂i,2013 . We reorder the estimated probabilities in each vector, and plot the predicted values as a function of their rank, as shown in Figure 7. The graph of predicted probabilities in year t at rank r is denoted by ρ̂t (r). Figure 7: Distribution of predicted probability of using M-PESA, 2009–2013 Probability of using M-PESA 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Median likelihood in 2013 = 63% 2013 2009 Median likelihood in 2009 = 22% 0 10 20 30 40 50 60 70 80 90 100 Percentile rank Under the assumption that the ordering does not change over time, – that is, that an individual at the pth percentile in 2009 would be at the same percentile in 2013 – this figure illustrates how access to and use of M-PESA changed over time. All but those with the very highest and very lowest predicted probabilities in 2009 saw their predicted probability increase in 2013. The predicted median probability of using M-PESA increased from about 22% to about 63%. The change in convexity of these functions (from convex to concave) from 2009 to 2013 reflects broad gains in access across the distribution. 7 By April 2011, the number of M-PESA agents was about 28,000 across the country, all deployed in the space of four years. By contrast, the number of bank branches across the country grew from 887 in 2008 to just 1,063 in 2010, during which time the ATM network expanded from 1,325 to 2,203.
The Digitisation of Financial Services 165 Rural versus urban differences To understand this growth in access further, we restrict attention to certain strata. To start, we calculate the distributions of access for rural and urban populations separately. The 2009 distributions are presented in Figure 8 – the median probability of using M-PESA amongst urban residents was about 50%, while it was only 15% in rural areas. Figure 8: Rural-urban divide in 2009 1 0.9 Probability of using M-PESA 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Median urban likelihood = 49% Urban Rural 0.1 Median rural likelihood = 15% 0 0 10 20 30 40 50 60 70 80 90 100 Percentile rank Figures 9 and 10 depict the changes in rural and urban usage between 2009 and 2013. The distribution in rural areas in 2013, ρ̂R2013 (r), closely matches that of urban areas in 2009, ρ̂U2009 (r), but the continued deepening of access in urban areas meant that rural residents were not able to catch up.
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Kenya’s Financial Transformation
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FSD Kenya 5th floor, KMA Centre Cor
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Dedication Ravi Ramrattan was FSD
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2 Kenya’s Financial Transformatio
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CHAPTER 1 Transformation of Kenya
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CHAPTER 2 The Geography of Financia
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CHAPTER 3 Formal Financial Inclusio
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CHAPTER 7 Financial Capability and
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