in the 21st Century

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174 Kenya’s Financial Transformation in the 21st Century The shift in the pattern of financial inclusion over time is illustrated in Figure 19, where we include both the 2009 and 2013 distributions (M-PESA users are not shown separately). We observe a more or less horizontal shift in the distribution, which can be interpreted as a move out of informal inclusion in the direction of formal inclusion, but while maintaining the likelihood of exclusion. This pattern suggests that it was difficult for individuals who had not entered the financial system to move up the ladder of financial inclusion, but that the integration of those who were already included in 2009 deepened over time. Figure 19: Change in financial inclusion, all individuals, from 2009 to 2013 Excluded Informally Included All households, 2009 All households, 2013 Formally Included Assessing the welfare impacts of changes in financial inclusion We care about changes in the distribution of financial inclusion only to the extent that they have welfare impacts. In this sub-section, we develop a simple framework to assess these welfare impacts. Let u s be the utility derived from being in financial inclusion state s = 1 (Formal inclusion), 2 (Informal inclusion), or 3 (Exclusion). Note that to simplify, we assume this utility is independent of other individual characteristics. Then individual i‘s expected utility at time t is U it 3 s = ∑ pˆ itu s= 1 s and we let aggregate welfare be the weighted sum of expected utilities across individuals,
The Digitisation of Financial Services 175 W n = ∑ω U t it it i= 1 where ω it is the sample weight of individual i, with ∑ n i=1 ω it = 1. Thus W t 3 = ∑α u s= 1 st s where α st = ∑ n i=1 ω it p s i and ∑ 3 s=1 α st = 1. Welfare at time t is constant for α st satisfying W = α + δα = k t 1t 2t where u δ = u − u − u 2 3 1 3 and k is a constant. If being informally financially included provides as much utility as being formally financially included, so u 2 = u 1 , then δ = 1, and indifference curves are horizontal straight lines in the simplex. On the other hand, if informal financial inclusion provides no more benefits than exclusion, so u 2 = u 3 , then δ = 0, and indifference curves are straight lines parallel to the side of the simplex opposite vertex 1 corresponding to full formal inclusion. A negative value of δ means that either informal or formal financial inclusion is worse than exclusion, but not both. A value larger than 1 would indicate that individuals gained greater utility from informal financial inclusion than from formal inclusion. Neither of these seems likely to be the case in practice. We now calculate the value δ* that would maintain welfare at a constant level between 2009 and 2013. In fact, we find that in order for welfare to have remained fixed, it would be necessary that δ* > 1. Thus, only if a move from informality to formality were to decrease utility could welfare have remained constant between 2009 and 2013. We infer then, under the assumption that formal financial inclusion increases utility, that average welfare must have increased. Our observations above, however, suggest that it is important to understand the distributional changes that took place in this window. One approach is to calculate the locations of both the average individual and the median individual in each of the two survey years. Of course, in order to do so, we need to make an assumption about the utility value of financial inclusion. Instead of choosing a single value of δ, we carry out this exercise under the two extreme assumptions identified above, that is δ = 0 and δ = 1. The results are shown in Figures 20 and 21.
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Kenya’s Financial Transformation
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Dedication Ravi Ramrattan was FSD
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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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in the 21st Century

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