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The Geography of F<strong>in</strong>ancial Services Providers <strong>in</strong> Kenya 101<br />

though this result is significant only when we compare ATMs of large privately<br />

owned banks with ATMs of foreign-owned banks. Our f<strong>in</strong>d<strong>in</strong>gs are <strong>in</strong> l<strong>in</strong>e with<br />

Upadhyaya and Johnson’s analysis of <strong>the</strong> evolution of <strong>the</strong> Kenyan bank<strong>in</strong>g<br />

sector, and support <strong>the</strong> view that large privately owned banks are <strong>in</strong>creas<strong>in</strong>gly<br />

target<strong>in</strong>g poor, f<strong>in</strong>ancially excluded populations.<br />

All <strong>in</strong> all, our results highlight <strong>the</strong> fundamental role played by mobile money<br />

agents <strong>in</strong> enhanc<strong>in</strong>g f<strong>in</strong>ancial access <strong>in</strong> Kenya, which we measure as <strong>the</strong> type<br />

of f<strong>in</strong>ancial services <strong>in</strong>dividuals have access to. Interest<strong>in</strong>gly, we do not f<strong>in</strong>d<br />

<strong>the</strong> same effect when we look at rates of credit usage, which suggests that<br />

o<strong>the</strong>r f<strong>in</strong>ancial services providers – like MFIs and SACCOs – are more likely<br />

to target poor populations <strong>in</strong> terms of credit products.<br />

At <strong>the</strong> same time, when we consider banks only, our f<strong>in</strong>d<strong>in</strong>gs suggest that<br />

large privately owned banks represent <strong>the</strong> bank segment that is mak<strong>in</strong>g <strong>the</strong><br />

greatest effort to fight f<strong>in</strong>ancial exclusion <strong>in</strong> Kenya, particularly among poorer,<br />

more underserved populations. Yet, <strong>the</strong> analysis of <strong>the</strong> geography of f<strong>in</strong>ancial<br />

services providers <strong>in</strong> Kenya also po<strong>in</strong>ts to a need for much greater effort by<br />

<strong>in</strong>stitutions to fully target f<strong>in</strong>ancially excluded populations. This is particularly<br />

important for those liv<strong>in</strong>g <strong>in</strong> areas characterised by low population densities,<br />

and where <strong>the</strong> lack of <strong>in</strong>frastructure makes <strong>the</strong> penetration of f<strong>in</strong>ancial services<br />

providers extremely difficult.<br />

References<br />

Allen, F., E. Carletti, R. Cull, J. Qian, L. Senbet and P. Valenzuela (2013),<br />

“Improv<strong>in</strong>g access to bank<strong>in</strong>g – Evidence from Kenya” World Bank Policy<br />

Research Work<strong>in</strong>g Paper No. 6593, Wash<strong>in</strong>gton, DC.<br />

Jack, W. and T. Suri (2014), “Risk Shar<strong>in</strong>g and Transactions Costs: Evidence<br />

from Kenya’s Mobile Money Revolution”, The American Economic Review<br />

104(1), pp. 183–223.<br />

Johnson, S., G. Brown, and C. Fouillet (2012), The Search for Inclusion <strong>in</strong> Kenya’s<br />

F<strong>in</strong>ancial Landscape: The Rift Revealed, Nairobi: FSD Kenya.<br />

K<strong>in</strong>g, M. (2012) “Is Mobile Bank<strong>in</strong>g Break<strong>in</strong>g <strong>the</strong> Tyranny of Distance to Bank<br />

Infrastructure? Evidence from Kenya”, IIIS Work<strong>in</strong>g Paper No. 412, Dubl<strong>in</strong>.<br />

Nielsen, K.B. and T. Sl<strong>in</strong>d (2013), “The power of mapp<strong>in</strong>g f<strong>in</strong>ancial services<br />

data”, Bill & Mel<strong>in</strong>da Gates Foundation, Seattle, WA.

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