in the 21st Century
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The Geography of F<strong>in</strong>ancial Services Providers <strong>in</strong> Kenya 79<br />
1 and Figure 5 – f<strong>in</strong>ancial services providers are more likely to be located <strong>in</strong><br />
more populated areas, which are also characterised by better <strong>in</strong>frastructure<br />
and lower levels of poverty. 13<br />
Indeed, counties like Nairobi, Kiambu, and Mombasa, with high population<br />
densities, also benefit from very large shares of mobile money services providers.<br />
Conversely, <strong>the</strong> presence of mobile money services providers <strong>in</strong> counties with<br />
a much more scattered population – like Marsabit, Isiolo or Tana River – is<br />
very limited. A potential explanation for <strong>the</strong> lack of mobile services providers<br />
<strong>in</strong> <strong>the</strong>se areas is that, at present, <strong>the</strong>re is no bus<strong>in</strong>ess case for sett<strong>in</strong>g up f<strong>in</strong>ancial<br />
access po<strong>in</strong>ts <strong>the</strong>re. Figure 6 reveals that more efforts are needed by <strong>in</strong>stitutions<br />
to facilitate <strong>the</strong> penetration of f<strong>in</strong>ancial access po<strong>in</strong>ts, particularly <strong>in</strong> counties<br />
where <strong>the</strong> population density is very low.<br />
Figure 6: Relationship between number of mobile money agents and population<br />
density at <strong>the</strong> county level (log-log scale)<br />
Number of mobile money agents (log scale)<br />
10<br />
8<br />
6<br />
4<br />
TAI<br />
GAR<br />
ISI WAJ TUR<br />
SAMLAM<br />
TAN<br />
MAR<br />
MAC<br />
UAS<br />
MER NYE KAK<br />
KIRKISI<br />
MUR<br />
HOM BUN<br />
KISU<br />
KIL<br />
MAK<br />
NYAN<br />
KIT<br />
KER SIAMIG<br />
LAI NAR<br />
EMB<br />
NAN TRA BUS<br />
BAR<br />
KWA<br />
NYAM<br />
THA<br />
VIH<br />
MAN<br />
WES<br />
KEI<br />
NAK<br />
BOM<br />
KIA<br />
NAI<br />
MOM<br />
1 3 5 7 9<br />
Population density (people per km 2 , log scale)<br />
County L<strong>in</strong>ear prediction<br />
Notes: BAR: Bar<strong>in</strong>go; BOM: Bomet; BUN: Bungoma; BUS: Busia; EMB: Embu; GAR: Garissa; HOM: Homa Bay; ISI:<br />
Isiolo; KAJ: Kajiado; KAK: Kakamega; KEI: Keiyo-Marakwet; KER: Kericho; KIA: Kiambu; KIL: Kilifi; KIR: Kir<strong>in</strong>yaga; KISI:<br />
Kisii; KISU: Kisumu; KIT: Kitui; KWA: Kwale; LAI: Laikipia; LAM: Lamu; MAC: Machakos; MAK: Makueni; MAN: Mandera;<br />
MAR: Marsabit; MER: Meru; MIG: Migori; MOM: Mombasa; MUR: Murang’a; NAI: Nairobi; NAK: Nakur; NAN: Nandi; NAR:<br />
Narok; NYAM: Nyamira;; NYAN: Nyandarua; NYE: Nyeri; SAM: Samburu; SIA: Siaya; TAI: Taita Taveta; TAN: Tana River;<br />
THA: Tharaka-Nithi: TRA: ; Trans Nzoia; TUR: Turkana; UAS: Uas<strong>in</strong> Gishu; VIH: Vihiga; WAJ: Wajir; WES: West Pokot.<br />
Source: Data on <strong>the</strong> area (sq. km) of counties <strong>in</strong> Kenya are taken from http://www.geohive.com/cntry/kenya.aspx.<br />
13 A similar pattern, although less pronounced, exists for <strong>the</strong> o<strong>the</strong>r categories of f<strong>in</strong>ancial services providers (ATMs,<br />
bank agents, bank branches, MFIs and SACCOs); <strong>the</strong> results for <strong>the</strong>se are available upon request.