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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.

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