Presentation on the challenges and opportunities of financing rural water supply in Africa, with evidence from Kenya. This presentation draws on work by the Water Programme, Smith School of Enterprise and the Environment, University of Oxford.

1. Are the rural water poor a bankable prospect?
- payment behaviours, digital data, performance models
maintenance service
provider but lack the
resources to pay in full
promote markets for
water-related goods
and services
to everyone with
resources to support
universal delivery
All and
sustainable
Most and
reliable
Some and
unreliable
InvestorsUser tariffs Government
Finance for maintaining water infrastructure, % of total
Population,%oftotal
Co-investing in maintenance costs of water infrastructure to ensure
reliable supplies for drinking, washing, bathing and laundry needs

2. Private and public finance for rural water supply in Africa
= countries with rural water cost recovery policy with
O&M under CBM; est. annual O&M expenditure c.
USD1bn p.a.
0 10 20 30 40 50 60 70 80 90
Liberia
Zimbabwe
Lesotho
Malawi
Zambia
Mali
South Africa
Madagascar
Tanzania
Nigeria
Weighted average
Kenya
Burkina Faso
Uganda
Mozambique
Ghana
Botswana
Senegal
Benin
Namibia
Cape Verde
Rural households paying for water (2008-09)1
1. Waterpoints analysed include standpipes, kiosks, handpumps and protected springs. Analysis excludes waterpoints located in urban areas.
Data drawn from publicly available waterpoint datasets (Virtual Kenya, 2015; National Water Sanitation and Hygiene Promotion Committee,
2014; Sierra Leone, STATWASH Portal 2014; Government of Tanzania, 2014; Government of Uganda, 2012). For additional data see Waterpoint
Data Exchange http://www.waterpointdata.org/

3. Private and public finance for rural water supply in Africa
0
10
20
30
40
50
60
70
80
90
100
Tanzania Kenya Uganda Liberia Sierra
Leone
Revenue collection rates1
Rural utility collection rate (piped schemes)
Standpipes/kiosks with revenue collection
Handpumps with revenue collection
8%
22%
13%
24%
10%
16%
34%
25%
43%
26%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Kenya Liberia Sierra
Leone
Tanzania Uganda
Rural waterpoint non-functionality rates
(n=183,149)1
With revenue collection Without revenue collection
1. Waterpoints analysed include standpipes, kiosks, handpumps and protected springs. Analysis excludes waterpoints located in urban areas.
Data drawn from publicly available waterpoint datasets (Virtual Kenya, 2015; National Water Sanitation and Hygiene Promotion Committee,
2014; Sierra Leone, STATWASH Portal 2014; Government of Tanzania, 2014; Government of Uganda, 2012). For additional data see Waterpoint
Data Exchange http://www.waterpointdata.org/

4. Multi-decadal payment analysis from coastal Kenya
Refs: Foster & Hope (2016); Foster & Hope (2017); Foster (2017)
0
10
20
30
40
50
60
70
80
90
100
Payment rate (% users paying)
(Monthly payments, 1990-2013)

5. Multi-decadal payment analysis from coastal Kenya
– systematic late payment or non-payment
Refs: Foster & Hope (2016); Foster & Hope (2017); Foster (2017)

6. Multi-decadal payment analysis from coastal Kenya
Refs: Foster & Hope (2016); Foster & Hope (2017); Foster (2017)
0
10
20
30
40
50
60
70
80
90
100
Payment rate (% users paying)
(Monthly payments, 1990-2013)
Data - 229 years of financial records
from 100 handpumps with >50,000
payment records
Predictors of payment:
• Distance to waterpoint
• Water quality (pH, taste)
• Productive use
• Seasonality
Ø Late payment and non-payment
are prevalent
Ø Payment behaviour a ‘socio-
ecological’ response
Ø Higher revenue collection, higher
unimproved use

7. From analogue to digital verification
- performance, planning, investment
Refs: Thomson et al. (2012); Hope et al. (2014); Foster et al. (2015)
Thomson (forthcoming);

8. From analogue to digital verification
- performance, planning, investment
Refs: Thomson et al. (2012); Hope et al. (2014); Foster et al. (2015)
Thomson (forthcoming);

9. From analogue to digital verification
- performance, planning, investment
Refs: Thomson et al. (2012); Hope et al. (2014); Foster et al. (2015)
Thomson (forthcoming);

10. From analogue to digital verification
- performance, planning, investment
Refs: Thomson et al. (2012); Hope et al. (2014); Foster et al. (2015)
Thomson (forthcoming);

11. From analogue to digital verification
- performance, planning, investment
Refs: Thomson et al. (2012); Hope et al. (2014); Foster et al. (2015)
Thomson (forthcoming);

12. From analogue to digital verification
- performance, planning, investment
Refs: Thomson et al. (2012); Hope et al. (2014); Foster et al. (2015)
Thomson (forthcoming);

15. Project performance data from FundiFix model
- KHSL (Kwale county) and Miambani Ltd. (Kitui county), Kenya

16. Project performance data from FundiFix model
- KHSL (Kwale county) and Miambani Ltd. (Kitui county), Kenya
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Feb-15
Mar-15
Apr-15
May-15
Jun-15
Jul-15
Aug-15
Sep-15
Oct-15
Nov-15
Dec-15
Jan-16
Feb-16
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Mar-17
Apr-17
May-17
Jun-17
Jul-17
Aug-17
Sep-17
Miambani Ltd. (Kitui) Collection efficiency

17. Project performance data from FundiFix model
- KHSL (Kwale county) and Miambani Ltd. (Kitui county), Kenya

18. Project performance data from FundiFix model
- KHSL (Kwale county) and Miambani Ltd. (Kitui county), Kenya
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Feb-15
Mar-15
Apr-15
May-15
Jun-15
Jul-15
Aug-15
Sep-15
Oct-15
Nov-15
Dec-15
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Feb-16
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Sep-16
Oct-16
Nov-16
Dec-16
Jan-17
Feb-17
Mar-17
Apr-17
May-17
Jun-17
Jul-17
Aug-17
Sep-17
Miambani Ltd. (Kitui) Collection efficiency
0%
5%
10%
15%
20%
25%
30%
Jan-16
Feb-16
Mar-16
Apr-16
May-16
Jun-16
Jul-16
Aug-16
Sep-16
Oct-16
Nov-16
Dec-16
Jan-17
Feb-17
Mar-17
Apr-17
May-17
Jun-17
Jul-17
Aug-17
Sep-17
Oct-17
Working Ratio-Kitui Working Ratio-Kwale
Management dispute

19. Water Services Maintenance Trust Fund
- pooling financial risk so no one is left behind