1. Academia contribution to the implementation of
the SDG related to WASH
Case Study: An improved monitoring framework
to support local level planning
Ricard Giné
Universitat Politècnica de
Catalunya

2. Who is
involved?
Background: the
Case Study
Improved framework
for planning
Decentralization offers the opportunity to define
strategies for equity-oriented planning and post-
project support. But various challenges
undermine effective targeting and prioritization:
 Local data is seldom available to support
evidence-based planning. Poorest locations
(50) in Kenya are targeted through the Central
Bureau of Statistics Poverty Index, which is
WASH non-specific and out-of-date (2003)
 Allocation procedures prevents the poor from
accessing the services
 Instruments for decision-making support are
not easy-to-use
 Lack of M&E framework hampers development
of long-term strategies
Key remarks

3. Who is
involved?
Background: the
Case Study
Improved framework
for planning
3
Recurrence of cholera, water-related diseases and
child mortality rates are high
Many and different initiatives in pursuing
achievements of the MDGs
Poor coordination of activities and lack
of sustained outcomes
Low access to safe water and
improved sanitation
Multi-Year WASH Sector Action / Investment Plan at
Districts of Homa Bay and Suba
Key remarks

4. Who is
involved?
Background: the
Case Study
Improved framework
for planning
Improved approach for local planning:
1. Identification of the neediest (data collection)
2. Prioritization to determine what gets done, and where (data analysis through planning
indices)
3. Development of tailored sector strategy (real needs) as road map to guide investments
(action plan)
+
WASH data collection, based on a Water Point
Mapping and a Household-based survey
Criteria to select targeted communities based
on simple planning indices, instead of
employing outdated and WASH-nonspecific
data
NAME OF INDEX FORMULA ACTION
Coverage index
250*
Population
IWPofNumber Construction of New water points
Functionality Index
100*
IWPTotal
IWPFunctofNumber
Rehabilitation of existing water
points.
Seasonality Index
100*
FIWPTotal
FIWPRound-YearofNumber Actions to increase reliability of the
source (catchment protection
actions, regulation of different uses)
and/or finding of additional sources
Management
Index 100*
FIWPTotal
FIWPManofNumber Management supporting activities
(establishment of WUEs and/or
support to establishment of tariff
collection systems).
Water Quality
Index 100*
FIWPTotal
FIWPSafeofNumber Actions to improve quality of water:
catchment protection, protection of
WP, etc… If salinity is high and
becomes dangerous, check other
alternative sources WP.
LOCATION
Population
2009
Total Number
WPs
LIST 1
Coverage Index
Rank 1
Priorities
North Kabuoch 5.088 0 0% 1
North Kanyamwa 9.286 0 0% 2
Central Kanyamwa 15.244 3 9% 3
Homa BayTown 35.816 8 11% 4
Gem Central 22.047 5 11% 5
Gem West 13.193 3 11% 6
West Kanyidoto 10.228 3 14% 7
East Kanyada 37.900 13 16% 8
East Kochia 13.917 5 17% 9
West Kabuoch 10.693 4 18% 10
West Kochia 12.637 5 19% 11
Gongo 9.569 4 20% 12
South Kabuoch 26.332 12 22% 13
Central Kabuoch 19.489 9 22% 14
West Kanyamwa 17.714 10 27% 15
East Kagan 12.012 7 28% 16
Gem East 11.619 7 29% 17
West Kwambwai 15.347 10 31% 18
West Kanyada 16.726 12 34% 19
South Kanyamwa 14.156 11 37% 20
East Kwambwai 16.249 13 38% 21
Central Kanyidoto 6.103 7 55% 22
South Kanyikela 3.180 6 90% 23
West Kagan 8.972 21 111% 24
North Kanyikela 3.103 9 138% 25
Key remarks

5. Who is
involved?
Background: the
Case Study
Improved framework
for planning
Coverage Index of Improved Water Points (IWP)
To estimate % of population covered by IWPs in a location, according to the
sector standards of service level (1 IWP serves 250 people). The index
shows those locations where coverage is a priority
  250*
Population
IWPofNumber
IndexCoverage Location
Location
Population
2009
WPs
Coverage
Index
Coverage
Priority
Coverage
Rank
North Kanyamwa 9286 0 0% High priority 1
North Kabuoch 5088 0 0% High priority 2
Homa BayTown 35816 8 5% High priority 3
Gem Central 22047 5 5% High priority 4
Central Kanyamwa 15244 3 5% High priority 5
Gem West 13193 3 5% High priority 6
West Kanyidoto 10228 3 7% High priority 7
East Kanyada 37900 13 8% High priority 8
East Kochia 13917 5 9% High priority 9
West Kochia 12637 5 9% High priority 10
West Kabuoch 10693 4 9% High priority 11
Gongo 9569 4 10% High priority 12
South Kabuoch 26332 12 11% High priority 13
Central Kabuoch 19489 9 11% High priority 14
West Kanyamwa 17714 10 13% High priority 15
East Kagan 12012 7 14% High priority 16
Gem East 11619 7 14% High priority 17
West Kwambwai 15347 10 16% High priority 18
West Kanyada 16726 12 17% High priority 19
East Kwambwai 16249 13 19% High priority 20
South Kanyamwa 14156 11 19% High priority 21
Central Kanyidoto 6103 7 27% Priority 22
South Kanyikela 3180 6 45% Priority 23
West Kagan 8972 21 56% No priority 24
North Kanyikela 3103 9 69% No priority 25
Key remarks

6. Who is
involved?
Background: the
Case Study
Improved framework
for planning
 Design of data collection methodology
 Supervision of field work
 Development of decision-making tools (planning indices, rankings and league
tables, poverty maps, etc.)
 Knowledge transfer to government staff
 Active participation in the data collection campaign
 Identification of sector needs / priorities
 Use of decision-making tools in WASH services delivery
 Overall coordination of the case study
 Funding
 Engage district offices (water, education and health) in the study
 Data collection
Key remarks

7. Key remarks
Who is
involved?
Background: the
Case Study
Improved framework
for planning
Remarks from the case study
 WASH-specific and updated data is essential to support evidence-based
planning. Cost of data collection is reduced in comparison with sector-related
investments (new infrastructure)
 These data can be easily exploited through simple planning indices to inform
planners / decision-makers and guide policy-making. Depending on the
problem at hand different actions might be planned:
Construction of new water points; Rehabilitation of non-functional existing
systems; Water quality improvement (water safety plans, surveillance, ...);
Sanitation Marketing; Hand-washing promotion, etc.
 The continued use of developed instruments requires effective appropriation
by decision-makers, which in turn depends on i) engagement of end-users
throughout the process, ii) design of user-friendly instruments, and iii)
continued support to local authorities.
Also, the monitoring framework needs to be rethought to allow data updating
and foster replicability

8. Key remarks
Who is
involved?
Background: the
Case Study
Improved framework
for planning
Academia contribution to WASH Post-2015
 Advocate for the importance of WASH in improving wellbeing (rigorous health
impact assessment of WASH interventions, identification and analysis of
WASH-related benefits, etc.)
 Promote and carry out research on WASH-related challenges (water quality,
sustainable exploitation of water resources, appropriate sanitation
technologies, reduction of inequalities, etc.)
 Enhance the planning process by improving availability of reliable information,
by improving access to information through data analysis, interpretation and
dissemination, and by encouraging the use of this information in decision-
making processes
 Ensure knowledge transfer to practitioners and government staff, in order to
accelerate progress towards post-2015 targets and indicators

9. Thank you!
ricard.gine@upc.edu