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

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(50) in Kenya are targeted through the Central Bureau of Statistics Poverty Index, which is WASH non-specific and out-of-date (2003)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

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

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)

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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Criteria to select targeted communities based on simple planning indices, instead of employing outdated and WASH-nonspecific data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

IWP ofNumber

Construction of New water points

Functionality Index

100*IWP Total

IWPFunct ofNumber

Rehabilitation of existing water points.

Seasonality Index 100*

FIWP Total

FIWP Round-Year ofNumber

Actions to increase reliability of the source (catchment protection actions, regulation of different uses) and/or finding of additional sources

Management Index 100*

FIWP Total

FIWPMan ofNumber

Management supporting activities (establishment of WUEs and/or support to establishment of tariff collection systems).

Water Quality Index 100*

FIWP Total

FIWP Safe ofNumber

Actions to improve quality of water: catchment protection, protection of WP, etc… If salinity is high and becomes dangerous, check other alternative sources WP.

LOCATIONPopulation

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 Bay Town 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

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

IWP ofNumber Index Coverage Location

LocationPopulation

2009WPs

Coverage

Index

Coverage

Priority

Coverage

Rank

North Kanyamwa 9286 0 0% High priority 1

North Kabuoch 5088 0 0% High priority 2

Homa Bay Town 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

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

Key remarksWho is

involved?Background: the

Case StudyImproved 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

Key remarksWho is

involved?Background: the

Case StudyImproved 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

Thank you!

ricard.gine@upc.edu