Making sense of a confusing picture: Reconciling sanitation data from different sources Ian Ross and Amos Chigwenembe WaterAid

1. What is data reconciliation? Which data? Focus on coverage, access or use of improved sanitation Collaboration What is reconciliation? Ensuring that - at the very least, different sources of data are comparable - better, national level monitoring is harmonised - even better, understand how national data corresponds to global data (JMP)

2. What’s the problem in general? A hypothetical situation (esp. at local level) - MoH: “survey says sanitation access is 50%” - MoI: “database says sanitation coverage is 60%” - JMP: “survey trends say sanitation use is 40%” What causes the problem? Different sources of data (surveys/databases), e.g. Pakistan district/provincial level, India database Different indicators/definitions (coverage/use, “hygienic”) e.g. “shared” in Nepal and most countries Different categories of improved (slab/traditional/shared) e.g. Bangladesh country report None is “wrong” Measuring different things – we need them all

3. Why does this matter? Conflicting data  hard to understand: -progress towards targets/ MDGs -impact of interventions -How to target investments to where they’re most needed Lack of coordination means: -Confusion, duplication Targeting!

4. State of sanitation monitoring in South Asia Positives Coordination forum often exists (e.g. task force), consultation on surveys Some progress on harmonisation of defs./cats. Most countries developed/ing an MIS Room for improvement Confusion or disagreement at district level  poor targeting Regular reconciliation between MIS/surveys Data not used enough for decision-making, and… There is no quick technical fix – a fancy database is not enough. Long-term dialogue and consensus building needed

EQUITY - Poverty – UNICEF wealth quintiles analysis, and beyond - Ethnicity / caste and more (nb. data is for households not individuals, so v hard to measure gender / disability) - India starting to monitor outcomes for scheduled castes/tribes INCLUSION What doesn’t get measured can’t get targeted

6. Experience from Malawi Data reconciliation regional approach -JMP regional workshop in Nepal in 2008, followed by national meetings in India/Bangladesh/Nepal WaterAid / JMP regional workshops in Southern Africa and Eastern Africa in 2010 and 2011 Preparation was crucial -Multi-stakeholder meeting to clarify national position -Review database and all surveys, definitions, categories etc. -Established an Action Group to coordinate assignments

7. Added value of regional discussion - Platform for cross-country critique and learning - Greater appreciation of JMP and coherence with it - Incentivizes collaboration between national agencies which wouldn’t otherwise have happened. Series of national multi-stakeholder meetings agreed: - Consensus on all definitions and indicators - Manual developed to guide future surveys - Specific WASH module for 2011 Welfare Survey - Process for inputting to database designed by AfDB Achievements during follow-up

8. Why did this result in progress 1. Shared understanding of the problem developed at regional level, clear action plan 2. Multi-stakeholder Action Group meets quarterly. Involvement of Planning ministry crucial 3. Ownership of the data reconciliation and harmonization process by government institutions

Conclusions and questions for discussion 1.Still discrepancies between data sources in many S.Asia countries, especially at the sub-national level. Can’t solve this today, but what must be done when we return home? Would a regional approach help? 2.Most countries could make better use of data for planning and targeting finance How can use of data for decision-making be improved? 3.There is little data on equity/inclusion, i.e. use of sanitation by poor and marginalised groups? What indicators could be realistically be used in existing monitoring systems (both surveys and MIS/database)?