ULBs and utilities are prone to drafting strategies based on weak evidence—which resource-crunched cities can ill-afford to do
Cities have emerged as hubs for economic development. Governance and management of cities should be evidence-based to produce the desired outcomes in quality of life. For that, cities should have high quality, up-to-date data to not only monitor service coverage, access and quality, but also take important decisions, including on resource allocation. The introduction of the system of annual service level benchmark (SLB) reports at the level of the urban local body) ULB was intended, among other things, to put in place a mechanism to eventually enable data-driven decision-making in cities. Many experts also recommend online publishing of SLB reports.
As things stand today, many ULBs do not publish SLB reports online, and several others have questionable data quality, leaving only a few big ULBs which serve the purpose of SLB reports—i.e. monitoring and reporting service coverage and quality. This is problematic since it is the smaller and medium sized ULBs who typically (barring exceptions) have more acute funding shortages, and have low own-source revenue mobilisation capacity—meaning the greater need to efficiently allocate available resources. SLBs cover 28 important indicators in the domains of water supply, wastewater management, solid waste management, storm water drainage, services coverage in slums, etc. Data on critical indicators such as piped water supply coverage, sewerage coverage, user fee collection efficiency, and others that are used in ULB-level service level improvement reports (SLIPs) and state-level state annual action plans (SAAPs)—under AMRUT—are also of low quality. The data quality is often acknowledged as poor in these reports. For example, the‘reliability score’ of reported data is often marked as ‘D’ in many documents—the lowest reliability level. This means ULBs and utilities are prone to drafting strategies, and even planning investments, based on weak evidence—which resource-crunched cities can ill-afford to do.
Following establishment of as-is (baseline) datapoints, it is important that ULBs routinely update the data. This is particularly imperative at a time of rapid urbanisation and rural-to-urban migration. On the supply side, supervisory control and data acquisition (SCADA) platforms will likely elevate the quality and frequency of systems data. Many ULBs are investing in SCADA, and smart and automatic metering, under the Smart Cities Mission and AMRUT. These will generate real time data including big data. But, it is also important that cities triangulate data from both the supply and demand sides to ensure greater reliability and validity of data. The SLB program was a useful step towards orienting a transition in focus from simply infrastructure development to results monitoring at the outcome level. Therefore, outcomes must also be tracked from the perspective of the end-user.
For some time, the SLB data was sourced only from the service providers (such as water board/utility) without sufficient quality checks. ULBs should cross-reference reported data with end-user data, in order to fully reflect the views of the citizens or service receivers. This is particularly important to factor in questions of barriers to access and inclusion parameters. This would also allow the engagement of citizenry and better enforcement of accountability of service providers. Existing research already highlights the gap in supply and demand side data. One research found that in the cities of Rae Bareli, Jabalpur and Varanasi, 42-73% of respondents reported at least one instance of dirty water supply in the preceding three months—while utility data in the corresponding cities reported 96-98% compliance with water quality standards.
The impacts of such variance is likely to be the most widespread amongst slums. Often, ULBs do not have a clear and up-to-date pictures of their slum populations. In this context, ULBs’ efforts at using GIS and drones for comprehensive slum mapping and profiling will be a useful data acquisition exercise too. For example, drone surveys for precision mapping is currently under implementation in Odisha.
It is useful, as cities grow in size, for ULBs to conduct routine feedback surveys on statistically representative samples to gauge the demand side data to validate the data reported by service providers. Finally, to fully utilise the wealth of data to inform decisions through use of analytics, it is useful for every ULB to have dedicated statisticians, and IT and MIS resource persons in order to manage data acquisition—not only for routine record-keeping purposes but also to analyse and derive insights to enable decision-making.
Abhirup Bhunia is with an international development consulting firm.