Unravelling NITI Aayog’s Composite Water Management Index: Veering to a pragmatic approach
An analysis of the effectiveness of the Composite Water Management Index as a policy-making tool
14 Jun 2019
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Image for representational purposes only. Image credit: India Water Portal


On 30th May, the new government took oath to serve the nation. The celebrations on that scorching summer evening at Rashtrapati Bhavan echoed hollow with more than 500 million people vulnerable to severe drought in the country. India is currently going through an extended dry spell with 13 of the last 18 years having deficit rainfall, resulting in acute water stress in almost 40 percent of the total landmass of the country. While water should have played an important role in the general election, it’s paradoxical how it hardly featured in the campaign of any party. A year back though, NITI Aayog released a report on the Composite Water Management Index (CWMI) on the 14th of June 2018. This triggered a lot of discussions centred around the looming water crisis and the statement on how 21 major cities were set to hit Zero Day by 2020. With this in mind, the report stated the necessity for a reliable and consistent database for monitoring the water situation and characterized CWMI as the first step towards making water governance effective by ranking states on nine key themes. The indicators were selected based on policies that are assumed to be promoting sustainable practices of water use. The objective of the index is to nudge the states that have been laggards in implementing these policies through 'Competitive Federalism'.


The Composite Water Management Index comprises of the weighted average of 9 thematic areas having separate sets of indicators. The themes are further divided into 28 indicators to score the states. Broadly, the indicators can be characterized into two types: 1) Continuous (76.67 % weight) and 2) Binary (23.33 % weight).


Each indicator within a theme has total weights assigned to them ex-ante and the index score depends upon the variation in performance across the states except for binary indicators which award 1 point straightaway if the criteria in the indicator is fulfilled. Although their contribution is limited to 23.33% of the total score, upon analyzing the distribution of the states on index scores, it becomes evident that binary indicators are significant enough to traverse the state’s score from the lowest quartile to the top-most quartile in their CWMI ranking. Moreover, these indicators mostly monitor introduction of new legislations that may help the state get brownie points. However, the report itself says that there is no strong correlation between legislation and their outcomes. There is also a lack of justification behind the weights assigned to the themes. Based on the premise if we redistribute the thematic indicators under 5 key dimensions of Access, Efficiency, Sustainability, Quality and Governance, we find that quality and access get the least weight which contradicts the very essence of the ranking. It is important to find a robust indicator that can reflect ground realities about water challenges, but many indicators in CWMI fail to achieve this.


For example, Indicator 16 about adherence to cropping pattern as per agro-climatic zoning, ranks Maharashtra 2nd with 99% of its area planted as per agro-climatic zoning. A recent study on water productivity by NABARD, though, highlights the need to re-align cropping pattern with natural water resource endowments with sugarcane cultivation in Maharashtra as a case in point. Similarly, Himachal Pradesh has a score of 100% for Indicator 22 on access to drinking water for urban population, whereas, Shimla, one of the major urban locations in the state, has faced severe water crisis in the summer of 2018. Similarly, there are numerous other counter examples that show adverse selection of indicators like using total expenses to indicate status of maintenance of structures, not considering the effect on health by quality of water and the list goes on.

Another limitation here is that most of the data is being provided by various state departments and reports. In such a scenario, when we have performance indicators wherein the score is based on the percentage of targets achieved it can incentivize state bodies to keep the target itself low. There is a strong need to employ a benchmark for each performance indicator for absolute comparison and for that, it is important to select indicators with historical evidence of causality with intended outcomes. Indicators that use expenditure as a benchmark cannot suffice. For example, one indicator assigns higher scores for higher expenditure in maintaining irrigation assets. This may paradoxically lead to disproportionate amounts of funds being allocated for maintenance of irrigation structures even when there is no need for maintenance.


While the effort by NITI Aayog to create an index for better water management in the country is laudable, there is definite scope for improvement. The index should not just restrict itself to becoming a common platform for water data. It is important that the compilation of this data in the form of an index should communicate the overall scenario of water sector in India.

From policy perspective, water management has four major dimensions: Access, Quality, Sustainability and Efficiency. In order to get better outcomes, each dimension can be developed as a simple index reflecting the performance of the states. To get a balanced picture of water management, the index can be derived as a compilation of these individual sub-indices. It can be done taking geometric mean of individual sub-indices, the way Human Development Index (HDI) compiles Education, Health and Income to explain overall human development.



As already underscored, the presence of a legal framework cannot ensure its implementation. Similarly, having a water data centre cannot provide assurance that relevant data is being captured. Therefore, we suggest dropping all indicators about legislative frameworks or those related to presence of a data centre. As providing drinking water is a priority and agricultural water use account for 80-85% of total water usage in India, for Water Availability, the focus should be on these two. For drinking water, we can simply measure the per capita volume of water supplied by respective water supply bodies with respect to demand (135 lpcd being the accepted norm) instead of looking at percentage of population or habitation under drinking water supply that does not provide information on actual water availability. For agricultural water availability, we use number of farm holdings instead of area since it is mostly small and marginal land holdings that do not have access to irrigation. Their share of total area may be low, but that of total land holdings will be much higher and provide a better picture of how many farmers lack access to irrigation.       

On water quality, along with looking at habitations affected by water quality issues, it is important to analyze the severity of these issues. Identifying the number of deaths per thousand individuals can provide a measure of this severity. Waste water management is another quality factor that the CWMI looks at by measuring the capacity of waste-water treatment plants. But, actual generation versus treatment data will provide better insight on the capacity necessary for treating all the waste water generated.

For Sustainability, it is important to account for both surface water as well as groundwater resources. As sustainability of groundwater reserves has been a prime concern, it is important to capture data on water table decline. A simple measure will be the proportion of observation blocks not in safe stage of groundwater development out of total observation blocks. Under Water Sustainability, suggested index is capturing data on sustainable practices such as micro-irrigation techniques and adherence to agro-climatic zoning prescribed cropping patterns. It is also necessary to account for efficient utilization of investments made in water resources. To measure this, under Water Efficiency index the indicators being considered are irrigation potential utilized out of the total irrigation potential created, irrigation service fee recovered and the revenue collected for agricultural water use against the power subsidies, since these subsidies reduce the marginal cost of groundwater pumping.


The NITI Aayog’s CWMI checks fewer boxes than it should due to its complex design and the failure of the indicators in mirroring the actual water scenario.

If there is intention to monitor water policies through CWMI in the long run, it should use indicators that can be sourced from public data sets to reduce measurement errors as well as do away with legislative indicators. Instead of measuring each indicator, it should rather evaluate outcomes that get affected directly by improvement in the water sector. Case in point, instead of performing the humongous task of preparing habitation level water quality data, using people affected by water-borne diseases acts as a better proxy. The suggested framework is intended to provide a guideline on creating a more reliable index with reduced complexity and scope of measurement errors.

The authors are both pre-doctoral fellows with the IWMI-Tata Programme. The views and opinions expressed in this article are those of the author/s and do not necessarily reflect the policy or position of India Water Portal.

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