Estimating the Magnitude of Water Supply and Sanitation Subsidies

The water supply and sanitation sector remains heavily subsidized around the world. Yet, theaccounting of water supply and sanitation subsidies globally has proved challenging due to utility-level data limitationsand their often implicit nature. This paper develops a methodology to estimate water supply and sanitationsubsidies that is adaptable to data scarce environments, while accounting for differences among service providerssuch as population served (to account for economies of scale), coverage of water and sanitation servicesindividually, and their level of operational efficiency in terms of water losses and staffing. This methodology isbased on Chile’s empresa modelo (model firm) approach to cost-reflective tariff estimation and uses utility-leveldata from the World Bank's International Benchmarking Network for Water and Sanitation Utilities database. Theresults suggest that the cost of subsidies associated with the operations, maintenance, and major repair andreplacement of existing water supply and sanitation infrastructure in much of the world (excluding, notably,China and India) is an estimated $289 billion to $353 billion per year, or 0.46 to 0.56 percent of thecountries' combined gross domestic product. This figure rises, shockingly, to 1.59 to 1.95 percent if only low- andmiddle-income economies are considered, an amount largely due to the capital subsidies captured in the estimation.Subsidies of operating costs account for approximately 22 percent of the total subsidy amount in the full sample andfor low-income economies separately. Annual subsidy amounts by region range from 0.05 to 2.40 percent of gross domesticproduct, and low-income economies are generally at the high end of this range. The estimations do not include capitalexpenditure for infrastructure expansion -- which tends to be fully subsidized -- or environmental costs. Therefore,the actual global magnitude of networked water supply and sanitation subsidies is much greater than the estimation.