Economic and environmental efficiency of UK and Ireland water companies: Influence of exogenous factors and rurality.


School of Natural Sciences, College of Environmental Sciences and Engineering, Bangor University, Gwynedd, LL57 2UW, UK. Electronic address: [Email]


For water companies, benchmarking their performance relative to other companies can be an effective way to identify the scope for efficiency gains to be made through infrastructure investment and operational improvements. However, a key limitation to benchmarking is the confounding effect of exogenous factors, which may not be factored in to benchmarking methodologies. The purpose of this study was to provide an unbiased comparison of efficiency across a sample of water and sewage companies, accounting for important exogenous factors. Bias-corrected economic and environmental efficiency estimates with explanatory factors were evaluated for a sample of 13 water and sewage companies in the UK and Ireland, using a double-bootstrap data envelopment analysis (DEA) approach. Bias correction for economic and environmental efficiency changed the rankings of nine and eight companies, respectively. On average, companies could reduce economic inputs by 19% and carbon outputs by 16% if they performed at the efficiency frontier. Variables explaining efficiency were: source of water, leakage rate, per capita consumption and population density. Population density showed statistical significance with both economic (p-value 0.002) and environmental (p-value 0.001) efficiency. Consequently, a rurality factor was defined for each company's operational area, which was then regressed against normalised water company performance data. More rural water companies spend more per property (R2 of 0.633), in part reflecting a larger number of smaller sewage treatment works serving rural populations (R2 of 0.823). These findings provide new insight into methods for benchmarking, and factors affecting, water company efficiency, pertinent for both regulators and water companies.


Data envelopment analysis,Double-bootstrap,Explanatory factors,Performance analysis,Urbanity,Water utilities,