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Franchise Bidding in the Water Industry — Auction Schemes and Investment Incentives

Abstract

Due to extensive shares of fixed costs, network industries such as electricity, gas, railways or water are widely seen as natural monopolies. In such case, it is cost minimising and therefore socially wanted when only one single firm serves the entire market. Usually these services are rendered by public enterprises or strongly regulated private companies. Harold Demsetz (1968) proposed franchise bidding as an alternative to regulation. He argued that auctioning the rights to a natural monopoly would lead to a similar outcome as regulation, but at lower costs. In fact, franchise bidding has often been used in practice. Even though, there is only some experience in the water sector — mainly from France. The success of the auctioning model in the French water sector is assessed ambivalent since competition at the re-auctioning stage is only minor intensive. However, in theory the main criticism of Demsetz’ proposal rather concerns investment incentives than competition intensity. It was Oliver Williamson (1976), who pointed out the problem of long-term specific investments. If the life-time of specific assets exceeds the contract length and transferring the ownership of assets is difficult, the franchisee faces a serious hold-up problem. As a result, re-auctioning a natural monopoly undermines investment incentives. The hold-up problem tends to be stronger in sectors where investment is very specific, long term oriented and hardly to evaluate by a third party. One can assume that investment in the capital-intensive water sector exactly corresponds to these characteristics. Water pipes have technological lifetimes up to 100 or more years and they can not be dug out and used elsewhere. Additionally, investment into the underground network can hardly be monitored by a third party.

Keywords

Bidding Strategy Price Auction Pipe Network Water Industry Investment Incentive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Deutscher Universitäts-Verlag | GWV Fachverlage GmbH, Wiesbaden 2006

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