Estimating the inverse demand function for transferring agricultural water in Taiwan

Changing climate has increasingly exacerbated droughts and floods in Taiwan; therefore, it is important to understand the actual demand of transferring Taiwan’s agricultural water. This estimation model could help the water resource agencies to develop appropriate mechanisms for transferring agricultural water, as well as bargaining tools for water-related negotiations. In this study, an inverse demand function estimation model for transferring agricultural water was established, and the water usage statistics derived from the water charge agreements, covering the period from January 1989 to December 2006 and including drought and non-drought periods, regarding the charging of water management fees and water usage fees, was applied to the estimation model in our empirical research. The agreements were made between irrigation associations and water companies, industrial water users, and science and industrial parks, for the purposes of strengthening irrigation management, building usage, and disposal of remaining water. The empirical research was conducted to estimate the demand for transferring agricultural water using double-log regression model for panel data, and analyzed with random effects models for regular conditions and drought periods. The results showed that the inverse demand function developed in this study was able to pass Largrange multiplier test, and adjusted R ² for the regression were high, fitting the random effects model showing good compatibility with the sample selection. From the results, we can verify the estimation models to forecasting models. The significant results not only prove that the model could provide important market information for the commercialization of water resources, but water resource agencies could also make use of this important information to develop suitable mechanisms for transferring agricultural water, as well as bargaining tools for negotiation of water transactions.