Files
Abstract
Conservation auctions are increasingly being used to procure public
environmental goods on private land. In the absence of demand-side
price information, the majority of conservation auctions in Australia have
been designed without a reserve price. In these instances bids have been
accepted in order of cost-effectiveness until the budget constraint binds. It
is widely recognised that in situations where auctions are run repeatedly
a reserve price strategy could allow for a more efficient allocation of funds
across multiple rounds, both spatially and temporally.
This paper provides a brief overview of methods for determining a
reserve price for application in conservation auctions. It is concluded that
information deficiencies and the high transaction costs involved in the
application of these methods to conservation auctions often render them
unsuitable for application to real-world auctions.
This paper presents an empirical approach to determining a reserve
price using data obtained during an auction - the supply curve. The
approach stems from the C4.5 algorithm, developed in the field of
data mining to construct decision trees from training data using the
concept of information entropy. The algorithm establishes a reserve price
by determining the cut-off price that results in the ”best fit” of two
normal distributions to the frequency distribution of bid-price per unit
environmental benefit.
Empirical data from conservation auctions in Victoria is used to
demonstrate the algorithm and compare auction results obtained using
the algorithm and traditional ”budget” methods. The paper presents a
discussion on the situations where the algorithm could be appropriately
used, and advantages and limitations of the approach are identified. The
paper concludes that the use of the algorithm can result in efficiency gains
over the traditional budget method in situations where alternative reserve
price strategies are impractical.