The cost-effectiveness of conservation auctions in the presence of asset specificity: An agent-based model

Abstract

Payments for Environmental Services are a financial incentive for land users to conserve and restore ecosystems. One of the challenges in their implementation is to maximize their cost-effectiveness, or put in other words, to maximize the provision of environmental services for a given budget. This study focuses on two aspects that endanger the cost-effectiveness of such schemes: asymmetric information and asset specificity. If land users are better informed about their own provision costs, compared to the agency, they can increase their rents by demanding higher payments. The presence of asset specificity makes land users vulnerable to being harmed by opportunism. To compensate this risk, they could require higher payments or an exante compensation, likely to compromise compliance. Auctions are claimed to reduce informational rents by revealing land users’ true provision costs. However, their costeffectiveness has been shown to deteriorate if they are repeated over time because bidders can learn and adapt their strategies. Social interaction is particularly important in this context, as it allows land users to gather information on the bid cap; and it allows for trust building, which can substitute the costly formulation and enforcement of contracts, and thus reduce contracting costs. So far, there are only few studies analyzing the effect of asset specificity on the cost-effectiveness of auctions. Our study fills this gap using an agent-based model to analyze the cost-effectiveness of uniform and discriminatory one-shot and repeated auctions. In our model, land users are assumed to be embedded in a social network through which they can interact and learn. Our results suggest that repeated auctions can increase the cost-effectiveness of payments schemes in the presence of asset specificity despite of learning effects over time if land users face liquidity constraints and high time preferences.