Optimal Design of Agri‐environmental Schemes under Asymmetric Information for Improving Farmland Biodiversity

Information asymmetry is one of the main obstacles to the effective design and implementation of agri‐environmental schemes (AES). The literature has generally addressed this issue through the use of principal‐agent models (PAM). We develop a PAM to support optimal design of a new AES for improving farmland biodiversity. We use the results of choice experiments to assess both the costs incurred by the agent for the provision of biodiversity and the resulting social benefits. We also make a number of novel contributions such as the inclusion of a non‐linear non‐compliance detection curve, a sensitivity analysis to identify which parameter estimates have a critical impact on PAM results, and analysis of the efficiency of different sanction scenarios. The results suggest that: (i) the second‐best solutions differ significantly from the optimal solutions attainable with perfect information, with farmers being strongly over‐compensated for the extra costs associated with improved biodiversity; (ii) monitoring levels should be higher; (iii) the sanction system should be tougher. Sensitivity analysis shows the need for accurate estimates of the marginal cost of public funds and the costs and benefits associated with the public goods, which represent the key parameters determining PAM results.