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

Abstract

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.

¿Resultaría económicamente eficiente un programa de reconversión ecológica para el olivar de montaña andaluz?

“…En este sentido, entre los instrumentos de la política pública disponibles, destaca la implementación de un nuevo programa agroambiental que fomente el abandono de la producción como estrategia para mejorar la provisión de tales BB.PP. (Gómez-Limón et al, 2019).…”

“…En segundo lugar, cabe indicar que el análisis realizado ha ignorado igualmente la existencia de costes de administración del programa en los que incurriría el sector público, que suponen también costes sociales que deberían tenerse en cuenta para determinar la eficiencia del programa propuesto. Gómez-Limón et al (2019) apuntan que estos costes podrían ser muy relevantes, dado el elevado coste de la gestión administrativa de los expedientes agroambientales, que en el caso de Andalucía podría llegar a ser de 108 euros por hectárea acogida. Como en el caso anterior, en la medida que pudiera disponerse de datos precisos al respecto, el análisis realizado podría afinarse para obtener resultados más robustos.…”

Estimating the effects of agri-environmental measures using difference-in-difference coarsened exact matching

“…This represents a recurring problem documented in several studies concerning AEMs (e.g. Evans and Morris, 1997;Falconer, 2000;Fraser, 2005;Hart and Latacz-Lohmann, 2005;Baylis et al, 2008;Canton et al, 2009;Unay-Gailhard and Bojnec, 2015;Gómez-Limón et al, 2018). Adverse selection may occur when farmers, whose usual farming practices already satisfy AEMs' commitmentsor are close to accomplishing themare more likely to participate in the program than farmers who are far from achieving the AEMs' environmental requirements; although the latter would represent the real target of the policy.…”

Farmers’ preferences for nutrient and climate-related agri-environmental schemes: A cross-country comparison

“…Studies are applied to a wide range of AES themes all over the world, e.g. rural development contracts (Villenueva et al 2017), conservation and biodiversity (Broch et al 2013;Adams et al 2014;Alló et al 2015;Gómez-Limón et al 2018), pesticide management (Christensen et al 2011;Kuhfuss et al 2016), land diversification requirements and production practices (Espinosa-Goded et al 2010;Jaeck and Lifran 2014;Schulz et al 2014), carbon sequestration and climate mitigation (Aslam et al 2017), and water quality improvements (Beharry-Borg et al 2013;Franzén et al 2016). A number of studies focus on AES contract characteristics, e.g.…”