I NTRODUCTION AND BACKGROUND

In recent years in developed countries there has been an increasing concern about the relationship between agriculture and the environment. In the European Union (EU), the Common Agricultural Policy (CAP) has introduced environmental measures in order to discourage negative environmental externalities and to promote positive externalities of agricultural activities. For example, negative externalities are sanctioned by a reduction in direct income payments if cross-compliance is not respected, while some ad hoc directives have been implemented for addressing some specific problems (i.e. the nitrates directive).

Positive externalities are encouraged by some Rural Development measures which promote environmentally sustainable farming practices, through payments that compensate farmers for the provision of environmental goods that the market does not reward. One of these measures are the agri-environmental schemes (AESs) introduced in the late 1980s as an option to be applied by Member States. Since 1992, with the Mac Sharry reform, the development of AE programs has become compulsory for all Member States with the Regulation 2078/92, while their application by farmers is still voluntary. Since 1999, with the Agenda 2000 CAP reform (EEC n. 1257/99) the AESs have become a section of the Rural Development Programs (RDPs) which represents Pillar II of the CAP.

Agri-environmental contracts are voluntary contracts, of at least 5 years, stipulated between the farmer and the government; under these contracts, the farmer provides environmental goods that go beyond the minimum requirements of the cross compliance and of the European and national compulsory environmental regulations and they receive a fixed per hectare payment to face the additional costs and the loss of income linked to these commitments. The main objective of the AESs consists of reducing the agricultural pollution risks as well as protecting biodiversity and landscape. AESs payments are co-financed by Member States and

24 they represent a large share of the public budget for Rural Development. They can be designed at national, regional or local level and this allows to take into account the heterogeneity of the natural characteristics and agricultural systems throughout the EU Member States. The agri-environmental and animal welfare programmes are the only measures that are compulsory in all RDPs and the AESs are the most significant measure in term of EU funding for Rural Development, 23.6%, followed by ‘Modernisation of agricultural holdings’, 12%, and ‘Payments to farmers in areas with handicaps, other than mountain areas’, 7.6%. In some EU countries the AESs are mainly defined at national level with little decision power at regional level (e.g. France and the Netherlands), while in other countries they are defined and implemented at regional or subregional level (e.g. Italy, Germany, Spain).

There is quite a lot of literature about AESs; most of it tries to analyze the factors affecting farmer’s participation to agri-environmental contracts (Vanslembrouck et al., 2002;

Defrancesco et al., 2007). Another widely studied topic concerns the analysis of the environmental effectiveness of farmers’ environmental practices; most of these studies outline the importance of accounting for farm heterogeneity, by applying more farm-specific measures (Aakkula et al., 2011). A few studies analyse the effects of AESs on farms’

practices and economic results. Sauer et al. (2012: 6) argued that “only a few studies so far have attempted to empirically measure the actual impact of being subject to AESs on producer behaviour at individual farm level using statistical or econometric tools”. The expectation is that farmers are heavily affected by participation to AESs, which may lead to a deep reorganisation of the farm and to a change in the sources of income.

An ex-post analysis tool recently applied to analyse the effects of agricultural policy measures on farm’s performances is the Propensity Score Matching (PSM). Propensity score analysis has been widely developed in the last thirty years as a program evaluation method based on observational data in a broad range of disciplines, such as medicine, epidemiology, psychology, social sciences, education. More recently it has been applied also to environmental economics and to the analysis of some measures of RDPs. The most recent applications integrates PSM with a Difference-in-Differences (DID) estimator.

Pufahl and Weiss (2009) and Chabé-Ferret and Subervie (2011) applied a DID PSM estimator in order to evaluate the effect of AESs on farm choices. Pufahl and Weiss analysed the impact of AESs adoption on input use and output produced of a large sample of German farms observed over the period 2000-2005. Their work showed that farmers participating in AESs

25 experience higher positive growth rates in sales, on-farm labour, area under cultivation, grassland and rented land compared to the ones non participating; by contrast, they have higher negative growth rates in livestock density, fertilizer and pesticide expenditure. Chabé-Ferret and Subervie (2011) investigated the land allocation changes linked to AESs participation on a sample of French farms as well as they analysed the different sources of bias and the windfall effect. The PSM estimator has also been applied to compare voluntary and compulsory environmental measures in terms of their impact on farm production choices.

Sauer et al. (2012) found that voluntary AESs affect farmers’ decisions heavier than non voluntary measures. Their analysis on a sample of the UK cereal farms showed that farmers participating into AESs do not reduce their efficiency as they efficiently adjust their production plan to the new constraints, especially by becoming less specialized and more diversified. The use of fertilizers and chemicals decreases, as well as the land and capital productivity, while labour productivity increases. Jaraitė and Kažukauskas (2012), applying a backward DID, showed that EU-15 farmers participating into voluntary AESs reduce chemical pollution more as compared to farmers not subject to voluntary AE program, indicating a cross positive effect between compulsory and voluntary measures, while the level of farm subsidy does not affect the degree of compliance with compulsory measures. PSM estimator has been also applied to the analysis of some Rural Development measures in North America.

The work of Liu and Linch (2011) studied the impact of development right programs on preventing farmland loss in six Mid-Atlantic US states. Tamini (2011) analysed the effect of extension advisory activities on farmer’s adoption of best management practices in Quebec.

His results showed that environmental advisory activities increase the environmental performance of farmers by increasing the rate of adoption of best management practices and this increase is larger in the case of practices related to compulsory regulations.

Despite the increasing use of PSM methods to study the effects of some Rural Development measures on farm decisions, to the best of our knowledge there are no studies that compare the effects of agri-environmental contracts on farmer’s choices and economic performances in different EU countries. In addition none of the studies found in the literature focus on the impact of AESs on the farm economic variables, such as farm income. AESs are expected to differently affect farmers in different EU Member States given the different climatic conditions, the different characteristics of agriculture as well as the different national implementations of AESs. The comparison among EU countries may indicate the countries

26 where the AESs adoption requires a deeper change of farmer’s production plans and where the effects on farm income are stronger. This may also indicate how the AESs perform in different Member States and the reasons of different participation rates. This paper aims at filling this gap by applying a DID PSM estimator in order to perform comparative analysis on the effects of AESs on farmer’s practises and economic performances across five EU Member States: France, Germany, Spain, the UK and Italy.