AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching

Abstract

A large share of the Common Agricultural Policy (CAP) is allocated to agri-environmental schemes (AESs), whose goal is to foster the provision of a wide range of environmental public goods. Despite this effort, little is known on the actual environmental and economic impact of the AESs, due to the non-experimental conditions of the assessment exercise and several data availability issues. The main objective of the paper is to explore the feasibility of combining the non-parametric statistical matching (SM) method and propensity score matching (PSM) counterfactual approach analysis and to test its usefulness and practicability on a case study represented by selected impacts of the AESs in Emilia-Romagna. The work hints at the potentialities of the combined use of SM and PSM as well as of the systematic collection of additional information to be included in EU-financed project surveys in order to enrich and complete data collected in the official statistics. The results show that the combination of the two methods enables us to enlarge and deepen the scope of counterfactual analysis applied to AESs. In a specific case study, AESs seem to reduce the amount of rent-in land and decrease the crop mix diversity.

The Effect of Investment, LFA and Agri‐environmental Subsidies on the Components of Total Factor Productivity: The Case of Slovenian Farms

“…As in Slovenian agriculture many farms are situated in LFA and the environmental component of agricultural production is significant, it is possible that the role of external effects is considerable (Bar ath et al, 2018; Unay-Gailhard and Bojnec, 2019. Several studies have examined the effect of AE schemes on farm performance (Pufahl and Weiss, 2009;Arata and Sckokai, 2016;D'Alberto et al, 2018), although the number of studies that have examined the effect of LFA subsidies is very limited (Mary, 2013). Garrone et al (2019) argue that the impact of LFA payments on agricultural productivity is not clear ex ante.…”

“…Researchers have used many different methods to evaluate the subsidy-economic performance link in EU countries, but, surprisingly, the number of papers that have used quasi-experimental methods to evaluate the effect of CAP subsidies is limited (Pufahl and Weiss, 2009;Michalek, 2012;Kirchweger and Kantelhardt, 2015;Arata and Sckokai, 2016;Nilsson, 2017;D'Alberto et al, 2018), while research has mainly been concentrated on the effect of agri-environmental schemes (e.g. Pufahl and Weiss, 2009;Arata and Sckokai, 2016;D'Alberto et al, 2018), with the exception of recent 2…”

Reviewing Counterfactual Analyses to Assess Impacts of EU Rural Development Programmes: What Lessons Can Be Learned from the 2007–2013 Ex-Post Evaluations?

“…Its suitability to assess programme impacts at micro and regional level was previously reviewed by Michalek [11,35]. PSM-DiD has been commonly applied to assess socioeconomic impacts of certain RDP measures, mainly agri-environmental payments [36][37][38][39] and farm investment measures [40][41][42]. From a geographical perspective, PSM-DiD has been applied to assess RDP impacts in several MS and non EU countries (21 examples of its application are noted in [10] (p. 13)).…”

Understanding the Spatial Agglomeration of Participation in Agri-Environmental Schemes: The Case of the Tuscany Region

“…Even after 20 years of application and a large share of expenditure allocated to AESs in national rural development budgets [2], evaluation reports and the scientific literature have determined that AESs have engendered lower-than-expected environmental impacts [3][4][5]. The economics literature points to poor targeting levels, low participation rates, the heterogeneity of compliance costs [6,7], the spatial distribution of participation, and the presence of information asymmetry between farmers and the government as the main reasons for unsatisfactory AES outcomes [8][9][10].…”

“…Poor spatial targeting [7,12] occurs when a programme cannot discriminate among different farms, areas, and environmental vulnerabilities, and thus reflects a lack of targeting in areas where the environmental benefits would otherwise be higher. These approaches are often constrained by the presence of high transaction costs [13] incurred by additional data needs [5] and changes in administrative procedures (e.g., different zoning, or different eligibility criteria and priorities). Other studies, in a bid to overcome the main limitation of uniform policy instruments, focus merely on spatial heterogeneity.…”