Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach

• Main Authors: De Castris, Marusca, Di Gennaro, Daniele
• Format: Journal Article
• Language: English
• Published: 15-03-2018
• Subjects: Statistics - Applications
• DOI: 10.48550/arxiv.1803.05659

During the last decades, public policies become a central pillar in
supporting and stabilising agricultural sector. In 1962, EU policy-makers
developed the so-called Common Agricultural Policy (CAP) to ensure
competitiveness and a common market organisation for agricultural products,
while 2003 reform decouple the CAP from the production to focus only on income
stabilization and the sustainability of agricultural sector. Notwithstanding
farmers are highly dependent to public support, literature on the role played
by the CAP in fostering agricultural performances is still scarce and
fragmented. Actual CAP policies increases performance differentials between
Northern Central EU countries and peripheral regions. This paper aims to
evaluate the effectiveness of CAP in stimulate performances by focusing on
Italian lagged Regions. Moreover, agricultural sector is deeply rooted in
place-based production processes. In this sense, economic analysis which omit
the presence of spatial dependence produce biased estimates of the
performances. Therefore, this paper, using data on subsidies and economic
results of farms from the RICA dataset which is part of the Farm Accountancy
Data Network (FADN), proposes a spatial Augmented Cobb-Douglas Production
Function to evaluate the effects of subsidies on farm's performances. The major
innovation in this paper is the implementation of a micro-founded quantile
version of a spatial lag model to examine how the impact of the subsidies may
vary across the conditional distribution of agricultural performances. Results
show an increasing shape which switch from negative to positive at the median
and becomes statistical significant for higher quantiles. Additionally, spatial
autocorrelation parameter is positive and significant across all the
conditional distribution, suggesting the presence of significant spatial
spillovers in agricultural performances.