A Non-Parametric Analysis of Technical Efficiency in Bulgarian Farms Using the Fadn Dataset

Abstract

The Bulgarian countryside has suffered a significant phenomenon of rural emigration since the early 1970s. The main consequence of rural depopulation has been a decline of investments in Bulgarian farms and in their own level of technical and economic efficiency. The aim of this research was to assess afterwards the enlargement of the European Union in 2007, the technical efficiency by a non-parametric approach such as the Data Envelopment Analysis (DEA), using some findings and variables investigated in the Farm Accountancy Data Network annual survey from 2007 till 2015. Farms have been stratified into functions of their typology of farming and their geographical localization. Research findings have pointed out that specialized farms as dairy farms and granivores ones have had the highest levels of technical efficiency compared to mixed farms and wine farms. To sum up, financial subsidies allocated by the Common Agricultural Policy have had a positive impact towards farmers, both increasing the technical efficiency and also in reducing the socio-economic marginalization of Bulgarian rural areas.

A Quantitative Assessment of the Rurality and an Efficiency Analysis of Emigration in Romania

“…In general, comparing different Romanian NUTS II counties, the district of Bucharest-Ilfov emerges as having suffered less socio-economic marginalisation and territorial disparities than Eastern and Southern regions (SURD et al, 2011). This implies a different impact of the financial supports allocated by the European Union before and after the enlargement in 2007, corroborating the hypothesis according to which the poorer a rural area is, the more modest its development will be (GALLUZZO, 2017a;2017b;2018a;2018b;SURD et al, 2011). Therefore, the focus of the National Rural Development Plan on stimulating measures of diversification in farming through agritourism, rural tourism, and the EU's LEADER initiatives, which is financed under the second pillar of the Common Agricultural Policy, represents a good opportunity for rural areas to reduce the socio-economic marginalisation suffered in many deprived contexts, even if the level of GDP per capita remains one of the most significant factors in the mitigation of socio-economic disparities and improving the environmental protection and sustainability of rural territories (BURJA & BURJA, 2014;GALLUZZO, 2017a;2017c;IORIO & CORSALE, 2010;ABRHAM, 2011).…”

Technical Efficiency in the Agricultural Business—The Case of Slovakia

“…Since Charnes, Cooper, and Rhodes [17] have created the non-parametric technique of data envelopment analysis, there are many studies and papers trying to evaluate the efficiency and productivity in the various fields of the economy, including agricultural holdings [18][19][20][21][22][23] or holdings in the food industry [24]. Since 1978 up through the year 2009, around 4500 Data Envelopment Analysis (DEA) related papers, both theoretically and practically oriented, have been published in the ISI Web of Science database [25].…”

“…Ezcurra et al [34] reveals a decrease in regional agricultural productivity disparities, while intra-distribution mobility is found to be relatively limited during the study period. Galluzzo [18] pointed out that specialized Bulgarian farms have had higher levels of technical efficiency compared to mixed farms, and financial subsidies allocated by the CAP have had a positive impact toward farmers, both increasing the technical efficiency and also reducing the socio-economic marginalization of Bulgarian rural areas. Marongiu et al [35] focused on the efficiency of the Italian and French dairy farms situated in the Alps.…”

Chinese liquor company management efficiency from a social responsibility perspective: a two stage dynamic directional distance function

“…There has been significant research into and comparative studies on alcohol and beverage company corporate efficiencies using DEA models (Aparicio et al, 2013;Giovanna et al, 2019;Goncharuk & Figurek, 2017;Lekic et al, 2018;Ormeci et al, 2018) that have considered both internal and external factors (Galluzzo, 2018;Sellers-Rubio et al, 2015). Two-stage DEA models have also been used to evaluate alcohol company efficiencies (Arunkumar & Ramanan, 2017;Carlucci et al, 2018), with many studies having included corporate environmental responsibility in the evaluation system and employed DEA, life cycle assessment (LCA) and other methods to study the relationships between social responsibility and corporate efficiency.…”