Tag: stochastic frontier analysis

Russia is often considered the most prominent country to become a leader on the world grain market. However, several issues slow down Russia’s agricultural progress, for example: a lack of infrastructure and investments, unequal regional development and inefficient use of production technologies. This study therefore examines the grain production potential of Russian regions by employing a modified approach to stochastic frontier analysis that allows us to include not only production technologies, but also indicators of the country’s heterogeneity and diversity among regions. The results obtained indicate that climate conditions in combination with the level of human and institutional development, and infrastructure have significant effects on the production structure of regions and therefore should not be neglected while assessing regional policies and production potential.

There is a continuously growing literature on the agricultural transformation in Central and Eastern European countries (see some surveys in Brooks and Nash 2002; Rozelle and Swinnen 2004). The research has focused on various aspects of transition, including land reform, farm restructuring, price and trade liberalisation, but even though Farm Accountancy Data Network (FADN) data are now available for some years, there are only a few studies (e.g. Bakucs et al. 2010, Fogarasi and Latruffe, 2007, Baráth et al., 2009) focusing on Hungarian farm performance. The objective of this paper is to shed light on some methodological issues that are needed to study Hungarian farm performance. Here we consider one aspect of farm performance, namely technical efficiency. This measure refers to whether farmers are capable of using existing technology to its full potential by producing the most possible from a given set of production factor quantities.