Application of the Luenberger index to estimating dynamics of the total factor productivity in Lithuanian family farms

Productivity and efficiency are the two interrelated factors influencing competiveness of a firm, a sector, or a state. It is therefore important to analyse the patterns of changes in productivity and efficiency in various sectors. This study employs the Luenberger productivity index for measuring the total factor productivity in Lithuanian family farms. Specifically, a sample of 200 farms that were operating in 2004–2009 was analyzed. The analyzed farms were classified into crop, livestock, and mixed ones by their output structure. The paper presents a survey on application of frontier techniques in research on the Lithuanian agricultural sector as well as frontier measures of efficiency. Special attention is paid to the Luenberger productivity index and decomposition thereof. The carried out analysis identified the sources of changes in productivity across different farming sectors. Compared to livestock farms both mixed farms and crop farms were characterised by higher efficiency and productivity gains caused by efficiency effect (catch–up) and lower gains from the overall technological change. Ke­y­wo­rds: productivity, efficiency, Luenberger index, data envelopment analysis, family farms. Introduction IIt is efficiency that provides a momentum for a non–inflatory economic development and increase in competitiveness. Moreover, as Latruffe (2010) points out, firm– and sector–specific efficiency does influence national competitiveness. Accordingly, the European Commission (2011) launched a flagship initiative under the Europe 2020 Strategy called A resource-efficient Europe. Furthermore, Henningsen (2009) argued that efficiency of the agribusiness is related to labour intensity, farm structure, technology and investment, managerial skills, and profitability. One thus needs to develop appropriate measures of efficiency and productivity. In order to perform appropriate benchmarking it is necessary to fathom the terms of effectiveness, efficiency, and productivity. One can evaluate effectiveness when certain utility or objective function is defined (Bogetoft, Otto, 2011). In the real life, however, this is not the case and the ideal behaviour can be described only by analyzing the actual data, i. e. by the means of benchmarking. Productivity means the ability to convert inputs to outputs. A distinction can be made between total factor productivity (Solow, 1957) and partial (single factor) productivity. Productivity growth is a source of a noninflatory growth and thus should be encouraged by means of benchmarking and efficiency management. Efficiency can be perceived as a ratio of the observed productivity level to the yardstick productivity level. Nauges et al. (2011) presented the following factors stressing the need for research into agricultural efficiency. First, agricultural producers typically own land and live on their farms, therefore the standard assumption that only efficient producers are to maintain their market activity usually does not hold in agriculture; moreover, suchlike adjustments would result in various social problems. Second, it is policy interventions – education, training, and extension programmes – that should increase the efficiency. Third, policy issues relating to farm structure are of high importance across many regions. The efficiency measures can be grouped into parametric and non-parametric ones as well as into deterministic and stochastic ones (MurilloZamorano, 2004; Coelli et al., 2005; Vinciūnienė, Rauluškevičienė, 2009). Lithuanian agricultural sector was analyzed by the means of regression analysis (Kriščiukaitienė et al., 2010), multi-criteria decision making methods (Baležentis, Baležentis, 2011a). Savickienė and Slavickienė (2012) employed correlation analysis and discussed some methodological issues regarding viability of farming business. The frontier measures were also employed (Vinciūnienė, Rauluškevičienė, 2009; Rimkuvienė et al., 2010; Baležentis, Baležentis, 2011b; Baležentis, 2012; Baležentis, Kriščiukaitienė, 2012; Baležentis et al., 2012). However, the productivity indices were not utilized to measure the dynamics of the total factor productivity in the Lithuanian agricultural sector so far.