Differences in farm efficiency in market and transition economies: empirical evidence from West and East Germany

Thiele, H

doi:10.1093/erae/26.3.331

Cited by 41 publications

(24 citation statements)

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“…2) supports the conclusion about the small allocative inefficiency of this input factor. Latruffe et al (2005) researching Polish farms reached a similar conclusion, as did Thiele and Brodersen (1999) when analysing efficiency of farms in West and the former East Germany. Insufficient fertilisation of Estonian agricultural land has led to three clearly negative results: First, only around 40-50% of the real yield potential of cereals is currently being realised (Roostalu et al 2001); secondly, the nutrient balances of arable soils are predominantly negative (Astover et al 2006a) and thirdly, Estonia's farms produce the lowest average cereal yield (in studied farms on average 1.9-2.3 Mg ha -1 ) in the EU25 countries.…”

Section: Discussionsupporting

confidence: 53%

“…2). In this study, we were unable to factor in differences in the quality of labour as an input variable although Thiele and Brodersen (1999) argue such differences could result in higher input slacks.…”

Section: Discussionmentioning

confidence: 93%

“…Input slacks in DEA may essentially be handled as allocative inefficiency (Ferrier and Lovell 1990) and indicates spare capacity to use inputs in optimal proportions. A high slack-input ratio may imply a large potential to reduce input nonproportionally or a large variability of input quality (Thiele and Brodersen 1999). The highest slackinput ratio of labour during the study period was during [2000][2001][2002][2003] (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…There is clearly a considerable unpredictability in efficiency among farms and this relatively high deviation points to a high diversity of management practices and skills between farms. Thiele and Brodersen (1999) discovered that the variance of farm efficiencies was higher in the former East Germany than in West Germany in 1995-1997 and argued that this was due to the greater inconsistency of management skills in the east during the transition period. Optimal size varies depending upon each farm's particular input-output configuration (Jaforullah and Whiteman 2000).…”

Section: Discussionmentioning

confidence: 99%

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Efficiency of Estonian grain farms in 2000 2004

Vasiliev

Astover²,

Mõtte³

et al. 2008

AFSci

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The aim of this study is to analyse the efficiency of Estonian grain farms after Estonia's transition to a market economy and during the accession period to the European Union (EU). The non-parametric method Data Envelopment Analysis (DEA) was used to estimate the total technical, pure technical and scale efficiency of Estonian grain farms in 2000-2004. Mean total technical efficiency varied from 0.70 to 0.78. Of the grain farms 62% are operating under increasing returns to scale. Solely based on the DEA model it is not possible to determine optimum farm scale and the range of Estonian farm sizes operating efficiently is extensive. The most pure technically efficient farms were the smallest and the largest but the productivity of small farms is low compared to larger farms because of their small scale. Therefore, they are the least competitive. Since pre-accession period to the EU, large input slacks of capital have replaced the former excessive use of labour and land. This raises the question about the effects on efficiency of the EU's investment support schemes in new member states.

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Section: Discussionsupporting

confidence: 53%

Section: Discussionmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Efficiency of Estonian grain farms in 2000 2004

Vasiliev

Astover²,

Mõtte³

et al. 2008

AFSci

View full text Add to dashboard Cite

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“…Noti ceably, the Central and East European countries are specific with these circumstances and thus re quire researches on the agricultural efficien cy and productivity (Gorton, Davidova, 2004). Consequently, Thiele and Brodersen (1999) ana lysed the performance of the West and East Ger man farms with respect to returns to scale. La truffe et al (2005) focused on the Polish farms while analysing the technical and scale efficien cies.…”

Section: Introductionmentioning

confidence: 99%

Returns to scale in the Lithuanian family farms: A quantitative approach

Baležentis¹,

Valkauskas²

2013

Žemės Ūkio Mokslai

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The paper analysed the returns to scale and scale elasticity prevailing in the Lithuanian family farms. The analysis was based on the farm-level data. Specifically, the three farming types were considered, viz. crop, livestock, and mixed farming. The non-parametric method, data envelopment analysis, was employed to define the production frontier and compute the estimates of the scale elasticity. The inefficient observations were projected on the production frontier and analysed both in input and output orientations. Meanwhile, the efficient observations were treated in accordance with Banker and Thrall (1992). The results enabled to estimate the most productive scale size for each of the considered farming types. The farm size was expressed both in absolute and relative terms. These estimates, indeed, might provide a momentum for discussions in regard to the farm structure and sustainable agricultural policy in Lithuania.

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Strategic Decision Making for the Triple Bottom Line

McWilliams¹,

Parhankangas²,

Coupet³

et al. 2014

Bus Strat Env

120

113

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Corporate social responsibility (CSR) has become a mandate for strategic managers and is often an important element of a differentiation strategy, but there is little research on how managers can make socially responsible decisions within the context of competitive strategy. In this study we explain how data envelopment analysis (DEA) results can be used to determine the trade‐offs inherent in managing the triple bottom line of profits, people and the planet. Once the trade‐offs are well understood, managers can implement sustainable competitive strategies that incorporate socially responsible decisions. Using public data from the electric power generation industry, we demonstrate how DEA can be utilized to determine the trade‐offs between efficiency, costs and pollution reduction, allowing managers to make and champion socially responsible decisions. We discuss the general applicability of our method for making strategic decisions incorporating the triple bottom line. Copyright © 2014 John Wiley & Sons, Ltd and ERP Environment

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Differences in farm efficiency in market and transition economies: empirical evidence from West and East Germany

Cited by 41 publications

References 0 publications

Efficiency of Estonian grain farms in 2000 2004

Efficiency of Estonian grain farms in 2000 2004

Returns to scale in the Lithuanian family farms: A quantitative approach

Strategic Decision Making for the Triple Bottom Line

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