Economics & Sociology

ISSN: 2071-789X eISSN: 2306-3459 DOI: 10.14254/2071-789X
Index PUBMS: f5512f57-a601-11e7-8f0e-080027f4daa0
Article information
Title: Dynamic Efficiency under Investment Spikes in Lithuanian Cereal and Dairy Farms
Issue: Vol. 10, No 2, 2017
Published date: 06-2017 (print) / 06-2017 (online)
Journal: Economics & Sociology
ISSN: 2071-789X, eISSN: 2306-3459
Authors: Virginia Namiotko
Lithuanian Institute of Agrarian Economics

Tomas Baležentis
Lithuanian Institute of Agrarian Economics
Keywords: dynamic efficiency, investment spikes, Lithuania, cereal farms, dairy farms, data envelopment analysis
DOI: 10.14254/2071-789X.2017/10-2/3
Index PUBMS: 96eea0db-004b-11e8-94c4-fa163e5d4f72
Language: English
Pages: 33-46 (14)
JEL classification: C44, Q12
Website: http://www.economics-sociology.eu/?493,en_dynamic-efficiency-under-investment-spikes-in-lithuanian-cereal-and-dairy-farms
Licenses:
Abstract

Lithuanian agriculture has been receiving investment support under the Common Agricultural policy since 2004. Indeed, the most profitable farming types – cereal and dairy farms – saw a particularly strong increase in the investment amounts. The measure of dynamic efficiency allows one analyze the performance of businesses in regards of inter-temporal optimization of the investment behavior. This paper, therefore, looks into the trends of dynamic efficiency in Lithuanian cereal and dairy farms. The research is based on the data from the Farm Accountancy Data Network covering the period of 2004-2014. The analysis carried out for different farm sizes indicates that scale inefficiency is the main source of technical inefficiency for smaller farms, whether cereal, or dairy ones. Farms experienced investment spikes showed slightly lower inefficiency. These technical efficiency gains are due to improved pure technical efficiency and scale efficiency. However, the latter source appeared as a more important one for the smallest farms (less than 30 ha).

Bibliography

1. Baležentis, T. (2016). Dynamic efficiency in Lithuanian cereal farms. Management Theory and Studies for Rural Business and Infrastructure Development, 38(2), 114-127.

2. Bilan, Y., & Strielkowski, W. (2015). Psychological decisions of polish rural entrepreneurs: daily targeting versus intertemporal substitution. Drivers for Progress in the Global Society, 123-128.

3. European Commission (2016). FADN Public Database, http://ec.europa.eu/agriculture/rica/database/database_en.cfm (2016 07 20).

4. Färe, R., Grosskopf, S. (1985). A nonparametric cost approach to scale efficiency. The Scandinavian Journal of Economics, 87(4), 594-604.

5. Färe, R., Grosskopf, S., Lovell, C. A. K. (1983). The structure of technical efficiency. The Scandinavian Journal of Economics, 85(2), 181-190.

6. Geylani, P. C., Stefanou, S. E. (2013). Linking investment spikes and productivity growth. Empirical Economics, 45(1), 157-178.

7. Grosskopf, S. (1986). The role of the reference technology in measuring productive efficiency. The Economic Journal, 96(382), 499-513.

8. Huggett, M., Ospina, S. (2001). Does productivity growth fall after the adoption of new technology? Journal of Monetary Economics, 48(1), 173-195.

9. Janda, K., Rausser, G., & Strielkowski, W. (2013). Determinants of Profitability of Polish Rural Micro-Enterprises at the Time of EU Accession. Eastern European Countryside, 19, 177-217. doi: https://doi.org/10.2478/eec-2013-0009

10. Kapelko, M., Oude Lansink, A. (2017). Dynamic multi-directional inefficiency analysis of European dairy manufacturing firms. European Journal of Operational Research, 257(1), 338-344.

11. Kapelko, M., Oude Lansink, A., Stefanou, S. (2016b). Assessing the Impact of Changing Economic Environment on Productivity Growth: The Case of the Spanish Dairy Processing Industry. Journal of Food Products Marketing, 1-14. DOI:10.1080/10454446.2014.10004

12. Kapelko, M., Oude Lansink, A., Stefanou, S. E. (2016a). Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms. Journal of Agricultural Economics. DOI: 10.1111/1477-9552.12188

13. Kapelko, M., Oude Lansink, A., Stefanou, S. E. (2015). Analyzing the impact of investment spikes on dynamic productivity growth, Omega, 54, 116-124.

14. Krylovas, A., Zavadskas, E. K., & Kosareva, N. (2016). Multiple criteria decision-making KEMIRA-M method for solution of location alternatives. Economic research – Ekonomska istraživanja, 29(1), 50-65.

15. Melnikienė, R. (ed.) (2016). Lietuvos žemės ir maisto ūkis 2015 = Agriculture and food sector in Lithuania 2015. Lietuvos agrarinės ekonomikos institutas.

16. Mikócziová, J. (2010). Sources of investment finance in firms in Slovakia. Journal of Competitiveness, 2(1), 67-73.

17. Power, L. (1998). The missing link: technology, investment, and productivity. Review of Economics and Statistics, 80(2), 300-313.

18. Rungsuriyawiboon, S., Hockmann, H. (2015). Adjustment costs and efficiency in Polish agriculture: a dynamic efficiency approach. Journal of Productivity Analysis, 44(1), 51-68.

19. Sakellaris, P. (2004). Patterns of plant adjustment. Journal of Monetary Economics, 51(2), 425-450.

20. Serra, T., Lansink, A. O., Stefanou, S. E. (2011). Measurement of dynamic efficiency: A directional distance function parametric approach. American Journal of Agricultural Economics, 93(3), 752-763.

21. Silva, E., Lansink, A. O., Stefanou, S. E. (2015). The adjustment-cost model of the firm: Duality and productive efficiency. International Journal of Production Economics, 168, 245-256.

22. Simionescu, M. (2016). The relation between economic growth and foreign direct investment during the economic crisis in the European Union. Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu, 34(1), 187-213.

23. Statistics Lithuania (2016). Official Statistics Portal, http://osp.stat.gov.lt/ (2016 08).