Productivity and efficiency in Czech agriculture: Does farm size matter?

https://doi.org/10.17221/384/2021-AGRICECONCitation:

Čechura L., Žáková Kroupová Z., Lekešová M. (2022): Productivity and efficiency in Czech agriculture: Does farm size matter? Agric. Econ. – Czech, 68: 1–10.

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This paper deals with the sources of total factor productivity (TFP), namely technical efficiency, scale efficiency, and technological change, considering the size of agricultural producers and using balanced panel data in the period 2014–2018 drawn from the Farm Accountancy Data Network (FADN) database for three sectors of Czech agriculture – cereals, milk, and beef. The investigation is based on the stochastic frontier (SF) modelling of an input distance function (IDF) with four error components (heterogeneity, statistical noise, persistent and transient inefficiency). The sector-specific models are estimated by a four-step estimating procedure with a system generalised method of moments (GMM) estimator to address the endogeneity problem. The results reveal inter- and intra-sectoral differences in productivity drivers. In particular, the smallest producers lag considerably behind the largest ones due to the scale effect (SEC). While large farms should focus on technological change, improvements in scale and technical efficiency have been identified as the main sources of coping with productivity losses for small farmers.

References:
Alvarez A., Arias C. (2004): Technical efficiency and farm size: A conditional analysis. Agricultural Economics, 30: 241–250. https://doi.org/10.1111/j.1574-0862.2004.tb00192.x
 
Arellano M., Bond S. (1991): Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58: 277–297. https://doi.org/10.2307/2297968
 
Barney J.B. (2001): Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view. Journal of Management, 27: 643–650. https://doi.org/10.1177/014920630102700602
 
Bokusheva B., Čechura L. (2017): Evaluating Dynamics, Sources and Drivers of Productivity Growth at the Farm Level. OECD Food, Agriculture and Fisheries Papers, No. 106. Paris, OECD Publishing: 1–64.
 
Caves D.W., Christensen L.R., Diewert W.E. (1982): The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50: 1393–1414. https://doi.org/10.2307/1913388
 
Čechura L. (2014): Analysis of the technical and scale efficiency of farms operating in LFA. AGRIS on-line Papers in Economics and Informatics, 6: 33–44.
 
Diewert W. (1976): Exact and superlative index numbers. Journal of Econometrics, 4: 115–145. https://doi.org/10.1016/0304-4076(76)90009-9
 
Griffin K., Khan A., Ickowitz A. (2002): Poverty and the distribution of land. Journal of Agrarian Change, 2: 279–330. https://doi.org/10.1111/1471-0366.00036
 
European Commission (2009): European Competitiveness Report 2008. European Commission. Available at https://ec.europa.eu/docsroom/documents/3399/attachments/1/translations/en/renditions/native (accessed May 2, 2021).
 
Eurostat (2021a): Price Indices of Agricultural Products, Output (2010 = 100) – Annual Data [apri_pi10_outa]. [Dataset]. Eurostat. Available at https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=apri_pi10_outa&lang=en (accessed June 6, 2021).
 
Eurostat (2021b): Price Indices of Agricultural Products, Output (2015 = 100) – Annual Data [apri_pi15_outa]. [Dataset]. Eurostat. Available at https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=apri_pi15_outa&lang=en (accessed June 6, 2021).
 
Eurostat (2021c): Price Indices of the Means of Agricultural Production, Input (2010 = 100) – Annual Data [apri_pi10_ina]. [Dataset]. Eurostat. Available at https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=apri_pi10_ina&lang=en (accessed June 6, 2021).
 
Eurostat (2021d): Price Indices of the Means of Agricultural Production, Input (2015 = 100) – Annual Data [apri_pi15_ina]. [Dataset]. Eurostat. Available at https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=apri_pi15_ina&lang=en (accessed June 6, 2021).
 
Eurostat (2018): Farms and Farmland in the European Union – Statistics. Eurostat. Available at https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Farms_and_farmland_in_the_European_Union_-_statistics#The_evolution_of_farms_and_farmland_from_2005_to_2016 (accessed May 10, 2021).
 
FADN (2021): Microeconomic Data of Czech Agricultural Holdings. [Unpublished raw data]. Institute of Agricultural Economics and Information.
 
Farsi M., Filippini M., Kuenzle M. (2005): Unobserved heterogeneity in stochastic cost frontier models: An application to Swiss nursing homes. Applied Economics, 37: 2127–2141. https://doi.org/10.1080/00036840500293201
 
Foster A.D., Rosenzweig M.R. (2017): Are There Too Many Farms in the World? Labor-Market Transaction Costs, Machine Capacities and Optimal Farm Size. NBER Working Paper No. 23909. Cambridge, National Bureau of Economic Research. Available at https://www.nber.org/system/files/working_papers/w23909/w23909.pdf (accessed Oct 2, 2021).
 
Greene W. (2005): Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126: 269–303. https://doi.org/10.1016/j.jeconom.2004.05.003
 
Hansen L.P. (1982): Large sample properties of generalized method of moments estimator. Econometrica, 50: 1029–1054. https://doi.org/10.2307/1912775
 
Jondrow J., Lovell C.A.K., Materov I.S., Schmidt P. (1982): On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19: 233–238. https://doi.org/10.1016/0304-4076(82)90004-5
 
Keizer T.H., Emvalomatis G. (2014): Differences in TFP growth among groups of dairy farms in the Netherlands. NJAS – Wageningen Journal of Life Sciences, 70–71: 33–38. https://doi.org/10.1016/j.njas.2014.03.001
 
Key N. (2019): Farm size and productivity growth in the United States Corn Belt. Food Policy, 84: 186–195. https://doi.org/10.1016/j.foodpol.2018.03.017
 
Kumbhakar S.C., Wang H.J., Horncastle A.P. (2015): A Practitioner's Guide to Stochastic Frontier Analysis Using Stata. New York, US, Cambridge University Press: 359.
 
Lafuente E., Leiva J.C., Moreno-Gómez J., Szerb L. (2020). A nonparametric analysis of competitiveness efficiency: The relevance of firm size and the configuration of competitive pillars. Business Research Quarterly, 23: 203–216. https://doi.org/10.1177/2340944420941440
 
Latruffe L. (2010): Competitiveness, Productivity and Efficiency in the Agricultural and Agri-Food Sectors. OECD Food, Agriculture and Fisheries Working Papers, No. 30. Paris, France, OECD Publishing: 62.
 
Man T.W.Y., Lau T., Chan K.F. (2002). The competitiveness of small and medium enterprises a conceptualization with focus on entrepreneurial competencies. Journal of Business Venturing, 17: 123–142. https://doi.org/10.1016/S0883-9026(00)00058-6
 
Mundlak Y. (1978): On the pooling of time series and cross section data. Econometrica, 46: 69–85. https://doi.org/10.2307/1913646
 
Novotná M., Volek T. (2015): Efficiency of production factors and financial performance of agricultural enterprises. AGRIS on-line Papers in Economics and Informatics, 7: 91–99. https://doi.org/10.7160/aol.2015.070409
 
Rada N.E., Fuglie K.O. (2019): New perspectives on farm size and productivity. Food Policy, 84: 147–152. https://doi.org/10.1016/j.foodpol.2018.03.015
 
Roodman D. (2009): How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9: 86–136. https://doi.org/10.1177/1536867X0900900106
 
Rudinskaya T., Hlavsa T., Hruska M. (2019): Estimation of technical efficiency of Czech farms operating in less favoured areas. Agricultural Economics – Czech, 65: 445–453. https://doi.org/10.17221/52/2019-AGRICECON
 
Sen A. (1962): An aspect of Indian agriculture. The Economic Weekly, 14: 243–266.
 
Šūmane S., Miranda D.O., Pinto-Correia T., Czekaj M., Duckett D., Galli F., Grivins M., Noble C., Tisenkopfs T., Toma I., Tsiligiridis T. (2021): Supporting the role of small farms in the European regional food systems: What role for the science-policy interface? Global Food Security, 28: 1–10. https://doi.org/10.1016/j.gfs.2020.100433
 
Tsionas E.G., Kumbhakar S.C. (2012): Firm heterogeneity, persistent and transient technical inefficiency: A generalized true random effects model. Journal of Applied Econometrics, 29: 110–132. https://doi.org/10.1002/jae.2300
 
Ullah S., Akhar P., Yaeferian G. (2018): Dealing with endogeneity bias: The generalized method of moments (GMM). Industrian Marketing Management, 71: 69–78. https://doi.org/10.1016/j.indmarman.2017.11.010
 
West G.P., De Castro J. (2001): The Achilles heel of firm strategy: Resource weakness and distinctive inadequacies. Journal of Management Studies, 38: 417–442. https://doi.org/10.1111/1467-6486.00243
 
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