DRIVERS OF ECONOMIC PERFORMANCE: WHAT CAN WE OBSERVE IN THE CZECH FOOD INDUSTRY?

dc.contributor.authorTrnková, Gabriela
dc.contributor.authorŽáková Kroupová, Zdeňka
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2021-09-15T08:08:07Z
dc.date.available2021-09-15T08:08:07Z
dc.description.abstractThis paper is focused on the investigation of the competitiveness drivers, namely technical and scale efficiency and technological change, and their relation to the profitability of the Czech food processing companies in the period 2016–2019. This investigation is based on the stochastic frontier modelling of an input distance function in the specification of the four-error-component model. The model is estimated with a multi-step procedure employing the generalized method of moments estimator addressing the potential endogeneity of netputs, and panel data gained from the Bisnode Albertina database. The results revealed (evaluated on the sample mean) that investigated food processing sectors were scale efficient in the analysed period, however, their production technologies exhibited prevailing technological regress. Moreover, the room for almost 17% cost reduction by the technical efficiency improvements was found out in all investigated sectors. Although inter-sectoral differences exist in the scale efficiency, technological change and technical efficiency dynamics, to increase the productivity and competitiveness of food processing companies, it is generally appropriate to focus on technical efficiency and technological change improvements. Both these competitiveness drivers connected with the cost reduction and minimizing of wastage of inputs are achievable through innovations. In general, the basic source of their financing is profit, the achievement of which is supported by cost minimization. However, it was found that sub-sectors, which are linked to sensitive sectors of agricultural production – that means sectors with the lowest national self-sufficiency, the highest level of imports and thus strong cost reduction pressure – have problem to translate the ability to produce efficiently into profitability. Although these food sectors, which have been also facing strong competition for a long time, which leads to significant pressure to reduce costs, achieved the highest technical efficiency, their profitability was lowest from the investigated sectors.en
dc.formattext
dc.identifier.doi10.15240/tul/001/2021-03-007
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/160961
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonAllendorf, J., & Hirsch, S. (2015). Dynamic productivity growth in the European food processing industry. In Proceedings of 55th Annual Conference of German Association of Agricultural Economists (GEWISOLA). September 23–25, 2015, Giessen, Germany.
dc.relation.isbasedonBadunenko, O., & Kumbhakar, S. C. (2016). When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models. European Journal of Operational Research, 255(1), 272–287. https://doi.org/10.1016/j.ejor.2016.04.049
dc.relation.isbasedonBaležentis, T., & Sun, K. (2020). Measurement of technical inefficiency and total factor productivity growth: A semiparametric stochastic input distance frontier approach and the case of Lithuanian dairy farms. European Journal of Operational Research, 285(3), 1174–1188. https://doi.org/10.1016/j.ejor.2020.02.032
dc.relation.isbasedonBaráth, L., & Fertő, I. (2015). Heterogeneous technology, scale of land use and technical efficiency: The case of Hungarian crop farms. Land Use Policy, 42, 141–150. https://doi.org/10.1016/j.landusepol.2014.07.015
dc.relation.isbasedonBaumol, W. J., Panzar, J. C., & Willig, R. D. (1982). Contestable Markets and the Theory of Industry Structure. New York, NY: Harcourt Brace Jovanovich.
dc.relation.isbasedonBlažková, I., & Dvouletý, O. (2017). Drivers of ROE and ROA in the Czech food industry in the context of market concentration. Agris On-line Papers in Economics and Informatics, 9(3), 3–13. https://doi.org/10.7160/aol.2017.090301
dc.relation.isbasedonBlažková, I., & Dvouletý, O. (2019). Investigating the differences in entrepreneurial success through the firm-specific factors: Microeconomic evidence from the Czech food industry. Journal of Entrepreneurship in Emerging Economies, 11(2), 154–176. https://doi.org/10.1108/JEEE-11-2017-0093
dc.relation.isbasedonBokusheva, B., & Čechura, L. (2017). Evaluating dynamics, sources and drivers of productivity growth at the farm level (Food, Agriculture and Fisheries Papers, No. 106). Paris: OECD Publishing. https://doi.org/10.1787/5f2d0601-en
dc.relation.isbasedonCaves, 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(6), 1393–1414. https://doi.org/10.2307/1913388
dc.relation.isbasedonCzech Statistical Office. (2021). Prices of producers – time series. Retrieved April 1, 2021, from https://www.czso.cz/csu/czso/ipc_cr
dc.relation.isbasedonČechura, L., & Hockmann, H. (2010). Sources of economical growth in the Czech food processing. Prague Economic Papers, 19(2), 169–182. https://doi.org/10.18267/j.pep.370
dc.relation.isbasedonČechura, L. (2014). Analysis of the Technical and Scale Efficiency of Farms Operating in LFA. Agris on-line Papers in Economics and Informatics, 6(4), 33–44. https://doi.org/10.22004/ag.econ.196526
dc.relation.isbasedonČechura, L., Hockmann, H., & Kroupová, Z. (2014). Productivity and Efficiency of European Food Processing Industry (Compete Working Paper N7). Halle: Leibniz Institute of Agricultural Development in Transition economies (IAMO). Retrieved February 20, 2021, from http://www.iamo.de/fileadmin/compete/files/working_paper/COMPETE_Working_Paper_7_Productivity_FP.pdf
dc.relation.isbasedonČechura, L., & Malá, Z. (2014). Technology and Efficiency Comparison of Czech and Slovak Processing Companies. Procedia Economics and Finance, 12, 93–102. https://doi.org/10.1016/S2212-5671(14)00324-4
dc.relation.isbasedonČechura, L., & Hockmann, H. (2017). Heterogeneity in Production Structures and Efficiency: An Analysis of the Czech Food Processing Industry. Pacific Economic Review, 22(4), 702–719. https://doi.org/10.1111/1468-0106.12217
dc.relation.isbasedonČechura, L., & Žáková Kroupová, Z. (2021). Technical Efficiency in the European Dairy Industry: Can We Observe Systematic Failures in the Efficiency of Input Use? Sustainability, 13(4), 1830. https://doi.org/10.3390/su13041830
dc.relation.isbasedonGschwandtner, A., & Hirsch, S. (2018). What Drives Firm Profitability? A Comparison of the US and EU Food Processing Industry. The Manchester School, 86(3), 390–416. https://doi.org/10.1111/manc.12201
dc.relation.isbasedonHirsch, S., Schiefer, J., Gschwandtner, A., & Hartmann, M. (2014). The determinants of firm profitability differences in EU food processing. Journal of Agricultural Economics, 65(3), 703–721. https://doi.org/10.1111/1477-9552.12061
dc.relation.isbasedonHirsch, S., & Schiefer, J. (2016). What Causes Firm Profitability Variation in the EU Food Industry? A Redux of Classical Approaches of Variance Decomposition. Agribusiness, 32(1), 79–92. https://doi.org/10.1002/agr.21430
dc.relation.isbasedonChambers, R. G. (1988). Applied Production Analysis: A Dual Approach. Cambridge: Cambridge University Press.
dc.relation.isbasedonChirinko, R. S., & Fazzari, S. M. (1994). Economic fluctuations, market power, and returns to scale: Evidence from firm‐level data. Journal of Applied Econometrics, 9(1), 47–69. https://doi.org/10.1002/jae.3950090105
dc.relation.isbasedonCoelli, T., & Perelman, S. (2000). Technical Efficiency of European Railways: A Distance Function Approach. Applied Economics, 32(15), 1967–1976. https://doi.org/10.1080/00036840050155896
dc.relation.isbasedonDimara, E., Skuras, D., Tsekouras, K., & Tzelepis, D. (2008). Productive efficiency and firm exit in the food sector. Food Policy, 33(2), 185–196. https://doi.org/10.1016/j.foodpol.2007.08.003
dc.relation.isbasedonFarrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 120(3), 253–290. https://doi.org/10.2307/2343100
dc.relation.isbasedonFilippini, M., & Greene, W. H. (2016). Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach. Journal of Productivity Analysis, 45(2), 187–196. https://doi.org/10.1007/s11123-015-0446-y
dc.relation.isbasedonGordon, M., & Davidova, S. (2004). Farm productivity and efficiency in the CEE applicant countries: a synthesis of results. Agricultural Economics, 30(1), 1–16. https://doi.org/10.1111/j.1574-0862.2004.tb00172.x
dc.relation.isbasedonGumbau-Albert, M., & Maudos, J. (2002). The determinants of efficiency: the case of the Spanish industry. Applied Economics, 34(15), 1941–1948. https://doi.org/10.1080/00036840210127213
dc.relation.isbasedonHailu, A., & Veeman, T. S. (2000). Environmentally Sensitive Productivity Analysis of the Canadian Pulp and Paper Industry, 1959–1994: An Input Distance Function Approach. Journal of Environmental Economics and Management, 40(3), 251–274. https://doi.org/10.1006/jeem.2000.1124
dc.relation.isbasedonHedvičáková, M., & Král, M. (2021). Performance Evaluation Framework under the Influence of Industry 4.0: The Case of the Czech Manufacturing Industry. E&M Economics and Management, 24(1), 118–134. https://doi.org/10.15240/tul/001/2021-1-008
dc.relation.isbasedonIrz, X., & Thirtle, C. (2005). Dual Technological Development in Botswana Agriculture: A Stochastic Input Distance Function Approach. Journal of Agricultural Economics, 55(3), 455–478. https://doi.org/10.1111/j.1477-9552.2004.tb00110.x
dc.relation.isbasedonKapelko, M. (2019). Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union. Annals of Operations Research, 278(1), 215–234. https://doi.org/10.1007/s10479-017-2497-0
dc.relation.isbasedonKaragiannis, G., Midmore, P., & Tzouvelekas, V. (2004). Parametric Decomposition of Output Growth Using a Stochastic Input Distance Function. American Journal of Agricultural Economics, 86(4), 1044–1057. https://doi.org/10.1111/j.0002-9092.2004.00652.x
dc.relation.isbasedonKeramidou, I., Mimis, A., Fotinopoulou, A., & Tassis, C. D. (2013). Exploring the relationship between efficiency and profitability. Benchmarking: An International Journal, 20(5), 647–660. https://doi.org/10.1108/BIJ-12-2011-0090
dc.relation.isbasedonKounetas, K., & Tsekouras, K. (2007). Measuring Scale Efficiency Change Using a Translog Distance Function. International Journal of Business and Economics, 6(1), 63–69.
dc.relation.isbasedonKumar, S. (2008). An Analysis of Efficiency–Profitability Relationship in Indian Public Sector Banks. Global Business Review, 9(1), 115–129. https://doi.org/10.1177/097215090700900108
dc.relation.isbasedonKumbhakar, S. C., & Knox Lovell, C. A. (2000). Stochastic Frontier Analysis. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139174411
dc.relation.isbasedonKumbhakar, S. C., Orea, L., Rodriguez-Álvarez, A., & Tsionas, E. G. (2007). Do we estimate an input or an output distance function? An application of the mixture approach to European railways. Journal of Productivity Analysis, 27(2), 87–100. https://doi.org/10.1007/s11123-006-0031-5
dc.relation.isbasedonKumbhakar, S. C., & Tsionas, E. G. (2012). Firm heterogeneity, persistent and transient technical inefficiency: a generalized true random effects model. Journal of Applied Econometrics, 29(1), 110–132. https://doi.org/10.1002/jae.2300
dc.relation.isbasedonKumbhakar, S. C., & Lien, G. (2009). Productivity and profitability decomposition: A parametric distance function approach. Acta Agriculturae Scandinavica, Section C – Food Economics, 6(3–4), 143–155. https://doi.org/10.1080/16507541.2010.481898
dc.relation.isbasedonKumbhakar, S. C., Lien, G., & Hardaker, J. B. (2014). Technical efficiency in competing panel data models: A study of Norwegian grain farming. Journal of Productivity Analysis, 41(2), 321–337, https://doi.org/10.1007/s11123-012-0303-1
dc.relation.isbasedonLatruffe, L. (2010). Competitiveness, Productivity and Efficiency in the Agricultural and Agri-Food Sectors (OECD Food, Agriculture and Fisheries Papers, No. 30). Paris: OECD Publishing. https://doi.org/10.1787/5km91nkdt6d6-en
dc.relation.isbasedonLien, G., Kumbhakar, S. C., & Alem, H. (2018). Endogeneity, heterodeneity, and determinants of inefficiency in Norwegian crop-producing farms. International Journal of Production Economics, 201, 53–61. https://doi.org/10.1016/j.ijpe.2018.04.023
dc.relation.isbasedonMathijs, E., & Vranken, L. (2000). Farm restructuring and efficiency in transition: evidence from Bulgaria and Hungary. Paper presented at the American Agricultural Economics Association Annual Meeting, Tampa, FL, USA, July 30–August 2. https://doi.org/10.22004/ag.econ.21886
dc.relation.isbasedonMezera, J., Plášil, M., & Náglová, Z. (2019). Panorama potravinářského průmyslu 2019 [Panorama of the Food industry 2019]. Prague: Ministry of Agriculture of the Czech Republic.
dc.relation.isbasedonMorroni, M. (2006). Knowledge, Scale and Transactions in the Theory of the Firm. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511617232
dc.relation.isbasedonMinistry of Industry and Trade. (2019). Panorama zpracovatelského průmyslu ČR 2018 [Panorama of the Manufacturing Industry 2018]. Retrieved April, 2021, from https://www.mpo.cz/cz/prumysl/zpracovatelsky-prumysl/panorama-zpracovatelskeho-prumyslu/-panorama-zpracovatelskeho-prumyslu-cr-2018--249524/
dc.relation.isbasedonNjuki, E., & Bravo-Ureta, B. E. (2015). The Economic Costs of Environmental Regulation in U.S. Dairy Farming: A Directional Distance Function Approach. American Journal of Agricultural Economics, 97(4), 1087–1106. https://doi.org/10.1093/ajae/aav007
dc.relation.isbasedonRoodman, D. (2009). How to do Xtabond2: An Introduction to Difference and System GMM in Stata. Stata Journal, 9(1), 86–136. https://doi.org/10.1177/1536867X0900900106
dc.relation.isbasedonRudinskaya, T., & Kuzmenko, E. (2019). Investments, Technical Change and Efficiency: Empirical Evidence from Czech Food Processing. Agris on-line Papers in Economics and Informatics, 11(4), 93–103. https://doi.org/10.7160/aol.2019.110409
dc.relation.isbasedonRudinskaya, T., & Náglová, Z. (2018). Impact of Subsidies on Technical Efficiency of Meat Processing Companies. AGRIS on-line Papers in Economics and Informatics, 10(1), 61–70. https://doi.org/10.7160/aol.2018.100106
dc.relation.isbasedonShephard, R. W. (1970). Theory of Cost and Production Functions. Princeton: Princeton University Press.
dc.relation.isbasedonSingbo, A., & Larue, B. (2016). Scale Economies, Technical Efficiency, and the Sources of Total Factor Productivity Growth of Quebec Dairy Farms. Canadian Journal of Agricultural Economics/Revue Canadienne D’agroéconomie, 64(2), 339–363. https://doi.org/10.1111/cjag.12077
dc.relation.isbasedonSoboh, R. A. M. E., Oude Lansink, A., & Van Dijk, G. (2014). Efficiency of European Dairy Processing Firm. NJAS – Wageningen Journal of Life Sciences, 70–71, 53–59. https://doi.org/10.1016/j.njas.2014.05.003
dc.relation.isbasedonTsionas, E. G., Kumbhakar, S. C., & Malikov, E. (2015). Estimation of Input Distance Functions: A System Approach. American Journal of Agricultural Economics, 97(5), 1478–1493. https://doi.org/10.1093/ajae/aav012
dc.relation.isbasedonUllah, S., Akhtar, P., & Zaefarian, G. (2018). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management, 71, 69–78. https://doi.org/10.1016/j.indmarman.2017.11.010
dc.relation.isbasedonVokoun, M., Polanecký, L., & Stellner, F. (2015). The impact of the recent economic crisis on the food industry in the Czech and Slovak Republic. Procedia Economics and Finance, 34(3), 142–148. https://doi.org/10.1016/S2212-5671(15)01612-3
dc.relation.isbasedonZelenyuk, V. (2015). Aggregation of scale efficiency. European Journal of Operational Research, 240(1), 269–277. https://doi.org/10.1016/j.ejor.2014.06.038.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectstochastic frontier analysisen
dc.subjecttechnical efficiencyen
dc.subjectprofitabilityen
dc.subjectfood processing industryen
dc.subjectthe Czech Republicen
dc.subject.classificationD24
dc.subject.classificationL66
dc.titleDRIVERS OF ECONOMIC PERFORMANCE: WHAT CAN WE OBSERVE IN THE CZECH FOOD INDUSTRY?en
dc.typeArticleen
local.accessopen
local.citation.epage127
local.citation.spage110
local.facultyFaculty of Economics
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue3
local.relation.volume24
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