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RESEARCH PAPER
Measuring the Efficiency of European Union Farms under Heterogeneous Technologies
1
Uniwersytet Ekonomiczny w Krakowie, Polska
Submission date: 2020-01-07
Final revision date: 2020-03-02
Acceptance date: 2020-07-16
Publication date: 2020-09-30
GNPJE 2020;303(3):111-137
KEYWORDS
production functionStochastic Frontier ModelsBayesian approachinternational comparisonrandom coefficients model
JEL CLASSIFICATION CODES
ABSTRACT
The aim of the present study was to derive the characteristics of the production process for crop farms in the European Union member states. The paper uses regional data on farms taken from the Farm Accountancy Data Network (FADN). Therefore, the models that account for heterogeneity among the analysed regions, were used in the present study. In particular, the paper considers two approaches to modelling heterogeneity: deterministic and stochastic. The deterministic approach is reflected in the paper with the usage of translog production function model, which allows output elasticities to depend on the input levels. The stochastic approach is represented by a stochastic frontier model with random coefficients. The application of the above-mentioned concept allowed to derive the Cobb-Douglas (C–D) production function model with individual parameters. The parameters of the four models were estimated using the Bayesian approach. The obtained results indicate that the C–D model is the best. In addition, it was observed that for the EU average, the highest production elasticity is with respect to materials, while the lowest w.r.t area. Surprisingly, the results suggest a high mean technical efficiency of the analysed regions (0.95), with very small dispersion of these scores.
REFERENCES (50)
1.
Aigner D., Lovell C. A. K., Schmidt P. [1977], Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6 (1): 21–37.
2.
Alvarez A., del Corral J. [2010], Identifying different technologies using a latent class model: extensive versus intensive dairy farms, European Review of Agricultural Economics, 37 (2): 231–250.
3.
Ball V. E., Bureau J-C., Butault J-P., Nehring R. [2001], Levels of farm sector productivity: an international comparison, Journal of Productivity Analysis, 15 (1): 15–29.
4.
Ball V. E., Butault J-P., San Juan C., Mora R. [2010], Productivity and international competitiveness of agriculture in the European Union and the United States, Agricultural Economics, 41 (6): 611–627.
5.
Baráth L., Fertő I. [2017], Productivity and convergence in European agriculture, Journal of Agricultural Economics, 68 (1): 228–248.
6.
Błażejczyk-Majka L., Kala R., Maciejewski K. [2011], Productivity and efficiency of large and small field crop farms and mixed farms of the old and new EU regions, Agricultural Economics – Czech, 58 (2): 61–71.
7.
Bocian M., Cholewa I., Tarasiuk R. [2017], Współczynniki Standardowej Produkcji „2013” dla celów Wspólnotowej Typologii Gospodarstw Rolnych, IERiGŻ-PIB, Warszawa.
8.
Čechura L., Grau A., Hockmann H., Kroupova Z., Levkovych I. [2014], Total factor productivity in European agricultural production, international comparison of product supply chains in the agri-food sector: determinants of their competitiveness and performance on EU and international markets, Compete Working Paper, 9, Halle.
9.
Christensen L., Jorgenson D., Lau L. [1973], Transcendental logarithmic production frontiers, Review of Economics and Statistics, 55 (1): 28–45.
10.
Coelli T. J., Rao D. S. P. [2005], Total factor productivity growth in agriculture: a Malmquist index analysis of 93 countries, 1980–2000, Agricultural Economics, 32 (1): 115–134.
11.
Czyżewski B., Matuszczak A., Brelik A. [2018], Endogeniczna wartość dóbr publicznych na obszarach wiejskich: przypadek Pomorza Zachodniego, Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu, 5: 48–54.
12.
Emvalomatis G. [2012], Productivity growth in German dairy farming using a flexible modelling approach, Journal of Agricultural Economics, 63 (1): 83–101.
13.
European Commission [2009], Typology handbook, RI/CC 1500 rev. 3, 05/10/2009 Brussels.
14.
Farrell M. [1957], The measurement of productive efficiency, Journal of the Royal Statistical Society, Seria A, 120 (3): 253–281.
15.
Floriańczyk Z., Osuch D., Płonka R. [2018], Wyniki Standardowe 2017 uzyskane przez gospodarstwa rolne uczestniczące w Polskim FADN, Część I. Wyniki Standardowe, IERiGŻ-PIB, Warszawa.
16.
Gelman A., Hill J. [2007], Data analysis using regression and hierarchical/multilevel models, Cambridge University Press, Cambridge.
17.
Gerdessen J. C., Pascucci S. [2013], Data envelopment analysis of sustainability indicators of European agricultural systems at regional level, Agricultural Systems, 118: 78–90.
18.
Greene W. H. [2005], Reconsidering heterogeneity in panel data estimators of the stochastic frontier model, Journal of Econometrics, 126 (2): 269–303.
19.
Griliches Zvi [1987], Productivity: measurement problems, in: J. Eatwell, M. Milgate and P. Newman (eds.), The New Palgrave A Dictionary of Economics, Stockton Press, New York, 3: 1010–1013.
20.
Heady D., Alauddin M., Rao D. S. P. [2010], Explaining agricultural productivity growth: an international perspective, Agricultural Economics, 41 (1): 1–14.
21.
Hildreth C., Houck J. P. [1968], Some estimators for a linear model with random coefficients, Journal of the American Statistical Association, 63 (322): 584–595.
22.
Huang H. [2004], Estimation of technical inefficiencies with heterogenous technologies, Journal of Productivity Analysis, 21 (3): 277–296.
23.
Kalirajan K. P., Obwona M. B. [1994], Frontier production function: the stochastic coefficients approach, Oxford Bulletin of Economics and Statistics, 56 (1): 87–96.
24.
Karagiannis G., Tzouvelekas V. [2009], Measuring technical ffficiency in the stochastic varying coefficient frontier model, Agricultural Economics, 40 (4): 389–396.
25.
Komorowska D. [2017], Wyniki produkcyjne i ekonomiczne gospodarstw specjalizujących się w uprawach polowych, Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu, 6: 135–140.
27.
Koop G., Osiewalski J., Steel M. [1997], Bayesian efficiency analysis through individual effects: hospital cost frontiers, Journal of Econometrics, 76, 1–2: 77–105.
28.
Koop G., Poirier D., Tobias J. [2007], Bayesian econometric methods, Cambridge University Press, Cambridge.
29.
Latruffe L., Balcombe K., Davidova S., Zawalińska K. [2004], Determinants of technical efficiency of crop and livestock farms in Poland, Applied Economics, 36 (12): 1255–1262.
30.
Lewis S., Raftery A. [1997], Estimating Bayes factors via posterior simulation with the Laplace-Metropolis estimator, Journal of the American Statistical Association, 92 (438): 648–655.
31.
Martinho V. J. P. D. [2017], Efficiency, total factor productivity and returns to scale in a sustainable perspective: an analysis in the European Union at farm and regional level, Land Use Policy, 68: 232–245.
32.
Marzec J., Osiewalski J. [2008], Bayesian inference on technology and cost efficiency of bank branches, Bank i Kredyt, 9: 29–43.
33.
Marzec J., Pisulewski A. [2019], The Measurement of time-varying technical efficiency and productivity change in Polish crop farms, German Journal of Agricultural Economics, 68 (1): 15–27.
34.
Marzec J., Pisulewski A., Prędki A. [2019], Efektywność techniczna i produktywność polskich gospodarstw rolnych specjalizujących się w uprawach polowych, Gospodarka Narodowa, 298 (2): 95–125.
35.
Meeusen W., van den Broeck J. [1977], Efficiency estimation from Cobb-Douglas production functions with composed error, International Economic Review, 18 (2): 435–444.
36.
Njuki E., Bravo-Ureta B. E., O’Donnell Ch J. [2019], Decomposing agricultural productivity growth using a random-parameters stochastic production frontier, Empirical Economics, 57 (3): 839–860.
37.
Orea L., Kumbhakar S. C. [2004], Efficiency measurement using a latent class stochastic frontiermodel, Empirical Economics, 29 (1): 169–183.
38.
Osiewalski J. [2000], Ekonometria bayesowska w zastosowaniach, Wydawnictwo Akademii Ekonomicznej w Krakowie.
39.
Osuch D., Goraj L., Skarżyńska A., Grabowska K. [2004], Plan wyboru próby gospodarstw rolnych polskiego, FADN.
40.
Pisulewski A., Marzec J. [2019], Heterogeneity, transient and persistent technical efficiency of Polish crop farms, Spanish Journal of Agricultural Research, 17 (1). e0106.
41.
Pitt M., Lee L. [1981], Measurement of sources of technical inefficiency in the Indonesian weaving industry. Journal of Development Economics, 9 (1): 43–64.
42.
Rezitis A., [2010], Agricultural productivity and convergence: Europe and the United States, Applied Economics, 42 (8): 1029–1044.
43.
Rossi P., Allenby G., McCulloch R. [2005], Bayesian statistics and marketing, John Wiley & Sons, Chichester.
44.
Skevas I. [2019], A hierarchical stochastic frontier model for efficiency measurement under technology heterogeneity, Journal of Quantitative Economics, 17 (3): 513–524.
45.
Špička J., Smutka L. [2014], The technical efficiency of specialised milk farms: a regional view, The Scientific World Journal, ID: 985149.
46.
Špička J. [2014], The regional efficiency of mixed crop and livestock type of farming and its determinants, Agris on-line Papers in Economics and Informatics, 1: 99–109.
47.
Swamy P. A. [1970], Efficient inference in a random coefficient regression model, Econometrica, 38 (2): 311–323.
48.
Tsionas E. G. [2002] Stochastic frontier models with random coefficients. Journal of Applied Econometrics, 17 (2): 127–147.
49.
Varian H. R. [2010] Intermediate microeconomics. A modern approach. Eight Edition, W. W. Norton and Company, New York.
50.
Zhu X., Lansink A. [2010], Impact of CAP subsidies on technical efficiency of crop farms in Germany, the Netherlands and Sweden, Journal of Agricultural Economics, 61 (3): 545–564.
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