In this paper, we aim to measure agricultural productivity change of 34 OECD countries between years 1990 and 2014. The methods employed are data envelopment analysis (DEA) and malmquist productivity index (MPI). DEA is a relative efficiency method in a production technology, whereas MPI is based on DEA to measure the changes in the production technology over time. Our challenge is the existence of missing data points over the years in the initial dataset, which correspond to approximately 9% of the data. Removing units, factors or years with missing data as commonly practiced in DEA, would cause loss of information and makes it very difficult to draw conclusions in such a macro-data. We present the idea of using averages of available data points for a given factor and average variations over the years in those data to produce intervals for the missing points and handle the problem without any dimension reduction in the available data. Fuzzy DEA approach is employed using the calculated factor-specific intervals followed by MPI calculations to conduct a productivity change analysis. We suggest and empirically illustrate that instead of narrowing down the scope of the analysis by excluding the points missing, applying fuzzy approaches is an option worth considering by which it can be possible to make the best out of the available information. The results of the analysis are interpreted with respect to years, countries, regions and economic size of the countries.
Accepté le :
DOI : 10.1051/ro/2018017
Mots-clés : Agricultural productivity change, data envelopment analysis, malmquist productivity index, Fuzzy data, missing data
@article{RO_2018__52_3_1003_0,
author = {Atici, Kazim Baris and Ulucan, Aydin and Bayar, Irmak Uzun},
title = {The measurement of agricultural productivity change in {OECD} countries with {Fuzzy} data},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {1003--1017},
publisher = {EDP-Sciences},
volume = {52},
number = {3},
year = {2018},
doi = {10.1051/ro/2018017},
zbl = {1405.90074},
language = {en},
url = {http://archive.numdam.org/articles/10.1051/ro/2018017/}
}
TY - JOUR AU - Atici, Kazim Baris AU - Ulucan, Aydin AU - Bayar, Irmak Uzun TI - The measurement of agricultural productivity change in OECD countries with Fuzzy data JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2018 SP - 1003 EP - 1017 VL - 52 IS - 3 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro/2018017/ DO - 10.1051/ro/2018017 LA - en ID - RO_2018__52_3_1003_0 ER -
%0 Journal Article %A Atici, Kazim Baris %A Ulucan, Aydin %A Bayar, Irmak Uzun %T The measurement of agricultural productivity change in OECD countries with Fuzzy data %J RAIRO - Operations Research - Recherche Opérationnelle %D 2018 %P 1003-1017 %V 52 %N 3 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro/2018017/ %R 10.1051/ro/2018017 %G en %F RO_2018__52_3_1003_0
Atici, Kazim Baris; Ulucan, Aydin; Bayar, Irmak Uzun. The measurement of agricultural productivity change in OECD countries with Fuzzy data. RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 3, pp. 1003-1017. doi : 10.1051/ro/2018017. http://archive.numdam.org/articles/10.1051/ro/2018017/
[1] , and , Drought in central and southwest Asia: La Nina, the warm pool, and Indian Ocean precipitation. J. Clim. 15 (2002) 697–700. | DOI
[2] and , Measuring agricultural productivity growth in MENA Countries. J. Dev. Agric. Econ. 1 (2009) 103–113.
[3] , and , The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50 (1982) 1393–1414. | DOI | Zbl
[4] , and , Measuring the efficiency of decision making units. Eur J Oper. Res. 2 (1978) 429–444. | DOI | MR | Zbl
[5] and , Total factor productivity growth in agriculture: a Malmquist index analysis of 93 countries 1980–2000. Agri. Econ. 32 (2005) 115–134. | DOI
[6] , and , IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manag. Sci. 45 (1999) 597–607. | DOI | Zbl
[7] , and , Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Springer, New York, NY (2007). | DOI | Zbl
[8] , and , The comparison of agricultural efficiency and productivty growth in the EU and Turkey 1980–2002. Int. J. Bus., Manag. Econ. 1 (2005) 109–124.
[9] , , and , Productivity changes in Swedish pharmacies 1980–1989: a nonparametric approach. J. Product. Anal. 3 (1992) 85–101. | DOI
[10] and , LDC agriculture: nonparametric Malmquist productivity indexes. J. Dev. Econ. 53 (1997) 373–390. | DOI
[11] , and , A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. Eur. J. Oper. Res. 214 (2011) 457–472. | DOI | MR | Zbl
[12] , and , Productivity growth and efficiency measurements in fuzzy environments with an application to health care. Int. J. Fuzzy Syst. Appl. 2 (2012) 1c35.
[13] and , Data envelopment analysis with missing data: an application to university libraries in Taiwan. J. Oper. Res. Soc. 51 (2000) 897–905. | DOI | Zbl
[14] and , Data envelopment analysis with missing data, in Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, edited by and Springer, US (2007) 291–304. | DOI | Zbl
[15] and , Total factor productivity and the effects of R&D in african agriculture. J. Int. Dev. 9 (1997) 529–538. | DOI
[16] and , Generalized fuzzy data envelopment analysis methods. Appl. Soft Comput. 19 (2014) 215–225. | DOI
[17] , Index numbers and indifference surfaces. Trabajos de Estatistica 4 (1953) 209–242. | DOI | MR | Zbl
[18] and , An assessment of sustainable agriculture in the OECD countries with special reference to Turkey. New Medit. 10 (2011) 4–17.
[19] , , and , Assessment of sustainability of the European Union and Turkish agricultural sectors. New Medit. 9 (2010) 13–21.
[20] and , Technical efficiency in African agriculture: is it catching up or lagging behind? J. Int. Dev. 26 (2014) 779–795. | DOI
[21] and , Getting implicit shadow prices right for the estimation of the Malmquist index: the case of agricultural total factor productivity in developing countries. Agric. Econ. 41 (2010) 349–360. | DOI
[22] and , Productivity growth, technical progress, and efficiency change in industrialized countries: comment. Am. Econ. Rev. 87 (1997) 1033–1039.
[23] , and , Assessing total factor productivity growth in Sub-Saharan African agriculture. J. Agric. Econ. 62 (2011) 357–374. | DOI
[24] , and , Efficiency analysis and ranking of DMUS with Fuzzy data. Fuzzy Optim. Decis. Mak. 1 (2002) 255–267. | DOI | Zbl
[25] , A fuzzy system approach in data envelopment analysis. Comput. Math. Appl. 24 (1992) 259–266. | MR | Zbl
[26] and , Asian agricultural productivity and convergence. J. Agric. Econ. 52 (2001) 96–110. | DOI
[27] , and , Productivity growth and convergence in Asian and African agriculture, in Comparing African and Asian Economic Development, edited by and Palgrave, Basingstoke (2001) 258–273.
[28] and , Agricultural productivity in the WANA region. J. Comp. Asian Dev. 10 (2011) 157–185. | DOI
[29] , and , A multilateral Malmquist productivity index approach to explaining agricultural growth in Sub-Saharan Africa. Dev. Policy Rev. 13 (1995) 323–348. | DOI
[30] and , A mathematical programming approach for measuring technical efficiency in a fuzzy environment. J. Product. Anal. 10 (1998) 85–102. | DOI
[31] , and , A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries. Renew. Sustain. Energy Rev. 40 (2014) 91–96. | DOI
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