EVALUATION OF OECD COUNTRIES' AGRICULTURAL TRADE PERFORMANCE: PROPOSAL OF A DYNAMIC MODEL

Yıl 2025, Cilt: 36 Sayı: 2, 236 - 270, 31.08.2025

Melis Almula Karadayı , Umut Aydın , Füsun Ülengin , Burç Ülengin

Öz

Throughout history, the agricultural sector has played an important role in the economies of both developed and developing countries and has contributed to employment, GDP, international commerce, and, most importantly, rural development. Within the scope of the study, the performance of the OECD nations on agricultural product trade is analyzed using the proposed dynamic model. Categorical Data Envelopment Analysis (DEA)-Malmquist Total Factor Productivity Index (TFPI) approaches are employed to examine in detail the changes that occurred in the OECD members' agricultural trade performance in efficiency, technology, and total factor productivity over time.Additionally, Dynamic Social Network Analysis is conducted to reveal the eigenvector centrality of the countries investigated in the same period for both import and export-related trade to reveal which countries have the highest trade connection with the highest trade volume to which countries. Finally, regression analysis takes those eigenvector centrality measures as dependent variables. The Categorical-DEA values, as well as maximum reservoir of water, reservoir minimum water area (% of total land area), Logistics Performance Index, and Red List Index, are used as independent variables in order to reveal the primary reasons behind these eigenvector centrality values and the actions that should be taken to improve them are specified.

Anahtar Kelimeler

Agricultural Products , Trade , Efficiency , Categorical-DEA , Malmquist Total Factor Productivity Index , Dynamic Social Network Analysis

OECD ÜLKELERİNİN TARIMSAL TİCARET PERFORMANSININ DEĞERLENDİRİLMESİ: DİNAMİK BİR MODEL ÖNERİSİ

Öz

Throughout history, the agricultural sector has played an important role in the economies of both developed and developing countries and has contributed to employment, GDP, international commerce, and, most importantly, rural development. Within the scope of the study, the performance of the OECD nations on agricultural product trade is analyzed using the proposed dynamic model. Categorical Data Envelopment Analysis (DEA)-Malmquist Total Factor Productivity Index (TFPI) approaches are employed to examine in detail the changes that occurred in the OECD members' agricultural trade performance in efficiency, technology, and total factor productivity over time.Additionally, Dynamic Social Network Analysis is conducted to reveal the eigenvector centrality of the countries investigated in the same period for both import and export-related trade to reveal which countries have the highest trade connection with the highest trade volume to which countries. Finally, regression analysis takes those eigenvector centrality measures as dependent variables. The Categorical-DEA values, as well as maximum reservoir of water, reservoir minimum water area (% of total land area), Logistics Performance Index, and Red List Index, are used as independent variables in order to reveal the primary reasons behind these eigenvector centrality values and the actions that should be taken to improve them are specified.

Anahtar Kelimeler

Agricultural Products , Trade , Efficiency , Categorical-DEA , Malmquist Total Factor Productivity Index , Dynamic Social Network Analysis

Kaynakça

• Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230. Doi : https://doi.org/10.1016/S0378-8733(03)00009-1
• Adler, N., & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. European Journal of Operational Research, 202(1), 273–284. Doi : https://doi.org/10.1016/j.ejor.2009.03.050
• Armagan, G., Ozden, A., & Bekcioglu, S. (2010). Efficiency and total factor productivity of crop production at NUTS1 level in Turkey: Malmquist index approach. Quality & Quantity, 44(3), 573–581. Doi : https://doi.org/10.1007/s11135-008-9216-y
• Aydın, U., Karadayı, M. A., Ülengin, F., & Ülengin, K. B. (2021). Enhanced performance assessment of airlines with integrated balanced scorecard, network-based superefficiency DEA and PCA methods. In Y. I. Topcu, Ö. Özaydın, Ö. Kabak, & Ş. Önsel Ekici (Eds.), Multiple criteria decision making. MCDM 2019. Contributions to management science (pp. 151–164). Springer. Doi : https://doi.org/10.1007/978-3-030-52406-7_9
• Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34(4), 513–521. Doi : https://doi.org/10.1287/opre.34.4.513
• Boakye, A., Lee, H., Annor, F., Dadzie, S. K., & Salifu, M. (2024). Data envelopment analysis (DEA) to estimate technical and scale efficiencies of smallholder pineapple farmers in Ghana. International Journal of Agricultural Economics, 9(2), 45–56. Doi : https://doi.org/10.11648/j.ijae.20240902.11
• 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(6), 1393–1414. Doi : https://doi.org/10.2307/1913388
• Chen, P.-C., Yu, M.-M., Chang, C.-C., & Hsu, S.-H. (2008). Total factor productivity growth in China’s agricultural sector. China Economic Review, 19(4), 580–593. Doi : https://doi.org/10.1016/j.chieco.2008.07.001
• Coelli, T. J. (1996). Measurement of total factor productivity growth and biases in technological change in Western Australian agriculture. Journal of Applied Econometrics, 11(1), 77–91. Doi : https://doi.org/10.1002/(SICI)1099-1255(199601)11:1
• 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(s1), 115–134. Doi : https://doi.org/10.1111/j.0169-5150.2004.00018.x
• Djoumessi, Y. F. (2022). New trend of agricultural productivity growth in sub-Saharan Africa. Scientific African, 18, e01410. Doi : https://doi.org/10.1016/j.sciaf.2022.e01410
• Dong, H. (2022). The impact of trade facilitation on the networks of value-added trade—Based on social network analysis. Emerging Markets Finance and Trade, 58(8), 2290–2299. Doi : https://doi.org/10.1080/ 1540496X.2021.1974393
• Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley. Hajihassaniasl, S. (2021). Measurement of total factor productivity in Iranian economic sectors: Malmquist index analysis. İzmir İktisat Dergisi, 36(3), 589–600. Doi : https://doi.org/10.24988/ije.202136306
• Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. Doi : https://doi.org/10.1098/rsta.2015.0202
• Karaman, S., & Özalp, A. (2017). Türkiye tarım sektörü bölgesel toplam faktör verimliliğinin Malmquist endeks ile belirlenmesi. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, 34(1), 209–217. Doi : https://doi.org/10.13002/jafag4200
• Kouriati, A., Tafidou, A., Lialia, E., Prentzas, A., Moulogianni, C., Dimitriadou, E., & Bournaris, T. (2023). The impact of data envelopment analysis on effective management of inputs: The case of farms located in the Regional Unit of Pieria. Agronomy, 13(8), 2109. Doi : https://doi.org/10.3390/agronomy13082109
• Kyriacou, A. P., Muinelo-Gallo, L., & Roca-Sagalés, O. (2019). The efficiency of transport infrastructure investment and the role of government quality: An empirical analysis. Transport Policy, 74, 93–102. Doi : https://doi.org/10.1016/j.tranpol.2018.11.008
• Liu, D., Liu, J. C., Huang, H., & Sun, K. (2019). Analysis of the international polysilicon trade network. Resources, Conservation and Recycling, 142, 122–130. Doi : https://doi.org/10.1016/j.resconrec.2018.11.025
• Lovrić, M., Da Re, R., Vidale, E., Pettenella, D., & Mavsar, R. (2018). Social network analysis as a tool for the analysis of international trade of wood and non-wood forest products. Forest Policy and Economics, 86, 45–66. Doi : https://doi.org/10.1016/j.forpol.2017.10.006
• Luttermann, S., Kotzab, H., & Halaszovich, T. (2020). The impact of logistics performance on exports, imports and foreign direct investment. World Review of Intermodal Transportation Research, 9(1), 27–46. Doi : https://doi.org/10.1504/WRITR.2020.106444
• Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadística, 4(2), 209–242. Doi : https://doi.org/10.1007/BF03006863
• Marchetti, D., & Wanke, P. (2017). Brazil’s rail freight transport: Efficiency analysis using two-stage DEA and cluster-driven public policies. Socio-Economic Planning Sciences, 59, 26–42. Doi : https://doi.org/10.1016/j.seps.2016.10.005
• Pınarbaşı, A., Aydın, U., Karadayı, M. A., & Tozan, H. (2022). An integrated performance measurement framework for restaurant chains: A case study in Istanbul. Endüstri Mühendisliği, 33(3), 484–499. Doi : https://doi.org/10.46465/endustrimuhendisligi.1087736
• Sultana, S., Hossain, M. M., & Haque, M. N. (2023). Estimating the potato farming efficiency: A comparative study between stochastic frontier analysis and data envelopment analysis. PLOS ONE, 18(4), e0284391. Doi : https://doi.org/10.1371/journal.pone.0284391
• Suroso, A. I. (2022). The effect of logistics performance index indicators on palm oil and palm-based products export: The case of Indonesia and Malaysia. Economies, 10(10), 261. Doi : https://doi.org/10.3390/economies10100261
• Şişman, Z., & Tekiner-Mogulkoc, H. (2022). Using Malmquist TFP index for evaluating agricultural productivity: Agriculture of Türkiye NUTS2 regions. Sigma Journal of Engineering and Natural Sciences, 40(3), 513–528.
• Telleria, R., & Aw-Hassan, A. (2011). Agricultural productivity in the WANA region. The Journal of Comparative Asian Development, 10(1), 157–185. Doi : https://doi.org/10.1080/15339114.2011.578490
• Tunca, H., & Deliktaş, E. (2015). OECD ülkelerinde tarımsal etkinlik ölçümü: Dinamik veri zarflama analizi. Ege Akademik Bakış, 15(2), 217–224.
• Ward, F. A., & Michelsen, A. (2002). The economic value of water in agriculture: Concepts and policy applications. Water Policy, 4(5), 423–446. Doi : https://doi.org/10.1016/S1366-7017(02)00039-9
• Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.
• Yin, R. R., Guo, Q., Yang, J. N., & Liu, J. G. (2018). Inter-layer similarity-based eigenvector centrality measures for temporal networks. Physica A: Statistical Mechanics and Its Applications, 512, 165–173. Doi : https://doi.org/10.1016/j.physa.2018.08.018
• Yu, J. K., & Ma, J. Q. (2020). Social network analysis as a tool for the analysis of the international trade network of aquatic products. Aquaculture International, 28(3), 1195–1211. Doi : https://doi.org/10.1007/s10499-020-00520-5