Measuring the Economic Efficiencies of OECD Countries in 2019 By Weight-Restricted Data Envelopment Analysis
DOI:
https://doi.org/10.51243/SAKA-TJMER.2021.7Keywords:
Economic Efficiency,, OECD, Data Envelopment Analysis (DEA), Weight-Restricted Data Envelopment Analysis (ARI DEA)Abstract
Data envelopment analysis (DEA) is a linear programming-based method that measures the relative efficiency of a large number of organizational decision-making units (DMU) by generating similar inputs and outputs. Apart from the classical DEA models, the value judgments and choices of decision-makers can be included in the model with the Assurance Region (AR) method, which is applied by putting weight restrictions on determined input and output variables. Because in classical DEA models, the fact that input and output weights can cause inconsistencies in the relative efficiency scores of decision-making units (DMU). Thus, a DMU that is usually inefficient may appear to be efficient. The purpose of this study is to analyze the economic efficiencies of 35 OECD countries in 2019 and to rank these countries according to their efficiency scores. Both the input-oriented CCR model and the weight-restricted model (ARI DEA) were used to measure the economic efficiency of countries. In the DEA, the binary comparison values used in determining the weight restrictions were specified according to the AHP scale. While the economies of the seven countries were found to be efficient in 2019 in the analyses carried out with the CCR model, the economies of the two countries were found to be efficient with the weight-restricted model. In the efficiency analysis carried out with the assurance region model, the efficiency values of the countries are very low compared to the input-oriented CCR model. Accordingly, in the end putting restrictions in the ARI model significantly changes the efficiency values, and the weight-restricted model is a superior method for identifying the active and inactive countries compared to the CCR model.
Downloads
References
Aksaraylı, M., & Pala, O. (2017). Veri zarflama analizi ve kümeleme analizi kullanılarak OECD’YE üye ülkelerin ekonomik performansları, yaşam memnuniyeti ve inovasyon düzeyleri açısından incelenmesi. Aydın İktisat Fakültesi Dergisi, 2(2), 67-80.
Bayrak, R., & Bahar, O. (2017). Economic efficiency analysis of tourism sector in OECD countries: An emprical study with DEA. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 20, 83–100.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Beasley, J.E. (1990). Comparing university departments, OMEGA, 18(2), 171-183.
Behdioğlu, S., & Özcan G. (2009). Veri zarflama analizi ve bankacılık sektöründe bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 14 (3): 301-326.
Brandao, L. C., Soares de Mello, J. C. C. B., & Del-Vecchio, R. R. (2020). A linear model for smooth DEA BCC frontiers. Computers and Industrial Engineering, 140(May 2019), 106222.
Budak, H. (2011). Veri zarflama analizi ve Türk bankacılık sektöründe uygulaması. Marmara Üniversitesi Fen Bilimleri Dergisi, 23(3), 95–110.
Charnes, A., W. Cooper, & E. Rhodes. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research 2(6), 429–444.
Chaparro, P., F., Jimenez, S., J., & Smith, P. (1997). On the role of weight restrictions in data envelopment analysis. Journal of Productivity Analysis, 8(2), 215–230.
Chien, T., & Hu, J. L. (2007). Renewable energy and macroeconomic efficiency of OECD and non-OECD economies. Energy Policy, 35(7), 3606–3615.
Çakın, E., & Özdemir, A. (2020). Ülkelerin inovasyon performansının ölçülmesinde yapar sinir ağları, bulanık demantel tabanlı analitik ağ süreci ve ağırlık kısıtlı veri zarflama analizi yaklaşımlarının bütünleşik olarak kullanılması ve bir uygulama. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 2(21), 287–314.
Demi̇r, A., & Bakırcı, F. (2014). OECD üyesi ülkelerin ekonomik etkinliklerinin veri zarflama analizi ile ölçümü. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 28 (2), 109-132.
Ela, M., Doğan, A., & Uçar, O. (2018). Avrupa bi̇rli̇ği̇ ülkeleri̇ ve Türki̇ye’ni̇n makroekonomi̇k performanslarının topsis yöntemi̇ i̇le karşılaştırılması. Osmaniye Korkut Ata Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2(2), 129–143.
Eroğlu, E., & Atasoy, M. C. (2006). Veri zarflama analizi ile etkinlik ölçümü ve etkin karar birimlerinin duyarlılık analizi. İ.Ü. İşletme Fakültesi Dergisi. 35 (2): 73-89.
Eyüboğlu, K. (2017). Türk dünyasında yer alan ülkelerin makro performanslarının karşılaştırılması. Bilig, 83, 331–350.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal ofthe Royal Statistical Society Series A (120), 253–290.
GhalehJough, R., S., Hosseinzadeh Lotfi, F., Jahanshahloo, G., Rostamy-Malkhalifeh, M., & Sharafi, H. (2020). Finding closest target for bank branches in the presence of weight restrictions using data envelopment analysis. Annals of Operations Research (C. 288).
Grossman, V., N., Mack, A., & Martinez-Garcia, E. (2013). Database of global economic indicators (DGEI): A methodological Note. Inside Federal Reserve Bank of Dallas, Globalization and Monetary Policy Institute Working Papers (C. 2013).
Gonzalez, E., Cárcaba, A., & Ventura, J. (2016). Weight constrained DEA measurement of the quality of life in Spanish municipalities in 2011. Social Indicators Research, 136(3), 1157–1182
Günay, B. (2015). BİST’de işlem gören KOBİ gıda işletmelerinin veri zarflama analizi yöntemi ile etkinliklerinin ölçülmesi. Akademik Bakış Dergisi. 47: 16-34.
Hsu, M., Luo, X., & Chao, G. H. (2008). The fog of OECD and non-OECD country efficiency: a data envelopment analysis approach. The Journal of Developing Areas, 81-93.
Hwang, S. N., Lee, H. S., Tang, S. C., & Hsu, S. S. (2013). Measuring quality of life using DEA-AR: Focusing on undesirable factors. Information Systems and Operational Research, 51(2), 84–91.
Kelly, E., Shalloo, L., Geary, U., Kinsella, A., & Wallace, M. (2012). Application of data envelopment analysis to measure technical efficiency on a sample of Irish dairy farms. Irısh Journal of Agricultural and Food Research. 51: 63-77.
Keskin, B., & Köksal, C. D. (2019). A hybrid AHP/DEA-AR model for measuring and comparing the efficiency of airports. International Journal of Productivity and Performance Management, 68(3), 524–541.
Kocakoç, İ. D. (2003). Veri zarflama anali̇zi̇’ndeki̇ ağırlık kısıtlamalarının beli̇rlenmesi̇nde anali̇ti̇k hi̇yerarşi süreci̇ni̇n kullanimi. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 18(2), 1–12.
Oikonomou, N., Tountas, Y., Mariolis, A., Souliotis, K., Athanasakis, K., & Kyriopoulos, J. (2016). Measuring the efficiency of the Greek rural primary health care using a restricted DEA model; the case of southern and western Greece. Health Care Management Science, 19(4), 313–325.
Özdemir, A., & Demierli, E. (2013). Ağırlık kısıtlı veri̇ zarflama anali̇zi̇ ile mevduat bankalarının etki̇nli̇k ölçümüne yöneli̇k bi̇r uygulama. Uluslararası Yönetim İktisat ve İşletme Dergisi, 9(19), 215–238.
Özden, H. Ü. (2008). Veri zarflama analizi ile Türkiye’deki vakıf üniversitelerinin etkinliğinin ölçülmesi. İstanbul Üniversitesi İşletme Fakültesi Dergisi. 37 (2): 167-185.
Ramanathan, R. (2003). An introduction to data envelopment analysis a tool for performance measurement. New Delhi: Sage Publications.
Samut, K., P., & Cafrı, R. (2016). Analysis of the efficiency determinants of health systems in OECD countries by DEA and panel tobit. Social Indicators Research, 129(1), 113–132.
Sarraf, F., & Nejad, S. H. (2020). Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies. Evaluation and Program Planning, 79(November 2019), 101762.
Stolzer, A. J., Friend, M. A., Truong, D., Tuccio, W. A., & Aguiar, M. (2018). Measuring and evaluating safety management system effectiveness using Data envelopment analysis. Safety Science, 104(January), 55–69.
Tarım, S. A., & Karan, M. B. (2001). Investment fund performance measurement using weight-restricted data envelopment analysis: An application to the Turkish capital market. Russian and East European Finance and Trade, 37(5), 64–84.
Thanassoulis, E., & Allen, R., (1998), Simulating weights restrictions in data envelopment analysis by means of unobserved DMUs, Management Science, Vol.44, No.2, 586-594.
Thompson, R.G., Singleton, F.D., Thrall, R.M., & Smith, B.A., (1986), Comparative site evaluations for locating a high-energy physics lab in Texas, Interfaces, 16:6, 35-49.
Thompson, R. G., Brinkmann, E. J., Dharmapala, P. S., Gonzalez-Lima, M. D., & Thrall, R. M.
(1997). DEA/AR profit ratios and sensitivity of 100 large U.S. banks. European Journal of Operational Research, 98, 213-229.
Tütek, H., Gümüşoğlu Ş. & Özdemir, A. (2012). Sayısal Yöntemler Yönetsel Yaklaşım. İstanbul. Beta Basım Yayım.
Vazhayil, J. P., & Balasubramanian, R. (2013). Optimization of India’s power sector strategies using weight-restricted stochastic data envelopment analysis. Energy Policy, 56, 456–465.
