Priorities for Effective Management of Health Expenditures in OECD Countries: Fuzzy AHP Application

Yıl 2025, Cilt: 33 Sayı: 63, 11 - 29

Erman Gedikli , Ersin Kocaman

https://doi.org/10.17233/sosyoekonomi.2025.01.01

Öz

This study aims to identify the determinants of health expenditures through a comprehensive literature review, contributing to the design of effective health policies. The Fuzzy AHP method was used to evaluate the determinants of health expenditures, categorising OECD member countries into developed and developing groups. In both country groups, health services emerged as the most significant determinant. Education, income, and economic changes were prominent in developed countries, while governance and education were key in developing countries. The study highlights the need to establish different strategic pathways based on the priorities of each country group, offering unique insights.

Anahtar Kelimeler

AHP, Fuzzy Logic, Health Expenditures, Health Policy

OECD Ülkelerinde Sağlık Harcamalarının Etkin Yönetimi için Öncelikler: Bulanık AHS Uygulaması

Öz

Bu çalışmanın amacı, kapsamlı bir literatür taraması yoluyla sağlık harcamalarının belirleyicilerini tespit etmek ve etkili sağlık politikalarının tasarlanmasına katkıda bulunmaktır. Bulanık AHS yöntemi, OECD üyesi ülkeleri gelişmiş ve gelişmekte olan gruplar olarak sınıflandırarak sağlık harcamalarının belirleyicilerini değerlendirmek için kullanılmıştır. Her iki ülke grubunda da sağlık hizmetleri en önemli belirleyici olarak ortaya çıkmıştır. Gelişmiş ülkelerde eğitim, gelir ve ekonomik değişimler öne çıkarken, gelişmekte olan ülkelerde yönetişim ve eğitim kilit rol oynamıştır. Çalışma, her ülke grubunun önceliklerine göre farklı stratejik yolların belirlenmesi ihtiyacını vurgulamakta ve benzersiz bulgular sunmaktadır.

Anahtar Kelimeler

AHS, Bulanık Mantık, Sağlık Harcamaları, Sağlık Politikaları

Kaynakça

• Akca, N. et al. (2017), “Determinants of health expenditure in OECD countries: A decision tree model”, Pakistan Journal of Medical Sciences, 33(6), 1490-1494.
• Astolfi, R. et al. (2012), “Informing policy makers about future health spending: A comparative analysis of forecasting methods in OECD countries”, Health Policy, 107(1), 1-10.
• Bac, C. & Y.L. Pen (2002), “An international comparison of health care expenditure determinants”, 10th International Conference on Panel Data, 1-22.
• Badulescu, D. et al. (2019), “The Relative Effects of Economic Growth, Environmental Pollution and Non-Communicable Diseases on Health Expenditures in European Union Countries”, Environmental Research and Public Health, 16(24), 5115.
• Blazquez-Fernandez, C. et al. (2014), “Disentangling the heterogeneous income elasticity and dynamics of health expenditure”, Applied Economics, 46(16), 1839-1854.
• Bloom, D.E. & J.E. Finlay (2009), “Demographic change and economic growth in Asia”, Asian Economic Policy Review, 4(1), 45-64.
• Boz, C. et al. (2020), “The impacts of aging, income and urbanization on health expenditures: A panel regression analysis for OECD countries”, Turk J Public Health, 18(1), 1-9.
• Büyüközkan, G. & G. Çifçi (2012), “A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers”, Expert Systems with Applications, 39(3), 3000-3011.
• Campbell, S. et al. (2020), “Purposive sampling: complex or simple? Research case examples”, Journal of Research in Nursing, 25(8), 652-661.
• Çelik, Y. et al. (2016), “Achieving value for money in health: a comparative analysis of OECD countries and regional countries”, International Journal of Health Planning and Management, 32, 279-298.
• Chaabouni, S. & K. Saidi (2017), “The dynamic links between carbon dioxide (CO2) emissions, health spending and GDP growth: A case study for 51 countries”, Environmental Research, 158, 137-144.
• De Meijer, C. et al. (2013), “The effect of population aging on health expenditure growth: a critical review”, Eur J Ageing, 10, 353-361.
• Di Matteo, L. (2003), “The income elasticity of health care spending A comparison of parametric and nonparametric approaches”, Eur J Health Econom, 4, 20-29.
• Eriksen, S. & R. Wiese (2019), “Policy induced increases in private healthcare financing provide short-term relief of total healthcare expenditure growth: Evidence from OECD countries”, European Journal of Political Economy, 59, 71-82.
• Gövdeli, T. (2019), “Health expenditure, economic growth, and CO2 emissions: evidence from the OECD countries”, Adiyaman University Journal of Social Sciences, 31, 488-516.
• Hartwig, J. & J.E. Sturm (2014), “Robust determinants of health care expenditure growth”, Applied Economics, 46(36), 4455-4474.
• Hosoya, K. (2014), “Determinants of Health Expenditures: Stylized Facts and a New Signal”, Modern Economy, 5(13), 1171-1180.
• İlgün, G. et al. (2022), “The Granger Causality Between Health Expenditure and Gross Domestic Product in OECD Countries”, Journal of Health Management, 24(3), 356-361.
• Ivanková, V. et al. (2020), “The governance of efficient healthcare financing system in OECD countries”, Polish Journal of Management Studies, 21(2), 179-194.
• Jakovljevic, M. et al. (2020), “Predictors of (in)efficiencies of Healthcare Expenditure Among the Leading Asian Economies - Comparison of OECD and Non-OECD Nations”, Risk Management and Healthcare Policy, 13, 2261-2280.
• Jiang, W. & Y. Wang (2023), “Asymmetric Effects of Human Health Capital on Economic Growth in China: An Empirical Investigation Based on the NARDL Model”, Sustainability, 15(6), 5537.
• Kahraman, C. et al. (2002), “Capital budgeting techniques using discounted fuzzy versus probabilistic cash flows”, Information Sciences, 142(1-4), 57-76.
• Karim, Z.A. et al. (2023), “The impact of population aging and fertility rate on economic growth in Malaysia”, Economic Journal of Emerging Markets, 15(2), 199-211.
• Kong, M. et al. (2020), “The Determinants of Health Care Expenditure and Trends: A Semiparametric Panel Data Analysis of OECD Countries”, Advances in Econometrics, 41, 191-216.
• Kraipornsak, P. (2017), “Factors Determining Health Expenditure in the Asian and the OECD Countries”, Economics World, 5(5), 407-417.
• Kumar, V. & K. Kumar (2008), “On the ideal convergence of sequences of fuzzy numbers”, Information Sciences, 178(24), 4670-4678.
• Kutlu, G. & E. Örün (2022), “The effect of carbon dioxide emission, GDP per capita and urban population on health expenditure in OECD countries: a panel ARDL approach”, International Journal of Environmental Health Research, 33(12), 1233-1242.
• Lago-Peñas, S. et al. (2013), “On the relationship between GDP and health care expenditure: A new look”, Economic Modelling, 32(1), 124-129.
• Meade, L.M. & J. Sarkis (1999), “Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach”, International Journal of Production Research, 37(2), 241-261.
• Mehrara, M. et al. (2010), “The Relationship between Health Expenditure and GDP in OECD Countries Using PSTR”, European Journal of Economics, Finance and Administrative Sciences, 24, 50-58.
• Middendorf, T. (2005), “Human Capital and Economic Growth in OECD Countries”, RWI Discussion Paper, No. 30.
• Mosca, I. (2007), “Decentralization as a determinant of health care expenditure: empirical analysis for OECD countries Ilaria Mosca Decentralization as a determinant of health care expenditure: empirical analysis for OECD countries”, Applied Economics Letters, 14, 511-515.
• Nghiem, S.H. & L.B. Connelly (2017), “Convergence and determinants of health expenditures in OECD countries”, Health Economics Review, 7(1), 29.
• OECD (2021), Health at a Glance 2021, OECD.
• Phi, G. (2017), “Determinants of Health Expenditures in OECD Countries”, Honors Thesis, Bryant University.
• Ramík, J. (2007), “A Decision System Using ANP and Fuzzy Inputs”, International Journal of Innovative Computing, Information and Control, 3(4), 825-837.
• Saaty, T. (1996), Decisions with the analytic network process (ANP), ISAHP 96.
• Sen, A. (2005), “Is health care a luxury? New evidence from OECD data”, International Journal of Health Care Finance and Economics, 5(2), 147-164.
• Sevkli, M. et al. (2012), “Development of a fuzzy ANP based SWOT analysis for the airline industry in Turkey”, Expert Systems with Applications, 39(1), 14-24.
• Sfakianakis, G. et al. (2021), “The impact of macro-fiscal factors and private health insurance financing on public health expenditure: evidence from the OECD countries for the period 2000-2017”, EuroMed Journal of Business, 16(1), 1-24.
• Sturm, J. E. & J. Hartwig (2012), “An Outlier-Robust Extreme Bounds Analysis of the Determinants of Health-Care Expenditure Growth”, KOF Working Papers, No. 307.
• Tian, F. et al. (2018), “A quantile regression approach to panel data analysis of health-care expenditure in Organisation for Economic Co-operation and Development countries”, Health Economics (United Kingdom), 27(12), 1921-1944.
• Tuzkaya, U.R. & S. Önüt (2008), “A fuzzy analytic network process based approach to transportation-mode selection between Turkey and Germany: A case study”, Information Sciences, 178(15), 3133-3146.
• UN (2022), World Economic Situation and Prospects.
• Vandersteegen, T. et al. (2015), “The impact of no-fault compensation on health care expenditures: An empirical study of OECD countries”, Health Policy, 119(3), 367-374.
• Wang, F. (2015), “More health expenditure, better economic performance? Empirical evidence from OECD countries”, INQUIRY The Journal of Health Care Organization, Provision, and Financing, 2015(52), doi:10.1177/0046958015602666.
• Wang, L. & Y. Chen (2021), “Determinants of China’s health expenditure growth: based on Baumol’s cost disease theory”, International Journal for Equity in Health, 20(1), 1-11.
• Wranik, D. (2012), “Healthcare policy tools as determinants of health-system efficiency: evidence from the OECD”, Health Economics, Policy and Law, 7(2), 197-226.
• Wu, D. et al. (2020), “The SARS-CoV-2 outbreak: What we know”, International Journal of Infectious Diseases, 94, 44-48.
• Xiaoqiong, W. et al. (2004), “Trapezoidal Fuzzy AHP for the Comprehensive Evaluation of Highway Network Programming Schemes in Yangtze River Delta”, in: Fifth World Congress on Intelligent Control and Automation IEEE, Cat. No.04EX788.
• Yetim, B. et al. (2020), “The Socioeconomic Determinants of Health Expenditure in OECD: An Examination on Panel Data”, International Journal of Healthcare Management, 14(4), 1265-1269.
• Yetim, B. et al. (2021), “The socioeconomic determinants of health expenditure in OECD: An examination on panel data”, International Journal of Healthcare Management, 14(4), 1265-1269.
• Younsi, M. et al. (2016), “Robust analysis of the determinants of healthcare expenditure growth: evidence from panel data for low-, middle-and high-income countries”, J Health Plann Mgmt, 31, 580-601.
• Yüksel, I. & M. Daǧdeviren (2010), “Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm”, Expert Systems with Applications, 37(2), 1270-1278.
• Zadeh, L.A. (1965), “Fuzzy sets”, Information and Control, 8(3), 338-353.
• Zhang, D. & K.M. Atikur Rahman (2020), “Government health expenditure, out-of-pocket payment and social inequality: A cross-national analysis of China and OECD countries”, Int J Health Plann Mgmt, 35(5), 1111-1126.