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The Relationship between Socio-Economic Development, Corruption and Health Indicators: Application of Partial Least Squares Structural Equation Modeling

Year 2017, , 191 - 206, 15.10.2017
https://doi.org/10.17093/alphanumeric.323277

Abstract

This study investigates the effects of corruption on health indicators and main cause of corruption by using structural equation modeling. Based on the heterogeneous dataset of 126 countries, structural equation model was estimated by using partial least square method where different development levels of countries were included. Findings indicate that GDP per capita, democracy levels and education level of women are three prominent variables that explain corruption in highly developed and developed countries. Corruption decreases as the regime becomes more democratic and GDP per capita increases. Furthermore, corruption has significantly displayed the effect it has on health indicators. As to middle and low-developed countries, the education level of women and health expenditure affect health indicators regardless of corruption and GDP per capita. And as the regime becomes more autocratic, corruption rises.

References

  • Aidt, T. S. (2009). Corruption, institutions, and economic development. Oxford Review of Economic Policy, 25(2), 271-291. doi:10.1093/oxrep/grp012
  • Akçay, S. (2000). Yolsuzluk, Ekonomik Özgürlükler ve Demokrasi. Muğla Üniversitesi Sosyal Bilimler Enstitüsü Dergisi,1(1),1-15.
  • Akçay, S. (2006). Corruption and Human Development. Cato Journal, 26(1), 29-48. Albayrak, M. (2010). Sağlık Sektöründe Yolsuzlular: Nedensellik Analizi. New World Sciences Academy, 5(3), 158-175.
  • Bağdiden, M& Dökmen, G.(2006). Yolsuzluklarla Kamu Harcamaları Arasındaki İlişkinin Ampirik Bir Analizi: Türkiye Örneği. ZKÜ Sosyal Bilimler Dergisi, 2(4), 23-38.
  • Chin, W.W., (2010). How to write up and report PLS analyses, In: Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H. (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (Springer Handbooks of Computational Statistics Series, vol. II). Springer, Heidelberg, Dordrecht, London, NewYork, pp. 655-690.
  • Distefano, C. (2002). The Impact of Categorization With Confirmatory Factor Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 9(3), 327-346. doi:10.1207/s15328007sem0903_2
  • Factor, R., & Kang, M. (2015). Corruption and population health outcomes: an analysis of data from 133 countries using structural equation modeling. International Journal of Public Health, 60(6), 633-641. doi:10.1007/s00038-015-0687-6.
  • Gupta, S., Mello, L. D., & Sharan, R. (2001). Corruption and military spending. European Journal of Political Economy, 17(4), 749-777. doi:10.1016/s0176-2680(01)00054-4.
  • Güneş, S., Polat, F., & Akın, T. (2014). Kalkınma Bağlamında Büyüme, Yolsuzluk ve Demokrasi İlişkisi. Economic Development: Social&Political Interactions. 13-21.
  • Hair,J.F., Hult, G.T, Ringle, & Sarstedt, C. (2014). A primer on partial least squares structural equation modeling (PLS-SEM), London, Sage.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods,6(1), 53-60.
  • Hox, J., Maas, C.J., & Brinkhuis, M. J., (2010). The Effect of Estimation Method and Sample Size in Multilevel Structural Equation Modeling. Statistica Neerlandica, 64(2), 157-170.
  • Hoyle, R. H. (2012). Handbook of structural equation modeling. New York: Guilford Press.
  • Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. New York: The Guilford Press.
  • Kwong, K., & Wong, K.(2013), Partial Least Squares Structural Equation Modeling Techniques Using SmatPLS, Marketing Bulletin, 24, Technical Note. Maccallum, R. C., & Austin, J. T. (2000). Applications of Structural Equation Modeling in Psychological Research. Annual Review of Psychology, 51(1), 201-226. doi:10.1146/annurev.psych.51.1.201.
  • Marshall MG, Jaggers K.& Robert Gurr T (2011), Polity IV Project:political regime characteristics and transitions,1800-2013. Dataset users’ manual. Center for Systemic Peace.
  • Mauro, P. (1998).Corruption and the Composition of Government Expenditure.Journal of Public Expenditure, 69, 263-279.
  • Rajkumar, A. S., & Swaroop, V. (2008). Public spending and outcomes: Does governance matter? Journal of Development Economics, 86(1), 96-111. doi:10.1016/j.jdeveco.2007.08.003.
  • Ravand, H., & Baghaei, P. (2016). Partial Least Squares Structural Equation Modeling with R, Practical Assessment Research&Evaluation, 21(11), 1-16.
  • Tiongson, E., Gupta, S., & Davoodi, H. (2001). Corruption and the provision of health care and education services. Routledge Contemporary Economic Policy Issues The Political Economy of Corruption. doi:10.4324/9780203468388.ch6.
  • Wei, S. (2000). How Taxing is Corruption on International Investors?. Review of Economics and Statistics 82 (1): 1–11.
  • Wold, H. (1973). Nonlinear Iterative Partial Least Squares (NIPALS) Modelling: Some Current Developments. Multivariate Analysis–III, 383-407. doi:10.1016/b978-0-12-426653-7.50032-6.
  • Yakar, S., & Cebeci, K. (2007). Yolsuzluğun Ekonomik Büyümeye Etkileri Üzerine Teorik Bir İnceleme. Çimento İşveren Dergisi, 4(21), 16-29.
  • The Economist Intelligence Unit, Democracy Index. http://www.transparency.org.nz/docs/2017/Democracy_Index_2016.pdf. Transparency International (2014). CPI 2014. https://www.transparency.org/research/cpi/overview.
  • United Nations Human Development Data. http://hdr.undp.org/en/data. United Nations, UNODC 2016 Report. http://www.anticorruptionday.org/documents/actagainstcorruption/print/materials2016/corr16_fs_DEVELOPMENT_en_PRINT.pdf).
  • World Bank (2014). World Development Indicators 2014, World Bank USA.

Sosyo-Ekonomik Kalkınma, Yolsuzluk ve Sağlık Göstergeleri Arasındaki İlişki: Kısmi En Küçük Kareler Yapısal Eşitlik Modeli Uygulaması

Year 2017, , 191 - 206, 15.10.2017
https://doi.org/10.17093/alphanumeric.323277

Abstract

Bu çalışma yolsuzluğun sağlık göstergeleri üzerindeki etkilerini ve yolsuzluğu etkileyen temel faktörleri yapısal eşitlik modeli ile ele alır. 126 ülkenin farklı kalkınma düzeyleri (veri kümesindeki heterojen yapı) dikkate alınarak Kısmi En Küçük Kareler yöntemi ile yapısal eşitlik modeli tahmin edilmiştir. Bulgulara göre, çok gelişmiş ve gelişmiş kategorisinde yer alan ülkelerde yolsuzluğu etkileyen en önemli faktörler, demokrasi düzeyi, kişi başına düşen gelir ve kadınların eğitim düzeyidir. Kişi başına düşen gelir arttıkça, rejim demokratikleştikçe yolsuzluk düşer. Yolsuzluğun sağlık göstergeleri üzerinde etkisi güçlüdür, yolsuzluk düştükçe sağlık göstergeleri iyileşme gösterir. Orta ve düşük gelişmiş kategorisinde yer alan ülkeler için ise, kadınların eğitim düzeyi ve sağlık harcamaları, yolsuzluktan ve kişi başına düşen gelirden bağımsız olarak sağlık göstergelerini iyileştiren faktörlerdir. Bununla beraber rejim otokratikleştikçe, yolsuzluk artar.

References

  • Aidt, T. S. (2009). Corruption, institutions, and economic development. Oxford Review of Economic Policy, 25(2), 271-291. doi:10.1093/oxrep/grp012
  • Akçay, S. (2000). Yolsuzluk, Ekonomik Özgürlükler ve Demokrasi. Muğla Üniversitesi Sosyal Bilimler Enstitüsü Dergisi,1(1),1-15.
  • Akçay, S. (2006). Corruption and Human Development. Cato Journal, 26(1), 29-48. Albayrak, M. (2010). Sağlık Sektöründe Yolsuzlular: Nedensellik Analizi. New World Sciences Academy, 5(3), 158-175.
  • Bağdiden, M& Dökmen, G.(2006). Yolsuzluklarla Kamu Harcamaları Arasındaki İlişkinin Ampirik Bir Analizi: Türkiye Örneği. ZKÜ Sosyal Bilimler Dergisi, 2(4), 23-38.
  • Chin, W.W., (2010). How to write up and report PLS analyses, In: Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H. (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (Springer Handbooks of Computational Statistics Series, vol. II). Springer, Heidelberg, Dordrecht, London, NewYork, pp. 655-690.
  • Distefano, C. (2002). The Impact of Categorization With Confirmatory Factor Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 9(3), 327-346. doi:10.1207/s15328007sem0903_2
  • Factor, R., & Kang, M. (2015). Corruption and population health outcomes: an analysis of data from 133 countries using structural equation modeling. International Journal of Public Health, 60(6), 633-641. doi:10.1007/s00038-015-0687-6.
  • Gupta, S., Mello, L. D., & Sharan, R. (2001). Corruption and military spending. European Journal of Political Economy, 17(4), 749-777. doi:10.1016/s0176-2680(01)00054-4.
  • Güneş, S., Polat, F., & Akın, T. (2014). Kalkınma Bağlamında Büyüme, Yolsuzluk ve Demokrasi İlişkisi. Economic Development: Social&Political Interactions. 13-21.
  • Hair,J.F., Hult, G.T, Ringle, & Sarstedt, C. (2014). A primer on partial least squares structural equation modeling (PLS-SEM), London, Sage.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods,6(1), 53-60.
  • Hox, J., Maas, C.J., & Brinkhuis, M. J., (2010). The Effect of Estimation Method and Sample Size in Multilevel Structural Equation Modeling. Statistica Neerlandica, 64(2), 157-170.
  • Hoyle, R. H. (2012). Handbook of structural equation modeling. New York: Guilford Press.
  • Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. New York: The Guilford Press.
  • Kwong, K., & Wong, K.(2013), Partial Least Squares Structural Equation Modeling Techniques Using SmatPLS, Marketing Bulletin, 24, Technical Note. Maccallum, R. C., & Austin, J. T. (2000). Applications of Structural Equation Modeling in Psychological Research. Annual Review of Psychology, 51(1), 201-226. doi:10.1146/annurev.psych.51.1.201.
  • Marshall MG, Jaggers K.& Robert Gurr T (2011), Polity IV Project:political regime characteristics and transitions,1800-2013. Dataset users’ manual. Center for Systemic Peace.
  • Mauro, P. (1998).Corruption and the Composition of Government Expenditure.Journal of Public Expenditure, 69, 263-279.
  • Rajkumar, A. S., & Swaroop, V. (2008). Public spending and outcomes: Does governance matter? Journal of Development Economics, 86(1), 96-111. doi:10.1016/j.jdeveco.2007.08.003.
  • Ravand, H., & Baghaei, P. (2016). Partial Least Squares Structural Equation Modeling with R, Practical Assessment Research&Evaluation, 21(11), 1-16.
  • Tiongson, E., Gupta, S., & Davoodi, H. (2001). Corruption and the provision of health care and education services. Routledge Contemporary Economic Policy Issues The Political Economy of Corruption. doi:10.4324/9780203468388.ch6.
  • Wei, S. (2000). How Taxing is Corruption on International Investors?. Review of Economics and Statistics 82 (1): 1–11.
  • Wold, H. (1973). Nonlinear Iterative Partial Least Squares (NIPALS) Modelling: Some Current Developments. Multivariate Analysis–III, 383-407. doi:10.1016/b978-0-12-426653-7.50032-6.
  • Yakar, S., & Cebeci, K. (2007). Yolsuzluğun Ekonomik Büyümeye Etkileri Üzerine Teorik Bir İnceleme. Çimento İşveren Dergisi, 4(21), 16-29.
  • The Economist Intelligence Unit, Democracy Index. http://www.transparency.org.nz/docs/2017/Democracy_Index_2016.pdf. Transparency International (2014). CPI 2014. https://www.transparency.org/research/cpi/overview.
  • United Nations Human Development Data. http://hdr.undp.org/en/data. United Nations, UNODC 2016 Report. http://www.anticorruptionday.org/documents/actagainstcorruption/print/materials2016/corr16_fs_DEVELOPMENT_en_PRINT.pdf).
  • World Bank (2014). World Development Indicators 2014, World Bank USA.
There are 26 citations in total.

Details

Journal Section Articles
Authors

Özlem Yorulmaz

Publication Date October 15, 2017
Submission Date June 22, 2017
Published in Issue Year 2017

Cite

APA Yorulmaz, Ö. (2017). The Relationship between Socio-Economic Development, Corruption and Health Indicators: Application of Partial Least Squares Structural Equation Modeling. Alphanumeric Journal, 5(2), 191-206. https://doi.org/10.17093/alphanumeric.323277

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