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AN ANALYSIS ON THE RELATIONSHIP BETWEEN INCOME DISTRIBUTION AND HUMAN DEVELOPMENT WITH PANEL DATA AND MACHINE LEARNING

Year 2022, Volume: 21 Issue: 2, 564 - 582, 30.04.2022
https://doi.org/10.21547/jss.883079

Abstract

This study examines the socioeconomic indicators such as human development, urbanization, and female employment rate in MIST countries on income distribution with both econometric and machine learning methods. Although there are many studies on growth in the economics literature, the income distribution has not found the place it should be. To fill this void, the intensity of studies between growth and income distribution has increased recently. For this purpose, we employed two separate analyzes using the panel data method and the Support Vector Regression method, which is one of the machine learning methods, by reaching the Gini coefficients, HDI, urbanization, and female labor force participation data of the MIST countries for the years 1990-2019. As a result, all determinants in the random-effects model have a statistically significant effect on income inequality. In the model, while HDI and urbanization are significant at 5%, female labor force participation is significant at 0.1%. Signs of all explanatory variables are negative, and therefore they have a decreasing effect on income inequality. As HDI, urbanization, and female labor force participation values of MIST countries have improved since 1990, the Gini coefficients have improved. In other words, the income distribution of these countries has become fairer. Unlike other studies in the literature, the Support Vector Regression model was used in this study, and SVR produced a more suitable model for analyzing income inequality.

References

  • Ahmed, N. K., Atiya, A., El Gayar, N. & El-Shishiny, H. (2010). An Empirical Comparison of Machine Learning Models for Time Series Forecasting, Econometric Reviews, 29 (5-6), 594-621. DOI: 10.1080/07474938.2010.481556.
  • Akça, H. ve Ela, M. (2012). Eğitim ve gelir dağılımı ilişkisi: Türkiye değerlendirmesi, Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 21(3), 241-260.
  • Alkira, S. ve Foster, J. (2010). “Designing the Inequality-Adjusted Human Development Index”, Oxford Poverty & Human Development Initiative (OPHI) Working Paper No. 37, United Nations Development Programme Human Development Report Office Background Paper No. 2010/2.
  • Alon, I., Qi, M., ve Sadowski, R. J. (2001). Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods, Journal of Retailing and Consumer Services, 8, 147–156.
  • Alvan, A. (2007). Forging a link between human development and income inequality: cross-country evidence, Review of Social, Economic & Business Studies, 7(8), 31-43.
  • Athey, S., Bayati, M., Imbens, G. ve Qu, Z. (2019). Ensemble methods for causal effects in panel data settings, AEA Papers and Proceedings, 109, 65–70.
  • Ayyıldız, F.V. (2017). Gelir adaletsizliğinin sebeplerinin araştırılması: ampirik analiz, Karadeniz Uluslararası Bilimsel Dergi, Cilt 34, Sayı 34, 131-141.
  • Baum-Snow, N ve Pavan, R. (2013). Inequality and city size, The Review of Economics and Statistics, 95(5), 1535-1548.
  • Belke, M. ve Bolat, S. (2016). The panel data analysis of female labor participation and economic development relationship in developed and developing countries, Economic Research Guardian, Weissberg Publishing, 6(2), 67-73.
  • Chen, G., Glasmeier, A., Zhang M. ve Shao, Yang (2016). Urbanization and income inequality in post-reform China: a causal analysis based on time series data, PLoS ONE, 11(7), 1-16.
  • Çelebi Boz, F., Bayramoğlu, T. ve Gültekin, Ö, F. (2019). BRICS ve MIST ülkelerinde Ar-Ge harcamaları ile yüksek teknolojili ürün ihracatı arasındaki ilişki üzerine bir araştırma, İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 8(2), 1111-1124.
  • Ekeocha, D. O. (2020). Urbanization, inequality, economic development and ecological footprint: searching for turning points and regional homogeneity in Africa, Journal of Cleaner Production, In Press, Corrected Proof, https://doi.org/10.1016/j.jclepro.2020.125244.
  • Erdoğan, B. E., Özöğür-Akyüz, S. ve Karadayı-Ataş, P. (2019). A novel approach for panel data: An ensemble of weighted functional margin SVM models, Information Sciences, https://doi.org/10.1016/j.ins.2019.02.045.
  • Eren, M. V. (2019). MENA ülkelerinde sektörel kadın istihdamı ve kalkınma arasındaki ilişkinin ampirik analizi, Yönetim ve Ekonomi Araştırmaları Dergisi, 17, 106-127.
  • Glaeser E., Resseger M. ve Kristina, T. (2009). Inequality in Cities, Journal of Regional Science, 49(4), 617-646.
  • Güleryüz, D. ve Özden, E. (2020). The prediction of brent crude oil trend using LSTM and Facebook prophet, Avrupa Bilim ve Teknoloji Dergisi, 20, 1-9.
  • Hausman, J. (1978). Specification tests in econometrics, Econometrica, 46(6), 1251-71.
  • Hicks, D. (1997). The inequality-adjusted human development index a constructive proposal, World Development, 25(8), 1283-1298.
  • Hill, T., O’Connor, M. ve Remus, W. (1996). Neural network models for time series forecasts, Management Science, 42, 1082–1092.
  • Ishan, P., Vivek, S. ve Jay, P. (2018). Estimation of the effect of income inequality on human development: a cross sectional study, Undergraduate Research Paper, Georgia Institute of Technology.
  • Kanbir, Ö. (2020). Ekonomik gelişmenin ölçümüne bir katkı: gelir dağılımı adaletine göre düzeltilmiş insani gelişme endeksi, İktisadi İdari ve Siyasal Araştırmalar Dergisi, Cilt 5, Sayı, 11, 1-20.
  • Kanbur R., Zhang, X. (1999). Which regional inequality? the evolution of rural–urban and inland–coastal inequality in China from 1983 to 1995”, Journal of Comparative Economics, 27(4), 686-701.
  • Kanbur, R. ve Zhuang, J. (2013). Urbanization and inequality in Asia”, Asian Development Review, 30(1), 131-147.
  • Karch, J. D., Brandmaier, A. M. and Voelkle, M. C. (2020). Gaussian process panel modeling—machine learning inspired analysis of longitudinal panel data, Frontiers in Psychology, Volume 11, Article 351, doi: 10.3389/fpsyg.2020.00351
  • Kennedy, P. (1992). A Guide to Econometrics, Oxford: Blackwell.
  • Kennedy, P. (2008). A Guide to Econometrics, 6th ed. Malden, MA: Blackwell Publishing.
  • Kuştepeli, Y. ve Halaç, U. (2004). Türkiye’de genel gelir dağılımının analizi ve iyileştirilmesi, Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt:6, Sayı:4, 143-160.
  • Kuznets, S. (1955). Economic growth and income inequality, The American Economic Review, 45(1), 1-28.
  • Liddle, B. (2017). Urbanization and inequality/poverty, Urban Science, 1(4): 35.
  • Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation, Technometrics, 12, 591–256.
  • Martinez, R. (2011). Inequality and the new human development index, Applied Economics Letters, 19(6), 1-3.
  • Mason, R. L., Gunst, R. F. & Hess, J. L. (1989). Statistical design and analysis of experiments: applications to engineering and science, New York: Wiley
  • Menard, S. (1995). Applied logistic regression analysis: sage university series on quantitative applications in the social sciences, Thousand Oaks, CA: Sage.
  • Menon, A. (2009). Large-Scale Support Vector Machines: Algorithms and Theory.
  • Mikk, J. (2008). The role of income inequality in human development, Social Research, 4(14), 78-83.
  • O’brien, R. M. (2007). A Caution Regarding Rules of Thumb for Variance Inflation Factors, Qual Quant, 41, 673–690. https://doi.org/10.1007/s11135-006-9018-6
  • Öztürk, E. ve Oktar, S. (2017). Kalkınma Gelir Eşitsizliği İlişkisi: Türkiye Örneği, Akademik Hassasiyetler, 103.
  • Shahriar K., David, B. ve Edwin, P. (2019). Linkages between poverty and income inequality of urban–rural sector: a time series analysis of India’s urban-based aspirations from 1951 to 1994, Applied Economics Letters, 26(6), 446-453.
  • Sohag, K., Umar, B. ve Alam, M. (2018). Stata command for time series analysis.
  • Sulemana, I., Amponsah, E. N., Codjoe, E. ve Andoh, J. A. N. (2019). Urbanization and income inequality in Sub-Saharan Africa, Sustainable Cities and Society, 48, 101544.
  • Swanson, N. R. ve White, H. (1995). A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks, Journal of Business and Economic Statistics, 13, 265–275.
  • Thiel, F. (2016). The effect of inequality on (human) development–insights from a panel analysis of the human development index, Master Thesis, Universitat de Barcelona.
  • Vapnik, V. (1995). The nature of statistical learning theory, Springer.
  • WDR (2013). Jobs. https://openknowledge. worldbank.org/handle/10986/11843 Erişim Tarihi: 23.01.2021.
  • Wooldridge, J. (2010). Econometric analysis of cross section and panel data. Cambridge, Mass.: MIT Press.
  • World Bank. (2012). Country gender assessment for Lao PDR: reducing vulnerability and ıncreasing opportunity, Washington, DC: World Bank and Asian Development Bank.
  • Wu, J. and Chen, E. (2010). A novel hybrid particle swarm optimization for feature selection and kernel optimization in support vector regression, Proceedings of the International Conference on Computational Intelligence and Security, (CIS '10), pp. 189–194.
  • Xin, H., X. Gu, H. Wu, Y. Hu, and Z. Yang, (2012). “Application of genetic algorithm-support vector regression (GA-SVR) for quantitative analysis of herbal medicines,” Journal of Chemometrics, vol. 26, no. 7, pp. 353–360.
  • Yapraklı, S. ve Bayramoğlu, T. (2016). Küreselleşme: Ekonomik ve Sosyal Eğilimler, Küreselleşme, Gelir Eşitsizliği ve Yoksulluk, Savaş Yayınları, Editörler: Turgut Bayramoğlu ve Enes Emre Başar.

PANEL VERİ VE MAKİNE ÖĞRENMESİ YÖNTEMİYLE GELİR DAĞILIMI VE İNSANİ GELİŞMİŞLİK ARASINDAKİ İLİŞKİ ÜZERİNE BİR ANALİZ

Year 2022, Volume: 21 Issue: 2, 564 - 582, 30.04.2022
https://doi.org/10.21547/jss.883079

Abstract

Bu çalışma, MİST ülkelerinde insani gelişme, şehirleşme ve kadın istihdam oranı gibi sosyoekonomik göstergelerin gelir dağılımı üzerindeki etkisini hem ekonometrik hem de makine öğrenmesi yöntemleriyle incelemektedir. Ekonomi yazınında büyüme ile ilgili çokça çalışma olmasına rağmen gelir dağılımı olması gerektiği kadar yer bulamamıştır. Bu boşluğu doldurmak için olsa gerek son zamanlarda büyüme ile gelir dağılımı arasındaki çalışmaların yoğunluğu artmıştır. Bu amaçla MİST ülkelerinin 1990-2019 yılı Gini katsayıları, İGE, şehirleşme ve kadınların iş gücüne katılımı verilerine ulaşılarak, panel veri yöntemi ve makine öğrenmesi yöntemlerinden biri olan Destek Vektör Regresyonu yöntemi aracılığı ile iki ayrı analiz yapılmıştır. Yapılan analizler sonucunda tesadüfi etkiler modelindeki tüm belirleyicilerin gelir adaletsizliği üzerinde istatistiksel olarak anlamlı etkiye sahip olduğu anlaşılmıştır. Modelde İGE ve kentleşme %5 düzeyinde anlamlıyken kadınların iş gücüne katılımı ise %0,1 oranında anlamlı çıkmıştır. Tüm açıklayıcı değişkenlerin işaretleri negatiftir ve dolayısıyla gelir adaletsizliğini azaltıcı yönde etkilerinin olduğu söylenebilir. Kısaca çalışmanın sonucunda MIST ülkelerinin 1990 yılından bu tarafa İGE, şehirleşme ve kadınların işgücüne katılım değerleri iyileştikçe Gini katsayıları iyileşmiş, yani bir başka ifadeyle bu ülkelerin gelir dağılımı daha adil olmuştur. (İngilizce) Bu çalışmada literatürde tespit edilen çalışmalardan farklı olarak Destek Vektör Regresyonu modeli de kullanılmış ve gelir adaletsizliğinin analizi için daha uygun bir model ürettiği gözlemlenmiştir.

References

  • Ahmed, N. K., Atiya, A., El Gayar, N. & El-Shishiny, H. (2010). An Empirical Comparison of Machine Learning Models for Time Series Forecasting, Econometric Reviews, 29 (5-6), 594-621. DOI: 10.1080/07474938.2010.481556.
  • Akça, H. ve Ela, M. (2012). Eğitim ve gelir dağılımı ilişkisi: Türkiye değerlendirmesi, Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 21(3), 241-260.
  • Alkira, S. ve Foster, J. (2010). “Designing the Inequality-Adjusted Human Development Index”, Oxford Poverty & Human Development Initiative (OPHI) Working Paper No. 37, United Nations Development Programme Human Development Report Office Background Paper No. 2010/2.
  • Alon, I., Qi, M., ve Sadowski, R. J. (2001). Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods, Journal of Retailing and Consumer Services, 8, 147–156.
  • Alvan, A. (2007). Forging a link between human development and income inequality: cross-country evidence, Review of Social, Economic & Business Studies, 7(8), 31-43.
  • Athey, S., Bayati, M., Imbens, G. ve Qu, Z. (2019). Ensemble methods for causal effects in panel data settings, AEA Papers and Proceedings, 109, 65–70.
  • Ayyıldız, F.V. (2017). Gelir adaletsizliğinin sebeplerinin araştırılması: ampirik analiz, Karadeniz Uluslararası Bilimsel Dergi, Cilt 34, Sayı 34, 131-141.
  • Baum-Snow, N ve Pavan, R. (2013). Inequality and city size, The Review of Economics and Statistics, 95(5), 1535-1548.
  • Belke, M. ve Bolat, S. (2016). The panel data analysis of female labor participation and economic development relationship in developed and developing countries, Economic Research Guardian, Weissberg Publishing, 6(2), 67-73.
  • Chen, G., Glasmeier, A., Zhang M. ve Shao, Yang (2016). Urbanization and income inequality in post-reform China: a causal analysis based on time series data, PLoS ONE, 11(7), 1-16.
  • Çelebi Boz, F., Bayramoğlu, T. ve Gültekin, Ö, F. (2019). BRICS ve MIST ülkelerinde Ar-Ge harcamaları ile yüksek teknolojili ürün ihracatı arasındaki ilişki üzerine bir araştırma, İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 8(2), 1111-1124.
  • Ekeocha, D. O. (2020). Urbanization, inequality, economic development and ecological footprint: searching for turning points and regional homogeneity in Africa, Journal of Cleaner Production, In Press, Corrected Proof, https://doi.org/10.1016/j.jclepro.2020.125244.
  • Erdoğan, B. E., Özöğür-Akyüz, S. ve Karadayı-Ataş, P. (2019). A novel approach for panel data: An ensemble of weighted functional margin SVM models, Information Sciences, https://doi.org/10.1016/j.ins.2019.02.045.
  • Eren, M. V. (2019). MENA ülkelerinde sektörel kadın istihdamı ve kalkınma arasındaki ilişkinin ampirik analizi, Yönetim ve Ekonomi Araştırmaları Dergisi, 17, 106-127.
  • Glaeser E., Resseger M. ve Kristina, T. (2009). Inequality in Cities, Journal of Regional Science, 49(4), 617-646.
  • Güleryüz, D. ve Özden, E. (2020). The prediction of brent crude oil trend using LSTM and Facebook prophet, Avrupa Bilim ve Teknoloji Dergisi, 20, 1-9.
  • Hausman, J. (1978). Specification tests in econometrics, Econometrica, 46(6), 1251-71.
  • Hicks, D. (1997). The inequality-adjusted human development index a constructive proposal, World Development, 25(8), 1283-1298.
  • Hill, T., O’Connor, M. ve Remus, W. (1996). Neural network models for time series forecasts, Management Science, 42, 1082–1092.
  • Ishan, P., Vivek, S. ve Jay, P. (2018). Estimation of the effect of income inequality on human development: a cross sectional study, Undergraduate Research Paper, Georgia Institute of Technology.
  • Kanbir, Ö. (2020). Ekonomik gelişmenin ölçümüne bir katkı: gelir dağılımı adaletine göre düzeltilmiş insani gelişme endeksi, İktisadi İdari ve Siyasal Araştırmalar Dergisi, Cilt 5, Sayı, 11, 1-20.
  • Kanbur R., Zhang, X. (1999). Which regional inequality? the evolution of rural–urban and inland–coastal inequality in China from 1983 to 1995”, Journal of Comparative Economics, 27(4), 686-701.
  • Kanbur, R. ve Zhuang, J. (2013). Urbanization and inequality in Asia”, Asian Development Review, 30(1), 131-147.
  • Karch, J. D., Brandmaier, A. M. and Voelkle, M. C. (2020). Gaussian process panel modeling—machine learning inspired analysis of longitudinal panel data, Frontiers in Psychology, Volume 11, Article 351, doi: 10.3389/fpsyg.2020.00351
  • Kennedy, P. (1992). A Guide to Econometrics, Oxford: Blackwell.
  • Kennedy, P. (2008). A Guide to Econometrics, 6th ed. Malden, MA: Blackwell Publishing.
  • Kuştepeli, Y. ve Halaç, U. (2004). Türkiye’de genel gelir dağılımının analizi ve iyileştirilmesi, Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt:6, Sayı:4, 143-160.
  • Kuznets, S. (1955). Economic growth and income inequality, The American Economic Review, 45(1), 1-28.
  • Liddle, B. (2017). Urbanization and inequality/poverty, Urban Science, 1(4): 35.
  • Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation, Technometrics, 12, 591–256.
  • Martinez, R. (2011). Inequality and the new human development index, Applied Economics Letters, 19(6), 1-3.
  • Mason, R. L., Gunst, R. F. & Hess, J. L. (1989). Statistical design and analysis of experiments: applications to engineering and science, New York: Wiley
  • Menard, S. (1995). Applied logistic regression analysis: sage university series on quantitative applications in the social sciences, Thousand Oaks, CA: Sage.
  • Menon, A. (2009). Large-Scale Support Vector Machines: Algorithms and Theory.
  • Mikk, J. (2008). The role of income inequality in human development, Social Research, 4(14), 78-83.
  • O’brien, R. M. (2007). A Caution Regarding Rules of Thumb for Variance Inflation Factors, Qual Quant, 41, 673–690. https://doi.org/10.1007/s11135-006-9018-6
  • Öztürk, E. ve Oktar, S. (2017). Kalkınma Gelir Eşitsizliği İlişkisi: Türkiye Örneği, Akademik Hassasiyetler, 103.
  • Shahriar K., David, B. ve Edwin, P. (2019). Linkages between poverty and income inequality of urban–rural sector: a time series analysis of India’s urban-based aspirations from 1951 to 1994, Applied Economics Letters, 26(6), 446-453.
  • Sohag, K., Umar, B. ve Alam, M. (2018). Stata command for time series analysis.
  • Sulemana, I., Amponsah, E. N., Codjoe, E. ve Andoh, J. A. N. (2019). Urbanization and income inequality in Sub-Saharan Africa, Sustainable Cities and Society, 48, 101544.
  • Swanson, N. R. ve White, H. (1995). A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks, Journal of Business and Economic Statistics, 13, 265–275.
  • Thiel, F. (2016). The effect of inequality on (human) development–insights from a panel analysis of the human development index, Master Thesis, Universitat de Barcelona.
  • Vapnik, V. (1995). The nature of statistical learning theory, Springer.
  • WDR (2013). Jobs. https://openknowledge. worldbank.org/handle/10986/11843 Erişim Tarihi: 23.01.2021.
  • Wooldridge, J. (2010). Econometric analysis of cross section and panel data. Cambridge, Mass.: MIT Press.
  • World Bank. (2012). Country gender assessment for Lao PDR: reducing vulnerability and ıncreasing opportunity, Washington, DC: World Bank and Asian Development Bank.
  • Wu, J. and Chen, E. (2010). A novel hybrid particle swarm optimization for feature selection and kernel optimization in support vector regression, Proceedings of the International Conference on Computational Intelligence and Security, (CIS '10), pp. 189–194.
  • Xin, H., X. Gu, H. Wu, Y. Hu, and Z. Yang, (2012). “Application of genetic algorithm-support vector regression (GA-SVR) for quantitative analysis of herbal medicines,” Journal of Chemometrics, vol. 26, no. 7, pp. 353–360.
  • Yapraklı, S. ve Bayramoğlu, T. (2016). Küreselleşme: Ekonomik ve Sosyal Eğilimler, Küreselleşme, Gelir Eşitsizliği ve Yoksulluk, Savaş Yayınları, Editörler: Turgut Bayramoğlu ve Enes Emre Başar.
There are 49 citations in total.

Details

Primary Language Turkish
Subjects Economics
Journal Section Economics
Authors

Erdemalp Özden 0000-0001-5019-1675

Ömer Faruk Gültekin 0000-0002-4832-4683

Turgut Bayramoğlu 0000-0003-0778-0516

Publication Date April 30, 2022
Submission Date February 20, 2021
Acceptance Date February 11, 2022
Published in Issue Year 2022 Volume: 21 Issue: 2

Cite

APA Özden, E., Gültekin, Ö. F., & Bayramoğlu, T. (2022). PANEL VERİ VE MAKİNE ÖĞRENMESİ YÖNTEMİYLE GELİR DAĞILIMI VE İNSANİ GELİŞMİŞLİK ARASINDAKİ İLİŞKİ ÜZERİNE BİR ANALİZ. Gaziantep University Journal of Social Sciences, 21(2), 564-582. https://doi.org/10.21547/jss.883079