Abstract
To explore the relationship between income inequality and economic development, and particularly to prove the existence of the Kuznets curve in Thailand’s economy along these two decades, we propose the simultaneous smooth transition kink equations (SKE) model in which each regression parameter can be present in two different regimes, lower and upper. The regression coefficients split into two parts based on the unknown kink and smooth parameters in the logistic function. Thus, the model provides a flexible structure to capture and explain the relationship between income inequality and economic development following the inverted-U curve called the Kuznets curve. Also, we extend the analysis of the SKE model by modelling its nonlinear dependence structure through various Copula functions. Thus, the model becomes more flexible in coupling together the different marginal distributions. Before investigating the Kuznets curve, we conduct a simulation study to confirm the performance and accuracy of our proposed model. The satisfactory results are obtained from this simulation study as the estimated coefficient results are close to the true values under a wide array of simulated data models and distributions together with three smooth transition functions. Finally, according to the present empirical results, the Kuznets hypothesis does not hold at the country level, but it does for the North and Northeast regions. Also, our findings show alternative ways to reduce income inequality by increasing private sector contribution, GDP per capita, government expenditure and government subsidy. Additionally, this study applies the same method for investigating the Kuznets curve in the regions to answer the question ‘Are there regional Kuznets curves in Thailand?’ Our model shows some exciting results in income distribution in the different areas that may support the theory of Kuznets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The Gini coefficient is a number on a scale measured from 0 to 1, where 0 represents perfect equality and 1 represents total inequality (Ray 1998).
References
Acemoglu, D., Johnson, S., & Robinson, J. A. (2005). Institutions as a fundamental cause of long-run growth. Handbook of Economic Growth, 1, 385–472.
Avdoulas, C., Bekiros, S., & Boubaker, S. (2018). Evolutionary-based return forecasting with nonlinear STAR models: Evidence from the Eurozone peripheral stock markets. Annals of Operations Research, 262(2), 307–333.
Banerjee, A. V., & Duflo, E. (2003). Inequality and growth: What can the data say? Journal of Economic Growth, 8(3), 267–299.
Boonyamanond, S. (2007). Can equality and growth be simultaneously achieved? Chulalongkorn Journal of Economics, 19(2), 135–160.
Bulir, M. A. (1998). Income inequality: Does inflation matter? International Monetary Fund, 148, 139–159.
Cheng, W. (2014). Understanding the Kuznets Process: An Empirical Investigation of Income Inequality in China 1978–2011 (No. 12-14). Monash University, Department of Economics.
Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula methods in finance. New York: Wiley.
Chow, G. C., & Lin, A. L. (1971). Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. Review of Economics and Statistics, 4, 372–375.
Dabla-Norris, M. E., Kochhar, M. K., Suphaphiphat, M. N., Ricka, M. F., & Tsounta, E. (2015). Causes and consequences of income inequality: A global perspective. International Monetary Fund.
Deininger, K., & Squire, L. (1996). A new data set measuring income inequality. The World Bank Economic Review, 10(3), 565–591.
Dollar, D., & Kraay, A. (2002). Growth is good for the poor. Journal of Economic Growth, 7(3), 195–225.
Easterly, W. (1999). Life during growth. Journal of Economic Growth, 4(3), 239–276.
Engle, R., & Granger, C. (1991). Long-run economic relationships: Readings in cointegration. Oxford: Oxford University Press.
Frazer, G. (2006). Inequality and development across and within countries. World Development, 34(9), 1459–1481.
Gallup, J. L. (2012). Is there a Kuznets curve. Portland State University, pp. 575–603 (working paper).
Galor, O., & Moav, O. (2004). From physical to human capital accumulation: Inequality and the process of development. The Review of Economic Studies, 71(4), 1001–1026.
Ganaie, A. A., & Kamaiah, B. (2015). Kuznets inverted U hypothesis of income inequality: Looking inside the available economic literature. Available at SSRN 2591284.
Ghazanchyan, M., Stotsky, J. G., & Zhang, Q. (2015). A new look at the determinants of growth in Asian Countries. IMF Working Paper.
Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2, 329–352.
Gregory, M. N., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107(2), 407–437.
Gustafson E. E., & Kessel W. C. (1978). Fuzzy clustering with a fuzzy covariance matrix. In Proceedings of the IEEE conference on decision and control (pp 761–766).
Gustafsson, B., & Johansson, M. (1999). In search of smoking guns: What makes income inequality vary over time in different countries? American Sociological Review, 64, 585–605.
Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica, 68(3), 575–603.
Hsing, Y., & Smyth, D. J. (1994). Kuznets’s inverted-U hypothesis revisited. Applied Economics Letters, 1, 111–113.
Ikemoto, Y., & Uehara, M. (2000). Income inequality and Kuznets’ hypothesis in Thailand. Asian Economic Journal, 14(4), 421–443.
Irons, J., & Bivens, J. (2010). Government debt and growth: Overreaching claims of debt threshold suffer from theoretical and empirical flaws. Economic Policy Institute, EPI briefing paper (271).
Kaasa, A. (2003).Factors influencing income inequality in transition economies. University of Tartu Economics and Business Administration Working Paper Series, (18).
Khalifa, S., & El Hag, S. (2010). Income disparities, economic growth, and development as a threshold. Journal of Economic Development, 35(2), 23.
Kimura, T., Kobayashi, H., Muranaga, J., & Ugai, H. (2003). The effect of the increase in the monetary base on Japan’s economy at zero interest rates: an empirical analysis. In Monetary policy in a changing environment, bank for international settlements conference series (Vol. 19, pp. 276–312).
Kuznets, S. (1955). Economic growth and income inequality. The American Economic Review, 45(1), 1–28.
Lazear, E., & Rosen, S. (1981). nRank order tournaments as optimal salary schemeso. Journal of Political Economy, 89(5), 841.
MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601–618.
Mahfoud, M., & Michael, M. (2012). Bivariate Archimedean Copulas: An application to two stock market indices. BMI Paper.
Maneejuk, P., Pastpipatkul, P., & Sriboonchitta, S. (2016). Economic growth and income inequality: Evidence from Thailand. In Integrated uncertainty in knowledge modelling and decision making: 5th international symposium, IUKM 2016, Da Nang, Vietnam, November 30-December 2, 2016, Proceedings 5 (pp. 649–663). Springer International Publishing.
Mathur, V. K. (1999). Human capital-based strategy for regional economic development. Economic Development Quarterly, 13(3), 203–216.
OECD (2012). Income inequality and growth: The role of taxes and transfers. In OECD Economics Department Policy Notes, No. 9. January 2012.
Ogus Binatli, A. (2012). Growth and income inequality: A comparative analysis. Economics Research International. https://doi.org/10.1155/2012/569890.
Pastpipatkul, P., Maneejuk, P., Wiboonpongse, A., & Sriboonchitta, S. (2016). Seemingly unrelated regression based copula: An application on thai rice market. In Causal inference in econometrics (pp. 437–450). Springer International Publishing.
Preechametta, A. (2015). Dynamics of income inequality under social identity hypothesis. Applied Economics Journal, 22(2), 1–24.
Ray, D. (1998). Development economics. Princeton: Princeton University Press.
Savvides, A., & Stengos, T. (2000). Income inequality and economic development: Evidence from the threshold regression model. Economics Letters, 69(2), 207–212.
Shahbaz, M., & Islam, F. (2011). Financial development and income inequality in Pakistan: An application of ARDL approach. Journal of Economic Development, 36(1), 35–58.
Sklar, A. (1973). Random variables, joint distribution functions, and Copulas. Kybernetika, 9(6), 449–460.
Tran, H. D., Pham, U. H., Ly, S., & Vo-Duy, T. (2017). Extraction dependence structure of distorted Copulas via a measure of dependence. Annals of Operations Research, 256(2), 221–236.
Trivedi, P. K., & Zimmer, D. M. (2007). Copula modeling: An introduction for practitioners. Foundations and Trends® in Econometrics, 1(1), 1–111.
Wichitaksorn, N., Choy, S. T. B., & Gerlach, R. (2006). Estimation of bivariate Copula-based seemingly unrelated Tobit models. Discipline of Business Analytics, University of Sydney Business School. NSW.
Acknowledgements
The authors would like to thank Dr. Laxmi Worachai for her helpful comments on an earlier version of the paper. The authors are also grateful for the financial support offered by Center of Excellence in Econometrics, Chiang Mai University, Thailand.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Maneejuk, P., Yamaka, W. & Sriboonchitta, S. Does the Kuznets curve exist in Thailand? A two decades’ perspective (1993–2015). Ann Oper Res 300, 545–576 (2021). https://doi.org/10.1007/s10479-019-03425-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-019-03425-6