Estimation of Gini Coefficient with Subject to the Size of Government by Using Fuzzy Nonlinear Regression

Document Type : Research Article


1 Assistant Professor, Department of Economics, Faculty of Management and Economics, University of Sistan and Baluchistan, Zahedan, Iran

2 Ph.D. student, Islamic Azad University, Kerman Branch, Kerman, Iran.


This article examines the effect of government size on the high, medium and low thresholds of the Gini coefficient in Iran. For this purpose, the auto regression model of soft fuzzy logistic transfer (FLSTAR) has been used for the period of 1997-2019. One of the reasons for using this model is flexibility in its application. The main focus of this paper is to calculate the Gini coefficient bands according to the size of government in the economy. Hence, we calculate the bands (high, middle and low) of the Gini coefficient. The study show that the threshold size of the government is equal 0.499. Findings of this research are applied in a real case which reveal that with increase of government share in economy the Gini coefficient increases as well. Therefore, the government should seriously pursue privatization policies.


Main Subjects

- Afonso, A.; Schuknecht, L. & Tanzi, V., 2010, “Public sector efficiency: evidence for new EU member states and emerging markets”. Applied Economics42(17): 2147-2164.
- Aghaeipoor, F. & Javidi, M. M., 2019, “On the influence of using fuzzy extensions in linguistic fuzzy rule-based regression systems”. Applied Soft Computing, 79: 283-299.
- Agranov, M. & Palfrey, T. R., 2015, “Equilibrium tax rates and income redistribution: A laboratory study”. Journal of Public Economics, 130: 45-58.
- Albanesi, S., 2007, “Inflation and inequality”. Journal of monetary Economics, 54(4): 1088-1114.
Allingham, M. G., 1972, “The measurement of inequality”. Journal of Economic Theory, 5(1): 163-169.
- Anderson, E.; d'Orey, M. A. J.; Duvendack, M. & Esposito, L., 2018, “Does government spending affect income poverty? A meta-regression analysis”. World Development, 103: 60-71.
- Armey, R. K. & Armey, D., 1995, The freedom revolution: the new republican house majority leader tells why big government failed. why freedom works, and how we will rebuild America. Regnery Pub.
- Aznarte, J. L.; Medeiros, M. C. & Benítez, J. M., 2010, “Linearity testing for fuzzy rule-based models”. Fuzzy Sets and Systems, 161(13): 1836-1851.
- Bal, C.; Demir, S. & Aladag, C. H., 2016, “A comparison of different model selection criteria for forecasting EURO/USD exchange rates by feed forward neural network”. International Journal of Computing, Communication and Instrumentalism Engineering, 3: 271-275.
- Bandyopadhyay, S. & Esteban, J., 2010, Redistributive taxation. public expenditure, and size of governent.
- Bechtel, M. M.; Liesch, R. & Scheve, K. F., 2018, “Inequality and redistribution behavior in a give-or-take game”. Proceedings of the National Academy of Sciences, 115(14): 3611-3616.
- Bhagwati, J. & Srinivasan, T. N., 2002, “Trade and poverty in the poor countries”. American Economic Review, 92(2): 180-183.
- Bulíř, A., 2001, “Income inequality: does inflation matter?”. IMF Staff papers, 48(1): 139-159.
- Champernowne, D. G., 1974, “A comparison of measures of inequality of income distribution”. The Economic Journal, 84(336): 787-816.
- Clements, B. J. & Kim, K. S., 1988, Foreign trade and income distribution: the case of Brazil (Vol. 108). Helen Kellogg Institute for International Studies, University of Notre Dame.
- Čok, M.; Urban, I. & Verbič, M., 2013, “Income redistribution through taxes and social benefits: the case of Slovenia and Croatia”. Panoeconomicus, 60(5): 667-686.
- Colletaz, G. & Hurlin, C., 2006, Threshold effects of the public capital productivity: an international panel smooth transition approach.
- Cysne, R. P.; Maldonado, W. L. & Monteiro, P. K., 2005, “Inflation and income inequality: A shopping-time approach”. Journal of Development Economics, 78(2): 516-528.
- De Mello, L. & Tiongson, E. R., 2006, “Income inequality and redistributive government spending”. Public finance review, 34(3): 282-305.
- Dotti, V., 2020, “Income inequality, size of government, and tax progressivity: A positive theory”. European Economic Review, 121: 103-327.
- Easterly, W., 1999, The effect of International Monetary Fund and World Bank programs on poverty. Available at SSRN 632587.
- Enders, W., 2008, Applied econometric time series. John Wiley & Sons.
- Ganjoei, R. A.; Akbarifard, H.; Mashinchi, M. & Esfandabadi, S. A. M. J., 2020, Estimation of upper and lower bounds of Gini coefficient by fuzzy data. Data in brief, 29, 105288.
- Ghasemzadeh, P.; Kalbkhani, H.; Sartipi, S.; & Shayesteh, M. G., 2019, “Classification of sleep stages based on LSTAR model”. Applied Soft Computing, 75: 523-536.
- Gini, C., 1912, Variabilit‡ e mutabilit. Reprinted in Memorie di metodologica statistica (Ed. Pizetti E.
- Gouveia, M. & Masia, N. A., 1998, “Does the median voter model explain the size of government?: Evidence from the states”. Public Choice, 97(1): 159-177.
- Hesamian, G. & Akbari, M. G., 2017, “Semi-parametric partially logistic regression model with exact inputs and intuitionistic fuzzy outputs”. Applied Soft Computing, 58: 517-526.
- John, R. I. & Innocent, P. R., 2005, “Modeling uncertainty in clinical diagnosis using fuzzy logic”. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(6): 1340-1350.
- Pernia, E. & Kakwani, N., 2000, What is Pro-poor Growth?.
- Kalwij, A. & Verschoor, A., 2007, “Not by growth alone: The role of the distribution of income in regional diversity in poverty reduction”. European Economic Review, 51(4): 805-829.
- Lee, C. C., 1990, “Fuzzy logic in control systems: fuzzy logic controller”. I. IEEE Transactions on systems, man, and cybernetics, 20(2): 404-418.
- Wang, L. X. & Mendel, J. M., 1992, “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning”. IEEE transactions on Neural Networks, 3(5): 807-814.
- Lofgren, H. & Robinson, S., 2008, Public spending, growth, and poverty alleviation in Sub-Saharan Africa: a dynamic general-equilibrium analysis. Public expenditures, growth, and poverty: lessons from developing countries.
- Lustig, N., 2015, The redistributive impact of government spending on education and health: Evidence from thirteen developing countries in the commitment to equity project. In Inequality and fiscal policy. International Monetary Fund.
- Lustig, N.; Pessino, C. & Scott, J., 2014, “The impact of taxes and social spending on inequality and poverty in Argentina, Bolivia, Brazil, Mexico, Peru, and Uruguay: Introduction to the special issue”. Public Finance Review, 42(3): 287-303.
- Moller, S.; Alderson, A. S. & Nielsen, F., 2009, “Changing patterns of income inequality in US counties, 1970–2000”. American journal of Sociology, 114(4): 1037-1101.
- Nixson, F. & Walters, B., 2006, “Privatization, income distribution, and poverty: the Mongolian experience”. World Development, 34(9): 1557-1579.
- Oniki, H. & Uzawa, H., 1965, “Patterns of trade and investment in a dynamic model of international trade”. The Review of Economic Studies, 32(1): 15-38.
- Perotti, R., 1992, “Income distribution, politics, and growth”. The American Economic Review, 82(2): 311-316.
- Perotti, R., 1994, “Income distribution and investment”. European Economic Review, 38(3-4): 827-835.
- Perotti, R., 1996, “Growth, income distribution, and democracy: What the data say”. Journal of Economic growth, 1(2): 149-187.
- Persson, T. & Tabellini, G., 1991, Is inequality harmful for growth? Theory and evidence.
- Ravallion, M., 2001, “Growth, inequality and poverty: looking beyond averages”. World development, 29(11): 1803-1815.
- Ravallion, M., 2007, “Looking beyond averages in the trade and poverty debate”. In: The impact of globalization on the world’s poor (pp. 118-144). Palgrave Macmillan, London.
- Salvatore, D., 2007, “Growth, international inequalities, and poverty in a globalizing world”. Journal of Policy Modeling, 29(4): 635-641.
- Sohn, S. Y.; Kim, D. H. & Yoon, J. H., 2016, “Technology credit scoring model with fuzzy logistic regression”. Applied Soft Computing, 43: 150-158.
- Son, H. H., 2004, “A note on pro-poor growth”. Economics letters, 82(3): 307-314.
- Son, H. H. & Kakwani, N., 2008, “Global estimates of pro-poor growth”. World Development, 36(6): 1048-1066.
- Sylwester, K., 2002, “Can education expenditures reduce income inequality?”. Economics of education review, 21(1): 43-52.
- Teräsvirta, T., 1994, “Specification, estimation, and evaluation of smooth transition autoregressive models”. Journal of the american Statistical association, 89(425): 208-218.
- Tsay, R. S., 1989, “Testing and modeling threshold autoregressive processes”. Journal of the American statistical association, 84(405): 231-240.
- Tong, H., 2012, Threshold models in non-linear time series analysis (Vol. 21). Springer Science & Business Media.
- Yen, J., 1999, Fuzzy logic: intelligence, control, and information. Pearson Education India.
- Yu, T. H. K.; Wang, D. H. M. & Chen, S. J., 2006, “A fuzzy logic approach to modeling the underground economy in Taiwan”. Physica A: Statistical Mechanics and its Applications, 362(2): 471-479.
- Zadeh, L. A., 1965, “Electrical engineering at the crossroads”. IEEE Transactions on Education, 8(2): 30-33.