تاثیر رشداقتصادی بر توزیع درآمد دراستان های ایران: رویکردپانل خودرگرسیونی NARDL
محورهای موضوعی : فصلنامه اقتصاد محاسباتیسوسن داراخانی 1 , اصغر ابوالحسنی هستیانی 2 * , فرهاد غفاری 3
1 - گروه اقتصاد ، دانشگاه آزاد علوم و تحقیقات ، تهران، ایران
2 - گروه اقتصاد ، دانشگاه آزاد علوم و تحقیقات ، تهران، ایران
3 - گروه اقتصاد ، دانشگاه آزاد علوم و تحقیقات ، تهران، ایران
کلید واژه: رشد اقتصادی, توزیع درآمد, رویکرد پانل خودرگرسیونی NARDL,
چکیده مقاله :
شناسایی و بررسی عوامل مؤثر بر توزیع درآمد از اهمیت بالایی برخوردار است، زیرا این عوامل می‌توانند به شکل‌گیری سیاست‌های اقتصادی و اجتماعی مؤثر کمک کنند. شناسایی این عوامل و بررسی تأثیرگذاری آن‌ها می‌تواند به سیاست‌گذاران کمک کند تا برنامه‌های مؤثرتری برای کاهش نابرابری و بهبود توزیع درآمد طراحی کنند. با توجه به پیچیدگی روابط بین این متغیرها، اتخاذ رویکردهای جامع و مبتنی بر داده‌ها برای تحلیل وضعیت اقتصادی کشورها ضروری است. از این رو هدف پژوهش حاضر تحلیل تاثیر رشد اقتصادی بر توزیع درآمد در استانهای ایران با رویکرد پانلی خودرگرسیونی با وقفه گسترده NARDL در طی دوره‌های 1385 تا 1401 برای استانهای ایران است. نتایج دادههای استانها نشان داد در رویکرد پانل NARDLنتایج نشان داده شد در بلندمدت، شوک منفی رشد تولید ناخالص داخلی سرانه بر ضریب جینی تاثیر مثبت و شوک مثبت رشد تولید ناخالص داخلی سرانه بر ضریب جینی تاثیر منفی داشته است. همچنین در بلندمدت شوک نرخ تورم، نرخ بیکاری و تسهیلات بانکی سرانه تأثیر مثبت و معنیداری بر ضریب جینی دارد. در مقابل شوک سرانه هزینهای دولت نیز تأثیر منفی و معنیداری بر ضریب جینی دارد بطوریکه با افزایش این متغیرها، نابرابری درآمد نیز کاهش خواهد یافت. مقدار ضریب کوتاهمدت نیز تأثیر منفی و معنیداری بر متغیر وابسته یعنی ضریب جینی داشته و ضریب آن برابر با 57/0- میباشد.
Extended Abstract
Purpose
Understanding the factors that influence how income is distributed is crucial for designing effective economic and social policies. By identifying and analyzing these factors, policymakers can develop targeted interventions that aim to reduce inequality and improve the fairness of income distribution. The relationship between economic growth and income inequality, however, is complex and multifaceted. As such, it is important to use robust, data-driven methodologies to capture the nuances of these relationships across different economic contexts. This study seeks to explore how economic growth affects income distribution in the provinces of Iran, employing the Nonlinear Autoregressive Distributed Lag (NARDL) model for panel data analysis from 2006 to 2022. The findings suggest that both positive and negative shocks to GDP growth per capita have significant implications for income inequality. Additionally, factors such as inflation, unemployment, banking facilities, and government spending per capita are found to influence the Gini coefficient, a common measure of income inequality, in both the short and long term. Introduction Income distribution has long been a central concern in economic research and policymaking. The distribution of income within a society has far-reaching implications for social stability, poverty levels, and overall economic well-being. While economic growth is often seen as a vehicle for improving national prosperity, its effects on income inequality are more nuanced and can vary depending on the specific economic, institutional, and social conditions of a country. This complexity has led to a growing body of literature that explores how different dimensions of economic growth—such as GDP growth, inflation, unemployment, and government spending—affect income distribution. In particular, developing countries like Iran face unique challenges in managing income inequality, as their economies often exhibit higher levels of volatility and external shocks.
Methodology
This study aims to shed light on how these factors interact with each other and contribute to the distribution of income in Iran’s provinces. Using the NARDL model, which is well-suited to capturing both short-term and long-term dynamics in economic relationships, this research seeks to provide a comprehensive analysis of the effects of economic growth on income inequality in Iran. Theoretical Framework The relationship between economic growth and income inequality is grounded in several theoretical perspectives. According to the Kuznets curve hypothesis, economic growth initially leads to higher inequality as wealth accumulates in the hands of a few, but over time, as economies mature and structural changes occur, inequality begins to decrease. However, empirical evidence on this relationship is mixed, with some studies supporting the Kuznets curve and others suggesting that growth can exacerbate inequality if not accompanied by inclusive policies. A more contemporary perspective, the “trickle-down” theory, posits that the benefits of economic growth eventually reach all sectors of society, leading to a reduction in inequality. On the other hand, some scholars argue that economic growth can increase inequality if it is concentrated in certain sectors or regions, leaving marginalized groups behind. In this context, factors such as inflation, unemployment, government policies, and access to financial resources play a crucial role in shaping the distribution of income. Methodology This study employs the Nonlinear Autoregressive Distributed Lag (NARDL) model to analyze the relationship between economic growth and income inequality in Iran’s provinces from 2006 to 2022. The NARDL model is particularly useful for capturing both the short-term and long-term dynamics of economic variables, allowing for the analysis of asymmetric effects of positive and negative shocks on the Gini coefficient, which measures income inequality.
Finding
The variables considered in this study include GDP growth per capita, inflation rate, unemployment rate, banking facilities per capita, and government spending per capita. These factors are selected based on their theoretical relevance and empirical importance in influencing income distribution. The Gini coefficient is used as the dependent variable to represent the level of income inequality in each province. Results The results of the NARDL analysis reveal several important findings regarding the relationship between economic growth and income inequality in Iran’s provinces. 1. GDP Growth and the Gini Coefficient The analysis shows that both positive and negative shocks to GDP growth per capita have a negative effect on the Gini coefficient, suggesting that economic growth, regardless of its direction, tends to reduce income inequality in the short term. Negative GDP growth shocks are found to have a stronger and more immediate impact on reducing inequality. This indicates that periods of economic downturns may lead to a more equal distribution of income, possibly due to reduced income opportunities for the wealthier segments of the population. 2. Inflation and Unemployment The long-term effects of inflation and unemployment on income inequality are significant and positive. Specifically, inflation and unemployment rates are found to increase income inequality, as both variables exacerbate the economic difficulties of lower-income groups. High inflation erodes the purchasing power of wages, disproportionately affecting poorer individuals, while unemployment reduces income opportunities, further deepening inequality. 3. Banking Facilities The availability of banking facilities per capita is found to have a significant positive impact on the Gini coefficient in the long run, meaning that increased access to financial resources may help reduce income inequality. This finding highlights the importance of financial inclusion in promoting more equitable income distribution, as access to banking services can facilitate investment, savings, and credit, particularly for the lower-income population. 4. Government Spending Government spending per capita has a negative and significant effect on the Gini coefficient, both in the short and long term.
Conclusion
This suggests that higher levels of government expenditure, particularly on social welfare programs, public services, and infrastructure, are associated with a reduction in income inequality. The findings underscore the importance of redistributive policies and government interventions in promoting income equality. Discussion The results of this study indicate that economic growth plays a complex role in shaping income distribution in Iran. While both positive and negative GDP growth shocks can reduce income inequality in the short term, factors such as inflation and unemployment tend to exacerbate inequality in the long run. The availability of banking services and government spending per capita emerge as key policy levers that can mitigate inequality, particularly through enhancing financial inclusion and redistributive spending. These findings suggest that policymakers should not only focus on achieving high rates of economic growth but also consider the distributional impacts of economic policies. In particular, targeted interventions aimed at reducing inflation and unemployment, along with increasing access to banking services and enhancing government spending on social programs, could help reduce income inequality and promote more inclusive economic development in Iran. Conclusion This study contributes to the understanding of how economic growth and various macroeconomic factors influence income distribution in Iran. By employing the NARDL model, the research highlights the importance of both short-term and long-term dynamics in shaping income inequality. The findings suggest that economic growth alone is not sufficient to ensure equitable income distribution and that complementary policies—such as reducing inflation, addressing unemployment, promoting financial inclusion, and increasing government spending—are crucial for achieving more balanced income distribution. Future research could explore the role of other factors, such as education, health, and labor market policies, in influencing income inequality in Iran and other developing countries.
-Acemoglu, D., Johnson, S., and Robinson, J. A. (2005). Institutions as a fundamental cause of long-run growth. Handbook of economic growth, 1(A), 385-472. Retrieved from https://doi.org/10.1016/S1574-0684(05)01006-3 /
-Adeleye, B. N., Gershon, O., Ogundipe, A., Owolabi, O., Ogunrinola, I. and Adediran, O. (2020). Comparative investigation of the growth-poverty-inequality trilemma in Sub-Saharan Africa and Latin American and Caribbean Countries. Heliyon, 6(12). Retrieved from https://doi.org/ 10.1016/j.heliyon. 2020.e05631/
-Aghion, P., Howitt, P. and Violante, G. (1998). Technology, knowledge and inequality. University College London, Mimeo.
-Ahluwalia, M. S. (1976). Inequality, poverty and development. Journal of development economics, 3(4), 307-342.
Retrieved from https://doi.org/10.1016/0304-3878(76)90027-4/
-Anselmann, C. (2020). The impact of an unequal distribution of income on economic growth: Theoretical considerations. Secular Stagnation Theories: A Historical and Contemporary Analysis with a Focus on the Distribution of Income, 153-162.
Retrieved from https://doi.org/10.1007/978-3-030-41087-2_6/
-Azevedo, L. F. D., Fonseca, P. C. D. and Missio, F. J. (2022). Income distribution and economic growth regime in Brazil: evaluation and propositions. Brazilian Journal of Political Economy, 42(1), 25-47.
-Banerjee, A. V. and Duflo, E. (2003). Inequality and growth: What can the data say?. Journal of economic growth, 8(3), 267-299.
-Barro, R. J. (2000). Inequality and Growth in a Panel of Countries. Journal of economic growth, 5, 5-32.
-Bassanini, A., Scarpetta, S. and Hemmings, P. (2001). Economic growth: the role of policies and institutions. Panel data evidence from OECD countries. Panel Data Evidence from OECD Countries (January 2001). Retrieved from http://dx.doi.org/10.2139/ssrn.265091/
-Bengtsson, E. and Stockhammer, E. (2021). Wages, Income Distribution and Economic Growth: Long-Run Perspectives in Scandinavia, 1900–2010. Review of Political Economy, 33(4), 725-745. Retrieved from https://doi.org/10.1080/09538259.2020.1860307/
-Benhabib, J. and Rustichini, A. (1996). Social conflict and growth. Journal of Economic growth, 1(1), 125-142. https://www.jstor.org/stable/40215884
-Blackburne, E. F. and Frank, M. W. (2007). Estimation of nonstationary heterogeneous panels. The Stata Journal, 7(2), 197-208.
-Bourguignon, F. (1981). Pareto superiority of unegalitarian equilibria in Stiglitz'model of wealth distribution with convex saving function. Econometrica: Journal of the Econometric Society, 1469-1475. Retrieved from http://www.jstor.org/stable/1911412/
-Chirwa, T. G. and Odhiambo, N. M. (2020). Public debt and economic growth nexus in the Euro area: A dynamic panel ARDL approach. Scientific Annals of Economics and Business, 67(3), 291-310. Retrieved from http://dx.doi.org/10.47743/saeb-2020-0016/
-De Haan, J. and Sturm, J. E. (2017). Finance and income inequality: A review and new evidence. European Journal of Political Economy, 50, 171-195. Retrieved from https://doi.org/10.1016/j.ejpoleco.2017.04.007/
-De La Croix, D. and Doepke, M. (2003). Inequality and growth: why differential fertility matters. American Economic Review, 93(4), 1091-1113. Retrieved from https://doi.org/ 10.1257/000282803769206214/
-esfandiari, M. and Rahimi, M. (2023). The effect of relative redistribution on economic growth, Rahbord-e-Tousee, 18(71) -21-49. Retrieved from http://dor.isc.ac/ 20.1001.1.17352460.1401.18.71.1.4/ (In Persian)
-Friedman, M. (1968). The Role of Monetary Policy. American Economic Review.
-Galor, O. and Tsiddon, D. (1997). Technological progress, mobility, and economic growth. The American Economic Review, 363-382. Retrieved from https://www.jstor.org/stable/2951350/
-Galor, O. and Zeira, J. (1993). Income distribution and macroeconomics. The review of economic studies, 60(1), 35-52. Retrieved from https://doi.org/10.2307/2297811/
-Granger, C. W. J. (1986). Developments in the study of cointegrated economic variables. Oxford Bulletin of Economics and Statistics, 48(3),213-228.
Retrieved from https://doi.org/10.1111/j.1468-0084.1986.mp48003002.x/
-Gries, T. and Naudé, W. (2020). Artificial Intelligence, Income Distribution and Economic Growth. VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224623, Verein für Socialpolitik / German Economic Association.
-Gupta, G. S. and Singh, R. D. (1984). Income inequality across nations over time: how much and why. Southern Economic Journal, 250-257.
-Haughton, J. and Khandker, S. R. (2009). Handbook on poverty+ inequality. World Bank Publications.
-Helpman, E. (Ed.). (1998). General purpose technologies and economic growth. MIT press.
-Hoff, K. and Stiglitz, J. E. (2004). After the big bang? Obstacles to the emergence of the rule of law in post-communist societies. American economic review, 94(3), 753-763. Retrieved from https://doi.org/ 10.1257/0002828041464533/
-Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of econometrics, 90(1), 1-44.
-Kazerooni, A, Asgharpour, H. & Tayyebi, S. (2020). The Impact of Slow Economic Growth on Inequality of Income Distribution with Emphasis on Thomas Piketty's Hypothesis, Qurterly Journal of The Economic Research,20(1),23-50. (In Persian)
-Krueger, A. B. (1993). How computers have changed the wage structure: evidence from microdata, 1984–1989. The Quarterly Journal of Economics, 108(1), 33-60.
-Maaboudi, R. and Dare Nazari, Z. (2022). An Analysis of the Share of Social Capital in Iran’s Economic Growth During the Years 2002-2019. Social Capital Management,8(4),633-663. Retrieved from https://doi.org/ 10.22059/JSCM.2021.331802.2219/(In Persian)
-Mdingi, K. and Ho, S. Y. (2021). Literature review on income inequality and economic growth. MethodsX, 8, 101402. Retrieved from https://doi.org/10.1016/j.mex.2021.101402/
-Musgrave, R. A. and Musgrave, P. B. (1989). Public Finance in Theory and Practice, McGraw-Hill, 1989: Public Finance in Theory and Practice (Vol. 1). Bukupedia.
-Nishi, H. (2022). Income distribution, technical change, and economic growth: A two-sector Kalecki–Kaldor approach. Structural Change and Economic Dynamics, 60, 418-432. Retrieved from https://doi.org/10.1016/j.strueco.2021.12.003/
-Nobahar E, Panahi H, Mehri Z. (2023). Investigating the Relationship between Income Inequality, Urbanization, and Economic Growth in Provinces of Iran. Economic and Planning Research, 28(1), 97-122.
Retrieved from https://doi.org/10.61186/jpbud.28.1.97/(In Persian)
-Panizza, U. (2002). Income inequality and economic growth: Evidence from American data. Journal of Economic Growth, 7(1), 25-41. Retrieved from
http://dx.doi.org/10.18235/0011000/
-Pedroni, P. (1996). Fully modified OLS for heterogeneous cointegrated panels and the case of purchasing power parity. Manuscript, Department of Economics, Indiana University, 5, 1-45. Retrieved from https://doi.org/10.1016/S0731-9053(00)15004-2/
-Perotti, R. (1993). Political equilibrium, income distribution, and growth. The Review of Economic Studies, 60(4), 755-776. Retrieved from https://doi.org/10.2307/2298098/
-Persson, T. and Tabellini, G. (1994). ls lnequality Harmful for Growth. American Economic Review, 84(3), 600-621.
-Piketty, T. (1997). The dynamics of the wealth distribution and the interest rate with credit rationing. The Review of Economic Studies, 64(2), 173-189. Retrieved from http://hdl.handle.net/10.2307/2971708/
-Piketty, T. (2014). Capital in the Twenty-First Century. Harvard University Press.
-Ravallion, M. (2016). The Economics of Poverty: History, measurement, and policy. The World Bank Group, Oxford University Press. Retrieved from https://doi.org/10.1093/acprof:oso/9780190212766.001.0001/
-Rebelo, S. (1991). Long-run policy analysis and long-run growth. Journal of political Economy, 99(3), 500-521.
Retrieved from https://www.jstor.org/stable/2937740/
-Robinson, S. (1976). A note on the U hypothesis relating income inequality and economic development. The American economic review, 66(3), 437-440. Retrieved from https://www.jstor.org/stable/1828182/
-Roine, J., Vlachos, J. and Waldenström, D. (2009). The long-run determinants of inequality: What can we learn from top income data?. Journal of public economics, 93(7-8), 974-988.
Retrieved from https://doi.org/10.1016/j.jce.2003.09.005/
-Saint-Paul, G. and Verdier, T. (1993). Education, democracy and growth. Journal of development Economics, 42(2), 399-407. Retrieved from https://doi.org/10.1016/0304-3878(93)90027-K/
-Shahiki Tash, M.N., and Alizadeh, S. (2021). Assessing the Relationship between Poverty, Income Distribution and Economic Growth in Iran (FLSR Fuzzy approach). Journal of Applied Economics Studies in Iran, 10(38), 39-54. Retrieved from https://doi.org/ 10.22084/AES.2021.23461.3239/ (In Persian)
-Shin, I. (2012). Income inequality and economic growth. Economic Modelling, 29(5), 2049-2057.
Retrieved from https://doi.org/10.1016/j.econmod.2012.02.011
-Smith, A. (2002). An Inquiry into the Nature and Causes of the Wealth of Nations. Readings in economic sociology, 6-17.
-Sonin, K. (2003). Why the rich may favor poor protection of property rights. Journal of comparative economics, 31(4), 715-731. Retrieved from https://doi.org/10.1016/j.jce.2003.09.005/
-Stiglitz, J. E. (2009). Economics of the Public Sector. W. W. Norton & Company.
-Thalassinos, E., Ugurlu, E. and Muratoglu, Y. (2012). Income Inequality and Inflation in the EU. European Research Studies Journal, 0(1), 127-140.
-Vieira, J. R., de Assis Libânio, G. and Cardoso, D. F. (2023). Economic growth, income distribution, and financial system: an analysis based on financial social accounting matrices for the brazilian economy (No. 664). Cedeplar, Universidade Federal de Minas Gerais.
-Weil, D.N. (2014). Economic Growth (3rd ed.). Routledge.
Retrieved from https://doi.org/10.4324/9781315510453/
-Zaman, K. and Shamsuddin, S. (2018). Linear and non-linear relationships between growth, inequality, and poverty in a panel of Latin America and the Caribbean countries: A new evidence of pro-poor growth. Social Indicators Research, 136(2), 595-619. Retrieved from https://doi.org/ 10.1007/s11205-017-1581-9