Assessing the Effect of Money Laundering on Income Inequality Using the PSTR Approach: A Case Study of Selected Islamic Countries

Document Type : Research Paper

Authors

1 PhD student in Islamic Economics, Department of Economics, Faculty of Economics and Management, Tabriz University, Tabriz, Iran

2 Professor, Department of Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran

Abstract

The objective of this study is to examine the nonlinear effect of money laundering on income inequality in selected Islamic countries, including Iran, Türkiye, Indonesia, Malaysia, Pakistan, Egypt, Kazakhstan, and Kyrgyzstan, over the period from 2012 to 2024. Given the limited availability of direct and comparable indicators of money laundering, the Corruption Perceptions Index and the ratio of money supply to gross domestic product (GDP) are used as proxy indicators for money laundering. In addition, inflation, per capita income, the square of per capita income, and economic openness are included in the model as control variables.
To examine nonlinear relationships and the dependence of the results on economic conditions, the Panel Smooth Transition Regression (PSTR) model is employed. Within this framework, three threshold variables, namely the exchange rate, economic growth, and economic openness, are considered to identify different economic regimes and allow for changes in the behavior of the variables at different levels of these indicators. The advantage of this approach is that, unlike linear models, it can capture gradual changes in coefficients and heterogeneity across countries under different economic conditions. The findings of the study show that the relationship between money laundering and income inequality is neither linear nor uniform, and its effect depends on macroeconomic conditions.
The results indicate that inflation persistently increases income inequality. Moreover, the coefficients of per capita income and its squared term confirm the existence of a nonlinear relationship between economic development and income inequality, consistent with the Kuznets hypothesis. In addition, the effects of the Corruption Perceptions Index and the ratio of money supply to gross domestic product are not uniform across all economic regimes, and the magnitude of their effects changes with variations in exchange-rate conditions, economic growth, and economic openness. Overall, the results show that macroeconomic and institutional variables play an important role in transmitting the effects of money laundering to income inequality.
 

Keywords

References

  1. Abiloro, T. O., Olawole, A., & Adeniran, T. E. (2019). Corruption, income inequality, and economic development in Nigeria. Sciences, 9(4), 304-319.
  2. Acemoglu, D., & Robinson, J. A. (2012). Why nations fail: The origins of power, prosperity, and poverty. Crown Business.
  3. Aghion, P., Akcigit, U., Bergeaud, A., Blundell, R., & Hémous, D. (2021). Innovation and top income inequality. Review of Economic Studies, 88(1), 1–45.
  4. Ajide, F. M., Ojeyinka, T. A., Egbewole, A. B., & Ridwan, I. (2025). Do less harm than good? Analyzing the effects of anti-money laundering regulations on income inequality in developing economies. Journal of Banking Regulation, 26(3), 215–233.
  5. Alfada, A. (2020). The destructive effect of corruption on economic growth in Indonesia: A threshold regression approach. Heliyon, 6(10), e05006.
  6. Alvaredo, F., Chancel, L., Piketty, T., Saez, E., & Zucman, G. (2023). World Inequality Database. https://wid.world/
  7. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.
  8. Asongu, S. A., & Odhiambo, N. M. (2021). Inequality, finance and renewable energy consumption in Sub-Saharan Africa. Renewable Energy, 175, 468–482.
  9. Atkinson, A. B., & Bourguignon, F. (2015). Handbook of Income Distribution (Vol. 2). Elsevier.
  10. Bai, J., & Carrion-i-Silvestre, J. (2009). Stationarity tests in panels with multiple structural breaks. Econometric Theory, 25(6), 1754–1790.
  11. Barone, R., & Kreuter, H. (2021). Money laundering and banking sector stability: New evidence from emerging Europe. Journal of Financial Crime, 28(3), 789–806.
  12. Barro, R. J. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, 5(1), 5–32.
  13. Bastagli, F., Hagen-Zanker, J., & Harman, L. (2019). Cash transfers: What does the evidence say? ODI Report.
  14. Beck, T., Demirgüç-Kunt, A., & Levine, R. (2010). Financial institutions and markets across countries and over time: The updated financial development and structure database. World Bank Economic Review, 24(1), 77–92.
  15. Beck, T., Levine, R., & Levkov, A. (2020). Big bad banks? The winners and losers from bank deregulation in the United States. Journal of Finance, 75(2), 1197–1235.
  16. Blackburn, K., Bose, N., & Capasso, S. (2017). Financial development, the shadow economy and income inequality. Journal of Economic Behavior & Organization, 141, 65–81.
  17. Breen, M., & Gillanders, R. (2015). Corruption, institutions and regulation. Economics of Governance, 16(3), 203–228.
  18. Breusch, T., & Pagan, A. (1980). The Lagrange multiplier test and its applications to model specification. Review of Economic Studies, 47(1), 239–253.
  19. Bruno, G. (2005). Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models. Economics Letters, 87(3), 361–366.
  20. Caner, M., & Hansen, B. E. (2004). Instrumental variable estimation of a threshold model. Econometric Theory, 20(5), 813–843.
  21. Chong, A., & Gradstein, M. (2019). Institutional persistence, income inequality, and individual attitudes. Journal of Economic Behavior & Organization, 166, 784–803.
  22. Cooley, A., & Heathershaw, J. (2021). Dictators without borders: Power and money in Central Asia. Yale University Press.
  23. Cooray, A., & Schneider, F. (2016). Does corruption promote shadow economy? A panel data analysis. International Tax and Public Finance, 23(4), 581–605.
  24. Cvijanović, D., & Spaenjers, C. (2021). “We’ll always have Paris”: The hedging value of urban real estate. Review of Financial Studies, 34(11), 5355–5401.
  25. Demirgüç-Kunt, A., & Levine, R. (2009). Finance and inequality: Theory and evidence. Annual Review of Financial Economics, 1, 287–318.
  26. Demirgüç-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2022). The Global Findex Database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. World Bank.
  27. Dollar, D., & Kraay, A. (2002). Growth is good for the poor. Journal of Economic Growth, 7(3), 195–225.
  28. Ehrlich, I., & Lui, F. (1999). Bureaucratic corruption and endogenous economic growth. Journal of Political Economy, 107(6), S270–S293.
  29. Friedman, E., Johnson, S., Kaufmann, D., & Zoido-Lobatón, P. (2000). Dodging the grabbing hand: The determinants of unofficial activity in 69 countries. Journal of Public Economics, 76(3), 459–493.
  30. González, A., Teräsvirta, T., van Dijk, D., & Medeiros, M. C. (2017). Panel smooth transition regression models. CREATES Research Paper 2017-36.
  31. Gupta, S., Davoodi, H., & Alonso-Terme, R. (2002). Does corruption affect income inequality and poverty? Economics of Governance, 3(1), 23–45.
  32. Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345–368.
  33. Hassan, M., & Schneider, F. (2016). Size and development of the shadow economies of 157 countries worldwide. IZA Discussion Paper, No. 10281.
  34. Hayakawa, K. (2009). First difference or forward orthogonal deviations—which transformation should be used in dynamic panel data models?: A simulation study. Economics Bulletin, 29(3), 2008–2017.
  35. International Monetary Fund. (2023). Middle East and Central Asia Regional Economic Outlook. IMF.
  36. Kar, D., & Spanjers, J. (2022). Illicit financial flows from developing countries: 2009–2018. Global Financial Integrity.
  37. Kaufmann, D., & Kraay, A. (2024). The worldwide governance indicators: Methodology and 2024 update. Available at SSRN 5154675.
  38. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). The worldwide governance indicators: Methodology and analytical issues. Hague Journal on the Rule of Law, 3(2), 220–246.
  39. Knoll, K., Schularick, M., & Steger, T. (2017). No price like home: Global house prices, 1870–2012. American Economic Review, 107(2), 331–353.
  40. Kuznets, S. (1955). Economic growth and income inequality. American Economic Review, 45(1), 1–28.
  41. Law, S. H., Tan, H. B., & Azman-Saini, W. N. W. (2020). Corruption and income inequality: A nonparametric approach. Journal of Economic Studies, 47(5), 1055–1077.
  42. Levine, R. (2005). Finance and growth: Theory and evidence. In P. Aghion & S. Durlauf (Eds.), Handbook of Economic Growth (pp. 865–934). Elsevier.
  43. Lustig, N. (2020). Inequality and social policy in Latin America. World Development, 134, 105–129.
  44. Mauro, P. (2016). Corruption and inequality: The impact on economic growth. IMF Working Paper.
  45. Menaldo, V. (2016). The institutions curse: Natural resources, politics, and development. Cambridge University Press.
  46. Narayan, P. K., & Narayan, S. (2010). Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy, 38(1), 661–666.
  47. North, D. C., Wallis, J. J., & Weingast, B. R. (2009). Violence and social orders: A conceptual framework for interpreting recorded human history. Cambridge University Press.
  48. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics, 22(2), 265–312.
  49. Phillips, P. C. B., & Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica, 75(6), 1771–1855.
  50. Reuter, P., & Truman, E. M. (2019). Chasing dirty money: The fight against money laundering. Peterson Institute for International Economics.
  51. Rodrik, D. (2000). Institutions for high-quality growth: What they are and how to acquire them. Studies in Comparative International Development, 35(3), 3–31.
  52. Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM. Stata Journal, 9(1), 86–136.
  53. Ross, M. L. (2015). What have we learned about the resource curse? Annual Review of Political Science, 18, 239–259.
  54. Saiz, A., & Wachter, S. (2022). Urban amenities and urban inequality. Journal of Urban Economics, 129, 103–132.
  55. Schneider, F. (2010). Shadow economies and corruption all over the world: New estimates for 145 countries. Economics: The Open-Access, Open-Assessment E-Journal, 1(9), 1–66.
  56. Sen, A. (1997). On Economic Inequality. Clarendon Press.
  57. Solt, F. (2020). Measuring income inequality across countries and over time: The Standardized World Income Inequality Database (SWIID v9.5). Social Science Quarterly, 101(3), 1183–1199.
  58. Sulemana, I., Kpienbaareh, D., & Osei-Koranteng, E. (2019). Corruption and economic development in Africa. Journal of Money Laundering Control, 22(4), 732–749.
  59. Bougatef, K. (2015). The impact of corruption on the soundness of Islamic banks. Borsa Istanbul Review, 15(4), 283–295.
  60. Tavares, J. (2003). Does foreign direct investment reduce corruption?. Economics Letters, 79(1), 103–109.
  61. Torgler, B., & Schneider, F. (2009). The impact of tax morale and institutional quality on the shadow economy. Journal of Economic Psychology, 30(2), 228–245.
  62. Unger, B., Addink, H., Busuioc, M., & Ferwerda, J. (2020). Combating fiscal fraud and empowering regulators. Oxford University Press
  63. World Bank. (2023). World Development Indicators. World Bank.
  64. Zellner, A. (1962). An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368.
Applied Theories of Economics
Volume 13, Issue 3 - Serial Number 50
September 2026
Pages 171-196

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