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

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