آثار سیاست‌های احتیاطی کلان بر توزیع ثروت در جامعه ایران؛ تحلیلی مبتنی بر الگوی DSGE با لحاظ بخش مسکن

Introduction: After the 2008 financial crisis, central banks realized that not only the stability of inflation and economic growth but also the soundness of individual institutions at the micro level does not guarantee the stability of the financial system. Therefore, in recent years, the application of macro-prudential policies has increased to enhance the resilience of the financial system by mitigating systemic risks on both the credit supply and credit demand sides. In Iran's economy, the most crucial component of the financial sector is the banking system, as banks play a prominent role in financing, and savers primarily rely on bank deposits. Under these circumstances, the investment and consumption choices made by businesses and households will be influenced by the financial cycles in the banking system, potentially increasing the likelihood of a crisis. Hence, the implementation of macro-prudential policies to enhance financial stability and prevent negative spillovers to the real sector in Iran's economy is imperative. Nonetheless, in recent years, some more recent studies have explored the side effects of macro-prudential policies, in addition to their effectiveness in establishing financial stability. These studies argue that, similar to fiscal and monetary policies, macro-prudential policies can also have significant distributional effects that may impact macroeconomic dynamics. Due to the asymmetric information in the banking sector, macro-prudential tools such as Loan-to-Value (LTV) ratios are utilized to mitigate the default risk, taking into account the loan amount as a proportion of the collateral assets' value, such as housing. Thus, while controlling credit risk reduces vulnerabilities in the financial system, discrepancies in households' access to collateral assets and housing can result in unequal access to credit, potentially affecting wealth and income inequality. Therefore, in this research, we aim to assess the impact of macroprudential policies on inequality by developing a dynamic stochastic general equilibrium model tailored to Iran's economy. While there have been limited studies in Iran investigating the effects of macroprudential policies, this research is the first to examine their distributional effects.

Methodology: To analyze the results, we employ a dynamic stochastic general equilibrium (DSGE) model that incorporates heterogeneity in the household sector. "This sector includes three types of households: savers, borrowers with high LTV, and borrowers with low LTV ratios. This classification helps in investigating the distributional effects. The loan provided to the first borrower is based on a proportion of their housing value, while for the second borrower, it is a proportion of their income. The rest of the model includes producers of intermediate and final goods in both the non-housing sector and the housing sector, as well as commercial banks, government, the central bank, and the macro-prudential authority. Banks collect deposits from saver households and provide loans to borrower households and firms based on their (LTV) ratios. They maximize their profit to determine the interest rates for deposits and loans in terms of the interest rate set by the central bank. The central bank employs a Taylor rule to determine the interest rate in terms of the inflation gap and the production gap. Finally, the macro-prudential authority determines LTV ratios in relation to the credit gap and the production gap. In this model, these tools exhibit countercyclical behavior to mitigate the reinforcing feedback between credit supply and economic booms. We used data from Iran's economy for the period 1989-2021.



Results and Discussion: The simulation results indicate that the torques derived from the model closely align with the torques found in the statistical data. Consequently, the presented model has proven to be successful in simulating Iran's economic data. The results suggest that the impact of macro-prudential policies on inequality varies depending on the different economic shocks experienced by the economy. Moreover, aside from their direct influence on household wealth, shocks in the housing sector also affect households' access to credit via the collateral channel. This can potentially lead to increased wealth for housing owners and higher levels of inequality. Hence, if macroprudential tools are defined with a focus on collateral-like assets, they may contribute to an increase in inequality. This result demonstrates the Matthew effect in economics, where wealth or credit is distributed among individuals according to how much they already have. Furthermore, if the loan-to-value ratio increases in the non-housing sector, it results in higher production and investment within this sector. As saving households own capital, their wealth grows, subsequently boosting their demand and contributing to inflation. This, in turn, leads to reduced consumption among other households. Consequently, the intended shock results in an increase in wealth and consumption inequality.

Conclusion: As discussed in recent studies, the use of macro-prudential tools, like other macroeconomic policies, also has side effects, with one of the most significant being changes in the distribution of income and wealth among various households. If access to credit is determined by creditworthiness and the ability of applicants to provide collateral, it is anticipated that the implementation of these policies will worsen income and wealth inequality. Given that prudential ratios exhibit countercyclical behavior, whether positive housing shocks increase access to credit or not depends on their direct impact on credit constraints and the indirect effect through prudential tools. According to the findings of this research, these factors ultimately lead to an increase in wealth inequality. Therefore, in addition to focusing on financial stability, it is essential to prevent shocks in the housing sector and factors that contribute to an intensified positive correlation between asset prices and credit. The existence of positive feedback loops between these two variables not only heightens the vulnerability of the financial sector but can also exacerbate wealth inequality.