Economic Research and Perspectives

Economic Research and Perspectives

Modeling the Impact of Monetary Policy with Different Patterns of Facility Repayment Rates on Macroeconomic Variables Using the Agent-Based Approach

Authors
Alireza Zarifian Abhari 1
Parastoo Mohammadi 2
1 M.A. in Systems and Industrial Engineering, Tarbiat Modares University, Tehran, Iran
2 Assistant Professor, Systems and Industrial Engineering Faculty, Tarbiat Modares University, Tehran, Iran
Abstract
Aim and Introduction:

The distribution of income and wealth in Iran is highly dependent on monetary policy. Iran's macroeconomic variables show that the country is experiencing an increase in inflation, liquidity and social inequality. Given that the facility repayment rate plays a role in channeling resources to investments, and given the role of the central bank in determining and regulating this variable in Iran, this study examines the impact of changing the facility repayment rate as a monetary policy tool on macroeconomics variables related to the distribution of income and wealth in society. The contribution of this research is to provide the agent-based model for Iran and to study the effects of different scenarios of decreasing, increasing and constant trend of facility repayment rate on the distribution of income and wealth, and other macroeconomic variables.

Methodology:

Two types of approaches can be used to model this problem: (1) simulation with the Dynamic Stochastic General Equilibrium (DSGE) approach and (2) simulation with the Agent-based Computational Economics (ACE) approach. DSGE models seek to find the optimal point, in which pricing is done through aggregating supply and demand by the Walrasian auctioneer, and none of the factors can decide on their variables. While in ACE simulations, changing process of variables and factors is examined and each factor has the ability to decide about its variables based on its observation of the system. The ACE approach has been used because of the proximity of the simulation to reality and the ability to examine the process. In this simulation, the effect of changing the facility repayment rate on the distribution of income and wealth and other macroeconomic variables is examined in three scenarios: decreasing, increasing and fixed facility repayment rate.

Agents that are considered in the proposed simulation are: (1) The Central bank as policy-maker agent that decides about facility repayment rate and money supply volume, (2) The bank is responsible for allocating credit to firms and depositing from households and distributing income from banking activities among depositors. It is also the responsibility of the bank to sell consumer goods and capital of bankrupt companies that have not been able to repay their facilities, (3) Firms are responsible for the production of consumer goods and capital and its sale in the market, and (4) A household that provides its labor force to firms in exchange for wages and provides consumer goods for its livelihood in the consumer goods market. In this simulation, policy-making is done by the central bank and the existence of the government, and fiscal policies are ignored and only the mentioned monetary policy (facility repayment rate) is investigated.

In this research, the market is defined in a way that each agent on the demand side observes a random list in terms of number of factors on the supply side and buys from the supplier agent that offers the lowest price. Model have 4 markets as follows: (1) the labor market, (2) consumer goods market, (3) capital goods market, and (4) credit market. The characteristic of this type of market is that the market mechanism is a random adaptation mechanism, and all agents on the demand side have incomplete information from suppliers and vice versa. None of agents see all the market prices and decide to buy only on the basis of incomplete observations of the system. Also, on the supplier side, the firm does not see the cumulative need of the market. It means that the firm estimates the amount of production for this period based solely on its own personal experience in the previous periods, and according to that estimate, it employs labor and produces goods and services. And firms set prices based on their experience on previous periods. This kind of market attitude in simulation has caused the simulated market to be closer to the real world.

Results and Discussion:

The result of this simulation shows that the repayment rate of incremental scenario caused the collapse of the simulation system and also the Gini coefficient increased, which indicates the disparity in the distribution of income and wealth in society. The fixed scenario does not show an effect on improving the Gini coefficient and on the other hand causes the bankruptcy of many firms in the long run. The best result is the reducing scenario. In this scenario, the system achieved sustainable economic growth, controlled liquidity, and a reduction in the Gini coefficient.

Conclusion:

In the absence of speculative markets, all the money generated in the banking system is directed to the production and development of economic activities. In addition, decreasing repayment rate of facilities can improve the distribution of income and wealth in society.
Keywords
Monetary policy
Income and wealth distribution
Agent-Based Modeling

Subjects

فخرایی سیدعنایت اله, منصوری سیدامین. (2010) برآورد میل نهایی به مصرف در گروه های درآمدی بر اساس فرضیه درآمد دائمی نسبی در ایران. نشریه دانش و توسعه. شماره 29 ; از صفحه 21 تا صفحه 38
پروین و طاهری فرد. (2009). تاثیر سیاست های پولی بر فقر و توزیع درآمد (مطالعه موردی اقتصاد ایران). پژوهش های رشد و توسعه پایدار (پژوهش های اقتصادی), 8(4), 95-128.‎
Carroll, C., Slacalek, J., & Tokuoka, K. (2014). The distribution of wealth and the marginal propensity to consume (No. 1655).
Coibion, O., Gorodnichenko, Y., Kueng, L., & Silvia, J. (2017). Innocent Bystanders? Monetary policy and inequality. Journal of Monetary Economics, 88, 70-89.
Dafermos, Y., & Papatheodorou, C. (2018). How does monetary policy affect income and wealth inequality? An agent-based stock-flow consistent analysis.
Dosi, G., Fagiolo, G., Napoletano, M., & Roventini, A.(2013). Income distribution, credit and fiscal policies in an agent-based Keynesian model. Journal of Economic Dynamics and Control, 37(8), 1598-1625.
Dosi, G., Fagiolo, G., Napoletano, M., Roventini, A., & Treibich, T. (2015). Fiscal and monetary policies in complex evolving economies. Journal of Economic Dynamics and Control, 52, 166-189.
Draghi, M. (2015). The ECB’s recent monetary policy measures: Effectiveness and challenges. Camdessus Lecture, IMF, Washington, DC, 14.
Fagiolo, G., & Roventini, A. (2016). Macroeconomic policy in DSGE and agent-based models redux: New developments and challenges ahead. Available at SSRN 2763735.
Fakhraee S. & Mansouri S., (2010). Estimation of final consumption tendency in income groups based on the relative permanent income hypothesis in Iran. Journal of Knowledge and development. 29; 21-38 {In Persian}
Flake, G. W. (1998). The computational beauty of nature: Computer explorations of fractals, chaos, complex systems, and adaptation: MIT press
Gatti, D.D., Fagiolo, G., Gallegati, M., Richiardi, M. & Russo, A. (2018). Agent-based models in Economics. UK: Cambridge University Press.
Gatti, D.D. & Dawid, H. (2018), Agent-Based Macroeconomics, Working Papers in Economics and Management, Bielefeld University, ISSN 2196−2723.
Giri, F., Riccetti, L., Russo, A., & Gallegati, M. (2019). Monetary policy and large crises in a financial accelerator agent-based model. Journal of Economic Behavior & Organization, 157, 42-58.
Katzner, D. W. (1989). The Walrasian Vision of the Microeconomy: An Elementary Exposition of the Structure of Modern General Equilibrium Theory: University of Michigan Press.
Leijonhufvud, A. (1967). Keynes and the Keynesians: A suggested interpretation. The American Economic Review, 57.410-401.(2)
Lengnick, M. (2013). Agent-based macroeconomics: A baseline model. Journal of Economic Behavior & Organization, 86, 102-120.
Montecino, J. A., & Epstein, G. (2015). Did Quantitative Easing increase income inequality? Institute for New Economic Thinking.
O’Farrell, R., Rawdanowicz, Ł., & Inaba, K.-I. (2016). Monetary policy and inequality.
Onaran, Ö., Stockhammer, E., & Grafl, L. (2011). Financialisation, income distribution and aggregate demand in the USA. Cambridge Journal of Economics, 35. 661-637.(4)
Parvin, Taheri Fard. (2009). The Impact of Monetary Policy on Poverty and Income Distribution (Case Study of Iranian Economy). Research on Sustainable Growth and Development (Economic Research), 8 (4), 95-128. {In Persion}
Riccetti, L., Russo, A., & Gallegati, M. (2015). An agent based decentralized matching macroeconomic model. Journal of Economic Interaction and Coordination, 10(2), 305-332.
Sarfati, H. (2015). OECD. In it together: Why less inequality benefits all. Paris, 2015. 332 pp. ISBN 978‐9264‐23266‐2. International Social Security Review, 68(4), 115-117.
Stockhammer, E. (2015). Rising inequality as a cause of the present crisis. Cambridge Journal of Economics, 39(3), 935-958.
Tesfatsion, L. (2006). Agent-based computational economics: A constructive approach to economic theory. Handbook of computational economics, 2, 831-880.
Economic Research and Perspectives
Volume 23, Issue 1
Spring 2023
Pages 55-84