The Effects of Microfinance on Income and Lifting the Poor out of Poverty: Agent-based Modeling

Document Type : Original Article

Authors

1 Associate Professor, Faculty of Industrial and Technology Management, School of Management, University of Tehran, Tehran, Iran.

2 Professor, Faculty of Natural Resources, School of Agriculture and Natural Resources, University of Tehran, Tehran, Iran.

3 Ph.D., Faculty of Industrial and Technology Management, School of Management, University of Tehran, Tehran, Iran.

4 PhD student, Faculty of Industrial and Technology Management, School of Management, University of Tehran, Tehran, Iran.

Abstract

Introduction: Microfinance, as a predominant poverty alleviation strategy aimed at lifting the poor out of poverty, provides financial services to impoverished individuals. This research examines the effect of microloans on the growth of individuals' income and their ability to rise above the poverty line. It also explores how changes in the duration of financial aid to Microfinance Institutions (MFIs) impact poverty alleviation. Additionally, the study investigates the effect of marketing and increased sales probability, as complementary services of microfinance, on income growth. Therefore, this research evaluates the influence of microfinance on income improvement and poverty reduction by considering two policies: changing the duration of financial aid to MFIs and increasing the probability of product sales.
Methods: In MFIs, the method of resource provision, different lending conditions, and the interactions among individuals create a complex environment. The heterogeneous characteristics and behaviors of individuals, along with their interactions in a dynamic setting, lead to complex events. Agent-based modeling (ABM) helps to understand and model these complexities. ABM is a simulation approach involving autonomous, independent, decision-making agents that are interconnected and aims to investigate system-level outcomes by modeling individual behaviors. This research employs the ABM approach.
Results and discussion: The simulation results indicate that the provision of microloans increases individuals' income and enables many to escape absolute poverty. This study assumes a zero-interest rate. The findings show that the resources of MFIs, based on savings and loan repayments, can increase, allowing for a growing number of loans even with a two-year donation policy. Thus, providing interest-free microloans and limiting the duration of assistance can create a sustainable microfinance system. The policy of extending financial aid from three to four years does not significantly increase income levels, as MFIs can sustain themselves through savings and repayments. However, extending the aid duration increases the number of loans, with 155, 221, and 278 loans given under the two-, three-, and four-year policies, respectively. Moreover, increasing the probability of sales from 60% to 80% results in significantly higher income and a greater number of individuals above the poverty line. Overall, the study reveals that increasing financial aid duration does not necessarily lead to higher income or poverty alleviation. Instead, marketing and boosting product sales are more effective.
Conclusions: Reducing the number of aid years and utilizing the released financial resources to create markets and enhance sales probability make poverty alleviation policies more effective. In other words, using financial resources to establish guaranteed or permanent markets for MFI members generates a greater leverage effect for income growth compared to simply extending the aid duration.

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  1. A collection of poverty monitoring reports (2022). Report No. 119. Available from: https://saman.mcls.gov.ir/ (In Persian(.
  2. Aghion, A., & Morduch, J. (2003). Microfinance Beyond Group Lending. Economics of Transition and Institutional change, 8(2), 401-420.
  3. Asgharizadeh, E., Sadeghi Moghadam, M.R., Safari, H., & Soltani Neshan, M. (2019). The effects of customers' decision making with Different Risk preference on Waranty Providers: Agent based Modeling. Industrial Management prespective, 9(1), 31-59. (In Persian(. Available from: https://akharinkhabar.ir/money/9634506.
  4. Badakhshan, E., Pishvaee, M.S., & Sahebi, H. (2016). A simulation-based optimization model for integrated planning of financial and physical flows in the supply chain. The Journal of Industrial Management prespective, 6(1), 31-51. (In Persian(
  5. Banerjee S.B., & Jackson, L. (2017). Micro-finance and the business of poverty reduction: critical perspectives from rural Bangladesh. Human Relations, 70(1), 63–91.
  6. Bhatt, N., & Tang, Sh. (2001). Delivering Microfinance in Developing Countries: Controversies and Policy Perspectives. Policy Studies Journals, 29(2), 319 - 333.
  7. Bourhime, S., & Tkiouat, M. (2018). Rethinking Microfinance in a Dual Financial System: An Agent-based Simulation. Scientific Annals of Economics and Business, 65(1), 13-29.
  8. Das, S.K., & Bhowal, A. (2013). Impact of self-help group on members and its involvement in social issues: core vs. peripheral issues. International Journal of Business and Management Invention, 2(12), 48–72.
  9. Farahbakhsh, M., Mahmoud Modiri, M., Khatami Firozabadi, S.M.A., & Pour Ebrahimi, A. (2023). Power Industry’s Life Cycle Simulation using Agent Based Modeling. The Journal of Industrial Management prespective, 12(4), 9-35. (In Persian(
  10. Global Issue. Ending poverty. (2018) Available from: https://www.un.org/en/global-issues/ending-poverty.
  11. Helms, B. (2006). Access for All: Building Inclusive Financial Systems. The World Bank.
  12. Joshi, M.Y., Flacke, J, & Schwarz, N. (2020). Do microfinance institutes help slum-dwellers in coping with frequent disasters? An agent-based modelling study. International Journal of Disaster Risk Reduction, 49, 1-22.
  13. Khaki, N. (2009). Poverty redusction and Microfinanace case study: Keshavarzi bank. Journal of Finanacial studies, 3, 111-136. (In Persian(
  14. Khandker S.R. (2005). Micro-finance and poverty: data from Bangladesh. World Bank Economic Review, 19(2), 263–286.
  15. Khosravi Nezhad, A.K. (2012). Estimating poverty and poverty indicators in urban and rural areas. Quarterly Journal of Economic Modelling, 6(2), 39-60. (In Persian(.
  16. Kraemer-Eis, H., Lang, F. (2012). The importance of leasing for SME finance. European Investment Fund (EIF), EIF Working paper No 2012/15.
  17. Lal, S., Kumar, D., & Murtaza Gh. (2023). Impact of Microfinance on Poverty Reduction: A Case Study of Khushhali Bank Mirpur Khas District Pakistan. Journal of Humanities and Social Sciences, 11(2), 953– 962.
  18. Ledgerwood, J. (1999). Microfinance handbook: An institutional and financial perspective. The World Bank, Washington, DC.
  19. Macal, C.M., & North, M.J. (2008). Agent-based modeling and simulation: ABMS examples. In Proceedings of the 40th Conference on Winter Simulation. 101- 112.
  20. Maeenuddin, Shaari Abd, H., Padzil Mohd, H., & Annuar MD, N. (2023). Predictors of microfinance sustainability: Empirical evidence from Bangladesh. Cogent Economics & Finance, 11(1), 1– 16.
  21. Morduch, J. (1999a). Between the Market and State: Can Informal Insurance Patch the Safety Net? World Bank Research Observer 14(2), 187 - 207.
  22. Morduch, J. (1999b). The Microfinance Promise. Journal of Economic Literature 37 (4), 1569 - 1614.
  23. Morduch, J. (2000). The Microfinance Schism . World Development Journal of Economic Literature, 28 (4), 617 - 629.
  24. Ozdemir, M., Savasan, F., & Ulev, S. (2023). Leveraging financial inclusion through Islamic microfinance: A new model proposal for participation banks in Turkiye. Borsa Istanbul Review, 23(3), 709– 722.
  25. Rashid, S., Yoon, Y., & Kashem, S.B. (2011). Assessing the potential impact of Microfinance with agent-based modeling. Economic Modelling, 28, 1907-1913.
  26. Saqalli, M., Gerard, B., Bielders, Ch., & Defourny, P. (2011). Targeting rural development interventions: Empirical agent-based modeling in Nigerien villages. Agricultural Systems, 104(4), 354-364.
  27. Singh, P.K., & Chudasama, H., (2020). Evaluating poverty alleviation strategies in a developing country. PLOS ONE, 15(1), 1- 23.
  28. Statistics report, 21 Microfinance Statistics You Need to Know. (2021). Available from: https://fitsmallbusiness.com/microfinance-statistics/
  29. Westover, J. (2008). The record of micro-finance: the effectiveness/ineffectiveness of microfinance programs as a means of alleviating poverty. Electronic Journal of Sociology. ISSN 1198 3655.
  30. Wright, G. A. N. (2000). Micro-finance Systems: designing quality financial services for the poor. Zed Books Ltd. London & New York, USA.
  31. Yunus, M., (2018). A world of three zeros: the new economics of zero poverty, zero unemployment, and zero net carbon emissions. Hachette Book Gorup, New York. Translated by Ghorbani, M. (In Persian(.