thesis on microfinance and poverty reduction

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The impact of microfinance institutions on poverty alleviation.

1. Introduction and Background

2. literature review, 3. methodology, 3.1. data and the variables, 3.2. empirical model specification, 4. empirical results, 4.1. descriptive statistics, stationarity and cointegration results, 4.2. regression results, 4.3. post estimation diagnostic tests, 5. summary and conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Abrar, Afsheen, and Attiya Y. Javaid. 2016. The impact of capital structure on the profitability of microfinance institutions. South Asian Journal of Management 10: 21–37. [ Google Scholar ] [ CrossRef ]
• Addae-Korankye, Alex. 2020. A Framework for Understanding Microfinance as a Tool for Poverty Reduction in Ghana . Cardiff: Cardiff Metropolitan University. [ Google Scholar ]
• Adjei, Joseph, Thankom Arun, and Farhad Hossain. 2009. Asset building and poverty reduction in Ghana: The case of microfinance. Savings and Development 33: 265–91. [ Google Scholar ]
• Aigbokhan, Ben E. 2008. Growth, Employment, Institutional Development, and Poverty Alleviation in Nigeria. New Growth and Poverty Alleviation Strategies for Africa: International and Regional Perspectives 13: 45. [ Google Scholar ]
• Bae, KwangBin, Dongsook Han, and Hosung Sohn. 2012. Importance of Access to Finance in Reducing Income Inequality and Poverty Level. International Review of Public Administration 17: 55–77. [ Google Scholar ] [ CrossRef ]
• Bank Supervision Division. 2019. Microfinance Industry Report for Quarter Ended 31 December 2019 . Harare: Reserve Bank of Zimbabwe. [ Google Scholar ]
• Bank Supervision Department. 2018. Selected South African Banking Sector Trends . Pretoria: Reserve Bank of South Africa. Available online: http//www.resbank.co.za/Research/statistics/pages/statistics-home.aspx (accessed on 28 April 2018).
• Bent, Bogale Berhanu. 2019. The Impact of Microfinance on Poverty Reduction and Women Empowerment. In Rais Collective Volume–Economic Science . Beltsville: Scientia Moralitas Research Institute, USA, pp. 72–86. [ Google Scholar ]
• Binswanger, Hans P., and Shahidur R. Khandker. 1995. The impact of formal finance on the rural economy of India. The Journal of Development Studies 32: 234–62. [ Google Scholar ] [ CrossRef ]
• Christensson, Linn. 2017. Microfinance Institutions and Poverty Reduction: Evidence from Nigeria. Available online: https://www.diva-portal.org/smash/get/diva2:1103573/FULLTEXT01.pdf (accessed on 13 February 2022).
• DFID. 2001. Maximising the Outreach of Microfinance in Russia. Research and Impact Assessment; Terms of Reference for for A . London: DFID. [ Google Scholar ]
• Do, Quynh Anh, Quoc Hoi Le, Thanh Duong Nguyen, Van Anh Vu, Lan Huong Tran, and Cuc Thi Thu Nguyen. 2021. Spatial Impact of Foreign Direct Investment on Poverty Reduction in Vietnam. Journal of Risk and Financial Management 14: 292. [ Google Scholar ] [ CrossRef ]
• Du, Yang, Albert Park, and Sangui Wang. 2005. Migration and rural poverty in China. Journal of Comparative Economics 33: 688–709. [ Google Scholar ] [ CrossRef ]
• Duong, Pham Bao, and Yoichi Izumida. 2002. Rural Development Finance in Vietnam: A Microeconometric Analysis of Household Surveys. World Development 30: 319–35. [ Google Scholar ] [ CrossRef ]
• Enisan, Akinlo Anthony, and Isaac Oluwafemi Oni. 2012. Impact of microfinance on poverty alleviation in Ondo State, Nigeria. Australian Journal of Business and Management Research 2: 31. [ Google Scholar ] [ CrossRef ]
• Félix, Elisabete Gomes Santana, and Teresa Freitas Belo. 2019. The impact of microcredit on poverty reduction in eleven developing countries in south-east Asia. Journal of Multinational Financial Management 52: 100590. [ Google Scholar ] [ CrossRef ]
• Gassner, Anja, David Harris, Kai Mausch, Anne Terheggen, Cristina Videira Lopes, Robert Finlayson, and Philip Dobie. 2019. Poverty eradication and food security through agriculuture in Africa: Rethinking objectives and entry points. SAGE Journals 48: 309–15. [ Google Scholar ]
• Ghalib, Asad K., Issam Malki, and Katsushi S. Imai. 2015. Microfinance and household poverty reduction: Empirical evidence from rural Pakistan. Oxford Development Studies 43: 84–104. [ Google Scholar ] [ CrossRef ]
• Goldberg, Nathanael. 2005. Measuring the Impact of Microfinance: Taking Stock of What We Know . Grameen Foundation USA Publication Series; Washington: Grameen Foundation USA, pp. 1–56. [ Google Scholar ]
• Gujarati, Damodar N., and Dawn C. Porter. 1999. Essentials of Econometrics . Singapore: Irwin/McGraw-Hill Singapore. [ Google Scholar ]
• Harber, Clive. 2002. Education, Democracy and Poverty Reduction in Africa. Comparative Education 38: 267–76. [ Google Scholar ] [ CrossRef ]
• Hickson, Robert. 2001. Reaching Extreme Poverty: Financial Services for the Very Poor. Prepared for Office of Development Studies, United Nations Development Program (UNDP) . Goondiwindi: Microfiance Services. [ Google Scholar ]
• Holger, Gorg, and Eric Strobl. 2005. Spillovers from foreign firms through worker mobility: An empirical investigation. The Scandinavian Journal of Economics 107: 693–709. [ Google Scholar ]
• Hulme, David. 2000. Impact assessment methodologies for microfinance: Theory, experience and better practice. World development 28: 79–98. [ Google Scholar ] [ CrossRef ]
• Imai, Katsushi S., Raghav Gaiha, Ganesh Thapa, and Samuel Kobina Annim. 2012. Microfinance and poverty—A macro perspective. World development 40: 1675–89. [ Google Scholar ] [ CrossRef ]
• Iqbal, Zahid, Shahid Iqbal, and Muhammad Ahmad Mushtaq. 2015. Impact of microfinance on poverty alleviation: The study of District Bahawal Nagar, Punjab, Pakistan. Management and Administrative Sciences Review 4: 487–503. [ Google Scholar ]
• Jaka, Hilda, and Elvin Shava. 2018. Resilient rural women’s livelihoods for poverty alleviation and economic empowerment in semi-arid regions of Zimbabwe. Jàmbá: Journal of Disaster Risk Studies 10: 1–11. [ Google Scholar ] [ CrossRef ]
• Jarque, Carlos M., and Anil K. Bera. 1987. A test for normality of observations and regression residuals. International Statistical Review 55: 163–72. [ Google Scholar ] [ CrossRef ]
• Kasali, Taofeek Aremu. 2020. Influence of Microfinance Intervention on Rural Poverty Alleviation in South-west Nigeria: An Application of Propensity Score Marching Technique. Asian Journal of Economics and Finance 2: 411–22. [ Google Scholar ]
• Khan, Arshad A., Sufyan U. Khan, Shah Fahad, Muhammad A. S. Ali, Aftab Khan, and Jianchao Luo. 2020. Microfinance and poverty reduction: New evidence from Pakistan. International Journal of Finance & Economics 26: 4723–33. [ Google Scholar ]
• Khandker, Shahidur R. 2003. Microfinance and Poverty: Evidence Using Panel Data from Bangladesh. The World Bank Economic Review 19: 263–86. [ Google Scholar ]
• Kim, Nguyen Thien, and Nguyen Minh Sang. 2018. Evaluating the efficiency of microfinance institutions in Vietnam and the Impact of this efficiency on poverty reduction. In Global Tensions in Financial Markets . Bingley: Emerald Publishing Limited. [ Google Scholar ]
• Klein, Michael, Carl Aaron, and Bita Hadjimichael. 2001. Foreign Direct Investment and Poverty Reduction . Policy Research Working Paper 2613. Washington: World Bank. [ Google Scholar ]
• Lee, Eddy, and Marco Vivarelli. 2006. The Social Impact of Globalisation in the Developing Countries. IZA Discussion Paper Series, No. 1925. Available online: http://ftp.iza.org/dp1925.pdf (accessed on 29 August 2022).
• Manzoor, Faiza, Longbao Wei, Mohammad Nurunnabi, and Qazi Abdul Subhan. 2019. Role of SME in poverty alleviation in SAARC via panel data analysis. Sustainability 11: 6480. [ Google Scholar ]
• Matzana, Velisaria, Aikaterina Oikonomou, and Michael Polemis. 2022. Tourism Activity as an Engine of Growth: Lessons Learned from the European Union. Journal of Risk and Financial Management 15: 177. [ Google Scholar ]
• Mhlanga, David, Steven Henry Dunga, and Tankiso Moloi. 2020. Financial inclusion and poverty alleviation among smallholder farmers in Zimbabwe. Eurasian Journal of Economics and Finance 8: 168–82. [ Google Scholar ]
• Morduch, Jonathan. 2000. The microfinance schism. World Development 28: 617–29. [ Google Scholar ]
• Morris, Gayle, and Carolyn Barnes. 2005. An assessment of the impact of microfinance: A case study from Uganda. Journal of Microfinance/ESR Review 7: 4. [ Google Scholar ]
• Oshora, Betgilu, Goshu Desalegn, Eva Gorgenyi-Hegyes, Maria Fekete-Farkas, and Zoltan Zeman. 2021. Determinants of Financial Inclusion in Small and Medium Enterprises: Evidence from Ethiopia. Journal of Risk and Financial Management 14: 286. [ Google Scholar ] [ CrossRef ]
• Pham, Duong Phuong Thao, and Thi Cam Ha Huynh. 2020. The impact of trade credit investment on manufacturing firms’ profitability: Evidence from Vietnam. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 68: 775–96. [ Google Scholar ]
• Samer, Sayed, Izaidin Majid, Syaiful Rizal, Mohd Razali Muhamad, and Nlizwa Rashid. 2015. The impact of microfinance on poverty reduction: Empirical evidence from Malaysian perspective. Procedia-Social and Behavioral Sciences 195: 721–28. [ Google Scholar ]
• Simatele, Munacinga, and Martin Kabange. 2022. Financial Inclusion and Intersectionality: A Case of Business Funding in the South African Informal Sector. Journal of Risk and Financial Management 15: 380. [ Google Scholar ] [ CrossRef ]
• Srinivas, Nidhi. 2004. Participation, Negotiation and Poverty: Encountering the Power of Images. International Journal of Voluntary and Nonprofit Organizations 15: 412–14. [ Google Scholar ]
• Tafamel, Ehiabhi Andrew. 2019. Analysis of the effect of microfinance institutions on poverty reduction in Nigeria. Academic Journal of Economic Studies 5: 114–19. [ Google Scholar ]
• Takaruva, Felicitate. 2016. The impact of Microfinance Institutions in alleviating poverty in Zimbabwe. A case study of JHM Microfinance. Doctoral dissertation, BUSE, Bindura, Zimbabwe. [ Google Scholar ]
• Ucal, Meltem, Mehmet Hüseyin Bilgin, and Alfred Haug. 2014. Income Inequality and FDI: Evidence with Turkish Data . MPRA Paper No. 61104. Fort Leonard Wood: MPRA. [ Google Scholar ]
• Van Rooyen, Carina, Ruth Stewart, and Thea De Wet. 2012. The impact of microfinance in sub-Saharan Africa: A systematic review of the evidence. World Development 40: 2249–62. [ Google Scholar ] [ CrossRef ]
• Winarno, S., Mustofa Usman, and D. Kurniasari. 2021. Application of Vector Error Correction Model (VECM) and Impulse Response Function for Daily Stock Prices. In Journal of Physics: Conference Series . Bristol: IOP Publishing, p. 012016. [ Google Scholar ]
• Zainal, Nurazilah, Annuar Md Nassir, Fakarudin Kamarudin, S. H. Law, Fadzlan Sufian, and Hafezali Iqbal Hussain. 2019. The social role of microfinance institutions in poverty eradication: Evidence from ASEAN-5 countries. Internafional Journal of Innovafion, Creafivity and Change 5: 1551–76. [ Google Scholar ]
• Zezza, Alberto, and Luca Tasciotti. 2010. Urban agriculture, poverty, and food security: Empirical evidence from a sample of developing countries. Food Policy 35: 265–73. [ Google Scholar ] [ CrossRef ]
• ZIMSTAT. 2019. Zimbabwe Poverty Update 2017–19 . ZIMSTAT and World Bank: Available online: https://www.zimstat.co.zw/wp-content/uploads/publications/Income/Finance/Zimbabwe_Poverty_Updat_2017_19_Final.pdf (accessed on 15 February 2022).
• ZIMSTAT. 2020. Zimbabwe Poverty Datum lines 2020. Available online: http://www.zimstat.co.zw/wp-content/uploads/2021/01/Poverty-Leaf-Logo-December-2020.pdf (accessed on 15 February 2022).

Share and Cite

Chikwira, C.; Vengesai, E.; Mandude, P. The Impact of Microfinance Institutions on Poverty Alleviation. J. Risk Financial Manag. 2022 , 15 , 393. https://doi.org/10.3390/jrfm15090393

Chikwira C, Vengesai E, Mandude P. The Impact of Microfinance Institutions on Poverty Alleviation. Journal of Risk and Financial Management . 2022; 15(9):393. https://doi.org/10.3390/jrfm15090393

Chikwira, Collin, Edson Vengesai, and Petronella Mandude. 2022. "The Impact of Microfinance Institutions on Poverty Alleviation" Journal of Risk and Financial Management 15, no. 9: 393. https://doi.org/10.3390/jrfm15090393

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Microfinance and Poverty Reduction in Nigeria: A Case Study of LAPO Microfinance Bank

--> Owolabi, Oluwatomi Ehagbor (2015) Microfinance and Poverty Reduction in Nigeria: A Case Study of LAPO Microfinance Bank. PhD thesis, University of Leeds.

From a contextual and service users’ perspective, this thesis investigates the poverty reducing effect of microfinance including the implementation processes and features of microfinance. Poverty is in this study conceptualised as ‘capability deprivation’ so that poverty reduction is achieved through improved capabilities for the poor. The literature review on microfinance begins with an attempt to locate the reasons for microfinance within the context of the finance-development and credit rationing literature. It also recognises the institutional-based approach to understanding the exclusion of the poor; and recognizes the role of lending group and social relations within the group in shaping individual and social capabilities of the poor. Next, it reviews some of the recent discourses in the microfinance literature. It uncovers a call in the literature for studies that look beyond impact to evaluate the features and characteristics of microfinance delivery and use from the clients’ perspective. The central arguments of this thesis include: (i) the utility or disutility of microfinance stems from the features of microfinance and its implementation strategies, as well as clients’ practices. (ii) The microfinance literature has not paid enough attention to the perspectives of service users, which can adversely affect microfinance assessments and its potential for poverty reduction. The study examines LAPO microfinance intervention in semi-urban and urban areas in Edo State, Nigeria. Given the research objective, design and methods, data collection and analysis were guided by the Interpretive, Capability and Poverty Participatory Assessment approaches. The mixed methods approach is selected as the most appropriate for addressing the research questions. Secondary data as well as primary data sourced from 35 interviews and 62 questionnaires were employed. Data analysis was conducted using qualitative, frequency distributions, cross-tabulations, Logit and OLS regression analysis. This study finds that service user perceptions of poverty place value on a stable source of livelihood, and the ability to meet basic material household needs. Hence, poverty reduction is measured as increased household capabilities as well as the increased ability to achieve successful business outcomes. The findings show that service users’ perceptions affirm the poverty reducing effect of microfinance. It also confirms the proposition that the various implementation processes and features of microfinance have unique effects on service users with differing potential for good or harm. The use of trust and personal relations as criteria for selecting group members, as well as the use of an individual guarantee to insure against risk due to imperfect information, suggests the possibility of exclusion. Heterogeneity in groups as well as low monitoring levels leave service users exposed to greater risk of moral hazard. Peer support enables service users to share ideas and build social networks vital to the success of their businesses and for raising social capital important for combating poverty. Despite the potency of the threat of social sanctions in enforcing repayment, the actual implementation of social sanctions and peer pressure comes at a cost to service users, including damaging social relations. While the targeting of women fosters their empowerment, the labelling of microfinance as a pro-women initiative has the potential to reinforce rather than challenge the prevailing gender bias in the Nigerian society. As regards loans, high repayment burden arising from the use of dynamic incentive creates the potential for harm. The use of savings as loan guarantee against peer defaults also has a similar effect. Although service users affirm the role of savings and non-financial services in the expansion of capabilities, the obscurity surrounding the access to some of the more criteria-determined non-financial services, is found to be damaging to service users’ experience. Hence, this study argues that while there are some links between poverty reduction and microfinance, these two are complexly related.

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Impact of Microfinance on Poverty and Microenterprises

• First Online: 26 April 2020

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• Sefa Awaworyi Churchill 2  

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In this chapter, Awaworyi Churchill conducts a meta-analysis to review the impact of microfinance on poverty reduction and microenterprise performance. He considers four proxies for poverty and three for microenterprise performance in order to examine the empirical evidence and to provide a general conclusion on the impacts of microfinance, while addressing issues of within and between-study variations. The chapter reports evidence of some positive effect on poverty, but this effect is weak.

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See Tedeschi ( 2008 ) for a review of some potential biases faced by microfinance impact-assessment researchers.

For detailed discussion on impact-assessment methods used in the literature as well as arguments concerning results validity, see, Morduch ( 1999 ), Roodman and Morduch ( 2013 ), Duvendack et al. ( 2011 ) and Berhane and Gardebroek ( 2011 ).

Keywords for microfinance include microfinance, micro-finance, microcredit, micro-credit, micro-lease, microloan, micro-Savings, micro-insurance, micro-banking, micro-bank, credit, MFI and small loan(s). Keywords for poverty and microenterprise include poverty, income, consumption, expenditure, assets, microenterprise, micro-enterprise, micro-business, small business and micro-franchise. The last search was conducted in January 2015, and thus our study captures only studies published during or before this period.

Statistical significance is determined by examining the confidence interval, thus studies with single estimates would not have a confidence interval in the context of out meta-analysis. Hence, we cannot indicate statistical significance for these studies.

Cohen indicated that an effect can be referred to as a ‘large effect’ if its absolute value is greater than 0.4, a ‘medium effect’ if between 0.10 and 0.4 and ‘small effect’ if less than 0.10.

It must be noted that this result emerges from a very small sample (drawn from three studies) and thus the conclusion here as well as others drawn from three or less studies must be taken with caution. This also reveals the need for more empirical studies.

There are six RCTs that examine one or more of the relationships we are interested in. However, at most, only two of such studies fall into the same cluster of interest. For instance, considering access to credit and assets, only Attanasio et al. ( 2011 ) report on this relationship and it is impossible to perform a PET/FAT test for this study only. Overall, the total estimates from only RCTs examining a particular outcome (using a specific microfinance measure) are not enough for a separate PET/FAT analysis.

It should be noted that these results are based on only nine estimates.

Given that moderating variables represent variations in the literature, different moderating variables appear in different regressions. For instance, the relationship between microcredit and consumption/expenditure has the highest number of primary studies and reported estimates. Thus, there is a higher likelihood for more variations to exist in this cluster as opposed to the relationship between access to microcredit and consumption/expenditure, which has relatively fewer estimates. Also, some moderating variables are specific to the microfinance measure being used. For instance, productive loan amount can be controlled for in the MRA that involves microcredit studies but not in the access to credit studies. For this reason, there are moderating variables that appear in Table 14.4b but are excluded from Tables 14.4a and 14.4c , and vice versa. Additionally, given that estimates in some categories are very few, we are not able to conduct MRAs for all clusters. In the end, we ran MRAs for only the microcredit-consumption/expenditure, access to credit-consumption/expenditure and the microcredit-income associations.

The Australian Business Dean’s Council (ABDC) and the Australian Research Council (ARC) present classifications for journal quality. Journals are ranked in descending order of quality as A∗, A, B and C. Thus, we introduce a dummy for A∗ and A ranked journals (high quality) in our MRA, and use other ranks as base.

Our meta-analysis includes publications from 1998 to 2013 (a period of 16 years). Fewer studies are published in the first eight years compared to the last eight. And most studies that fall in the category of the last eight years (after 2005) used larger panel datasets compared to previous studies, which in most cases used cross-sectional datasets.

Ideally, another characteristic to capture is the lending type, whether primary studies examine individual lending or group lending. However, this is not possible given that for this dimension, fewer variations exist in the primary studies found in each microfinance-outcome variable cluster.

Note that the constant term and the intercept coefficient have now interchanged positions, while the error term is newly defined as \( {\varepsilon}_i \) .

Abou-Ali, H., El-Azony, H., El-Laithy, H., Haughton, J., & Khandker, S. R. (2009). Evaluating the impact of Egyptian social fund for development programs . Policy Research Working Paper Series 4993. The World Bank.

Google Scholar  

Alam, S. (2012). The effect of gender-based returns to borrowing on intra-household resource allocation in rural Bangladesh. World Development, 40 (6), 1164–1180.

Article   Google Scholar  

Alptekin, A., & Levine, P. (2012). Military expenditure and economic growth: A meta-analysis. European Journal of Political Economy, 28 (4), 636–650.

Ashraf, N., Karlan, D., & Yin, W. (2010). Female empowerment: Impact of a commitment savings product in the Philippines. World Development, 38 (3), 333–344.

Attanasio, O., Augsburg, B., Haas, R., Fitzsimons, E., & Harmgart, H. (2011). Group lending or individual lending? Evidence from a randomised field experiment in Mongolia . IFS Working Papers: W11/20. Institute for Fiscal Studies.

Augsburg, B., De Haas, R., Harmgart, H., & Meghir, C. (2012). Microfinance at the margin: Experimental evidence from Bosnia and Herzegovina. Rochester.

Banerjee, A., Duflo, E., Glennerster, R., & Kinnan, C. (2009). The miracle of microfinance? Evidence from a randomized evaluation . Department of Economics Massachusetts Institute of Technology MIT Working Paper, pp. 1–40.

Berhane, G., & Gardebroek, C. (2011). Does microfinance reduce rural poverty? Evidence based on household panel data from Northern Ethiopia. American Journal of Agricultural Economics, 93 (1), 43–55.

Card, D., & Krueger, A. (1995). Time-series minimum-wage studies: A meta-analysis. American Economic Review, 85 (2), 238–243.

Cohen, J. (1988). Statistical power analysis for the behavioural sciences . Hillsdale, NJ: Lawrence Erlbaum Associates.

Collins, D., Morduch, J., Rutherford, S., & Ruthven, O. (2009). Portfolios of the poor: How the world’s poor live on $2 a day . Princeton and Oxford: Princeton University Press.

Copestake, J. (2002). Inequality and the polarizing impact of microcredit: Evidence from Zambia’s Copperbelt. Journal of International Development, 14 (6), 743–755.

Copestake, J., Bhalotra, S., & Johnson, S. (2001). Assessing the impact of microcredit: A Zambian case study. The Journal of Development Studies, 37 (4), 81–100.

Copestake, J., Dawson, P., Fanning, J., McKay, A., & Wright-Revolledo, K. (2005). Monitoring the diversity of the poverty outreach and impact of microfinance: A comparison of methods using data from Peru. Development Policy Review, 23 (6), 703–723.

Cotler, P., & Woodruff, C. (2008). The impact of short-term credit on microenterprises: Evidence from the Fincomun-Bimbo program in Mexico. Economic Development & Cultural Change, 56 (4), 829–849.

Cuong, N. V. (2008). Is a governmental micro-credit program for the poor really pro-poor? Evidence from Vietnam. The Developing Economies, 46 (2), 151–187.

Daley-Harris, S. (2009). State of the Microcredit Summit Campaign Report 2009 . Washington, DC: Microcredit Summit.

De Dominicis, L., Florax, R., & Groot, H. (2008). A meta-analysis on the relationship between income inequality and economic growth. Scottish Journal of Political Economy, 55 (5), 654–682.

de Mel, S., McKenzie, D., & Woodruff, C. (2008). Returns to capital in microenterprises: Evidence from a field experiment. The Quarterly Journal of Economics, 123 (4), 1329–1372.

Doucouliagos, H., & Ulubasoglu, M. (2008). Democracy and economic growth: A meta-analysis. American Journal of Political Science, 52 (1), 61–83.

Dupas, P., & Robinson, J. (2013). Savings constraints and microenterprise development: Evidence from a field experiment in Kenya. American Economic Journal: Applied Economics, 5 (1), 163–192.

Duvendack, M., Palmer-Jones, R., Copestake, J., Hooper, L., Loke, Y., & Rao, N. (2011). What is the evidence of the impact of microfinance on the well-being of poor people? London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.

Egger, M., Smith, G., Schnieder, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315 , 629–634.

Feigenberg, B., Field, E. M., & Pande, R. (2010). Building social capital through microfinance . John F. Kennedy School of Government, Harvard University.

Garikipati, S. (2008). The impact of lending to women on household vulnerability and women’s empowerment: Evidence from India. World Development, 36 (12), 2620–2642.

Gertler, P., Levine, D. I., & Moretti, E. (2009). Do microfinance programs help families insure consumption against illness? Health Economics, 18 (3), 257–273.

Hawkes, D., & Ugur, M. (2012). Evidence on the relationship between education, skills and economic growth in low-income countries: A systematic review. London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.

Henmi, M., & Copas, J. B. (2010). Confidence intervals for random effects meta-analysis and robustness to publication bias. Statistics in Medicine, 29 (29), 2969–2983.

Hoque, S. (2004). Micro-credit and the reduction of poverty in Bangladesh. Journal of Contemporary Asia, 34 (1), 21–32.

Hoque, S. (2005). Micro-credit and empowerment of women: Evidence from Bangladesh. Asian Economic Review, 47 (3), 411–420.

Hulme, D. (2000). Impact assessment methodologies for microfinance: Theory, experience and better practice. World Development, 28 (1), 79–98.

Hulme, D., & Mosley, P. (1996). Finance against poverty . London: Routledge.

Imai, K. S., Arun, T., & Annim, S. K. (2010). Microfinance and household poverty reduction: New evidence from India. World Development, 38 (12), 1760–1774.

Imai, K. S., & Azam, M. D. S. (2012). Does microfinance reduce poverty in Bangladesh? New evidence from household panel data. Journal of Development Studies, 48 (5), 633–653.

Imai, K. S., Gaiha, R., Thapa, G., & Annim, S. K. (2012). Microfinance and poverty—A macro perspective. World Development, 40 (8), 1675–1689.

Islam, A. (2009). Three essays on development and labour economics . PhD, Monash University, Australia.

Islam, A. (2011). Medium- and long-term participation in microcredit: An evaluation using a new panel dataset from Bangladesh. American Journal of Agricultural Economics, 93 (3), 847–866.

Kabeer, N. (2005). Direct social impacts for the Millennium Development Goals. In J. Copestake, M. Greeley, S. Johnson, N. Kabeer, A. Simanowitz, & K. Knotts (Eds.), Money with a mission, volume 1: Microfinance and poverty reduction . London: ITDG.

Kaboski, J. P., & Townsend, R. M. (2012). The impact of credit on village economies. American Economic Journal: Applied Economics, 4 (2), 98–133.

Karlan, D., Goldberg, N., & Copestake, J. (2009). Randomized control trials are the best way to measure impact of microfinance programmes and improve microfinance product designs. Enterprise Development and Microfinance, 20 (3), 167–176.

Karlan, D. S., & Zinman, J. (2007). Expanding credit access: Using randomized supply decisions to estimate the impacts . CEPR Discussion Papers: 6180.

Karnani, A. (2007). Microfinance misses its mark. Stanford Social Innovation Review, 5 (3), 34–40.

Kevane, M., & Wydick, B. (2001). Microenterprise lending to female entrepreneurs: Sacrificing economic growth for poverty alleviation? World Development, 29 (7), 1225–1236.

Khandker, S. R. (2005). Microfinance and poverty: Evidence using panel data from Bangladesh. World Bank Economic Review, 19 (2), 263–286.

Korth, M., Stewart, R., Rooyen, C., & Wet, T. (2012). Microfinance: Development intervention or just another bank? Journal of Agrarian Change, 12 (4), 575–586.

Kouassi, M. J. (2008). Microfinance and health: A study of selected countries . PhD, Howard University.

Levine, R. (2004). Finance and growth: Theory and evidence . NBER Working Paper No. 10766.

Li, X., Gan, C., & Hu, B. (2011). The welfare impact of microcredit on rural households in China. Journal of Socio-Economics, 40 (4), 404–411.

McKernan, S.-M. (2002). The impact of microcredit programs on self-employment profits: Do noncredit program aspects matter? Review of Economics and Statistics, 84 (1), 93–115.

Morduch, J. (1998). Does microfinance really help the poor? New evidence from Flagship Programs in Bangladesh . Woodrow Wilson School of Public and International Affairs Working Papers. Princeton University.

Morduch, J. (1999). The microfinance promise. Journal of Economic Literature, 37 (4), 1569–1614.

Nghiem, S., Coelli, T., & Rao, P. (2012). Assessing the welfare effects of microfinance in Vietnam: Empirical results from a quasi-experimental survey. Journal of Development Studies, 48 (5), 619–632.

Nino-Zarazua, M. M., & Copestake, J. (2008). Financial inclusion, vulnerability and mental models: From physical access to effective use of financial services in a low-income area of Mexico City. Savings and Development, 32 (4), 353–379.

Pitt, M. M., & Khandker, S. R. (1998). The impact of group-based credit programs on poor households in Bangladesh: Does the gender of participants matter? The Journal of Political Economy, 106 (5), 958–996.

Roodman, D., & Morduch, J. (2013). The impact of microcredit on the poor in Bangladesh: Revisiting the evidence . NYU Wagner Research Paper No. 2231535, pp. 1–49.

Rosenberg, R. (2010). Does microcredit really help poor people? CGAP Focus Note, Number 59.

Sebstad, J., & Chen, G. (1996). Overview of studies on the impact of microenterprise credit . Washington, DC: Management Systems International.

Shaw, J. (2004). Microenterprise occupation and poverty reduction in microfinance programs: Evidence from Sri Lanka. World Development, 32 (7), 1247–1264.

Stanley, T. (2008). Meta-regression methods for detecting and estimating empirical effects in the presence of publication selection. Oxford Bulletin of Economics and Statistics, 70 (2), 103–127.

Stanley, T. (2010). Meta-regression models of economics and medical research. In I. Shemilt, M. Mugford, L. Vale, K. Marsh, & C. Donaldson (Eds.), Evidence-based decisions and economics: Health care, social welfare, education and criminal justice (2nd ed.). Oxford, UK: Wiley-Blackwell.

Stanley, T., & Doucouliagos, H. (2007). Identifying and correcting publication selection bias in the efficiency-wage literature: Heckman meta-regression . Economics Series 2007/11. Deakin University.

Stanley, T., Jarrell, S., & Doucouliagos, H. (2009). Could it be better to discard 90% of the data? A statistical paradox . Economics Working Papers No. SWP 2009/13. Deakin University.

Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression approximations to reduce publication selection bias. Research Synthesis Methods, 5 (1), 60–78.

Stewart, R., van Rooyen, C., Dickson, K., Majoro, M., & de Wet, T. (2010). What is the impact of microfinance on poor people? A systematic review of evidence from sub-Saharan Africa . Technical Report. London: EPPI-Centre, Social Science Research Unit, University of London.

Stewart, R., Van Rooyen, C., Korth, M., Chereni, A., Rebelo Da Silva, N., & de Wet, T. (2012). Do micro-credit, micro-savings and micro-leasing serve as effective financial inclusion interventions enabling poor people, and especially women, to engage in meaningful economic opportunities in low- and middle-income countries? A systematic review of the evidence . EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.

Takahashi, K., Higashikata, T., & Tsukada, K. (2010). The short-term poverty impact of small-scale, collateral-free microcredit in Indonesia: A matching estimator approach. Developing Economies, 48 (1), 128–155.

Tedeschi, G. A. (2008). Overcoming selection bias in microcredit impact assessments: A case study in Peru. Journal of Development Studies, 44 (4), 504–518.

Ugur, M. (2013). Corruption’s direct effects on per-capita income growth: A meta-analysis. Journal of Economic Surveys, 28 , 472–490.

Vaessen, J., Leeuw, F., Bonilla, S., Rivas, A., Lukach, R., Bastiaensen, J., & Holvoet, N. (2012). The effects of microcredit on women’s control over household spending in developing countries . 3ie Systematic Reviews—SR003-1.

van Rooyen, C., Stewart, R., & de Wet, T. (2012). The impact of microfinance in Sub-Saharan Africa: A systematic review of the evidence. World Development, 40 (11), 2249–2262.

Wood, G., & Sharif, I. (1997). Who needs credit?: Poverty and finance in Bangladesh . London: Zed Books.

Wydick, B. (1999). The effect of microenterprise lending on child schooling in Guatemala. Economic Development and Cultural Change, 47 (4), 853–869.

Zeller, M., Sharma, M., Ahmed, A. U., & Rashid, S. (2001). Group-based financial institutions for the rural poor in Bangladesh: An institutional- and household-level analysis . Research Report 120. Washington, DC: International Food Policy Research Institute.

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Editors and affiliations, appendix: empirical design.

This study conducts a meta-analysis of the data collected and this is done in three stages. The first stage involves the calculation of the fixed effects estimates (FEEs) for the weighted mean of the various estimates that have been reported for each study. Stanley, Jarrell, and Doucouliagos ( 2009 ) propose that FEEs are efficient given that the estimates reported by the original studies are derived from the same population and have a common mean. In addition, FEES are more reliable than simple means, and compared to random-effects weighted means, they are less affected by publication bias (Henmi & Copas, 2010 ; Stanley, 2008 ; Stanley & Doucouliagos, 2014 ).

Second, to test if reported FEEs are affected by publication selection bias, we conduct precision effect tests (PETs) and funnel asymmetry tests (FATs). The PET/FAT makes it possible to test if a particular microfinance measure has ‘genuine effects’ on the various outcome measures after controlling for biases like publication selection bias. In the last stage of the meta-analysis, we examine if variations in reported estimates can be attributed to study characteristics such as publication year and type, econometric methodology, data type, and borrower differences. Thus, a multivariate meta-regression is conducted in order to test for genuine effects on the outcome variables after controlling for various biases and the effects of moderating variables (variations) such as those mentioned earlier. This process is conducted using partial correlation coefficients (PCCs) derived from estimates extracted from the chosen studies.

PCCs are used because they measure the association between microfinance and the outcome variables while other independent variables are held constant. Basically, they are comparable across different studies as they are independent of the metrics used in measuring both the dependent and explanatory variables, and they are also widely used in meta-analysis (see for example Alptekin & Levine, 2012 ; Doucouliagos & Ulubasoglu, 2008 ; Ugur, 2013 ).

The PCC for each effect estimate is calculated as follows:

Similarly, the standard error of the above coefficient is calculated as

where \( {r}_i \) and \( S{E}_{ri} \) represent the PCC and the standard error of the PCC respectively. The standard error represents the variance attributed to sampling error, and it is used in the calculation of the FEEs for the study-based weighted means. \( {t}_i \) represents the t-statistic associated with the given effect-size estimate, and \( d{f}_i \) represents the degrees of freedom that correspond with the estimates as reported in the studies.

For the weighted means used in this study, the approach used by Stanley and Doucouliagos ( 2007 ), Stanley ( 2008 ), and De Dominicis, Florax, and Groot ( 2008 ) was adopted. They report that weighted means can be calculated using the relation:

where \( \overline{X} \) is the weighted mean of the reported estimates, \( {r}_i \) , is the partial correlation coefficient as calculated in Eq. ( 14.1 ) and \( {w}_i \) , is the weight that varies depending on whether \( \overline{X} \) is a random effect mean or fixed effect mean.

For fixed effect estimates (FEEs), the weight \( {w}_i \) is given as the inverse of the square of the standard error associated with the PCCs as derived in Eq. ( 14.2 ). Thus, Eq. ( 14.3 ) can be re-expressed as Eq. ( 14.4 ) as the fixed effect estimates for the weighted mean of the partial correlations.

where \( {\overline{X}}_{FEE} \) is the fixed effect estimate weighted mean, and \( {r}_i \) and \( S{E}_{ri} \) remain as they are above. The fixed effect estimate weights account for the within-study variations by distributing weights, such that less precise estimates are assigned lower weights, while more precise estimates are assigned higher weights. Thus, the fixed effects weighted means are more reliable compared to the simple means.

The PET/FAT analysis involves the estimation of a bivariate weighted least square (WLS) model. Egger, Smith, Schnieder, and Minder ( 1997 ) propose the following model to test for publication selection bias:

where \( {r}_i \) is the effect estimate, \( {\beta}_0\kern0.28em \mathrm{and}\kern0.5em {\alpha}_0 \) represent the constant term and the slope coefficient respectively, while \( S{E}_{ri} \) is the standard error of the estimate. Egger et al. ( 1997 ) suggest that publication bias is present if the slope coefficient is significantly different to zero. Furthermore, the model also suggests that in the absence of bias (that is the slope coefficient is not significantly different to zero), the effect estimate would randomly vary around the true effect, which is the intercept term. Nonetheless, Eq. ( 14.5 ) would not be efficient in determining whether the effect estimates are genuine since it is heteroskedastic in nature (Hawkes & Ugur, 2012 ; Stanley, 2008 ) and the variance of the reported effect estimates are not constant. In this regard, Stanley ( 2008 ) recommends that Eq. ( 14.5 ) be converted into a weighted least square (WLS) model by dividing through it by \( S{E}_{ri} \) to yield Eq. ( 14.6 ). Stanley ( 2008 ) demonstrates that this WLS model can be used to test for both publication selection bias (which is the FAT) and for genuine effect beyond selection bias.

Here, the \( t \) -value becomes the dependent variable and the coefficient of the precision ( \( 1/S{E}_{ri} \) ) becomes the measure of genuine effect. Footnote 13 The funnel asymmetry test involves testing for the following null and alternate hypotheses (Eq. (14.7 )) and if the null hypothesis is rejected, this means that asymmetry exists.

The precision effect test, also known as the test for genuine effect, involves testing of the following null and alternate hypotheses:

Stanley ( 2010 ) indicates that the reported estimates and their associated standard errors have a nonlinear relationship given that the FAT/PET results point to the co-existence of the presence of both publication selection bias and genuine effect. In situations like this, they propose that a precision effect test with standard errors (PEESE) be conducted to account for any nonlinear relationships that may exist. They propose the following PEESE model:

Dividing this PEESE model by \( S{E}_{ri} \) , which suppresses the constant term with the aim of addressing any potential heteroskedasticity problems, we obtain the following;

Given that \( \frac{r_i}{S{E}_{ri}}={t}_i \) and \( {u}_i\left(\frac{1}{S{E}_{ri}}\right)={v}_i \) , we get

Equation ( 14.11 ) tests whether \( {\beta}_0=0 \) and helps determine if genuine effects are present. The genuine effect in this case takes into account any nonlinear relationship that may exist with the standard error.

The use of the PET/FAT and PEESE analysis makes it possible to make precise inferences regarding the existence of genuine effects. However, these tests work with the assumption that any moderating variable that may potentially be related to specific study characteristics, or sample differences, are equal to their sample means and are independent of the standard error. As a result, the PET/FAT and PEESE do not include moderating variables. Based on this understanding, this study also conducts a multivariate meta-regression (MRA), which takes into account various moderating variables and allows us to examine the role of such variables on estimated effect-sizes. The MRA specification (Eq. 14.12 ) is usually used to model heterogeneity.

where \( {t}_i \) is the \( t \) -value associated with each reported estimate, \( {Z}_{ki} \) is a vector of binary variables that account for variations in the studies, and \( {\beta}_k \) are the coefficients to be estimated, which explain the effect of each moderating variable on the estimate effect size.

Equation ( 14.12 ) is often estimated by OLS, which is a consistent estimator if the estimated effect sizes retrieved from primary studies are independent from one to another. However, given that primary studies often provide more than one estimate, this potentially brings into question the independency among estimates (De Dominicis et al., 2008 ). Thus, we estimate Eq. ( 14.13 ) using a multi-level model (hierarchical model) to account for any issues of data dependency. Hence, we estimate the follow model:

Here, \( {t}_{ji} \) is the \( i \) th \( t \) -value associated with the \( j \) th study and \( k \) represents the number of moderating variables. \( {Z}_{ki} \) remains as explained, and \( {\epsilon}_j \) is the study-specific error term. Both error terms \( {\epsilon}_j \) and \( {u}_{ji} \) are normally distributed around the PCCs’ mean values such that \( {\epsilon}_i\sim N\kern0.28em \left(0,S{E}_{ri}^2\right) \) , where \( S{E}_{ri}^2 \) is the square of the standard errors associated with each of the derived PCC, and \( {u}_i\sim N\kern0.28em \left(0,{\tau}^2\right) \) , where \( {\tau}^2 \) is the estimated between-study variance.

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Awaworyi Churchill, S. (2020). Impact of Microfinance on Poverty and Microenterprises. In: Awaworyi Churchill, S. (eds) Moving from the Millennium to the Sustainable Development Goals. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-15-1556-9_14

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The role of microfinance institutions on poverty reduction in Ethiopia: the case of Oromia Credit and Saving Share Company at Welmera district

• Dejene Adugna Chomen   ORCID: orcid.org/0000-0003-4327-3298 1  

Future Business Journal volume  7 , Article number:  44 ( 2021 ) Cite this article

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The purpose of this study is to assess the contribution of Oromia Credit and Saving Share Company microfinance institution on poverty alleviation in Welmera district, Oromia Special Zone Surrounding Finfine, Oromia Regional State, Ethiopia. Both random and purposive sampling techniques were used for data collection. Three hundred and fifty-seven respondents were selected from twelve different villages for the data collection. The study used a binary logistic regression to identify the key determinants of the income improvement of respondents. The findings confirmed that education level, voluntary saving, and utilization of loan for the intended purposes are statistically significant and positively contributed to the income improvement of the respondents in the study area. The finding revealed that most of the respondents' income improved after they joined the program which impacted positively in improving their standards of living.

Introduction

Initially, microfinance was introduced to the globe by Muhammad Yunus in 1976 in Jobra's village in Bangladesh [ 29 ]. It has currently been an effective instrument for poverty reduction [ 4 ]. However, the contribution of microfinance services in poverty reduction got more attention in 2005, after the United Nations (UN) announced the year of international microcredit. Many microfinance institutions have arisen and have attracted the poorer communities and have developed new strategies to realize their vision [ 29 ]. Since then, most developing countries have been using microfinance as the best strategy to eradicate poverty [ 19 , 45 ].

Several microfinance institutions (MFIs) emerged in Africa to fulfill the limitless need for financial services and provide different benefits in the past decades. For instance, some of these microfinance institutions successfully address materials shortage, the tangible materials like goods, intangible (services) to realize them. Once properly managed, microfinancing’s materials benefits can range beyond the household into the community [ 45 ]. However, some scholars describe that few of these microfinance institutions focus on offering loans, whereas others offer both credit facilities and gather the deposit[ 12 ].

In Ethiopia, microfinance was introduced in 1995 to reduce poverty, and since then, Ethiopia's government has stimulated the expansion of modern financial services in the country. Presently, around 31 licensed microfinance institutions are operating throughout the country [ 17 ]. In recent times, the government of Ethiopia developed various developmental strategies such as a poverty reduction strategy paper which is aimed at enhancing and supportable growth, among other documents, considered microfinance as the best reference in achieving the intended development objectives and limiting the risky trends in poverty problem and meeting the millennium development goals [ 46 ]. Normally, almost most of Ethiopia's microfinance institutions have common goals: poverty reduction by providing loans and saving services by using the group-approach lending system [ 20 ].

The Oromia Credit and Saving Share Company (OCSSCo) microfinance institution was introduced in the Oromia region in 1996 to deliver microfinance facilities (credit and saving) to the farmers in the rural areas in the region [ 44 ]. The OCSSCo microfinance institution is one of the country's biggest microfinance institutions in terms of loan provision and having a high number of customers [ 16 ].Many research findings in Ethiopia link microfinance to poverty reduction. For instance, Abebe [ 1 ] on the Specialized Financial Promotion Institute (SFPI) showed that after the households took the loan from the institution, their average monthly income increased and the intervention improved the beneficiaries' living standard. Similarly, a study of the Dedebit Credit and Saving Institution (DECSI) indicated that the institution contributed to the beneficiaries' financial and physical well-being (household consumption and housing improvement) [ 22 ].

Even though various studies were carried out to identify the importance of MFIs in reducing poverty in Ethiopia, to the best of the author’s knowledge, this is the first research to access the contribution of microfinance institutions in poverty alleviation in this study area. Therefore, this research's broad aim is to assess the contribution of Oromia Credit and Saving Share Company in poverty reduction at Welmera district, Oromia Special Zone Surrounding Finfine, Oromia Regional State, Ethiopia.

This study has the following specific objectives: (1) to assess the contribution of microfinance on children's education enrollment and the saving attitude of the beneficiaries after they join the program and (2) to identify factors that determine the income improvement of the beneficiaries.

Literature review

Microfinance can be effective in poverty reduction if it is integrated with other developmental strategies that work to meet the poor's basic needs to take them from poverty [ 5 , 32 , 33 ]. Several studies in different disciplines used different approaches to assess the impacts of microfinance in poverty reduction. For instance, the interventions of the microfinance program influence social associations somewhat through their economic impacts. In various examples, credit systems implementers have demanded that the work leads to advanced social transformation, by authorizing women and shifting gender relations in the household and the community [ 2 ]. Likewise, Nichols [ 38 ] tried to conclude the influence of microfinance on the poor in China's rural areas. He found that microcredit had a range of positive impacts on a poor community in central China. A study done in Vietnam exhibited that after the beneficiaries joined the microfinance institutions, they saw significant progress in their beneficiaries' income and consumption [ 15 ]. Another study carried out in India revealed that the poor improve their microenterprises, smooth consumption, rise returns making ability, and appreciate a better quality of life because of microcredit's efficient provision [ 4 ].

In a panel data approach/framework, Khandker [ 30 ] investigate the relationship between microfinance and poverty for a sample size of 1,789 households drawn from 87 villages in 29 Thanas in Bangladesh and show that access to microfinance helped both poor female participants in the program and the local economy. The study confirms that there is evidence of reducing overall poverty decline at the community level. Research conducted in Pakistan also shows that microfinance programs have positively contributed to household expenditure and children's education [ 39 ]. Correspondingly, a study conducted in Zanzibar concluded that after the household became the microfinance institution customer, they could improve their income to expand their business [ 23 ].

Similarly, a report conducted in Nigeria confirmed that microfinance contributed to eradicating poverty among women after receiving loans by improving their economic status and social and political conditions [ 25 ]. Besides, participation in an MFI program empowers clients to change managing their enterprise ones [ 34 ]. Likewise, a study undertaken in Kenya showed that microfinance institutions could contribute to poverty reduction [ 40 ]. The study also actively exhibited the loan use helped the beneficiaries have their dairy farm and small retail shops. Correspondingly, research conducted by Chowdhury [ 13 ] and Abebe [ 1 ] found that the households' living conditions improved because of microfinance intervention. Contrary to these views, Mosley and Hulme [ 35 ] found that microfinance institutions' clients could not generate income for the poor sections of the people. Similarly, research conducted in Bangladesh suggested that no result to provision assertions that programs raise consumption level and/or the program's contribution that shows incremental education enrollment for children [ 33 ]. To sum up, extant works of literature are inconsistent and inconclusive. Furthermore, previous study works have not examined the role of Oromia Credit and Saving Share Company microfinance institution on poverty alleviation by targeting the study area. This study, therefore, intends to contribute to the literature by filling these gaps.

The study was carried out in Welmera District which is found in Oromia Special Zone Surrounding Finfine, Oromia Regional State, Ethiopia. The district's capital town Holeta is at a distance of 29 km to the west along the main road to Ambo from Addis Ababa (which is the capital city of the country). The district is bounded on the south by the Sebeta Hawas district, on the west by Ejere district, on the north by Mulo district, on the northeast by the Sululta district, on the east by the city of Burayu. The Welmera district comprises 23 rural villages and 3 towns.

Data sources and sample size determination

The study used several primary data collection methods like survey/questionnaire, direct observation, and key informant interviews (KII). The questionnaire was a combination of closed and open-ended questions. The choice of sampling size/designs depends on the type of research and the type of conclusion the researcher wants to draw[ 31 ]. To meet the objectives of this study, we have implemented both purposive and random sampling techniques. To select the respondents from the district through random sampling techniques, the first step was to identify the total number of villages known as beneficiaries of the institution using purposive sampling technique. Out of eighteen (18) villages, twelve (12) were selected from the district using a random sampling method. To select the required number of respondents, we applied random sampling. According to Neuman [ 37 ], a population size less than 1000 must take 30% a sample ratio. However, as Saunders (2005) cited in Muiruri [ 36 ], a sample is considered adequate if the sample size is greater than 30 and over 10% of the population.

In contrast to Neuman and Saunders, this study used 15% of the total population as a sample (see Table 1 ).

Model specifications

Most of the time, the binary logistic regression framework depicts the connections between one or more independent variables as well as a binary outcome variable [ 18 ]. Furthermore, binary logistic regression is the most applicable model when the dependent variable is a dummy variable [ 43 ]. Thus, to identify the key factors for the beneficiaries' improvement in income, this study used binary logistic regression analysis.

The generalized linear model was written as follows:

where X 1 to X 4 represents a quantitative/continuous independent variable and X 5 to X 10 stands for independent variables that are considered as a dummy and µ is the error term

Specifically, binary logistic regression was expressed as below:

where Y is the probability of income improvement; B 0 is constant; and B 1 … +  B N are parameters.

Thus, the model is specifically expressed as follows:

where INIMPr stands for income improvement, and age,fms, dwm,dfm,s,tr,ul,ms,edus,and gend stand for age, family size, duration with microfinance, distance from the lending institution, saving, training, utilization of loan, marital status, education status, and gender, respectively (see Table 2 ).

Results and discussion

Purpose of loan and loan usage of the respondents.

All beneficiaries took the loan from the institution to satisfy their unlimited needs. In this study, since all beneficiaries are farmers, they used the loan for agricultural and related activities. 51.26% of respondents took the loan for purchasing agricultural inputs (such as fertilizer and improved seed (see Table 3 ). However, 16.5% of them used loans for animal fatting. But almost less than two (1.16%) of the respondents used the loan for other (poultry production). Thus, this study confirms that most of the respondents in the study area used the loan to purchase agricultural inputs and animal fatting. Besides, the study tried to figure out whether the respondents used the loans for their intended purpose or not. A majority (94.67%) of the respondents used the loan for the proposed purpose from the survey. However, a few respondents have used the loan for the unintended purpose (see Table 3 ). Some key informants also confirmed that some beneficiaries did not use the loan for the intended purpose; sometimes, they could not repay the loan. This shows that if the beneficiaries miss the loan's use for the intended purpose, they cannot improve their income and cannot repay the loan.

Impacts of microfinance at the beneficiaries’ level

Impacts on beneficiaries income.

One of the measures of microfinance institution loan effectiveness is its ability to generate income for its beneficiaries. An increase in income can be a component of a better life. In other ways, the major objectives of microfinance are to help in generating income for low-income households and help in alleviating poverty. The majority of respondents' incomes were increased after they took loans from a microfinance institution (see Table 4 ). However, due to the improper use of the loan, the income of a few beneficiaries in the study area was decreased (see Table 4 ). Thus, as compared to before taking the loan, after they took a loan a large number of the respondents' income increased.

Besides, key informant's interviews also confirmed that the beneficiaries’ status of income was improved after they took loans. Thus, it was found that the poor have undoubtedly benefited from the institution in several ways. From this, the study concluded that the microfinance institution has a great contribution to improve the living standards of the households in the study area. This result is confirmed with the previous studies done by Abebe [ 1 ] and Gutu and Mulugeta [ 21 ] in different parts of the country.

Impacts on education

A service provided to the beneficiaries is believed in one way or another way to promote the education status of children. This study observed that the beneficiaries' numbers of children attending school were increased after the beneficiaries joined the institution. As presented in the table above, 69% of the beneficiaries responded that after they took the loan, they could buy materials for their children and educate them as compared to before (see Table 4 ). From this, the results conclude that the OCSSCO microfinance brought an important influence on children's education enrollment which in turn helps to lift their families out of poverty. This study promotes the findings of Bantige [ 9 ]; Bhuiya [ 11 ], and Mosley and Rock [ 35 ], who found that the loan from microfinance help the respondents to give better education to their children. Correspondingly, another study that has been done on the Amhara Credit and Saving Institute (ACSI) in other regions of the country also points out that all beneficiaries in the study area could send their children to the school after the beneficiaries became the clients of the institution [ 6 ]. However, this finding converses with the finding of Banerjee et al. [ 7 ] which suggested that there is no change in the probability that children or teenagers are enrolled in school after the beneficiaries took the loan.

Impacts on nutrition improvement

Access to microfinance can create improvement in the nutritional intake of the beneficiaries and their families based on their effectiveness in the program. Regarding the improvement of food, the respondents were asked to reflect their responses about food diet improvement like increase consumption of food in the amount and variety. As stated in Table 5 from the total respondents, about 204 (57.14%) gave their responses from strongly agree to agree that there is an increase in the quantity of consumption of food after they joined the program. However, about 153 (42.85%) of the respondents don’t agree with the improvement. Furthermore, about 192 (53.78%) of the respondents gave their responses from strongly agree to agree for the existence of an increase in food in variety after they joined the institution.

About 165 (46.22%) gave their response regarding increase food in variety from neutral to strongly disagree (see Table 5 ).

This shows that how microfinance contributes to improving beneficiaries' food intake. Thus, since the beneficiaries' diet improved, it is possible to conclude that OCSSCo microfinance has an important role in reducing poverty by lifting the people from poverty. This result confirms another study done in the other region of the country by Doocy et al. [ 14 ], which indicated that microfinance programs have a significant influence on the improvement of nutritional position as well as on the well-being of the women participants and their families. Similarly, Ajit and Anu [ 4 ] report the same result.

Impacts on the beneficiaries saving attitude

In most cases, knowledge of microfinance improved attitude toward microcredit and the saving behavior of its beneficiaries [ 26 ]. Table 6 confirms that 236 (66.10%) of the respondents responded that Oromia Credit and Saving Share Company (OCSSCo) has developed their culture of saving after they joined the institution. However, 121 (33.89%) responded negatively. Correspondingly, research done by Gutu and Mulugeta [ 21 ] indicated improvement of profit, saving, and diet after the beneficiaries joined the institution. Furthermore, this study observed that about 87% of the beneficiaries know the benefit of credit and saving institutions on poverty reduction (see Table 6 ). From this, it is possible to conclude that most of the respondents have a good awareness of credit and saving and the benefit of the institution on poverty reduction. Thus, this indicates that OCSSCo had a major role to improve the saving habits of the beneficiaries which are the source of capital and finally a key for the economic development of the country.

Training is very crucial to develop private outcomes and inclusive well-being for beneficiaries and to progress institutional consequences for the microfinance institutions [ 27 ]. In this regard, in addition to financial services, Oromia Credit and Saving Share Company provides nonfinancial services for the beneficiaries to build up their capacity. A positive response was obtained from the respondents which indicated about 96% of them got training from the institution especially on saving, loan utilization, and about the market and general training (see Table 7 ). Nevertheless, insignificant numbers of respondents (4%) do not get training. Therefore, the study concludes that the given training helped the beneficiaries in adapting the behavior of saving and investing the loan in income-generating activities.

Challenges of the beneficiaries to access the service

Microfinance institutions in Ethiopia are facing many challenges in operating efficiently [ 10 ]. Similarly, the beneficiaries of microfinance institutions face various constraints and challenges. Concerning challenges and constraints, more than (29%) of the beneficiaries said their great problem for accessing funds from this microfinance institution was the rate of interest they were charged for assessing funds (see Table 8 ). Likewise, a large share of the respondents also sees the distance from the lending institute (26%) as another big challenge. Even though the two mentioned above are the major challenges, group lending, time of credit available, time of repayment, working ethics, and working time of the institute were also seen as challenges by the respondents in the study area (see Table 8 ). This study agrees with the study done by Sabit and Mohammed [ 42 ] on the same microfinance institution in the southwest part of the country.

Econometric analyses

Result of multicollinearity test.

Multicollinearity problems will occur while two or more predictor variables in a multiple regression model are extremely interrelated [ 3 ]. As a measure of multicollinearity in the model, the variance inflation factor (VIF) and tolerance levels are used. It is an indication of multicollinearity when tolerance levels may be less than 0.40 and VIF is higher than 2.50 [ 3 ]. Therefore, as it is observed from Table 9 , the model has no multicollinearity problem (see Table 9 ).

Test of goodness fit and model summary

The goodness-of-fit tests can be utilized when our data are discrete or continuous data and the final incorporating grouped continuous data through addition [ 24 ]. Most of the time, the Hosmer–Lemeshow goodness-of-fit test is the most appropriate and often used for binary logistic regression models [ 18 ]. To say the model is statistically fit to describe data, our p -value must be insignificant. Consequently, the below table shows that ψ 2 (df = 8, N  = 357, X 2  = 8.800) and its p  = 0.359 which is a more than 5% significance level (see Table 10 ). Furthermore, the Omnibus test of the model coefficient in the last iteration reveals that the addition of each variable into the model is statistically significant when p  = 0.000, which is less than that of a cutoff value of 0.05. Moreover, the share of the total variation of the dependent variable is measured using Nagelkerke R Square. Accordingly, Nagelkerke R Square indicated that the model described around 61.7% of the variation in the data (see Table 10 ). Likewise, the predictive correctness of the model was calculated using the classification table. Thus, the study suggested that the overall percentage of correction of prediction was 89.7% (see Table 10 ).

Result of the binary logistic regression

The binary logistic regression is specified as a more exact and effective estimation system due to its capacity to provide the essential binary nature of the combination of the dependent variable [ 41 ]. Additionally, it allows the estimate of group association when independent variables are continuous, discrete, or both [ 8 ]. Table 11 reveals the logistic regression coefficient results, such as the standard errors, Exp( β ), Wald test statistics, p -value for each predictor. Accordingly, the statistical significance and the result of each of the predictor variables are described in Table 11 .

Therefore, from Table 11 , gender, education level, having voluntary savings, distance from the lending institute, and utilization of loans for intended purposes are determinants of income improvement. Gender has a significant effect on beneficiaries' income improvement: In the above table, gender (1) the reference category (1) shows males. The negative B (coefficient of the independent variable) shows that the probability of income improvement decreases from male to the next gender: while the probability of the next gender, female household 0 in income improvement increases by 0.400 (see Table 11 ). This is because females are more savers and take care of the risk than males. Educated people (literate) will have the probability of income improvement by 7.658 times more than illiterate. This is because educated persons can use the loan properly as stated on their plan than illiterate groups. They have a better understanding of how to invest the loan in income-generating activities than those uneducated groups.

Beneficiaries who have voluntary savings have a chance of income improvement by 2.348 than those without voluntary savings (see Table 11 ). This is because more savings will bring more opportunities to investments in income-generating activities which help them in income improvement. This result confirmed the country's study by Kebede and Menza [ 28 ], which found that the total number of voluntary and savers increases from year to year. Besides, this study tried to look at the utilization of the loan. Utilization of the loan for the intended purposes helped those who used the loan accordingly more than those who not used the loan for the intended purposes by 64.169. However, the study shows that as the distance from financial institutions increases by one unit, the improvement of beneficiaries' income decreases by 0.91 (see Table 11 ). This is because as beneficiaries far away from the lending institution, they lack access to market and information, and even they are not getting regular supervision from the institution compared to those living nearest to the institution.

In this study, we targeted at evaluating the role of microfinance institutions in poverty reduction in Ethiopia by taking Oromia Credit and Saving Sharing Company (OCSSCo) as an example. This study points out that the intervention of OCSSCo in the study area helped the beneficiaries improve their level of living conditions. The study discovered that after the beneficiaries joined the institution, their income increased, nutrition intake improved, and most beneficiaries could buy necessary items and materials to educate their children. Besides these, the bulk of the beneficiaries argued that they developed the habit of voluntary savings after they joined the institution. This implies that the microfinance institutions made efforts to increase the beneficiaries' income by putting proper monitoring and follow an up system in place. However, regardless of these achievements, most of these microfinance beneficiaries in the study area strongly disagree with the institution's rate of interest when they take loans'.

From the binary logistic regression result, the study concluded that education level, gender, distance from the lending institute, utilization of the loan for the intended purposes, and voluntary savings are significant in the model to determine income improvement of the beneficiaries. In contrast, other explanatory variables like age, family size, marital status, membership duration with the institution, and training with income improvement are not as strong. So, they do not significantly affect income improvement. This study concludes that Oromia Credit and Saving Share Company microfinance positively impacted beneficiaries' living conditions. This study recommends that to change many peoples' lives; the institution has to reduce the interest rate charged to the respondents when accessing financial products.

Availability of data and materials

On request, the author can provide the data used for this study.

Abbreviations

Amhara Credit and Saving Institute

Dedebit Credit and Saving Institutions

Microfinance institutions

Oromia Credit and Saving Share Company

Variance inflation factor

Specialized Financial and Promotion Institution

Key informant interviews

United Nations

Abebe T (2006) Impact of microfinance on poverty reduction in Ethiopia, the case of three branches of Specialized Finance and Promotional Institution(SFPI): Addis Ababa University, Masters Thesis, Ethiopia

Ackerly BA (1995) Testing the tools of development: credit programmes, loan involvement, and women’s empowerment. IDS Bull 26(3):56–68

Article   Google Scholar  

Adeboye N, Fagoyinbo IS, Olatayo T (2014) Estimation of the effect of multicollinearity on the standard error for regression coefficients. IOSR J Math 10(4):16–20

Ajit KB, Anu B (2012) Microfinance and poverty reduction in India. Integr Rev J Manag 5(1):31–35

Google Scholar  

Alemu KT (2008) Microfinance as a strategy for poverty reduction: a comparative analysis of ACSI and wisdom microfinance institution in Ethiopia. Master Theis, The Netherlands, 1–57

Asmamaw YC (2014) Impact of microfinance on living standards, empowerment and poverty alleviation of poor people in Ethiopia. J Field Actions 5:43–67

Banerjee A, Duflo E, Glennerster R, Kinnan C (2015) The miracle of microfinance? Evidence from a randomized evaluation. Am Econ J Appl Econ 7(1):1–53

Barbara GT, Linda SF (2012) Using multivariate statistics. In: California State University, Northridge: Vol. Fifth Edit

Batinge BK (2014) An assessment of the impact of microfinance on the rural women in North Ghana. Eastern Mediterranean University

Beklentİlerİ ZV, Alemu S (2018) The performance of Ethiopian microfinance institutions, challenges and prospects. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi 8(16):307–324

Bhuiya M (2016) Impact of microfinance on health, education, and income of rural households: evidence from Bangladesh. Master Thesis, University of Dhaka, Bangladesh

Blavy R, Basu A, Yülek MÂ (2004) Microfinance in Africa: experience and lessons from selected African Countries. IMF Working Papers 04(174):1–23

Chowdhury A (2009) Microfinance as a poverty reduction tool—a critical assessment. DESA Working Paper No. 89, 1–13

Doocy S, Teferra S, Norell D, Burnham G (2005) Credit program outcomes: coping capacity and nutritional status in the food insecure context of Ethiopia. Soc Sci Med 60(10):2371–2382

Duong HA, Nghiem HS (2014) Effects of microfinance on poverty reduction in vietnam : a pseudo-panel data analysis. J Acc Finance Econ 4(2):58–67

Ebisa D, Getachew N, Fikadu M (2013) Filling the breach: microfinance. J Bus Econ Manag 1(1):10–17

Eshete A (2010) Assessment of the role of microfinance Institutions in urban poverty Alleviation. Addis Ababa University, Master Thesis

Fagerland MW, Hosmer DW (2012) A generalized Hosmer-Lemeshow goodness-of-fit test for multinomial logistic regression models. Stata J 12(3):447–453

García-Pérez I, Fernández-Izquierdo MÁ, Muñoz-Torres MJ (2020) Microfinance institutions fostering sustainable development by region. Sustainability (Switzerland) 12(7):1–23

Gobezie G (2009) Sustainable rural finance: prospects, challenges, and implications. Int NGO J 4(2):012–026

Gutu F, Mulugeta W (2016) The role of microfinance on women’s economic empowerment in Southwest Ethiopia. Glob J Manag Bus Res B Econ Commerce 16(6):1–10

Guush B, Cornelis G (2011) Does microfinance reduction rural poverty? Evidence-based on household panel data from Northern Ethiopia. Am J Agric Econ 93:43–55

Haji YH (2013) The contribution of microfinance institutions to poverty reduction at south District in Zanzibar. The Open University of Tanzania

Haschenburger JK, Spinelli JJ (2005) Assessing the goodness-of-fit of statistical distributions when data are grouped. Math Geol 37(3):261–276

Irobi CN (2008) Microfinance and poverty alleviation. A case of Obazu Progressive Women Association Mbieri, Imo State -Nigeria. Master Thesis, SLU

Jebarajakirthy C, Lobo A, Hewege C (2015) Enhancing youth’s attitudes towards microcredit in the bottom of the pyramid markets. Int J Consum Stud 39(2):180–192

Karlan D, Valdivia M (2006) Teaching entrepreneurship: impact of business training on microfinance clients and institutions. Yale University, Center Discussion Paper No.941, 1–46

Kebede Menza S, Kebede T (2016) The impact of microfinance on household saving: the case of amhara credit and saving institution feres bet Sub-Branch, Degadamot Woreda. J Poverty J 27:64–82

Khan M, Rahaman MA (2007) Impact of microfinance on living standards, empowerment and poverty alleviation of poor people: a case study on microfinance in the Chittagong District of Bangladesh. Umea School of Business, Master Thesis, 5(13), 1–95

Khandker SR (2005) Microfinance and poverty: evidence using panel data from Bangladesh. World Bank Econ Rev 19(2):263–286

Kothari C (1990) Research methodology: methods and techniques ((Second Re). New Age International Publishers

Lakwo A (2006) Microfinance, rural livelihoods, and women’s empowerment in Uganda. In African Studies Centre

Morduch J (2000) The microfinance schism. Princeton University, New Jersey, USA. World Development, 28(4), 617–629

Morris G, Barnes C (2005) An assessment of the impact of microfinance: a case study from Uganda. J Microfinance 7(1):40–54

Mosley P, Rock J (2004) Microfinance, labor markets and poverty in Africa: a study of six institutions. J Int Dev 16(3):467–500

Muiruri PM (2014) The role of micro-finance institutions to the growth of micro and small enterprises (MSE) in Thika, Kenya (Empirical Review of Non- Financial Factors). Int J Acad Res Acc Finance Manag Sci 4(4):249–262

Neuman WL (2014) Social research methods: qualitative and quantitative approaches (Seventh Ed)

Nichols S (2004) A case study analysis of the impacts of microfinance upon the Lives of the Poor in Rural China.RMIT University,Melbourne,Australia

Noreen U, Imran R, Zaheer A, Saif MI (2011) Impact of microfinance on poverty : a case of Pakistan. World Appl Sci J 12(6):877–883

Okibo BW, Makanga N (2014) Effects of microfinance institutions on poverty reduction in Kenya. Int J Curr Res Acad Rev 2(2):76–95

Owolabi OE (2015) Microfinance and poverty reduction in Nigeria: A case study of LAPO microfinance bank: The University of Leeds. Ph.D. Dissertation, 1–235

Sabit J, Mohammed A (2015) Role of credit and saving share company in poverty reduction in rural communities of Gumay District, Jimma Zone, South West Ethiopia. Int J Appl Econ Finance 9(1):15–24

Santoso DB (2016) Credit accessibility: the impact of microfinance on rural indonesian households. Master Thesis

Tiruneh TB (2015) A comprehensive examination of the sustainability status of microfinance institutions in Ethiopia. Eur J Bus Manag 7(19):17–31

United Nations (2006) Microfinance in Africa: combining the Best Practices of Traditional and Modern Microfinance Approaches towards Poverty Eradication

Wolday A (2004) Managing growth of microfinance institutions (MFI S): Balancing sustainability and reaching a large number of clients in Ethiopia. Ethiopian J Econ XIII(2):62–102

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Chomen, D.A. The role of microfinance institutions on poverty reduction in Ethiopia: the case of Oromia Credit and Saving Share Company at Welmera district. Futur Bus J 7 , 44 (2021). https://doi.org/10.1186/s43093-021-00082-9

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IMPACT OF MICROFINANCE ON POVERTY REDUCTION IN ETHIOPIA

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Abdinasir gedi

Zewdu Teshome

zewdu teshome

IAEME PUBLICATION

IAEME Publication

Purpose: The main objective of this study is analyzing the impact of ACSI (Amhara credit and saving institution) credit access on households’ poverty reduction in terms income and welfare of Ethiopian public. And also to study is estimating average treatment effect (ATT) on the borrowers in terms of income and welfare. Design/methodology/approach: After a meticulous review of the relevant literature on Impact and role of microfinance institutions in reducing poverty. The study has used cross sectional data with structured questionnaire to collect relevant data with a sample of 422 households. The study sample was divided in to two parts, half of the surveyed households were credit holders and another half did not take the credit. The outcome variables of the study are income and welfare of households. The study has employed both descriptive and econometric model to achieve the objective of this research. The study has used non experimental data-the propensity score matching was employed in the econometric part of the study to estimate the impact of the credit on outcome variable in research. Propensity score matching was used to match treatment unit with its counterpart. Among matching methods, Kernel (0.1) was employed since it satisfies the criterion of best matching between treatment unit and controlled unit. Findings: The study found that average treatment effect (ATT) on the treated group in terms of income is less than its counterpart. However, average treatment effect (ATT) on treated group in terms of welfare is greater than the average treatment effect (ATT) on controlled group. The study has also identified factors that determine the decision of households to take credit in ACSI by using logit model. The logit regression result shows that sex of household, age, age square, marital status, and livestock ownership are main determinants to take credit from microfinance institutions. The Ethiopian responsible body should aware borrowers to allocate the loan in productive area of economic activity

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