The Role Of Microfinance In Poverty Reduction: Countries Experiences by Regions 2000-2018


Poverty, a “pronounced deprivation in well-being” Boonperm, Haughton, and Khandker (2009), is a worldwide concern. As per the World Bank, more than 1.29 billion individuals live on under $1.25 per day, representing just about 22% of the absolute populace of emerging nations. However, the poverty rate diminished by over half from 1990 to 2010, one billion individuals lived in outrageous neediness by the end of 2015 (Beegle & Christiaensen, 2019). The global extreme poverty rate fell to 9.2 percent in 2017, from 10.1 percent in 2015. In 2017, almost 700 million individuals live underneath the World Bank poverty line of $1.90 each day, and at higher poverty lines, 24.1 percent of the world lived on less than $3.20 a day and 43.6 percent on less than $5.5 a day (Munoz Boudet, Bhatt, Azcona, Yoo, & Beegle, 2021). Starting in 2018, close to a large portion of the world- almost 4 billion individuals- lives with a family pay beneath $2.50 per day In 2018 the World Bank uncovered that generally, 50% of these individuals - 368 million-live in only five nations; India, Nigeria, the Democratic Republic of Congo, Ethiopia, and Bangladesh, where they comprise the most significant number of individuals residing in poverty. The very poor live in shortage without help, watching monetary development and prosperity pass them. The world economy overlooks them. Surprisingly, despite the fact that the impoverished constitute a significant portion of the global population, this segment is regarded as too risky to be supplied by financial institutions.

Microfinance institutions (MFIs) thus started providing micro‐financial services for this " risky segment" and, in turn, help in alleviating poverty in developing countries (Battilana & Dorado, 2010). The impact of MFI services on the poor's lives, on the other hand, and researchers studying the impact of microfinance in Africa, Asia, Latin America, and the Caribbean elucidate the positive impact of microfinance programs on; raising household income and consumption, women's empowerment, and children's education (Vaessen et al., 2014). Microfinance is one of the major contributors to poverty reduction. It also works by giving a wide range of financial services, credits, instalment administrations, cash moves, and protection to poor and low-pay families and their miniature endeavours. Furthermore, considering their colossal commitment as far as occupation creation and income age for the public authority, the United Nations perceived Microfinance as perhaps the most effective apparatus to lighten poverty (Saad & Duasa, 2010). Microfinance has been identified as a tool for economic growth and poverty alleviation (Wilkes, 2005).

MFIs usually offer standardized products in developing countries, fundamentally minor credits to countless unbanked individuals. In agricultural nations, private companies' finance and microcredit go about as supplements rather than substitutes (Bauchet & Morduch, 2013). In developed countries- since banking coverage and social safety nets- MFIs usually target a limited number of micro-entrepreneurs who commercial banks disregard, MFIs in developed countries mainly aim to facilitate self-employment. In Europe, microfinance's main target is job creation, advancement of small undertakings, monetary and social consideration, and strengthening objective gatherings. In 2011, dynamic MFIs in the European Union (E.U.) disbursed more than 204,080 loans worth of EUR 1,047 million in total. Furthermore, most MFIs in the E.U. benefit from government subsidies, which they usually require during their startup phase. It comes in various forms, both immediate and backhanded; insurance against default risk, charge impetuses, credits at special rates, and business advancement administrations. The situation in developed countries is a bit different. The distinction between organizations served by normal banks and MFIs is muddled, where some MFIs serve clients who are qualified for banks' advances. The response of the financial area to the improvement of microcredit exercises is mixed; some banks created MFIs or collaborated with MFIs. On the other hand, some banks have advocated for tighter market restrictions and more microfinance activity. Micro-entrepreneurs prefer to borrow from MFIs rather than traditional banks since the screening procedures are less stringent, and MFIs are more socially oriented and supported. They offer business advice, technical assistance, and other appealing credit conditions and products such as savings and insurance, and subsidised MFIs are viewed as a challenge to traditional banks (Bendig, Unterberg, & Sarpong, 2014).

The purpose of this article is to investigate and evaluate the impact of microfinance provisions on poverty reduction in several developing and a few developed nations across different regions and evaluate the impact of regions and time on the performance of microfinance business.


The fame gained by the Microfinance industry addresses a massive achievement taken inside a verifiable system. It changed preconceived notions of the poor as purchasers of monetary administrations, broke preconceived notions of the poor as unbankable, presented a variety of loaning strategies demonstrating that it is possible to offer savvy economic types of assistance to poor people, and activated a large sum of "social speculation" for poor people (Mutua, Nataradol, Otero, & Chung, 1996). The main motive behind the microfinance movement was poverty reduction. Microfinance institutions could pay for themselves and earn some profit while trying to provide the poor with the needed repayable funds. This could result from the worldwide spread of microfinance and being institutionalized globally. “Microfinance is perhaps the best procedure for poverty lightening in emerging nations. Albeit generally upheld by non-legislative associations and socially arranged financial backers, microfinance institutions (MFIs) have progressively shown their worth on a stand‐alone premise, offering various items/instruments to serve the less "wanted".

In Thailand, the impact of funds on income and expenditure levels was examined via an empirical study using panel data from the Thailand Socioeconomic Surveys of 2002 and 2004 using the propensity score matching. Launched in 2001, the Thailand Village and Urban Community Fund (VF) provided working capital for local credit associations with a total of US$2 billion for Thailand's 78,000 rural villages. A fixed-effects model using a panel of rural households for these years concluded that VF borrowing is associated with, on average, 3.5% more current spending and 1.4% more income. A very similar result was found using a propensity score matching model applied to nationwide data in 2004 found that VF loans are associated with the acquisition of more durable goods, i.e., more spending (Boonperm, Haughton, & Khandker, 2013).

Microfinance's impact on two important needs indices is being investigated; to increase family utilisation and housing improvement, a four-round board informational collection on ranch families approaching microfinance was conducted in northern Ethiopia. Because of time-invariant undetected heterogeneity and individual patterns, the outcomes of utilising fixed impacts and irregular pattern models to minimise prospective decision predispositions showed that getting for certainly boosted utilisation and lodging improvements. Borrowings show long-term consequences for these outcomes, according to a flexible specification that takes into account recycled borrowings, meaning that transient effect estimates may exaggerate credit impacts (Berhane & Gardebroek, 2011).

Bangladesh was the spark spot from which microfinance new era spread internationally. Many studies hence examining the effect on poverty level took place in Bangladesh, especially in rural areas. In 2012 (Imai & Azam, 2012 ) studied the impact of funds from microfinance institutions (MFI) on reducing poverty using a family board with four rounds from 1997 to 2004. The effects of general microfinance advances and advances for useful purposes on pay, food utilization and ladies' Body Mass Index were assessed. The broad implications of MFI advances on pay and food utilization were positive, and the reason for the credit was a significant component in foreseeing which family government assistance marker is getting to the next level. Elective assessment strategies affirm a positive effect of MFI credits on food utilization development the diminishing poverty impacts of microfinance in Bangladesh.

Rajper, Ghumro, Mangi, and Lund (2018) analyzed the impact of loans given to the customers of Khushhali Bank Limited in the Sukkur region Sindh- Pakistan. They studied the effect of the income generated on alleviating the poverty level of a sample size of 370 persons. The study determined a significant relationship among the different variables measured: Occupation, Residential Area, Age, Gender, Education, income, and microloans disbursed, and that microfinance is a key factor for alleviating poverty among the beneficiaries. Moreover, microfinance institutions can easily improve their performance and play a significant role in alleviating the poverty of bank customers in Sindh.

Data of the Indian economy shows that a large section of the poor and marginalized sections of the Indian economy are financially excluded. They do not have access to the various financial services the institutional set-up provides, and access to financial services is not uniform throughout the economy. This needs the development of an effective system for implementing financial inclusion. Prathap, Dr Mahesh, and Karthik (2018) studied the impact of microfinance initiatives on the income level of the respondents, the impact of SHG/MFIs loans on the beneficiaries and their standard of living, and its impact on providing better employment opportunities. Through structured questionnaires, the population for the study targeted members of Self Help Groups living in Pavagada and Kunigal region of Karnataka State- India, and have been engaged in microfinance activities for at least two years. The study showed that 82% of borrowers were able to start their businesses, and 61 % were able to start their businesses. The descriptive statistics showed a positive impact on the income level that has increased, and better employment opportunities had been created, with an overall improvement in living standards.

Aggarwal, Klapper, and Singer (2013) examined the role of microfinance through availing access to credit on achieving long-term economic growth for small businesses in Sub Saharan Africa. The study shows that access to financial results in business growth. However, economic gains from microfinance are lower than expected because of the mobilization of household savings and empowerment from financial service access. The only positive outcome from microfinance is the smoothing of consumption expenses. Thus there is a need to promote methods for saving, which would be more efficient at reducing poverty than loans.

The way to long term microfinance achievement is the "graduation rate". Microfinance "graduates" who could accomplish long term benefits come as frequently from saving as from having business achievement. Ahlin and Jiang (2008) concentrated on the drawn-out monetary impacts of microfinance. Their investigation showed that the character of microfinance advances could be improved by increasing the useful productivity of independently employed borrowers in order to profit sustainability and achieve long-term benefits. This is achieved through data sharing, automatic exchange, and the development of programmes for borrowers. Incentives for MFI loan officers and measures of performance evaluation and MFI success should also be organised.

In terms of MFI manageability, the concept of microfinance sustainability may be divided into four interconnected ideas: economic practicality, financial appropriateness, institutional feasibility, and borrower reasonability. Monetary practicality connects with how a loaning organization ought to compare the expense per every unit of money loaned to the value it charges its borrowers (i.e., the financing cost). Financial suitability connects with meeting the monetary payment of assets (opportunity set us back) utilized for acknowledging and different tasks for the pay it produces from its loaning exercises. In terms of monetary manageability, Khandker (2005) suggested that credit reimbursement (interest rate) might be another indicator of MFI monetary manageability, as a low default rate would help with future loaning acceptance. Meyer (2002) noticed that the poor expected to approach economic assistance on a long term premise rather than only a onetime monetary help. The momentary advance would demolish the government assistance of poor people.

Moreover, the monetary un-sustainability in the MFI emerges because of low reimbursement rate or un-emergence of assets guaranteed by givers or states. "Measuring financial sustainability requires that MFIs maintain good financial accounts and follow recognized accounting practices that provide full transparency for income, expenses, loan recovery, and potential losses" (Meyer, 2002). The microfinance business has arisen as possibly the most fascinating monetary conveyance development. It is generally considered to be a neediness apparatus to help the economic turn of events and is perceived as important monetary assistance and a decent business opportunity; a number of MFIs across the world have shown the feasibility of contacting poor people and being beneficial simultaneously.


3.1. Micro Finance Institutions’ Performance

Micro Financing Institutions (MFIs) have been proving to be a key factor for poverty alleviation through loan provision to the poor or those who have no or limited access to credit through commercial banks. With 139 million clients and a credit arrangement of USD 114 billion every 2017. Microfinance keeps decreasing monetary prohibition, with 69% of grown-ups approaching financial administrations in 2017, an improvement of 7% versus 2014. Over the last decade, microfinance institutions (MFIs) have lent hundreds of billions of dollars, with an average annual growth rate of 11.5% since 2014.

Figure 1.Worldwide MFI performance Source

With almost 66% of worldwide borrowers in 2017, South Asia stays the worldwide forerunner as many borrowers. India drove the way among nations dynamic in microfinance with 50.9 million borrowers and an extraordinary EUR 17.1 billion, trailed by Bangladesh, Vietnam, Mexico and the Philippine (Barometer, 2019).

3.2. Number of Microfinance institutions

Though the revolution of the microfinance era sparked in Bangladesh in Asia, data available at Mix-market shows that Latin America and Caribbean Region is the region with the largest number of microfinance institutions followed by Sub-Saharan Africa, and the region with the lowest number is the MENA region. And it is the region with the biggest volume of loans disbursed, followed by East Asia and Pacific and East Asia. Yet, the number of borrowers benefiting from these loans is highest in South Asia, and that with the highest microfinance intensity is Sub Saharan Africa.

As of 2018, the top 100 MFIs constituted 76% of the total loans disbursed of $US124 Bill, and 80% of the beneficiaries are women. The number of MFIs have been growing with an increase of 9.4%, 15.6% and 8.5% for the years 2016, 2017 & 2018, respectively, along with an increase in the number of borrowers (Microfinance Barometer 2019). South Asia remains the global leader in terms of the number of borrowers, where they constituted 60% of the global borrowers in 2017. India led the way among active countries in microfinance in the world in 2017, with 50.9 million borrowers and an outstanding gross loan portfolio of $ 19.5 billion, followed by Bangladesh, Vietnam, Mexico and the Philippines.

3.3. Number of Borrowers and Gross Loan Portfolio

In 2018, 139.9 million borrowers profited from the administrations of MFIs, contrasted with just 98 million out of 2009. Of these 139.9 million borrowers, 80% are ladies, and 65% are rustic borrowers, extents that have stayed stable throughout recent years, regardless of the expansion in the number of borrowers (Barometer, 20).

Geopolitical situations and economic models affect the performance of Micro Finance Institutions, especially when it comes to profitability terms, and this varies from one locale to the next. In East Europe and Central Asia, MFIs saw a decrease in execution, while returns in Africa were positive. Performance was up in South Asia, upheld by high efficiency and adequacy.

Based on Microfinance Institutions’ data collected via Mix-Market, the African continent exhibits one of the fastest-growing MFI bases. And the gross loan portfolio of African microfinance institutions increased between 2002 and 2014, and the total amount of loans disbursed incrementally grew from $0.6 billion to $8.48 billion within the same time interval. The growing loan portfolio is propelled by the large consumer base opting for this alternative financing solution. Between 2002 and 2011, depositors grew from 3 to 20 million people. Similarly, the bottom of active borrowers has grown from 3 to 6 million. The demand for microfinance products stimulated the offer to satisfy the mounting consumer base; the time lapse between 2002 and 2011 recorded a linear growth in MFIs numbers from 177 to 394.

South Asia keeps on overwhelming worldwide microfinance; it is the district with the biggest number of borrowers (85.6 million of every 2018). This number becomes faster than in different areas (+13.8% somewhere in the range of 2017 and 2018). It also has the best three business sectors: borrowers, India, Bangladesh (where the kickoff spark for micro-credit by Professor Yunus was first ignited), and Vietnam. With 73% female clients and 79% rural borrowers. With 500 MFIs over the Period 200-2018, India constitutes half of them (250 MFIs), followed by Bangladesh (86 MFIs). The region’s Microfinance loans disbursed continued to develop, with an arrangement of $21.5 billion out of 2018. That same year, 20.8 million recipients acquired from microfinance organizations around here (+10.2% versus 2017). As of

The Asia Pacific microfinance sector has been undergoing transformational change. It grew extensively, as innovations in the financial industry helped MFIs boost their diverse product offerings. Moreover, enterprises served by these microfinance institutions are becoming more dynamic, along with the growing complexity of the financing requirements. Sharp contrasts arise across the locale, as MFIs undergo different changes in their constructions, adjusting their plans of action into various monetary foundations, like little banks.

East Asia & Pacific and Latin America, and Caribbean districts have the most significant Gross Loan portfolios. These two districts for their own for 44% of the all-out microfinance area portfolio, with $48 billion in extraordinary credits (+5% each year since 2012). These areas were the main locales to incorporate microfinance in their economy after accomplishing Bangladesh's insight. This district is the second biggest for several borrowers, with 23 million clients in 2018, a marginally lower figure (- 0.3%) following quite a while of development.

South Asia region has the highest number of borrowers, followed by Latin America and the Caribbean. On the other hand, financial inclusion in the MENA region is still one of the lowest worldwide, with only 18% of the population having a formal bank account and only 13% of the female population with the lowest number of borrowers least amounts of microloans disbursed. Microfinance is an effective method for growing the degrees of financial access. However, explicit microfinance regulations and guidelines and an effective help structure are expected to reinforce the area in the MENA locale. In addition, there should be the provision of training, sharing of information, monitoring techniques, capacity building for those working in MFIs, whereby they can scale up and expand their outreach and be able to better provide borrowers with the advice needed to ensure their businesses success and the ability to repay the loans which will, in turn, ensure the sustainability and success of MFIs.


This section studies the effect of microfinance on the poverty headcount rate, controlling for all other variables that have been mentioned in the literature that they- in most circumstances -affect poverty. The dependent variable is the poverty headcount ratio at the three poverty lines $1.90, $3.2 and $5.5 a day. In addition to microfinance variables that might affect poverty rates which are: the number of microfinance institutions, the number of gross loan portfolios disbursed, and the percentage of microfinance loans to GDP – referred to as microfinance intensity -, other determinants of poverty rates are included in the model as control variables. Following the literature, the other determinants include inflation, employment rate, gross capital formation, trade openness, agriculture and industry value added (% GDP).

Each microfinance variable/ indicator will be tested separately along with all the other control variables that may affect the poverty rate. This is to test for each microfinance indicator’s effect and its significance on reducing poverty rates. More precisely, the paper used an enhanced model following the model that was adopted by to estimate the effect of microfinance on the poverty headcount ratios: $1.90, $3.2 and $5.5 a day. A panel data model is applied to study 91 countries clustered on to six regions: Sub Saharan Africa, Middle East and North Africa, East Asia and Pacific, South Asia, Europe and Central Asia during 2000-2018.

The Model is as follows:PHRit = β0+ β1 MFit + β2 Pop growthit + B3 Inflationit + β4 Trade %GDPit + β5 GCF2it + β6 Agg% GDPit +β7 Ind% GDPit + β8 Employment Rateit + uit, i= 1, 2,….N , t=1,2,…T

Microfinance variables, the number of microfinance institutions, total loans, and microfinance intensity are examples of explanatory factors for microfinance (MF). The control variables; Trade % GDP, Gross capital formation, Employment rate (male and female %), Agriculture and Industry value added (% of GDP). The coefficient Βit measures the partial effect of microfinance or in any other control variables on poverty headcount ratios, and uitis the error term in country i at time t. Hausman test is performed on the panel data to determine whether to choose Random Effects or Fixed Effects. Due to Data limitation, the model tests the effect of these variables for 6 regions over 5 periods (averages). Thus dummy variables for the regions and other ones for the periods are included in the model to reflect the effect of the countries being in a certain region and the effect of time. And thus, pooled OLS will be used to test for the dummies.


Data is estimated using Fixed Effect, and Random Effect OLs and Hausman test is used to decide on the more appropriate model to be used. Since the results of the Hausman test for all models’ variations rejected the null hypothesis as all the P values were less than 0.05, then based on this, the FE model is the appropriate estimate, and the results are interpreted accordingly. The model is estimated individually at each poverty level, and at each poverty level, only one of the three microfinance indicators is tested alongside the control variables. This is to see the impact of each microfinance indicator on its own.

Table 1 represents the estimated results at Poverty headcount ratio (PHR) at $1.9 using three FE models to test for the effect of Total Gross loans disbursed, the total number of microfinance institutions, and the microfinance intensity (number of microfinance institutions % GDP) each on its own, along with the control. The estimated results show that all the microfinance indicators have a significant negative effect on PHR $1.9. The greater the number of microfinance available in the economy, and the higher the volume of loans disbursed and the per cent it constitutes of the GDP, the lower the poverty rates. This demonstrates the good impact microfinance has on reducing poverty and improving the living standards of the disadvantaged. As for the control variables: Gross capital formation (GCF) and agriculture value-added have a significant negative effect on poverty in the 3 variations of the model, where the higher the GCF and the higher the contribution of the agriculture sector in the GDP, the lower the poverty rates. At that level of poverty, the greater the industrial sector's contribution to GDP, the higher the poverty rates. As a result, the greater the poor's involvement in the agricultural sector, the better off they are at this poverty level. Estimates show that the higher the Percent of female employment, the lower the poverty rate, which reflects the importance of encouraging female contribution to the labour force. Literature reviews highlighted that female engagement in microfinance projects are usually higher than that of males. Moreover, females have the highest repayment rates, which appears in the estimated models where the Male employment percentage has increased poverty. The higher the Trade % GDP, the higher the Population growth rate, and the higher the inflation rate, the higher is the poverty rate; except for the first model, inflation has an insignificant negative impact.

Model 1 Testing for Gross Loan Model Testing for Number of Microfinance Institutions Model 3 Testing for Microfinance Intensity
Gross Loan Portfolio (-1.17e-09***) No of Microfinance Institutions (-0.0970)** Microfinance Intensity (-0.450)***
0.000 (0.04) (0.15)
Population Growth 2.148* Population Growth 1.628 Population Growth 2.053
(1.251) (1.310) (1.26)
Inflation -0.00405 Inflation 0.104 Inflation 0.00518
(0.080) (0.11) (0.08)
Trade(%GDP) 0.024 Trade(%GDP) 0.034 Trade(%GDP) 0.0191
(0.03) -0.0297 (0.03)
Gross Capital Formation (-0.544)*** Gross Capital Formation (-0.580)*** Gross Capital Formation (-0.527)***
(0.102) (0.10) (0.10)
Aggriculture Value Added (% GDP) (0.681)*** Aggriculture Value Added (% GDP) (0.700)*** Aggriculture Value Added (% GDP) (0.670)***
(0.14) (0.15) (0.14)
Industry Value Added (% GDP) (0.282)* Industry Value Added (% GDP) (0.355)** Industry Value Added (% GDP) 0.248
(0.17) (0.18) (0.17)
Per cent of Female Employment -0.115 Percent of Female Employment -0.239 Percent of Female Employment (-0.316)*
(0.19) (0.19) (0.18)
Percent of Male Employment (0.560)** Percent of Male Employment (0.619)*** Percent of Male Employment (0.621)***
(0.23) (0.23) (0.23)
Constant -24.27 Constant -24.18 Constant -18.03
(17.70) (15.46) (14.98)
No of Obsv 266 No of Obsv 265 No of Obsv 264
R-squared 0.445 R-squared 0.41 R-squared 0.413
Table 1. Estimated results of the Model at Poverty headcount ratio $ 1.9

Source: Calculated by author. Note: ***p<0.01, **p<0.05, *p<0.1

Table 2 represents the estimated results of the estimation of the three FE models on Poverty headcount ratio (PHR) at $3.2. The estimated results show that all the microfinance indicators have a significantly negative effect; the higher the microfinance indicators, the lower the poverty rate. As for the control variables, the higher the Gross capital formation (GCF), the greater the contribution of Female employment, and the higher the trade % GDP, the lower the poverty rate. At this PHR, Agriculture value, added % GDP and Industry value % GDP added increase poverty and Inflation, Population growth, and Percent of male employment.

Model 1 Testing for Gross Loan Model Testing for Number of Microfinance Institutions Model 3 Testing for Microfinance Intensity
Gross Loan Portfolio (-1.16e-09***) No of Microfinance Institutions (-0.119)** Microfinance Intensity (-0.669)***
0.000 (0.06) (0.19)
Population Growth 0.169 Population Growth -0.487 Population Growth -0.00531
(1.63) (1.72) (1.62)
Inflation 0.105 Inflation (0.322)** Inflation 0.118
(0.10) (0.14) (0.10)
Trade(%GDP) -0.0151 Trade(%GDP) -0.00238 Trade(%GDP) -0.0214
(0.04) (0.04) (0.04)
Gross Capital Formation (-0.547)*** Gross Capital Formation (-0.600)*** Gross Capital Formation (-0.514)***
(0.13) (0.14) (0.13)
Aggriculture Value Added (% GDP) (1.307)*** Aggriculture Value Added (% GDP) (1.327)*** Aggriculture Value Added (% GDP) (1.283)***
(0.18) (0.19) (0.18)
Industry Value Added (% GDP) (0.685)*** Industry Value Added (% GDP) (0.806)*** Industry Value Added (% GDP) (0.625)***
(0.22) (0.23) (0.22)
Percent of Female Employment (-0.423)* Percent of Female Employment (0.806)** Percent of Female Employment (-0.726)***
(0.24) (0.25) (0.29)
Percent of Male Employment 0.468 Percent of Male Employment (0.573)* Percent of Male Employment (0.541)*
(0.29) (0.31) (0.29)
Constant -1.514 Constant -4.408 Constant 9.43
(19.50) (20.23) (19.28)
No of Obsv 266 No of Obsv 265 No of Obsv 264
R-squared 0.445 R-squared 0.41 R-squared 0.413
Table 2. Estimated results of the Model at Poverty headcount ratio $ 3.2 Source: Calculated by the Author. Note: *** p<0.01, ** p<0.05, * p<0.1

Table 3 represents the estimated results of the estimation of the three FE models on Poverty headcount ratio (PHR) at $5.5. The estimated results show that all the microfinance indicators significantly reduce poverty. As for the control variables, the higher the Gross capital formation (GCF)- except in the last model-and, the higher the engagement of the female labour force, the less the poverty rate. Higher Inflation rates increase poverty, and also at this poverty rate Agriculture, and Industry % GDP do not improve poverty. The higher the Trade % GDP, the lower is the poverty rate, yet the impact is insignificant. At the same time, as the other previous models, Male employment per cent doesn’t improve the poverty rate, and the higher the inflation rate, the higher the poverty rate.

Model 1 Testing for Gross Loan Model Testing for Number of Microfinance Institutions Model 3 Testing for Microfinance Intensity
Gross Loan Portfolio (-2.05e-09***) No of Microfinance Institutions (-0.114)* Microfinance Intensity (-0.909)***
0.000 (0.07) (0.22)
Population Growth -2.773 Population Growth (-3.497)* Population Growth -2.916
(1.89) (1.99) (1.86)
Inflation (0.239)*** Inflation (0.541)*** Inflation (0.254)***
(0.12) (0.16) (0.12)
Trade(%GDP) -0.0185 Trade(%GDP) -0.00703 Trade(%GDP) -0.0261
(0.04) (0.05) (0.04)
Gross Capital Formation (-0.280)* Gross Capital Formation (-0.335)** Gross Capital Formation -0.24
(0.150) (0.15) (0.15)
Aggriculture Value Added (% GDP) (1.350)*** Aggriculture Value Added (% GDP) (1.379)*** Aggriculture Value Added (% GDP) (1.309)***
(0.21) (0.22) (0.21)
Industry Value Added (% GDP) (0.613)*** Industry Value Added (% GDP) (0.738)*** Industry Value Added (% GDP) (0.558)***
(0.24) (0.26) (0.24)
Percent of Female Employment (-0.801)*** Percent of Female Employment (-1.016)*** Percent of Female Employment (-1.142)***
(0.28) (0.29) (0.27)
Percent of Male Employment (0.377)** Percent of Male Employment 0.545 Percent of Male Employment 0.449
(0.34) (0.36) (0.33)
Constant (41.05)* Constant 35.14 Constant (53.93)**
(17.70) (23.54) (22.25)
No of Obsv 266 No of Obsv 265 No of Obsv 264
R-squared 0.445 R-squared 0.41 R-squared 0.413
Table 3. Estimated results of the Model at Poverty headcount ratio $ 5.5 Source: Calculated by the author.

Microfinance can play a role in poverty reduction by providing poor people with the necessary funds to start and develop their businesses. It will generate income that will allow them to provide a better quality of life to their families, including better nutrition, education, and health, as shown in the above estimates at various poverty headcount ratios. Moreover, it can lift off the burden of unemployment by creating job opportunities for the entrepreneurs themselves and through induced job opportunities. Microfinance encouraged females to join the labour force, besides the high repayment rates proved by females, which enabled their business sustainability aside from the estimated findings, the larger the percentage of employed females, the lower the poverty rates at all levels. So policies and regulations should encourage female employment and provide them with the needed support and help to enable their engagement and contribution to their economy. Microfinance should also be provided with the needed means to increase and strengthen its contribution in the business sector, not only through availing more microfinance institutions and more loans but to work on enriching the business environment with instruments needed to ensure sustainability of this sector. Pieces of training, technical assistance, capacity building, follow up, guidance, support, and needed policies are all means needed to elevate the performance of all partners involved in the microfinance business.

Pooled OLS is used to test for the effect of different regions and the effect of time on poverty rates. Countries are divided across the 6 regions, on a 5 years average period. MENA region is taken as the reference region and the last Period (P 5) is the reference period. The estimated results at PHR $1.9 show that all regions show higher significant poverty rates than the reference period, except for Europe & Central Asia and Latin America & Caribbean, results are not significant. As for the effect of time/ periods on poverty has higher rates, yet the first and fourth periods are insignificant.

At PHR $3.2, Europe & Central Asia is less in poverty rates yet insignificant than the MENA region; all other regions are significantly higher in poverty rates, except for Latin America& Caribbean, which shows insignificant results. All periods also have higher significant poverty rates, except for the first period, as it has a less yet insignificant poverty rate. The effect of regions and time shows that Europe and Central Asia have a lower but insignificant poverty rate, Latin America and the Caribbean have insignificantly higher poverty rates, and the other three regions, Sub-Saharan Africa, East Asia Pacific, and South Asia, have significantly higher poverty rates than the MENA region at PHR $5.5. As for the effect of time, all periods are significantly higher in poverty rates except for the first period, which is significantly lower than the last period under study.


Poverty is one of the world's most prominent issues. This study attempts to empirically study the effect of microfinance provision on the welfare of those who have access to it and how it should be widely considered and targeted as a means of poverty reduction. Literature has discussed the efficacy of microfinance in further developing government assistance of the poor and low-pay populace. This limited quantity of credits given by microfinance establishments or business banks needn't bother with any insurance or authority archive, suitable for the poor who usually lack this required document. Generally, the microfinance sector in MENA and Sub Saharan Africa regions are not very much evolved and are still a long way from arriving at its possible interest. What is more outstanding regarding this industry in the MENA area is that many of these nations are confronting the absence of a fitting monetary administration framework, checking and data frameworks, lack in item broadening and development in microfinance programs considering clients' requests. Microfinance likewise can be an effective apparatus in enabling ladies by getting them associated with social exercises, especially that they proved their success in sustaining their Business and proved their ability of loans repayment. The estimated results show that the higher the number of microfinance institutions available, the greater the number of gross loans disbursed. The higher the share of loans to the GDP, the lower the poverty rates. This in addition to the impact of engaging more females in the workforce to reduce poverty. The results imply the importance of the microfinance industry in reducing poverty and the need to enhance its role in the economy, especially for the regions that suffer from high poverty rates and low levels of financial inclusion to the poor segments. Regulations, legislative reforms and policies are needed to improve and enhance the business environment to ensure the sustainability of loans’ provision. At the same time, continuous follow-up, training, capacity building and guidance are needed. It is crucial to design and implement the right criteria for choosing the beneficiaries to ensure the repayment of loans and that these loans are being used efficiently to establish and grow businesses.


There are some caveats faced during the analysis. Due to some data limitations, not all countries in the regions under study were covered, especially data regarding poverty rates, and that is why a 5-years average was considered. Microfinance data available at MIX Market as well was not available for all countries over the whole period under study 2000-2018. Also, microfinance literature available for all countries, especially developed countries adopting microfinance in their financial system, is still insufficient; that is why studying the impact of microfinance and its performance in such economies is challenging. Such caveats might be considered as future research questions that require more updated data and further investigation; more research and empirical studies are needed for developed countries as well as the MENA regions and Arab countries to be able to identify their performance, the gaps and the required means of enhancement and improvement.

Governments who seek to reduce poverty should work on the development of a better legal framework to legitimize and encourage the inclusion and expansion of microfinance activities, which will, in turn, increase the level of confidence in the system and contribute to poverty reduction efforts. Microfinance needs not to continue relying on grants or charity and to be able to transform to self-sufficiency and financial sustainability. The need for creating incentive mechanism design for microfinance along with the provision of needed supervision, training, technical advisory, assistance, and management tools is crucial to enhance and develop this sector and enable it to have a greater impact on poverty reduction.