thesis on effects of microfinance on poverty

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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.

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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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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 \( {\varepsilon}_i \) .

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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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Please note you do not have access to teaching notes, literature review on effect of microfinance institutions on poverty in south asian countries and their sustainability.

International Journal of Emerging Markets

ISSN : 1746-8809

Article publication date: 8 July 2021

Issue publication date: 11 August 2023

The authors present a systematic literature review on microfinance institutions’ (MFIs) effect on poverty and how they can ensure their sustainability. The purpose of this article is to review the effect of MFIs on poverty in South Asian countries. The analysis and review of the selected corpus of literature also provide avenues for future research.

Design/methodology/approach

A total of 95 papers from 49 journals in 4 academic libraries and publishers were systematically studied and classified. The authors define the keywords and the inclusion/exclusion criteria for the identification of papers. The review includes an analysis of the selected papers that give insights about publications with respect to themes, number of themes covered in individual publications, nations, scope, methodology, number of methods used and publication trend.

The literature indicates the positive effect of microfinance on poverty but with a varying degree on various categories of poor. The relation between poverty and microfinance is, however, dependent on the nation under the scanner. While sustainability and outreach co-exist, their trade-off is still a matter of debate.

Originality/value

This is the first systematic literature review on MFIs’ effect on poverty in South Asian nations. Additionally, the authors discuss the literature on the trade-off between sustainability and outreach for MFIs.

• Microfinance
• Sustainability
• Systematic literature review

Gupta, P.K. and Sharma, S. (2023), "Literature review on effect of microfinance institutions on poverty in South Asian countries and their sustainability", International Journal of Emerging Markets , Vol. 18 No. 8, pp. 1827-1845. https://doi.org/10.1108/IJOEM-07-2020-0861

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• Corpus ID: 169091245

Analysis of the Effects of Microfinance on Poverty Reduction

• J. Morduch , Barbara A. Haley
• Published 1 November 2001

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Contributions of microfinance institutions in poverty reduction in morogoro: a case of elgibo saccos limited, assessing contribution of microfinance institutions toward poverty reduction in urban communities: a case of mwashita saccos in dodoma urban, the channels of poverty reduction in malawi : a district level analysis, sustainable institutions or sustainable poverty targeting: the case of microfinance, micro finance a means to poverty alleviation: a case of masara n’ariziki farmers association, origin of development policy and the effect on economic growth: a proposed theory with historical grounding in south korea and tanzania., microfinancing in bangladesh: impact on households, consumption and welfare, the role of microfinance in poverty reduction: evidence from south asia, access to financial services and women empowerment, through microfinance: a case of palestine, poverty and the impact of microcredit : a theological reflection on financial sustainability in lusaka rural, zambia, 177 references, defying the odds: banking for the poor, microfinance and poverty: questioning the conventional wisdom, poverty alleviation and enterprise development: the need for a differentiated approach, finance against poverty, assessing the efficiency and outreach of micro-finance schemes, credit for aileviation of rural poverty : the grameen bank in bangladesh, reaching the poor with effective microcredit: evaluation of a grameen bank replication in the philippines, successes in anti-poverty, managing to empower: the grameen bank's experience of poverty alleviation, conflicts over credit: re-evaluating the empowerment potential of loans to women in rural bangladesh, related papers.

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IMPACT OF MICROFINANCE ON POVERTY REDUCTION IN ETHIOPIA

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Feleke Borga

Habtamu Baynes

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Milkessa A S F E W Abdeta

Poverty is highly affecting the life's of the population of Less Developed Countries (LDCs) in general and that of rural people in particular. High vulnerability, lack of education and medical services, less participation in different decision-making activities are some of the major problems the rural people faced. To overcome these and other related socioeconomic problems, micro finance Institutions (MFIs) targeted the poor in general and the rural people in particular. The main objective of this review is to find out whether the provision of microfinance services has brought changes on the living standards of clients. It is expected that microfinance services create employment opportunities, increasing income, enhancing empowerment and in aggregate improve the livelihood of the poor. Even though the performance of microfinance increase from time to time there are many problems facing microfinance institutions in Ethiopia. These includes inaccessibility for a foreign capital which may foster their loan portfolio, failure to repay loan at all or partly or not paying on time which causes serious problems on sustainability of the institutions, lack of research to understand client needs and lack of follow up of the clients. Reviewing the impact of microfinance intervention is important to know its viability on poverty reduction. The impact assessment of microfinance is conducted both at household and institutional outreach and sustainability. The impact of the program is assessed at household level based on average income, which in turn affects access to education, access to medical facilities, nutritional status, savings, employment generation and empowerment, among others, which are indicators of poverty. If outreach has been expanded and institution is sustainable, then the program is judged to have a positive impact as it has widened the financial market. Loan repayment performance is affected by a number of socioeconomic, institutional and natural factors, some of which are believed to impact on repayment negatively while others have positive impact. Major socioeconomic variables that affect credit repayment include education, age of household head, family size, gender of household head, farm size, loan size, livestock ownership, annual farm revenue, loan diversion, frequency of contact with development agent, group effect and location of borrowers from lending institution.

Bogale Berhanu Benti

As per the World Bank report (2015), in 2000 Ethiopia had one of the highest poverty rates in the world, with 56 percent of the population living on less than (U.S.) $1.25 purchasing power parity (PPP) a day. According to International Fund Agricultural Development (2008), "Under the IFAD, initiated Rural Financial Intermediation Programme (RUFIP), impressive results have been achieved over the past five years in expanding outreach in the delivery of financial services by operationally sustainable microfinance institutions (MFIs) and RUSACCOs, with the clientele growing from about 700,000 to nearly 2 million poor rural households. The programme has demonstrated the potential of rural finance in enabling a large number of poor people to overcome poverty. Women account for about 30 per cent and 50 per cent of beneficiaries of MFIs and RUSACCOs respectively. However, much remains to be done, particularly in improving management information systems and expanding outreach to access-...

Zelalem Hailu

Microfinance is currently being promoted as a key development strategy for promoting poverty reduction and empowerment of people economically. This is because of its potential to effectively address poverty by granting financial services to households who are not served by the formal banking sector. This study attempted to investigate the effects of MFIs on poverty reduction. The study focused on places located in Addis Ababa, especially in Akaki Kality sub city as a case study. It intended to cover credit facilities provided by the MFIs and clients perception on income improvement and/or reduced poverty levels. The study used descriptive survey design. The target population was one staff/administrators and 50 clients or recipients of the MFIs. The study employed stratified sampling technique to select staff of the selected MFIs and clients. Both qualitative and quantitative data analysis methods were used. The study revealed that as a microfinance institution has been providing microfinance services to different groups of youth specially women - productive or active poor and that the institution uses various strategies to deliver its services such as granting small loans to women to help them start businesses, grow their businesses and educate their children. To enhance client’s business skills to use credit and establish market channels for their products, the study recommends that microfinance institutions can arrange mechanisms to improve technical and business skills of the poorest through training and loan utilization. The study also recommended that MFIs should put in place micro-insurance schemes which could help clients to pool risk or share losses.

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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