Alternative strategies of for-profit, not-for-profit and state-owned Nepalese microfinance institutions for poverty alleviation and women empowerment

Abstract

Microfinance is the provision of financial services to disadvantaged people and the financially excluded, often with a social mission of poverty alleviation and women empowerment. There are many different forms of microfinance institutions (MFIs): for-profit, not-for-profit and state-owned, all of which use different strategies to improve socio-economic status of their clients. The objective of this paper is to examine the alternative strategies of MFIs in Nepal. Primary data was collected through structured questionnaires from 240 women clients of three MFIs. Parametric and non-parametric tests, and exploratory factor analysis have been applied for analysis. The results show that MFIs have different segmentation strategies for their clients, focusing on income levels, total consumption and the number of children. Surprisingly, it was found that the private MFI was reaching poorer people than other MFIs. Our results show that MFIs look at total consumption expenditure rather than total income. Private MFIs target different activities for giving loans compared to government-owned MFIs. The communication strategy of the MFIs is different since the clients of government-owned MFI are better educated and are more likely to read the newspaper. The exploratory factor analysis shows that respondents perceived poverty alleviation and empowerment. The most influencing factors are related to an increase in consumption expenditure, followed by an increase in capital expenditure.

Keywords:

• Microfinance institutions
• segmenting
• strategy
• targeting
• creating shared value

Jel classification:

• G21
• L25
• L31
• L32
• O53
• P36

1. Introduction

Today, with the increasing importance of societal concerns, theories and concepts such as Strategic Corporate Responsibility (Bowman & Haire, 1975; Brooks, 2005), Instrumental Stakeholder Theory (Jones, 1995; Jones et al., 2018), and Creating Shared Value (M. E. Porter & Kramer, 2011) are influencing the competitive strategies of the firms. These concepts are similar (Crane et al., 2014). What is essential is that the firms seek to show investors and consumers that they are engaged. Still, their mushrooming suggests that firms are paying more and more attention to adding value to society through actions that do not necessarily add to their financial bottom line. However, considerable work needs to be done to apply these frameworks to contexts outside the developed world and learn from other contexts (Jones et al., 2018), such as microfinance.

Microfinance is the provision of formal financial services to unemployed or low-income individuals or groups who lack access to various financial products and services at a reasonable price. It includes a wide range of financial services, such as micro-savings, micro-credit, micro-insurance, and payment services. In addition to financial intermediation, many Microfinance institutions (MFIs) provide social intermediation services: group formation, financial literacy programs, and capabilities development among group members (Ashta, 2016; Ledgerwood, 1999). Microfinance has become a significant part of development finance to address poverty reduction, employment creation, and socio-economic empowerment of the people (Ganle et al., 2015; Garikipati, 2012; Johnson, 2005; Kabeer et al., 2012; Mayoux, 2001; Ngo & Wahhaj, 2012). MFIs target disadvantaged women and develop a range of financial products after understanding the need of the customers. They consider that they indirectly bring about a positive change in the family of the clients and the community by enhancing the family income, improving the living conditions, increasing the social capital, and reducing their vulnerability to life. Their social performance concerns not only the outcomes but also the effort of reaching poor people. There is always a scope for a continuous effort to enhance the social performance dimension of MFIs.

Microfinance can have a positive impact on the economic, social, and psychological empowerment of women borrowers (Datta & Sahu, 2022). Yet, women may still not be receiving as many loans as men in many countries (Quigley & Patel, 2022), even though some research suggests that female borrowers may be funded faster than men for basic loans (Gafni et al., 2021). Therefore, more work and more clarity are required on the factors that can influence the empowerment level. For example, while it is widely believed that technology may make a difference in empowerment, there are studies showing that poor women, especially older ones, are not able to use ICT, and therefore ICT is not always a solution for women empowerment (Triyono & Nuariyani, 2019). Another factor could be that women do not have access to information on loans (Deepa & Vanitha, 2022), and therefore MFIs need to develop communication strategies. Very little work has examined whether women empowerment or poverty alleviation depends on the type of MFI engaged: private-owned, government-owned, or NGO. Our research objective is to shed light on whether the type of MFI can influence poverty alleviation or women empowerment through alternative strategies.

There is considerable literature on the difference between social entrepreneurs and commercial entrepreneurs (Austin et al., 2006; Clark et al., 2018; Estrin et al., 2013) and on the transformation of NGO social enterprises to for-profit social enterprises (Ashta, 2020; Ashta et al., 2015; D’Espallier et al., 2017). However, we do not find a study of how a government-owned social enterprise differs from private and NGO social enterprises in reaching the poorest and delivering social outcomes. A priori, we would expect that NGOs would be the most engaged in reaching the poorest and that their actions would help the poor to engage with their community and reduce subjective poverty. We would expect the private-owned MFIs would be most focused on the financial bottom line, and their clients would, therefore, not feel more empowered and may not find a significant reduction in subjective poverty. In this study, we present three case studies showing that this intuition is not vindicated. This counterintuitive result means that donors, governments, investors and academics may need to reorient their biases towards one form of microfinance organisation rather than another.

As microfinance expanded, the number of MFIs increased, and many markets became saturated. By 2016, the competitive strategy became the most crucial risk (CSFI, 2016). The competitive strategy theory outlines three strategies: cost leadership strategy, differentiation strategy, and focus strategy (M. Porter, 1980). A firm’s competitive strategy positively boosts performance quality on various dimensions (Chen et al., 2016; Haddad et al., 2015; Teti et al., 2014). The choice of a firm’s competitive strategy depends on business size, location, and structure (Olubunmi et al., 2014). The future and long-term sustainability of a firm depend on the selected competitive strategy (Morgan, 2015).

The study’s objective is to examine the alternative strategies of MFIs of different legal forms to understand how they differentiate from each other in reaching the poor and delivering social outcomes. The research question is whether MFIs with different forms use different strategies and whether they have different impacts on poverty alleviation and women empowerment. The following sections present the literature on the strategy and performance of MFIs, the research methodology, the context and the case description, the results and discussion of the study, and the conclusion.

2. Literature review on microfinance strategies and performance

Microfinance is a popular development tool, particularly in financial inclusion and poverty reduction. It provides a collateral-free loan to unbanked people who cannot pledge any collateral for the loan. MFIs apply a unique credit delivery mechanism that provides loans to marginalised and disadvantaged people under the group-based lending system. It is considered an effective tool for financial inclusion and poverty reduction in developing countries like Nepal. The concept of microfinance became popular when Professor Muhammad Yunus, the founder of Grameen Bank, was awarded the Nobel Peace Prize in 2006. It enhances the marginalised and low-income people’s socio-economic status through a sustainable business model (M. T. Rahman, 2020).

Micro-credit encourages the establishment of micro-enterprises or small businesses that can be profitable quickly. Borrowers and their family members may have adequate skills or know-how of the activities to improve productivity in their usual occupations or setting of new businesses. The borrowers generally initiate the small or micro-enterprises in the form of sole ownership. Therefore, microfinance helps to create self-employment or may generate employment opportunities. As we can see, microfinance is a social entrepreneurship sector that focuses on a double-bottom line.

As microfinance expanded, competition increased, and the market became saturated. Hossain et al. (2020) investigated how competition affects MFIs’ double-bottom-line performance. They conducted a cross-country dataset of 59 countries over ten years. The study finds competition harms MFIs’ economic sustainability and undermines the breadth of outreach but enhances their depth of outreach.

The firm’s strategy is increasingly important for MFIs as competition becomes more intense. Many MFIs suffer in saturated markets such as Bangladesh, where there is high penetration of microfinance, aggressive expansions of the MFIs, and institutional leakages of information (Mia, 2017). While developing the strategy, MFIs consider all the stakeholders: clients, investors, managers, employees, government, media, researchers, and other interested third parties (Gandja et al., 2015; Milana & Ashta, 2012; Panda, 2016). As mentioned earlier, recent theories such as Strategic Corporate Responsibility (Bowman & Haire, 1975; Brooks, 2005), Instrumental Stakeholder Theory (Jones, 1995; Jones et al., 2018), and Creating Shared Value (M. E. Porter & Kramer, 2011) are influencing the competitive strategies of the firms by giving greater importance to stakeholders.

Successful organisations include stakeholders in their vision and mission statements (Bartkus et al., 2006). Entrepreneurial vision is important because it influences strategy, innovation, control systems, and performance. For example, a shared value vision will lead to strategic alignment with stakeholder goals, responsible innovations, impact measurement and monitoring, and better economic performance of the firm and of its stakeholders (Mühlbacher & Böbel, 2019). The strategic alignment will also reinforce innovation and performance if it comes from a pro-active CSR strategy (related, therefore, to the mission) rather than a responsive CSR strategy (Bocquet et al., 2013).

The stakeholders are very important for MFIs that source funding from donor agencies, using narratives based on promises. Performance efficiency depends upon the achievement of commitments efficiently, especially in achieving the promised social outcomes. If MFIs fail to fulfill the social promises, many donors may withdraw subsidised support to MFIs, and MFIs may become commercial (Ashta, 2019).

Most of the MFIs use debt as a source of funds, but it creates financial stress and other problems, leading to poor social performance and financial inclusion (Kanyurhi, 2017). To minimise these problems, for-profit MFIs must raise funds through equity. There are many ways of issuing equity, such as private equity, public equity, and micro-equity, either online or offline (Kim & Moor, 2017).

MFIs target their activities in areas where there is high gender discrimination. MFIs tend to respond to local culture and mainly focus on women, where they are likely to be financially excluded, especially in South Asia (Drori et al., 2020). MFIs tailor their practices to each group instead of using the standardised McDonald’s approach. There are no strict rules and restrictions while borrowing and repaying loans from MFIs (Goodman, 2017). However, MFIs should follow corporate governance principles to maintain the trust that ultimately helps to reduce risks (Khan & Ashta, 2013).

The diversification of microfinance activities enhances microfinance profitability and sustainability. The MFIs should diversify their revenue-generating activities to take advantage of diversification benefits. After MFIs become self-sustainable, they can meet their core objective of financial inclusion (Zamore, 2018).

Dhungana et al. (2016) conducted a community-based cross-sectional study in four western Nepal districts. They interviewed 500 microfinance clients representing different ethnic groups (upper caste, adibasi and janajati, and dalit), using systemic random sampling. They compared health awareness and practices among different ethnic groups, using logistic regression after adjusting for age, level of education, sex of household heads, occupation, and residence. The study finds a positive effect of microfinance on health awareness and practices among different ethnic groups in Nepal. They recommend further evaluation of the impact of microfinance on improving Nepalese people’s health and economic conditions.

Subsidised and unsubsidised MFIs use different strategies across different regions. Subsidised MFIs are generally more efficient than unsubsidised MFIs (Hudon & Traca, 2011). Asian and African MFIs compensate for non-subsidisation by charging higher interest rates. In Eastern Europe and Central Asia, unsubsidised MFIs target their services to fewer poor clients. Unsubsidised Latin American MFIs tend to reduce their share of female borrowers (D’Espallier et al., 2013).

Chan and Ghani (2011) examined micro-loans impact in vulnerable remote areas of Malaysia. This study examined whether microfinance programs reach the intended target in vulnerable remote villages and whether the borrowers improve their lives through microfinance. Ninety-three percent of the borrowers observed an increase in income, assets, and spending on family members. The findings suggest that small loans can encourage rural enterprises’ development, skills, and confidence in rural women and the social standing of rural women.

S. Rahman (2010) examined the consumption difference between micro-credit borrowers and non-borrowers in Bangladesh. The research investigates the consumption behaviour of borrowers of two major MFIs in Bangladesh and compares that with non-borrowers. The control-group method has been used to compare the differences in consumption patterns between the two groups. The estimation of linear and quadratic models suggests that borrowers of micro-credit programs are better off in terms of consumption than non-borrowers.

The microfinance industry has effectively reached millions of poor people, provided them with financial services, and reduced their poverty (Simanowitz & Walter, 2002). Microfinance programs help poor borrowers over time and meet their immediate needs (Khandker, 2001). However, most studies show no impact in the short-term (Duvendack & Mader, 2019). There are a few pieces of evidence of the positive impact of microfinance, mainly through increasing income (Chan & Ghani, 2011; Khandker, 2001; McGuire & Conroy, 2000; Wright, 2000), increasing consumption of households (Berhane & Gardebroek, 2011; McGuire & Conroy, 2000; S. Rahman, 2010) and reducing vulnerability (Salia & Mbwambo, 2014; Wright, 2000; Zaman, 2000).

Owing to the failure of many microfinance programs supported by subsidies (Adams et al., 1984), MFIs need to focus on win-win practices that satisfy both financial development and social impacts. The argument is that microfinance activities that are supported by proper banking activities will be sustainable, as demonstrated by the examples of Bangladesh and Indonesia. A win-win strategy not only empowers investors to invest in financially sustainable businesses but also help to reduce financial cost by helping to create flexible financial products and services. However, Morduch (2000) indicates that the win-win strategy is not optimal but that there is space for a diversity of institutions, some focusing on profits and some on subsidised loans.

It is not profit maximisation that makes a program efficient. Instead, what matters is having a hard budget constraint, something possible even with subsidies. Nor is it so that subsidisation necessarily leads to mistargeting. Fear of mistargeting may limit the size of the optimal subsidy … Nor is it so that savings mobilisation is necessarily held down by charging interest rates on loans that are below levels needed to break even. (Morduch, 2000, p. 626–7)

Since then, it is generally hypothesized that the schism in microfinance involves for-profit institutions charging high-interest rates and giving bigger loans to richer clients and not-for-profit institutions providing subsidised interest rates and smaller loans to more impoverished clients. Due to competition, the pressure on both kinds of MFIs to create various financial and non-financial products and demonstrate social impacts increases. In this comparative study, we will focus on different types of Nepalese MFIs used to serve clients and their effects on living standards.

Competing firms are interested in communicating with borrowers to promote their products better. For this communication, they need to know-how entrepreneurs obtain their resources. The extant research, based on low competition level, focused on how entrepreneurs access resources. Entrepreneurs either use pro-active strategies in which they communicate their plans, or they use interpersonal strategies by which they leverage their existing ties with potential resource providers or with their network to obtain information about potential resource providers (Rawhouser et al., 2017). Social ties reduce the cost of debt, and bankers use these to obtain guarantees (Granovetter, 2005). It is considered that weak ties are more useful in business relationships (Granovetter, 1973). However, in microfinance, where poor people, often illiterate, have information deficits, it is often through their strong ties that they find financial resource providers, although more educated people can exploit weaker ties better (Ashta et al., 2016).

However, as competition increases, MFIs need to seek borrowers rather than wait for borrowers to come to them, and for this purpose, they need to develop communication strategies. Micro entrepreneurs need financing to survive, but they do not care whether the funding comes from family, friends, or a particular financial institution (Mor et al., 2020). Many have minimal networks and very little time for networking (Ozdemir et al., 2016). We have witnessed a considerable improvement in information flows during the last few years, thanks to information and communication technologies. Therefore, for all the above reasons, MFIs would need to know-how the poor borrowers access information to better formulate their communication strategies. We have not found anything in the social entrepreneurship literature that examines whether the communication needs of poor clients and the communication strategies of public sector, private and NGO enterprises differ.

3. Data and methods

The research objective is to examine the alternative strategies of MFIs in inducing socio-economic changes in clients. The study aims to evaluate the alternative strategy of MFIs in terms of socio-economic changes on various dimensions such as occupation, consumption, investment, social awareness, and income. Since there is very little research done on competitive strategy in microfinance, our study is exploratory and uses a case study design (Saunders et al., 2009). To enable us to explore, we prefer a variety of cases, as recommended by Yin (2013). Therefore, we chose three MFIs with different legal statuses assuming that they have different visions, missions, and strategies. We selected one government-owned MFI (Nepal Grameen Bikas Bank), one private-owned MFI (Chhimek Laghubitta Bittiya Sanstha Limited), and one non-government MFI (NESDO). The government-owned MFI is 30 percent owned by the State and 3 percent owned by Nepal’s central bank.

The research uses primary data collected through structured questionnaires from the three different MFIs (Government-owned MFI, Private MFIs, and Non-government MFIs) of Kaski District, Nepal. The survey included 240 microfinance clients, randomly selecting 80 clients from each MFI, and screening them for having a minimum of five years of involvement in microfinance activities. The long-term screening criteria are to allow sufficient time for an impact to be observed. We applied a margin of error of 5%, a confidence level of 95% and assumed a response rate of 95 percent. The population size of the government-owned MFI clients with five years of experience was 2255, NGO- based MFI was 1860, and Private-owned MFI was 2763. Using the largest population and applying the sample size formula, we found the minimum sample size required was 72 clients. To allow for redundancy, we used 80 clients from each microfinance institution.

Both parametric (independent sample t-test, one-way ANOVA, regression, and factor analysis) and non-parametric (chi-square test) tests are applied to analyse performance effectiveness.

We used a structured questionnaire for data collection. The questionnaire included demographic information such as age, marital status, education, and household size. Besides, there were questions related to income, consumption expenditure, capital expenditure, and savings of clients. Some questions were related to the client’s perception of alternative strategies that MFIs followed to improve living standards, such as helping in creating a new business, moving from fragile to more stable occupations, professionalising agriculture and livestock practices, and providing loans for the emigration of clients’ family members. Some questions were related to information media used, such as radio, television, mobile, newspaper, and social networking sites.

A set of statements were provided concerning the respondents’ perception of poverty alleviation and women empowerment. Other statements were added on the factors affecting the living standard of clients. The factors to measure the living standard were transfer income, job satisfaction, consumption expenditure, usage of communication media, and capital expenditure. These statements were assessed on a 5-point Likert scale to measure how clients agreed with the given statements. We denote 1 as strongly disagree, and 5 as strongly agree.

4. Context and case description

A successful organisation mentions the firm’s stakeholders (employees, society) and its values in the vision statement (Bartkus et al., 2006). According to Ashta (2018), NGOs and for-profit enterprises differ in their vision and mission statements: The vision statement of for-profit enterprises tends to focus on the enterprise and where it will be in the future, while the vision statements of not-for-profit focus on society and where the society will be in the future. The three most frequently stated missions of MFIs are poverty alleviation, women’s empowerment, and rural financial inclusion, often with some combination. Mersland et al. (2019) find that MFIs usually conform to their stated mission. The vision and mission of selected MFIs are presented in the following table.

From the above, we can see that the NGO-based organisation’s vision statement has a broad scope and visualises society. The for-profit institution’s vision statement focuses on itself (to be a socially conscience bank), but it also adds the poverty reduction objective. The vision statement of government-owned MFI goes directly into solving the problem of poverty. None of the vision statements refers to the employees of the firm.

In our study, the mission statements of all three MFIs focus on poverty alleviation, and two of them also mention a focus on rural financial inclusion (The NGO does not). Women’s empowerment seems, therefore, not their overall goal, but the same two MFIs mention it in their target objectives (again, the NGO does not). It is surprising because all three MFIs target only women. All of our respondents are women. Therefore, we can qualify the results of Mersland et al. (2019) and indicate that the vision and mission statements have to be understood in the background of a country’s cultural context. If all MFIs target women, they may no longer be mentioned in mission and vision statements, although all correspond to this frequently accepted image of microfinance.

Usual indicators of reaching poor people include average loan size, the number of credit clients, and the loan method (Mersland et al., 2019). Since all the MFIs are giving credit and are using group lending or individual lending depending on the purpose of the loans, in our study, instead of average loan size, we asked our clients their family income and their consumption expenditure to provide indications of poverty and depth of outreach.

For rural outreach, the usual indicator is to see the percentage of rural clients or agricultural loans percentage (Mersland et al., 2019). In our study, we looked at the different kinds of agricultural activities as well as non-agricultural activities. While all agricultural activities are rural, some of the non-agricultural activities could be rural or urban (Table 1).

Table 1. Effect of microfinance on some variables according to previous literature

Variables	Impact of Microfinance	Source
Poverty	Reduce	Ayuub (2013); Cai et al., (2020); Chapagain and Dhungana (2020); Goldberg (2005); Pitt and Khandker (1998)
	No change	Roodman and Morduch (2014)
	More successful in reaching the poor but less successful in reaching vulnerable	Amin et al. (2003); Duong and Izumida (2002)
Income level	Increase	Cai et al. (2020); Chapagain and Dhungana (2020); Setboonsarng and Parpiev (2008)
	No change	Roodman and Morduch (2014)
Consumption expenditure	Increase	Cai et al. (2020); Khalily (2004); Pitt and Khandker (1998); Setboonsarng and Parpiev (2008)
	No change	Roodman and Morduch (2014)
Saving	Increase	Chapagain and Dhungana (2020); Pitt and Khandker (1998); Weiss et al. (2005)
	No change	Roodman and Morduch, 2014
Capital expenditure	Increase	Chapagain and Dhungana (2020); Pitt and Khandker (1998)
	No change	Roodman and Morduch (2014)
Empowerment	Increase	Cai et al. (2020); Weiss et al. (2005)

5. Results and discussion

Based on the demographic information, a one-way ANOVA, multinomial logistic regression, and exploratory factor analysis have been used to conclude the impact of the MFI strategy on the change in clients’ socio-economic empowerment.

5.1. Demographic information of clients involving in microfinance

Clients’ demographic information, such as marital status, number of children, educational level, monthly family income, and age structure, are presented below, along with the status of each MFI. It should be noted that people living below Rs 24,000 can be considered to be living in extreme poverty, keeping in mind a Nepalese rupee PPP rate of 34.93 to a dollar (https://www.indexmundi.com/facts/nepal/ppp-conversion-factor) and a poverty line at USD 1.9 (Ferreira et al., 2015). Therefore, the first two categories concern extremely poor people, the third and fourth categories concern poor people, and the last concern is middle-income people. Further, the chi-square test has been used to identify the association between microfinance status and demographic characteristics (See Table 2).

Table 2. The vision and mission of MFIs

Name and Legal form of MFI	Number of Clients	Vision	Mission	Source
Government-owned microfinance: Grameen Bikash Laghubitta Bittiya Sanstha Ltd (GBLBS)	221,928	To reduce poverty by creating self-employment opportunities for rural needy people through microfinance services at their doorsteps.	To uplift the social and economic condition of the rural poor people in Nepal.	https://gblbs.com.np/index.htm
NGO- based microfinance(non-government-owned): National Education and Social Development Organization (NESDO)	78,935	Establishment of a poverty-free new Nepal in terms of human, financial, and physical resources.	Sensitisation, unification, and income generation of the destitute community, Identification and optimal mobilisation of local resources Capacity building of local community and self-reliance group Promotion and advancement of self-reliant initiatives, Institutional development, and professionalism.	https://nesdonepal.org/about/vision-missions/
Private-owned Microfinance: Chhimek Laghubitta Bittiya Sanstha Ltd (CLBSL)	369,692	To be a social conscience bank that enables the poor to contribute equally to a prosperous, self-reliant rural society through self-employment and social awareness and to help reduce poverty in Nepal.	To extend financial services and social awareness to the poor in under-served and unserved areas of Nepal in a sustainable manner.	https://chhimekbank.org/en/about-us/vision-mission-and-objectives/

Source: Website of MFIs and their Annual Reports of 2021.

Table 3 shows the demographic information of respondents. The demographic profile indicates that most of the respondents (93.33 percent) were married, the majority of respondents (69.17 percent) had two children, and the majority of the respondents were literate, but they had less than the SLC level education. Besides, 86 (35.83 percent) respondents had more than Rs. 40000 monthly household income, and most of the respondents (69.58 percent) were 31 to 60 years old.

Table 3. Demographic information of respondents

Demographic variables	Categories	Grameen (Government-owned)	Chhimek (Private)	NESDO (Non- government)	Total	P-value
Marital Status	Married	71	77	76	224
	Widow	9	3	4	16
	Total	80	80	80	240	.125
No of children	zero to one	11	30	20	61
	two to three	64	47	55	166
	four and above	5	3	5	13
	Total	80	80	80	240	.036**
Education Level	Illiterate	2	10	2	14
	Literate but not Formal Education	36	31	41	108
	Under SLC#	32	30	27	89
	SLC#	8	6	8	22
	Above SLC#	2	3	2	7
	Total	80	80	80	240	.170
Family Monthly Income (NRS)	Below 10,000	0	8	3	11
	10,000–20,00	11	21	12	44
	20,000–30,000	15	22	16	53
	30,000–40,000	24	10	12	46
	Above 40,000	30	19	37	86
	Total	80	80	80	240	.001***
Age	Up to 30	17	28	16	61
	31 to 60	60	47	60	167
	61 and above	3	5	4	12
	Total	80	80	80	240	.142

Notes: NRS denotes Nepalese Rupees, and 1 $ = NRS 128.08 on 24 July 2022.

#SLC denotes School Leaving Certificate.

*** denotes significance at a 1 percent level and ** denotes significance at a 5 percent level.

Source: Field survey 2020 and authors’ calculation.

A Chi-square test with clients’ demographic variables shows an association between microfinance status and the number of children in their families. The government-owned MFI (Grameen) targeted clients with more children, whereas the private-owned MFI (Chhimek) targeted clients with fewer children. The non-government MFI (NESDO) was in a position in between the others.

The initial hypothesis based on Morduch (2000) was that private MFIs would target middle-income people, government MFIs aimed at poor-income people, and NGO MFIs aimed at extremely poor people. However, counter-intuitively, we found that NGOs aim at middle-income people, government-owned at poor people, and private-owned MFIs at extremely poor people.

5.2. Economic conditions of microfinance clients

Here, the economic conditions of clients of each MFI were measured by calculating average monthly income, consumption expenditure, average annual capital expenditure, and average monthly savings. Thereafter, the approximate average consumption expenditure of different items in each MFI was calculated. The one-way ANOVA test identified the significant mean difference between each economic variable for the clients involved in the different MFIs. It was hypothesized that different MFIs would reach people with different characteristics of monthly income, consumption expenditure, and saving habits. The results are presented in Table 4.

Table 4. Key economic conditions of microfinance clients

Particulars	Grameen (Government-owned)	Chhimek (Private)	NESDO (non-government)	ANOVA
Items	Mean(S.D)	Mean	Mean	Sig.
Monthly income (Rs.)	40838 (20455.85)	29356(18955.01) (18955.01)	39938 (19808.16)	0.001***
Monthly consumption expenditure	18731 (7144.505)	16063(8848.7) (8848.7)	20431 (9355.41)	0.005**
Annual capital expenditure	119400 (96820.68)	67000(64448) (64448)	106313 (76657.81)	0.001***
Monthly savings	16281 (14917.89)	9891(10314.45) (10314.45)	18156 (34365.64)	0.052*
Expenditure on information	1765 (1823.899)	1456(756.21) (756.21)	1778 (1788.711)	0.328
Consumption of food	8838 (2609.349)	7725(5032.74) (5032.74)	9363 (4113.097)	0.034**
Consumption of clothing	2075 (1427.798)	2125(1369.884) (1369.884)	2419 (3511.684)	0.601
Consumption expenditure on education	3753 (3653.158)	2168(1938.143) (1938.143)	4325 (5419.41)	0.002***
Consumption expenditure on health	1331 (903.382)	1743(1604.643) (1604.643)	1376 (1638.094)	0.136
Consumption expenditure on Communication	1108 (1006.658)	919(697.123) (697.123)	1288 (1099.755)	0.052*
Consumption expenditure on electricity	760 (911.057)	617(637.48) (637.48)	535 (441.929)	0.117
Consumption expenditure on drinking water	228 (200.774)	246(303.75) (303.75)	210 (148.861)	0.596
Consumption expenditure on entertainment	634 (685.832)	602(66.37) (660.37)	635 (768.90)	0.945

Notes: *** denotes significance at a 1 percent level and ** denotes significance at a 5 percent level and * denotes significance at a 10 percent level.

Source: Field survey 2020 and authors’ calculations.

Table 4 shows the one-way ANOVA analysis. This analysis is used to indicate the significant mean differences of scale variables, which have more than two categories of independent variables. The table shows a significant difference in monthly income, monthly consumption expenditure, annual capital expenditure, monthly savings, consumption on education, and communication consumption.

Once again, it was found that for all the indicators, the private-owned MFI is targeting the poorest people. The post hoc analysis shows a significant difference in income between households of government-owned MFI and private-owned MFI and between non-government MFI and private-owned MFI. Clients involved in government-owned and non-government MFIs had higher monthly incomes than clients involved in privately owned MFIs. At the same time, clients of non-government MFIs had the greatest consumption expenditure, and this expenditure was significantly different than clients of private-owned MFIs. Clients of government-owned MFIs had the highest capital expenditure, and clients of privately owned MFIs had the lowest. The difference was significant between government-owned MFIs and private-owned and private-owned and non-government-owned MFIs. Previous research has shown microfinance borrowers have higher consumption than non-borrowers (S. Rahman, 2010) and that microfinance increases consumption (Cai et al., 2020; Khalily, 2004; Pitt & Khandker, 1998; Setboonsarng & Parpiev, 2008), but our study is the first that shows that private-owned MFIs target poorer borrowers.

Moreover, non-government MFI clients had the highest saving and which is significantly different from clients of privately owned MFI. Further, private-owned MFI’s clients spent less on education and communication. The education expenses were significantly less than government-owned MFIs and non-government MFIs, and communication expenses were significantly less than non-government MFIs. The curious reader will find it strange that with major differences in averages in total income and much less differences in averages of total consumption, both have significance at a 1% level. However, savings are also considerably different but have significance at only a 5% level. The answer lies in the standard deviations: the standard deviations are high for income and for savings, especially in relation to the means for the latter. As a result, the dispersion is high, and ANOVA is reduced for savings. What this means is that MFIs are targeting people with a certain level of total expenditure which is more visible than total income, especially in the informal sector.

For some expenditures, such as clothing, drinking water, electricity, health, and entertainment, the amount expensed was very low. This low amount may be the reason that clients categorised these expenses as fixed expenses and indicated a fixed amount on these categories. Further, it can be seen that people in rural areas were not focusing much on health and entertainment.

5.3. Segmenting strategies: Different MFIs target different livelihoods

Here, the occupational status of clients involved in different MFIs was measured. The basic objective was to identify each MFI’s strategy for uplifting the occupational status by looking at the occupational status of the household head and the emigration of family members. Clients’ occupational status was classified under different headings such as professional agriculture, traditional agriculture, professional livestock, traditional livestock, wage-based work, creation of new business, and other service and jobs. The null hypothesis would be there is no difference in MFIs targeting the different activities.

Multinomial logistic regression was used to identify the likelihood of membership of clients in each microfinance organisation based on their occupational status. Here, a higher likelihood score for one of the MFIs indicates that its strategy is more focused on this occupational sector than other MFIs. The government-owned MFI served as a reference category and compared to clients’ likelihood to be involved in private-owned MFIs and non-government MFIs. For the occupational status of the household head, unstable occupation was taken as a reference category; and for other variables, “no” was taken as a reference category of independent variables. The results are provided in Table 5.

Table 5. Likelihood of clients’ microfinance membership compared with government-owned MFI

Name of Microfinance	Variables	P-value	Odds ratio
Private-owned MFI	Intercept	.086
	Occupational status of household head= stable	.016**	3.511
	Involved in professional agriculture= yes	.569	0.738
	Involved in traditional crop production=yes	.119	0.531
	Involved in professional livestock= yes	.556	0.743
	Involved in unmanaged livestock= yes	.260	1.717
	Involved in poultry farming= yes	.013**	0.324
	Involved in daily wage work= yes	.017**	0.172
	Creation of small business= yes	.001***	0.201
	Involved in service/job= yes	.174	0.494
	The household member involved in foreign job = yes	.001***	0.218
Non-government MFI	Intercept	.572
	Occupational status of household head (modern vs traditional)	.010**	3.656
	Involved in professional agriculture (yes vs no)	.523	0.724
	Involved in traditional crop production (yes	.429	0.741
	Involved in professional livestock=yes	.801	1.119
	Involved in unmanaged livestock=yes	.676	1.230
	Involved in poultry farming=yes	.740	0.882
	Involved in daily wage work=yes	.943	1.044
	Creation of small business=yes	.027**	0.420
	Service job= yes	.961	0.977
	The household member involved in foreign job= yes	.389	0.729

*** denotes significance at a 1 percent level, ** denotes significance at a 5 percent level, and * denotes significance at a 10 percent level.

Source: Field survey 2020 and authors’ calculations.

Table 5 shows that clients whose family head had a stable occupation had 3.51 times more chances to become a client of private-owned MFI as compared to government-owned MFI. It also shows that clients who are involved in poultry farming, created a new business, involved in wage base work, and whose family members emigrated for foreign employment were less likely to become the member of private-owned MFI by 67.6 percent, 82.8 percent, 79.9 percent, and 78.2 percent respectively. On the other hand, clients who were involved in unmanaged livestock had more chances to become a member of private-owned MFI.

Moreover, compared to government-owned MFI as a reference category, clients whose family head had a stable occupation had 3.656 times more chances to be involved in non-government MFI and it was statistically significant. However, those clients who created new businesses were about 58 percent less likely to be involved in non-government MFI, and it was also statistically significant. All the other occupational status variables were not statistically significant for NGOs versus government-owned MFIs.

From the above analysis, it was inferred that private MFI looked at the stable occupational status of the household head while giving membership to their clients. But, government-owned MFI and non-government MFI provide relatively more important to modern occupational status. Compared to private MFI and non-government MFI, the government gave relatively more importance to borrowers starting a small business. Moreover, compared to private MFI, government-owned MFI focused more on clients in poultry farming, daily wage work, and foreign employment of their family members. There seems to be no difference in targeting between government-owned MFI and non-government MFI on these indicators. Government-owned MFI and non-government MFI mostly focused their activities on financing poultry farming, daily wage work, and professional emigration.

5.4. Strategies: Different MFIs use different promotion media

The likelihood of using various information tools by microfinance clients to get information was compared. It is assumed that using various information tools increases the power to get more information about business and society and helps to empower them socially. Further, it is assumed that microfinance organisations’ training and development program helps to use more information sources to better inform about society and business. The null hypothesis is that there is no difference among MFIs in targeting people with different information seeking habits.

Table 6 shows the multinomial results of logistic regression analysis using government-owned MFIs as a reference. The results show that clients who used a phone were equally likely to become clients of private-owned MFI and non-government MFI. Clients who read newspapers or listened to the radio were less likely to involve in private-owned MFI as compared to Government-owned MFI, and these variables were statistically significant.

Table 6. Likelihood of uses of different information sources

Particulars	Variables	P-value	Odds ratio
	Intercept	.034
Private-owned MFI	Have a phone= yes		1.000
	Listening to Radio= yes	.027**	0.326
	Watching a Television= yes	.539	0.627
	Reading a Newspaper= yes	.042**	0.393
	Using social sites= yes	.368	0.716
Non-governmentMFI MFI	Intercept	.004
	Have a Phone= yes		1.000
	Listening to Radio= yes	.001***	0.168
	Watching a television= yes	.482	0.585
	Reading Newspaper= yes	.088*	0.447
	Using social sites= yes	.206	0.622

P-value of model=0.0001, Pseudo R square=0.13.

Notes: The reference category is Grameen Bikas Bank, and coefficient no is taken as a reference category of independent variables.

Source: Field survey 2020 and authors’ calculations.

Similarly, clients of non-government MFI were less likely to listen to the radio and read newspapers than clients of government-owned MFI, and these variables were also statistically significant. In conclusion, clients who used these information sources were more likely to involve in government-owned MFI.

It is inferred that clients of government-owned MFI used different communication media in a balanced way than clients of other MFIs. Although information expenses of clients of non-government MFI were higher as depicted, they were less likely to use radio, television, and social sites. The reason may be that they invested most of their amount and time on the phone. But clients of government-owned MFI gave equal emphasis to all communication media. The reason may be that the clients of the government-owned MFI are richer and better educated.

5.5. Factors affecting the living standards of microfinance clients

An exploratory factor analysis was used to identify the factors influencing the living standard of the clients. Table 5 shows the factors influencing living standards and their standard loadings. The main factors that affect living standards are an increase in consumption expenditure, an increase in capital expenditure, satisfaction with job status, and an increase in income level. The detailed results are presented in Table 7.

Table 7. Factors loading across different factors

Factors	Dependent Variables		Loadings of the Independent Variables
	Poverty alleviation	Empowerment	F1	F2	F3	F4	F5
Microfinance is a tool for financial inclusion	0.916
Microfinance facilitates poverty reduction	0.714
MFIS services reach to marginalised and poor people	0.555
I am satisfied with microfinance services		0.710
Microfinance intervention has changed my daily activities		0.609
Support of microfinance for social information awareness		0.552
My amount of consumption for the electricity has increased			.724
My amount of consumption for the drinking water has increased			.713
My amount of consumption for the entertainment has increased			.660
My amount of consumption for the clothing has increased			.594
My amount of consumption for the education has increased			.499
My amount of consumption for the health matters electricity has increased			.431
My investment in furniture has increased				.797
My investment in electronic appliances has increased				.752
My investment on accessories has increased				.725
My investment on the house has increased				.674
My investment on land has increased				.660
I have emotional attachment to my occupation					.828
My job security has increased					.743
I have freedom from burden by self-employment					.562
I have better occupational status than before					.479
My usage of television has increased						.796
My usage of phone has been increased						.479
My usage of social sites has increased						.404
My family income from foreign employment has increased							.501
I received government subsidy regularly							-.492

Source: Field survey 2020 and authors’ calculation.

Notes: F1 is related to consumption expenditures, F2 is related to capital expenditures, F3 is related to occupational satisfaction, F4 is related to information and communication technology, and F5 is related to transfer income.

Exploratory factor analysis was done to identify the factors that influence the mission of the MFIs. For this identification, the variables on which we collected information were divided into dependent and independent. The SPSS software was used to reduce these variables into factors. Before determining the appropriateness of running exploratory factor analysis, KMO and Bartlett’s tests were performed. As suggested by Malhorta and Birks (2006), the minimum accepted value of factor loading is 0.3. Table 7 reports the factor loadings using a cut-off value of 0.3.

For dependent variables, the KMO value was 0.78 and Bartlett’s test of sphericity had a significant value of chi-square that signifies it is appropriate to run exploratory factor analysis. The factor analysis was done by using the principal axis factoring method by using direct_oblimin rotation. The variance of eigenvalues of the factor was almost 66 percent, which shows 66 percent of the variance in our model is explained by these factors. The dependent variables were loaded into two factors: one was related to reducing poverty, and the other was empowerment through satisfactory services.

For independent variables, the KMO value was 0.80 and Bartlett’s test of sphericity had a significant value of chi-square that signifies it is appropriate to run exploratory factor analysis. When factors were loaded under criteria eigenvalue greater than one, seven factors were obtained, but the result was difficult to explain owing to cross-loading and theoretical mismatch of results. An analysis of six factors resulted in the same problem. Finally, five factors were used for the analysis. The variance of eigenvalues of the factors is almost 61.46 percent. Then factor analysis was done by using the principal axis factoring method by using varimax rotation. Since the correlation between factors is very low, the varimax rotation method provides better results. Variables were loaded with good loading in five factors, as shown in F1, F2, F3, F4, and F5. The F1 is related to consumption expenditures, F2 is related to capital expenditures, F3 is related to occupational satisfaction, F4 is related to information and communication technology, and F5 is related to transfer income.

For the factor related to consumption (F1), electricity consumption, drinking water, entertainment, clothing, education, and health were loading. Before the involvement of microfinance, the clients were not able to spend on these items, but now their capacity to spend on these has increased. This increased consumption is positively affecting poverty reduction and empowerment.

Similarly, for the factor related to capital expenditure (F2), after involvement in microfinance activities, clients’ potential to purchase electronic appliances, furniture, accessories, and improvement to the house increased.

Furthermore, their occupational satisfaction (F3) increased significantly. This includes an emotional attachment to the occupation, job security, autonomy thanks to self-employment, and change in occupational status.

Besides, the capacity to use information and communication technology (F4) such as phone, television and social sites increased significantly since they did not have access to these before involvement in microfinance activities.

Perhaps the most interesting findings are on how the transfer income of clients (F5) influenced poverty reduction and empowerment. The loading of government compensation is negative, indicating that people who are not satisfied with the government subsidies are the ones who take steps to reduce poverty and increase empowerment. The second interesting thing is that those who took loans to finance their family members’ professional emigration to foreign countries found a reduction in their poverty and increased empowerment.

6. Conclusion and suggestions

The impact of MFIs of different legal forms on poverty and women empowerment has been studied. We have examined the impact in terms of socio-economic changes of women clients in government-owned MFI, private-owned MFI, and non-government MFI. The study finds that all MFIs were engaged to empower clients both from an economic and social perspective.

From an economic perspective, MFIs motivate clients by assisting in income-generating activities, providing assistantship to invest in small businesses, increasing savings and investment, and maintaining a balanced consumption pattern. These strategies are best followed by government-owned MFI followed by non-government MFI, and least by private-owned MFI.

From a segmentation strategy perspective, it was expected that private MFIs would lend to higher-income women, government MFIs to middle-income women, and NGO MFIs to the poorest segment. Surprisingly, it was found that the MFI lending to high-income women was the government MFI, the NGO was lending to middle-income women, and it was the private MFI that was reaching ultra-poor women. The same trend was also observed for consumption expenditure. Judging by the standard deviations of the respondents, it is possible that MFIs segment their customers based on total consumption, which may be more visible rather than total income, which is difficult to observe for informal sectors. This performance of private MFIs is good news for impact investors who are searching for social impact through private firms.

Moreover, this is the first study that shows that the government-owned MFI (Grameen) targeted clients with more children, whereas the private-owned MFI (Chhimek) targeted clients with fewer children. The non-government MFI (NESDO) was in a position in between the others.

The government MFIs targeted new businesses, had the highest savings and investment, and had the greatest consumption expenditure. This result indicates that government-owned MFIs target relatively higher income women, not the poorest of the poor. Government MFIs and NGO MFIs need to study how a private MFI can make money by reaching out to the poorest of the poor.

From a product strategy perspective, we found that the private MFI was targeting different occupations of the clients compared to the state-owned MFI. However, we did not find many differences between the state-owned MFI and NGO MFI regarding the occupation of the clients. From an innovation perspective, the clients of government-owned MFI are more likely to create businesses and be involved in professional farming. This result may be because their clients have higher income and are more literate.

From a communication strategy perspective, the study found that the clients of the government-owned MFI used more communication media to get information about business and society. The clients of government-owned MFI used all media in a balanced way, but clients of non-government MFI and private-owned MFI used phones more. The reason may be that the clients of the government-owned MFI in Nepal have higher income and are more educated.

From a strategic perspective, the private-owned MFI and the non-government MFI targeted the household head’s stable occupation status, but the government-owned MFI targeted loans without looking at the occupation of the household head of the client.

Finally, the exploratory factor analysis shows that women perceived poverty alleviation and empowerment. The policy implication is that governments should continue to encourage microfinance and support a multitude of different forms of MFIs since they target different types of clients. Future researchers should create a longitudinal study to confirm these results.

An obvious limitation of this study is that the MFIs selected may not be representative of all the MFIs of their kind. Thus, the results are not generalizable, as is often true for case studies. Future studies need to repeat our study with more MFIs of each kind.

This paper focused on the alternative strategy formulation of different kinds of MFIs based on research data gathered before the Covid pandemic. However, COVID has reversed poverty reduction and has had adverse consequences for women (Agarwal, 2021). It would be interesting to study the strategic restructuring that has taken place during the pandemic for the sustainability of MFIs and see its impact on poverty alleviation and women empowerment.

Acknowledgments

We are thankful to all the participants of three different MFIs (Government-owned MFI, Private MFIs, and Non-government MFIs) of Kaski District, Nepal. We also acknowledge Professor Claudio Zara, Bocconi University, Italy, for providing valuable input.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data will be available on request to corresponding authors.

Additional information

Funding

The authors received no funding for this work.

Notes on contributors

Bharat Ram Dhungana

Bharat Ram Dhungana is an Associate Professor at the School of Business, Pokhara University, Nepal. He has more than two decades of teaching and research experience in the field of management. He has published a dozen of peer-reviewed research papers in national and international journals and authored many books. He has presented several papers at national and international conferences.

Ramkrishna Chapagain

Ramkrishna Chapagain is a Lecturer at School of Business, Pokhara University, Nepal. He is currently doing his PhD from University of Delhi, Delhi School of Economics. He has published some papers on national and international Journals and participated in many international conferences.

Arvind Ashta

Arvind Ashta is a professor of finance at the Burgundy School of Business, Université Bourgogne Franche-Comté in Dijon, France and member of the research center CEREN, EA 7477 and the research associations RRI, CERMi, PRME Anti-poverty Working Group. He is also associated with the European Microfinance Platform. He has published extensively in international journals, authored, and edited books on microfinance and social entrepreneurship.