Evolution of microfinance for the financial inclusion of the poor

Until the 1990s, state intervention in donation and subsidy-based finance played a key role in financially including people experiencing poverty, [29]. This poverty lending approach involved government-funded credit at subsidised rates, [30]. Since the 1990s, there has been a paradigm shift toward sustainable finance, aiming to ensure the inclusion of low-income individuals without compromising the sustainability of financial institutions. A moot question till the 1990s was whether access to finance for people with low incomes is affordable. The concept of community lending formats based on social capital gradually emerged as a substitute for physical collateral, enabling people with low incomes to access finance [31]. Due to access to local market information, the microfinance institutions and self-help groups are more effective in achieving financial inclusion, [32].

From a sociological perspective, the main factors impacting the financial behaviour of poor people are social norms and culture [33]. During this period, several microfinance models leveraging social capital emerged. One of the most notable microfinance formats based on social capital was ROSCAs, which involved pooling the savings resources and rotating them as borrowings among the group members. This initiative is based on trust and operates through social pressure. It fosters social cohesion and mutual support among group members. ROSCAs (Rotating Savings and Credit Associations) were informal savings institutions that did not offer any interest to the savers and were vulnerable to the risk of strategic default. Due to the risk of moral hazard cropping from information asymmetry, the growth of ROSCAs declined, [34]. Similarly, cooperative societies provided credit and financial services to the members at a lower rate. These societies are democratically controlled institutions designed to meet the needs of their members. These cooperative societies suffered from moral hazards, the risk of strategic default by the members, and weaker controls. These societies failed due to governance issues and political interference, resulting in higher non-performing assets and increased fraud [35]. One of the milestones in microfinance was the setting up the Grameen Bank, which Muhammad Yunus founded in Bangladesh, aimed at providing microfinance to the poor women at the bottom of the pyramid. As per Grameen Bank, credit is extended based on social capital, [8]. Various village-level institutions, banks, and institutions also emerged in rural areas, providing loans from the pooled savings, [36]. Despite several initiatives due to the information asymmetry regarding the creditworthiness of people with low incomes, these models had limited success, with Grameen Bank a notable exception. Self-help groups based on social capital emerged as vehicles for the social emancipation of low-income people, [37]. It enabled people with low incomes without a credit history to access microfinance, [38,39]. SEWA by Ela Bhatt was the first self-help group, enabling the poor and marginalised to access finance, [15,40]. India adopted a graduation approach to financial inclusion through the self-help group bank linkage programme. Beginning with NGOs like MYRADA in 1980, which promoted self-help groups, the approach was institutionalised with the NABARD self-help group linkage programme in 1982, [13]. This initiative allowed the poor and marginalised to generate social capital through networked relationships, bonding, and bank linkages. Moreover, policy programmes such as Swarnjayanti Gram Yojana further reinforced the provision of international recognition at the 1997 World Microcredit Summit [41]. Furthermore, state-promoted initiatives such as Kudumshree Kerala and Velegu in Andhra Pradesh have demonstrated how small savings and credit groups can evolve into platforms for livelihood generation and empowerment. The subsequent graduation of self-help groups into policy initiatives, such as Jeevika in Bihar and Tripti in Odisha, led to scaling up these community initiatives into federations and clusters. Eventually, these initiatives culminated in Deen Dayal Antodaya Yojana under the National Rural Livelihood Mission. These milestones illustrate the evolution of small informal savings groups into bank-linked intermediaries [42]. These self-help groups demonstrated high repayment rates and efficient loan disbursal. They emerged as a panacea to the problem of financial exclusion of the poor and marginalised sections of society due to information asymmetry.

Table 1 provides details regarding the evolution of microfinance. Information asymmetry increases the likelihood of adverse selection, whereby the banks might select members with a higher probability of default, [43]. Theoretical models of information asymmetry have emphasized that banks might misinterpret the risk profile of borrowers. This information asymmetry underlies credit rationing among rural borrowers. Within self-help groups, members select each other and are, by definition, already familiar with one another. This a priori information mitigates the risk of adverse selection and improves the group quality. This peer selection leverages social capital and networked relationships to mitigate information asymmetry and transaction costs, [44]. Community lending and saving participation signals good repayment behaviour. Group members are selected based on homogeneity of risk profiles, business correlations, and social ties, ensuring low-risk group composition, [45]. Theoretical foundations in the domain of information asymmetry in the context of self-help groups and community lending groups are given below:

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Table 1. Evolution of microfinance (Source: Author’s own work).

https://doi.org/10.1371/journal.pone.0341674.t001

Table 2 provides the theoretical frameworks in information asymmetry as challenge to financial inclusion. Despite the success narratives, the self-help groups are fraught with various limitations, such as the risk of default. Such as the risk of overlapping loans, strategic defaults, and use of loans for income smoothing purposes, [46]. Earlier community lending models were devoid of any social contract and joint liability. Self-help groups operate based on joint liability with dynamic incentives for additional bank loans contingent on good repayment behaviour, [47]. They operate through peer monitoring, peer selection, and higher repayment rates mitigate strategic default, [27]. Social capital motivates members to monitor one another and sanction defaulters within homogeneous groups. [48] highlight that the influence of peer monitoring and peer pressure on loan repayment is not uniform and differs with the size of the group, loan size, demographics, including literacy levels, and gender, [24]. Thus, although the theory suggests that peer mechanisms help improve the repayment rates and financial sustainability of the groups, the empirical evidence on the impact of peer mechanisms on repayment behavior in the group is extremely limited. There is a research gap regarding what factors influence the impact of the peer mechanism on loan repayments in a community lending group. Fig 1 depicts how peer selection, peer monitoring and peer sanction impact information asymmetry, in the form of moral hazard and adverse selection.

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Table 2. Theoretical frameworks in information asymmetry as a challenge to financial inclusion (Source: Author’s own work).

https://doi.org/10.1371/journal.pone.0341674.t002

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Fig 1. Conceptual framework for reducing information asymmetry (Source: Author’s own work).

https://doi.org/10.1371/journal.pone.0341674.g001

Fig 1 provides the conceptual framework for reducing information asymmetry

H0: Peer mechanism does not impact the group quality

H1: Peer mechanism has no impact on adverse selection

H2: Peer selection and monitoring do not have an impact on internal loss default

H3: Peer monitoring & selection does not have an impact on external loan default

H4: Peer monitoring and selection do not impact misuse of funds

Despite various case studies on how peer mechanisms have ensured higher repayment rates, certain case groups have disintegrated due to excessive loan burdens. One notable example of community lending failure is the overindebtedness and loan default of microborrowers with Grameen Bank. Similarly, many self-help groups, linked with microfinance institutions, faced repayment defaults in Andhra Pradesh. In the case of APMAS (Andhra Pradesh Mahila Abhivruddhi Society), heavy reliance on joint liability created peer pressure, leading to political backlash, political tensions, and suicides. While ROSCAs thrive on trust, the primary reason for their failure was the informal institutional framework and the lack of legal recourse. Similarly, wealthier members in the village dominated decision-making, leading to the failure of community lending initiatives. Thus, internal factors influence how peer pressure affects repayment within a group. Any heterogeneity among the group members regarding financial literacy, education, social status, and peer pressure might not work equally, and loan default might rise, [49]. Additionally, external factors, such as leadership, may also influence how peer pressure affects loan repayment and default. Another exogenous factor impacting loan repayment and default is digital technology, [50]. Thus, no empirical evidence exists to suggest that peer pressure has an equal impact in different group conditions. Moreover, this research study explores the impact of various factors that account for why peer pressure does not work equally, leading to differences in loan repayment.

Ethical consideration

The data for the study was collected with the consent of the participants by the qualified researcher/leader in the self-help promoting institution. Due consideration was given to the senstivity of the participants and the consent was taken from the participants verbally. There were no minors involved in the study and the consent was winessed by the an NGO representative, who was present throughout the data collection. The data were collected verbally and witnessed and documented by the research assistant with SHPI (Self-Help Promoting Institution). And the phone numbers and contact details of the participants were also give. For their time all participants were given a nominal amount to compensate for their loss of work during work hours. Moreover, none of the participants were contacted during their work hours or on official holidays.

Results of ANOVA

The results of the ANOVA analysis highlight important differences among members in terms of risk propensity. In terms of income growth after joining the group, after self-help group membership, this is not uniform across all groups. The F-statistic for the difference among groups is F = 4.248, with a significant p-value of 0.015. This implies that while some members report substantial improvement, others perceive little change in income growth. In terms of business stability, there is no significant variation across the clusters (F = 0.006, p = 0.994). Hence, this question has been dropped. Regarding preference, there is a substantial difference between the clusters. Members’ preference for unsecured loans shows a strong difference across the clusters (F = 3.759, p = 0.000), indicating that while some members are comfortable with taking unsecured loans, others refrain from taking unsecured loans. Similarly, tolerance for paying higher transaction costs, the member’s choices differ across the clusters with F = 17.76, p = 0.000. There are also significant differences among the clusters in terms of preference for a higher interest rate (F = 316.25, p = 0.000). Finally, preferences for long-term loans show significant variation across clusters. Overall, the findings suggest that there is a significant difference across the clusters in terms of their propensity to assume risk.

Table 3 provides the results of ANOVA. It indicates that a statistically significant difference exists in how members perceive their income after joining the group. Among clusters, there is a very strong and significant difference in preference for unsecured lending. With respect to their willingness to pay higher transaction cost and their tolerance for a high rate of interest, significant differences exist across the clusters. In addition, members demonstrate extremely strong and significant differences in their preference for loan tenure, suggesting diverse borrowing needs.

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Table 3. Results of ANOVA analysis (Source: Author’s own calculation).

https://doi.org/10.1371/journal.pone.0341674.t003

Table 4 provides the results of cluster analysis. The ANOVA results show a statistically significant difference across the clusters. Table 4 indicates the results of cluster analysis. The sample was divided into 3 clusters (Cluster 1, Moderate Risk = 101 members), Cluster 2 (Low risk = 211) and Cluster 3 (High risk = 88).

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Table 4. Results of cluster analysis (Source: Author’s own calculation).

https://doi.org/10.1371/journal.pone.0341674.t004

Table 5 provides the result of impact of peer mechanism on group quality. As per Table 5, financially literate members have a 12% predicted probability of being all risky. Financial literacy reduces the probability of being in the moderate risk group by 7.1% and the safe group by 6%. Overall, literate individuals are more likely to take more risks. Similarly, strong peer sanctions reduce the probability of being in safe clusters. Peer sanctions have an adverse effect, reducing the likelihood of being in a safe group by 5% and being in a moderate-risk group by 5.1%. Thus, negative reinforcement increases moral hazard and increases the likelihood of being in a risky group. Similarly, frequency of meetings as per peer monitoring reduces the probability of being in all risky groups by 10% and slightly riskier groups by 8%, while increasing the likelihood of being in the moderate risk group by 6.2% and the safe group by 5.4%. Thus, peer monitoring reduces the riskiness of the group. Members with technology access are less likely to be in the risky group by 11% and decrease the likelihood of being in the riskiest group by 2.9%. With access to technology, the likelihood of being in a moderate group is 6.9%, and the likelihood of being in a safe group is 7.9%. Technology strongly encourages safer behaviour. Leadership shows no significant impact on the likelihood of being risky. Being a moderately risky customer increases the likelihood of being moderate risk (3.9%) or safe (4%). Low-risk members are less likely to be in any high-risk group, slightly riskier by 1.1%, and more likely to be in the moderate group by 5.1%. Additionally, they are slightly riskier by 1.1%. They are also more likely to be in the moderate group by 5.1% and are considered safe (4.6%).

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Table 5. Impact of Peer mechanism, Peer selection & Peer sanction on Group Quality (Source: Author’s own work).

https://doi.org/10.1371/journal.pone.0341674.t005

The low-risk members (proxy for peer selection) are significantly less likely to be in the high-risk member group and significantly more likely to be in the safe member group. This confirms strong peer selection, which leads to a reduction in adverse selection among group members. Hence, hypothesis 1 (H1) that Peer selection through a priori information about the credit behaviour of other members reduces adverse selection in the group is accepted.

Table 6a provides the results of logistic regression for Model 1, Model 2 and Model 3. According to Table 6a, in Model I, leadership reduces the likelihood of internal loan default, as indicated by a negative β coefficient of −0.462, which is highly significant. Access to technology is negative and significant, with a β coefficient of −0.742, at a high significance level, suggesting that technology reduces the risk of internal loan default. Low-risk members are less likely to default internally compared to high-risk members, with a β coefficient of −0.665 and a high significance level. Other variables, peer pressure, peer sanction, and financial literacy, are not statistically significant. Internal loan default rates are reduced by effective leadership, access to technology, and a low-risk member composition.

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Table 6. (a) Results of logistic regression for Model 1, Model 2, Model 3 (Source: Author’s own calculation). (b): Marginal effects of peer mechanism on internal default, external default, and misuse of funds. (Source: Author’s own work).

https://doi.org/10.1371/journal.pone.0341674.t006

Hence, the H2 is partially supported, indicating that peer selection reduces internal loan default repayment. Low-risk members in a group reduce the probability of internal loan default. Peer sanction and peer pressure do not have an impact on internal loan default and repayment.

In Model II, the analysis of the model reveals that the willingness to help others is negative and significant, with a coefficient of β equal to −0.48 and a high level of significance, suggesting that regular meetings strengthen repayment discipline towards external lenders. Moderate-risk members are less likely to default externally compared to high-risk members, with a β coefficient of −0.932 and a high level of statistical significance. Other variables such as literacy, education, leadership, and peer effects are not significant. External loan default or default on bank loans is significantly reduced by cooperative behaviour, frequent meetings and having few high-risk members.

From the analysis of the statistical results, strong leadership significantly reduces internal loan default, and peer selection also reduces the risk of internal default significantly. This leads to the rejection of the hypothesis that peer selection significantly reduces the risk of selecting low-risk members.

In model III, peer pressure is negative and significant, with a β coefficient of −0.525 and a high level of significance, indicating that peer pressure reduces the likelihood of misuse of funds. Peer sanction has a β coefficient of 0.713 and a high level of significance, indicating that peer sanction or negative reinforcement increases the likelihood of misuse of funds. Financial literacy, with a β coefficient of −0.869 and a high significance level, indicates that literate members are less likely to misuse funds. The education coefficient is negative and significant, with a β of −0.854 and a high significance level, indicating that higher education reduces the likelihood of fund misuse. The business correlation coefficientβ = 0.362 is also positive and significant, indicating that a positive business correlation increases the likelihood of default. Both moderate- and low-risk members are less likely to misuse funds, with β coefficients of −0.907 and −0.961, respectively, and a high level of significance. Misuse of funds is significantly reduced by financial literacy, education, peer pressure, and moderate and low risk. While peer sanctions and business correlation increase the probability of misuse of funds.

Based on the analysis, hypothesis 3 is accepted that peer pressure reduces the misuse of funds. And low-risk and moderate-risk members reduce the instances of misuse of funds.

Table 6b provides the marginal effects of peer mechanism. Internal default risk is statistically reduced by leadership quality, access to technology, and the presence of low-risk members. Leadership reduces the probability of internal loan default by 15.8%. And low-risk members are 15.2% less likely to default internally. External loan default is reduced by poor monitoring and members in low-risk categories. Willingness to help others reduces the probability of default by 9.6% and frequency of meetings reduces the probability of default by 11.4%. Moderate-risk members are 22.2% less likely to default externally compared to high-risk members. Table 4(b) provides the results of logistic regression.

To classify borrowers into various risk types, cluster analysis was employed following one-way ANOVA. There was a significant difference among the risk types, and cluster scores were generated as a proxy of risk class. For generating clusters based on riskiness, the members were classified on the basis of [1] income growth, [2] debt terms. For forming the clusters, the questions used are: [1] How do members rank their income growth after joining the group? And how do they rank the groups based on debt growth and preference of loan covenants, which include [1] maturity of loans, [2] cost of loan, and [3] transaction costs. These findings are further corroborated through the use of One-way ANOVA, and the results show a significant difference among groups. These questions were asked because small entrepreneurs are difficult for formal financial institutions, such as banks, to assess; they often lack information about them. The basic premise is that low-quality borrowers are insensitive to higher borrowing and transaction costs.

Hypothesis I: Peer selection through a priori information about the credit behaviour of other members reduces adverse selection in the group

The study substantiates that peer selection mitigates the adverse selection, and this is consistent with earlier findings in the study, [51]. Thus, having prior information about the riskiness of the members enables peer selection and risk matching, thereby reducing the risk of adverse selection. However, the literature mentions the problem of moral hazard or the change in financial behaviour of the members after joining the group. With a priori information, the literature emphasises the problem of strategic default or collusion among the members of the group, [52]. In our analysis, Model II discusses the impact of peer mechanisms on internal and external loan repayment defaults.

Similarly, peer monitoring through frequent meetings increases the likelihood that the group is safe. This is consistent with the findings of the earlier studies, [53]. [54] highlights that when group meetings were reduced from fortnightly to monthly, repayment delays and defaults increased in the group. Besides that, reducing the number of meetings eroded the group’s trust, leading to repayment defaults. Thus, meetings definitely help to improve accountability and trust among the group members. But there is a potential downside: a very high frequency of meetings can lead to group fatigue and impose psychological cost on members by reducing participation quality and imbalance in the family and personal lives of the members in the group, [55]. A more extensive study can be undertaken in the future to account for the influence of a higher number of group meetings and geographical context on loan repayment behaviour and group quality.

An interesting finding is that peer sanctions negatively impact group quality, contrasting with earlier studies’ findings, [56]. According to the literature, peer sanctions are implemented to ensure compliance and reduce free-riding behavior among group members. [53] In his study, he has emphasised that peer sanctions are designed to reduce free riding in a group. However, the findings of our analysis indicate that peer sanctions lead to a higher propensity to take risks. Sanctions can erode trust, cohesion, cooperation, and solidarity within a microfinance self-help group. Due to the threat of sanctions, the group members might decline to cooperate and support each other in case of a contingency, [57]. Thus, according to our study, peer sanctions may lead to the erosion of trust and social capital within the group.

External factors such as financial literacy also play a significant role in shaping group quality, aligning the results with the theory of peer monitoring, which emphasises the role of the peer mechanism and reduces adverse selection, [58]. Surprisingly, financial literacy increases the likelihood of risk-taking. This could be due to procyclicity and overconfidence bias, which means that the literate members might take calculated risks, thinking that they can manage risk better, [59]. Alternatively, members with more information and literacy might expose themselves to more borrowing options, making them more vulnerable to credit risk. Due to higher procyclicity and inclination to take risks, the borrowers with higher financial literacy might face more credit risk

Hypothesis II: Repayment of internal loans is impacted by peer mechanisms (Peer selection, peer sanction, peer pressure, peer monitoring through meetings, leadership)

Additionally, earlier studies [60] have highlighted that peer sanctions reduce internal loan defaults. Our study, in contrast, argues that peer sanctions are positively associated with internal loan default. A plausible explanation is that cohesive groups and groups with strong leadership are less reliant on negative sanctions to facilitate repayment of loans, [61]. Thus, according to the analysis of the data, strong leadership enhances group cohesion and reduces the need for punitive sanctions. Good leadership in a group fosters a culture of repayment and discipline. Furthermore, although financial literacy negatively impacts internal loan default, the results are not statistically significant. This suggests that although financial literacy may provide a better understanding of financial obligations, it does not necessarily prevent opportunistic behavior. However, the use of technology reduces the misuse of funds by bringing about better disclosure and transparency in the utilisation of funds, [62] The group linkage programme is also supported by various Government and non-government microfinance Institutions. An earlier study highlights the importance of a pluralistic microfinance ecosystem in achieving empowerment goals [27]. This will help to improve the performance of the groups [23]. However, this study is limited to the linkage between self-help groups and banks, and further research can be conducted in this area.

Hypothesis III: Peer monitoring, sanctions and pressure reduce the moral hazard in a group

The data analysis reveals that peer monitoring decreases the likelihood of external loan default within a group. However, peer monitoring undertaken through regular meetings and informal interactions promotes greater accountability and financial discipline among group members. Regular meetings and evaluation of repayment schedules reduce the likelihood of default among the members of the group, [60]. Thus, an optimum number of meetings and repayment schedules reduces the likelihood of external loan default. This is in line with the findings of an earlier study [63] However, an excessive number of meetings results in personal fatigue and psychological costs, which can result in strategic default, [54]. Additionally, the willingness of members to help one another reduces the risk of external default among group members. Willingness to help others leads to higher solidarity, and better cohesion leads to trust and reciprocity among the members of the group, [64]. Indeed, social capital serves as the basis for lending to group members. Thus, social capital improves access to formal finance while reducing external loan default. Several studies emphasize the significance of microfinance and group lending in enhancing the living standards of households. In the context of reduced external loan default, more research can be undertaken to explore the impact of microfinance on household living standards [63].

Hypothesis IV: Peer pressure and peer sanctions reduce the misuse of funds among the members of the group

The study’s findings show that peer pressure, which utilizes social influence and informal norms, reduces the misuse of funds among group members. This is supported by the findings of an earlier study, [65]. Further, [66] in his analysis has highlighted that the increasing misuse of funds in the case of a large-scale default episode in Andhra Pradesh, India. The study’s findings indicate that social pressure and peer pressure do not stabilize repayment behavior during the crisis; rather, they exacerbate the financial plight of the group, leading to widespread defaults. Thus, peer pressure can have negative effects in the event of a crisis. While peer pressure and social influence reduce the misuse of funds in the group, as per [67]. Thus, although our study indicates that social influence and peer pressure reduce the misuse of funds, there is still scope for further study regarding the factors that influence the strength and direction of the relationship between peer pressure and misuse of funds. The size of the group and regional variations might act as moderators in influencing the strength of the relationship between the peer mechanism and misuse of funds. Additionally, an earlier study [24] has highlighted the role of loan size on group productivity, and further research can be conducted in this area.