This is Part 2 of a 3-part series based on the recently released NCAER-MFIN report, “Assessing the Effectiveness of Regulated Small Borrowing in India” (March 2026). In the previous blog, we examined a set of foundational aspects of the report – namely, the survey timeline, sample representativeness, expenditure patterns, and the reported end use of microfinance loans. In this blog, we turn to some of the broader claims advanced in the report regarding the wider benefits of microfinance and assess whether these claims hold up when situated in a broader empiricalcontext.
The empirical evidence on the developmental impact of microfinance is mixed. Early studies were optimistic about its poverty-reducing potential. Pitt and Khandker (1998) examined Grameen Bank and two other MFIs in Bangladesh and reported positive effects on household expenditure and asset accumulation[1]. Similarly, Khandker (2005) found promising developmental impacts like reductions in poverty at both the household and the broader local economy through spillover effects[2]. The more recent wave of studies has increasingly focused on specific contexts or product innovations rather than offering generalisable assessments of microfinance credit as a whole. For instance, Cai et al. (2020) found positive effects of microcredit in rural China, including higher incomes and reduced poverty, in a setting characterised by very low baseline access to formal finance, high returns to off-farm employment, and relatively borrower-friendly loan terms[3]. Bari et al. (2024) studied asset-based microfinance (where the asset itself serves as collateral which allowed approval of much larger loans) and found statistically significant increases in assets and profits which in turn, increased household income and consumption[4]. Similarly, Battaglia et al. (2024) found that greater flexibility in loan repayment led borrowers to invest more in their businesses, which increased assets, revenues and profits with lower rates of loan default, particularly among poorer borrowers[5].
However, evidence from randomised evaluations (Banerjee, Karlan, and Zinman 2015; Banerjee et al. 2015; Meager 2019) across multiple countries found no uniform developmental impact of microfinance with gains being very modest and far less transformative than originally claimed[6],[7],[8]. While access to credit increased borrowing and supported business activity in some contexts, average effects on broader welfare indicators (like, poverty alleviation, consumption increase, educational and health improvements, etc.) were mostly non-existent, often with outcomes varying significantly across settings and borrower characteristics. Furthermore, Banerjee et al. (2024) who examined the long-term impact of microfinance, found that for the vast majority of poor households, microfinance fails to release them from the poverty trap and provides zero long term economic benefits[9].
Furthermore, the academic literature on microfinance emerging from mainstream economics (covered above) has not adequately incorporated perspectives on microfinance emanating from other disciplines. A more complete picture of the impact of microfinance is not possible without such incorporation. For instance, the mainstream economics literature has ignored the inherent dynamics of credit cycles within the sector and the endogenous booms and busts that it generates, causing millions of borrowers to find themselves in conditions of significant debt distress as the cycle peaks, and then, once the bust arrives, suddenly without any formal credit. International Political Economy scholar Philip Mader (2018) has shown how Minsky’s Financial Instability Hypothesis, which accounts for these kinds of cycles, could successfully explain the microfinance crisis of 2010 in Andhra Pradesh[10]. He points out that destabilising dynamics in commercial competitive microfinance and India’s political economy were the causes of that crisis. Similarly, sociologist Isabelle Guérin has written quite extensively about microfinance based on almost two decades of ethnographic fieldwork in South India. In Guérin (2014), one of a dozen or so papers on the subject, she argues that households use microcredit not as an entrepreneurial tool, but as one of many debts they constantly ‘juggle’ to manage financial obligations[11]. She also characterizes micro-borrowings as a social act that shapes a person’s community standing and often reinforces local power hierarchies instead of erasing them – contrary to the emancipatory claims of earlier microfinance studies. Although these perspectives offer a compelling complementary lens to understand the nuances of microfinance, neither academia nor industry has given serious attention to them.
The literature review we have presented above poses problems for the celebratory tone of the NCAER-MFIN report, which appears to showcase only the positive impacts of microfinance without performing a sufficiently systematic or impartial reading of the evidence from across the disciplinary spectrum. As we have described above, even within the discipline of economics itself, there remains considerable debate about the purported benefits of microfinance. Not only does the NCAER-MFIN report not engage with competing viewpoints, but it also advances several broader claims regarding the benefits of microfinance that have not been systematically studied in the academic literature – particularly its role in displacing informal credit, increasing awareness around insurance, and promoting digital financial inclusion. It is these broader claims that this blog examines more closely.
The Diverse Sources of Household Credit Need More Careful Consideration
The report argues that microfinance institutions are displacing informal credit, with lower borrowing costs cited as a key driver. In support of this argument, the survey finds that only 1% of borrowers reported relying on informal sources such as moneylenders, relatives, friends, or local shopkeepers alongside formal loans. These findings merit closer scrutiny for two reasons: first, they appear difficult to reconcile with larger datasets on household borrowing patterns; second, they may be influenced by the survey’s structure.
Data from the Centre for Monitoring Indian Economy’s (CMIE) Consumer Pyramids Household Survey (CPHS) indicate that informal borrowing continues to constitute a substantial component of household debt, even if its overall share has declined over time[12]. In particular, shop loans and borrowing from relatives or friends accounted for roughly 21% and 12% of total borrowing, respectively, during September–December 2024. While the CMIE and NCAER–MFIN measures are not directly comparable, the magnitude of the difference raises questions about whether informal borrowing is underreported in the survey sample.
As discussed in our previous blog, the report itself acknowledges that borrowers were identified and approached with the assistance of regulated entities (REs) and their field staff. In such settings, particularly in centre-based interviews, respondents may faceincentives to underreport their true level of indebtedness or their reliance on informal borrowing. This raises concerns about the interpretation of self-reported indebtedness within a purposive sampling framework.
In the case of household expenditure, the report attempts to situate its findings within broader survey evidence such as HCES and NABARD estimates. A similar exercise for informal borrowing patterns would have strengthened the credibility of its conclusions regarding the displacement of informal credit.
Microinsurance Uptake May Signal Coercive Lending Practices
The report also suggests that microfinance institutions have contributed to improving borrowers’ financial awareness, including awareness regarding insurance products. The report’s positive framing of the high uptake of credit-linked life insurance is embedded within this broader narrative. This, however, raises an important question regarding how such insurance coverage should be interpreted.
The report notes that credit-linked life insurance was effectively treated as “mandatory” for loan approval and that, at an average premium of ₹1,547, it accounted for roughly 9.5% of the total loan cost. At the same time, the report presents these insurance products as both popular and beneficial, implicitly interpreting their uptake as evidence of borrower acceptance. Yet, the report does not adequately examine the implications of such products being perceived as a condition for accessing credit.
Importantly, credit life insurance is neither a regulatory requirement, nor are lenders are permitted to make its purchase a formal precondition for loan approval[13]. Nevertheless, survey responses suggest that many borrowers perceived such insurance as effectively mandatory for obtaining credit. The report itself notes that such products are “generally bundled with credit across the industry”. This suggests that the observed uptake of credit-linked life insurance may reflect institutional practices of mis-selling more than independent consumer demand.
While lenders may have legitimate incentives to promote such products to mitigate default risk arising from borrower mortality, the report does not explicitly examine how the benefits and risks associated with these products are distributed between borrowers and lenders. This omission is notable given the report’s own qualitative findings. Out of the 32 focus group discussions (FGDs) summarised in the annexure, seven[14] refer to the linkage of third-party products – predominantly credit life insurance – with loans, suggesting that the practice of bundling insurance products alongside credit is widespread despite repeated regulatory caution regarding coercive tying practices[15].
Taken together, these patterns suggest that the high uptake of credit-linked insurance may not necessarily reflect broad-based voluntary demand for insurance products. The relatively uniform uptake of life insurance across wealth quartiles further suggests a supply-side-driven outcome, shaped more by institutional practices than by differentiated consumer demand. At the same time, the uptake of non-mandatory insurance products remains limited. For instance, the report notes that crop insurance coverage is as low as 2.6%, while several other insurance products remain disproportionately concentrated among relatively wealthier households. These patterns suggest that insurance adoption among borrowers may not be readily taken as a signal of microfinance’s positive impact on borrowers – indeed, quite the opposite.
The Narrative on Digital Adoption Is Problematic
The concluding section of the executive summary states that the microfinance sector has been effective in “promoting digital adoption/inclusion to some extent by providing options to their borrowers to repay … using digital channels.” While the report qualifies this claim in several places, some aspects of the narrative on digital adoption warrant closer examination.
The report notes that 61% of surveyed borrowers owned smartphones and that 54% of smartphone owners had installed digital payment applications. It further characterises the finding that 12% of borrowers use digital channels for loan repayment as a “promising indicator of digital adoption being on the rise.” This raises a methodological concern: the report interprets these findings as evidence of a “rise” in digital adoption, yet presents no temporal or comparative data to evaluate such a trend.
The report notes that only 25% of borrowers making digital loan repayments transact themselves while the rest transact via an intermediary like family members, peers, or group members. This raises the possibility that lenders are encouraging or only permitting digital repayments, making it difficult to interpret digital usage as evidence of borrowers’ independent adoption of digital finance. . It is therefore likely that the report overstates the extent of meaningful digital financial inclusion, as many transactions are mediated.
Further, the report finds that the lowest levels of independent digital repayment capability were concentrated in Tamil Nadu, Telangana, and Uttar Pradesh. This observation appears difficult to reconcile with both broader digital payment indicators and someof the report’s own findings. Tamil Nadu and Telangana consistently rank among the leading states in terms of UPI transaction volume and value during the relevant survey period[16]. Moreover, the report itself notes that Telangana (79%) and Tamil Nadu (66%) are among the leading states in terms of the installation of digital payment applications among surveyed borrowers. Against this backdrop, the report’s findings regarding weak digital repayment capability in these states appear difficult to reconcile both with its own observations on app installation and with broader indicators of digital payment usage.
Taken together, these patterns suggest that the report’s characterisation of a promising rise in digital adoption, alongside an implicit attribution of this shift to the microfinance sector, appears insufficiently contextualised when viewed alongside both the report’s internal findings and broader indicators of digital payment usage.
Conclusion
In this blog, we examined some of the broader claims advanced in the NCAER–MFIN report regarding informal credit displacement, insurance awareness, and digital financial inclusion. Across these themes, the report’s findings deserve more nuanced interpretation or more supporting evidence than the headline narratives sometimes suggest. Informal borrowing may remain an important, ifpotentially underreported, component of household debt; insurance uptake appears closely intertwined with product bundling practices; and the narrative of rising digital adoption appears less straightforward when viewed alongside broader usage patterns and the report’s own internal findings.
Collectively, these observations call for a more cautious interpretation of the report’s conclusions, particularly where descriptive findings, rather than rigorous empirical work to support causal claims , are interpreted as evidence of broader developmental transformation.
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Footnotes
[1] Pitt, Mark M., and Shahidur R. Khandker. “The Impact of Group‐Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?” Journal of Political Economy 106, no. 5 (1998): 958–96. https://doi.org/10.1086/250037
[2] Khandker, Shahidur R. “Microfinance and Poverty – Evidence Using Panel Data from Bangladesh.” Policy Research Working Paper Series, Policy Research Working Paper Series, 2003. https://ideas.repec.org//p/wbk/wbrwps/2945.html
[3] Cai, Shu, Albert Park, and Sangui Wang. “Microfinance Can Raise Incomes: Evidence from a Randomized Controlled Trial in China.” HKUST Business School Research Paper No. 2020-006, 2020. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3670721
[4] Bari, Faisal, Kashif Malik, Muhammad Meki, and Simon Quinn. “Asset-Based Microfinance for Microenterprises: Evidence from Pakistan.” American Economic Review 114, no. 2 (2024): 534–74. https://doi.org/10.1257/aer.20210169
[5] Battaglia, Marianna, Selim Gulesci, and Andreas Madestam. “Repayment Flexibility and Risk Taking: Experimental Evidence from Credit Contracts.” Review of Economic Studies 91, no. 5 (2024): 2635–75. https://doi.org/10.1093/restud/rdad107
[6] Banerjee, Abhijit, Esther Duflo, Rachel Glennerster, and Cynthia Kinnan. “The Miracle of Microfinance? Evidence from a Randomized Evaluation.” American Economic Journal: Applied Economics 7, no. 1 (2015): 22–53. https://doi.org/10.1257/app.20130533
[7] Banerjee, Abhijit, Dean Karlan, and Jonathan Zinman. “Six Randomized Evaluations of Microcredit: Introduction and Further Steps.” American Economic Journal: Applied Economics 7, no. 1 (2015): 1–21. https://doi.org/10.1257/app.20140287
[8] Meager, Rachael. “Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments.” American Economic Journal: Applied Economics 11, no. 1 (2019): 57–91. https://doi.org/10.1257/app.20170299
[9] Banerjee, Abhijit, Emily Breza, Esther Duflo, and Cynthia Kinnan. “Can Microfinance Unlock a Poverty Trap for Some Entrepreneurs?” Working Paper No. 26346. Working Paper Series. National Bureau of Economic Research, 2024. https://doi.org/10.3386/w26346
[10] “The Instability of Commercial Microfinance : Understanding the Indian Crisis with Minsky.” In The Rise and Fall of Global Microcredit. Routledge, 2018. https://doi.org/10.4324/9781315228693-10
[11] Guérin, Isabelle. “Juggling with Debt, Social Ties, and Values: The Everyday Use of Microcredit in Rural South India.” Current Anthropology 55, no. 9 (2014). https://doi.org/10.1086/675929
[12] Sharma, Misha, and Shree Harini V. “How Have Household Balance Sheets Changed Post the Pandemic? A Descriptive Analysis of Household Portfolios Using CMIE’s Consumer Pyramid Household Survey Dataset.” Dvara Research, November 7, 2024. https://dvararesearch.com/how-have-household-balance-sheets-changed-post-the-pandemic-a-descriptive-analysis-of-household-portfolios-using-cmies-consumer-pyramid-household-survey-dataset/
[13] There shall be no `linkage’ either direct or indirect between the provision of financial services offered by the NBFC to its customers and use of the insurance products. See Para 30 of Reserve Bank of India. 2025. Reserve Bank of India (Non-Banking Financial Companies – Undertaking of Financial Services) Directions, 2025. https://www.rbi.org.in/Scripts/BS_ViewMasDirections.aspx?id=12961#C3C
[14] We looked at FGDs numbered 16,18, 19, 25, 26, 28, 31 given in the Annexure
[15] Reserve Bank of India, Draft Amendment Directions for “Advertising, Marketing and Sales of Financial Products and Services by Regulated Entities”, February 11, 2026, https://rbi.org.in/Scripts/BS_PressReleaseDisplay.aspx?prid=62207
[16] See July – September 2024 of Ecosystem Statistics of UPI https://www.npci.org.in/product/ecosystem-statistics/upi
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Part 1 of the series is available here
Part 3 of the series is available here
