Microfinance: Issue 3

Several recent studies consider variations on ‘asset-based microfinance’ – that is, financial products that are explicitly tied to investment in a specific asset. It is now well-established, through a body of experimental field work, that the returns to providing appropriate fixed assets to microenterprises are high and sustained. This is true of urban microenterprises (see, for example, De Mel et al. 2008, De Mel et al. 2012, Fafchamps et al. 2014, and Hussam et al. 2022), and of asset transfers in rural agricultural settings (see, for example, Banerjee et al. 2015b, Bandiera et al. 2017, and Balboni et al. 2022).[1]1. For contrasting evidence, see Bauchet et al. (2015), who study an ultra-poor programme in Andhra Pradesh and find “no lasting net impact on income or asset accumulation”.

It remains an open question, however, whether high returns can also be achieved through credit products; if so, this could open exciting possibilities for providing large fixed assets in a way that is financially sustainable for microfinance institutions. A few recent papers consider this kind of asset-based microfinance. 

The impact of asset-based microfinance

Jack et al. (2023) work with a Kenyan dairy savings and credit cooperative making loans to farmers for the purchase of large water tanks. The cooperative’s standard credit contract requires clients to have deposits with the cooperative worth one-third of the value of the loan; this acts as collateral for one-third of the loan, with the remaining two-thirds secured by guarantors (either through savings or shares held in the cooperative). This contract has a take-up rate of only about 2%. Jack et al. randomly offer some borrowers the opportunity instead to take a contract in which 96% of the value of the loan is collateralised through the water tank itself (with the remaining 4% being a standard deposit requirement). The authors find that this innovation massively increases take-up (from about 2% to about 40%) – and conclude that, under the standard contract, “95% of potential tank purchasers would have been prevented from purchasing tanks due to credit constraints”. The authors find an increase in average late balances, but with a very small magnitude (less than 1% of the total loan value). 

The results of Jack et al. suggest exciting possibilities for contractual innovations that tie loans more directly to the purchase of investment assets. Specifically, the key result indicates that asset collateralisation may make it viable for lenders to extend larger loans to credit-constrained borrowers. This basic result aligns with earlier quasi-experimental research; in particular, Assuncao et al. (2014) study a 2004 Brazilian legal reform that made it easier for borrowers to sell cars that had been repossessed as collateral for failed auto loans. Assuncao et al. find that the reform “expanded credit to riskier, self-employed borrowers who purchased newer, more expensive cars” – but that it also increased loan delinquency and default. 

Following these insights, Bari et al. (2024) partnered with a Pakistani MFI to offer asset-based financing to graduated microenterprise borrowers in and around Lahore. The MFI randomly offered some borrowers a contract to purchase a fixed asset for their business. Repayments were made over 18 months, using a ‘hire-purchase’ arrangement where, as in Jack et al. (2023), the asset served as collateral for the loan. This allows the MFI to make much larger loans than would otherwise be commercially feasible in such a context. Specifically, the MFI agreed to finance assets worth up to US$ 1,900 per borrower: around four times the size of the MFI’s maximum standard loan. The authors find large and significant increases in business assets, business profits, household income, and household consumption. 

Interestingly, Bari et al. find that their results are very stable over time: the authors conduct follow-up surveys at three, six, 12, 18, and 24 months, and find essentially the same treatment effects across each follow-up wave. Viewed through the lens of a standard household intertemporal optimisation framework, this poses something of a mystery: given that household returns to microenterprise capital investments are high, why does the control group not simply accumulate capital through small incremental investments each period? To answer that question, Bari et al. build and calibrate a dynamic structural model in which household enterprises face a ‘dual-asset conundrum’: they can hold wealth either in a low-return liquid asset or in a high-return fixed asset that has large non-convex adjustment costs. (Specifically, following Field et al. (2013), Bari et al. require that, if the household is to invest in fixed capital, it must make a large investment: “a household cannot buy or sell a rickshaw one wheel at a time”.)[2]2. For earlier models of capital lumpiness and microfinance, see, for example, Banerjee et al. (2015a), Kaboski and Townsend (2011) and Besley et al. (1993).  The authors’ calibration results imply an important role for such costs; this implies that – at least for graduated borrowers – there may be substantial welfare gains through microfinance contracts that provide a large collateralised asset, rather than contracts that seek to encourage enterprises to accumulate such assets through incremental increases in wealth.

Asset-based financing for women

An exciting area of recent work has focused specifically on the effects of asset-based financing for women, highlighting the potential for transformative positive impacts from providing financing for lumpy investments aimed at benefitting women. Van Doornik, Gomes, Schoenherr, and Skrastins (2024b) examine the impact of asset financing for motorcycles through ”consorcios” –  a widespread group-lending mechanism for financing durable goods in Brazil, with more than 6.7 million participants annually. The authors focus on motorcycle consorcios, which primarily include credit-constrained individuals seeking to invest in personal mobility. Approximately one-third of motorcycles in Brazil are sold through consorcios – and between 2009 and 2016, more than 10 million individuals (or 6.6% of the working-age population) participated in a motorcycle consorcio. Each month, participants in a consorcio make equal contributions, which are then allocated as credit to a subset of members for motorcycle purchases, determined through lotteries and auctions. Consorcios resemble rotating savings and credit associations (ROSCAs), which have been extensively studied in the development economics literature (Besley et al. 1993, Besley and Coate 1995) – and, as noted in Section 4A, provided foundational insights for joint-liability group lending in microfinance. Van Doornik et al. find that women who gained access to credit for motorcycle purchases through consorcios experienced an 18% reduction in mortality, primarily due to fewer fatal assaults in both public and domestic settings. Access to individual mobility enables women to avoid dangerous areas and provides a means of escape from potentially violent situations – underscoring the safety and empowerment benefits of mobility-focused credit. In related work, Van Doornik, Schoenherr, and Skrastins (2024) show that this same credit programme significantly boosts employment rates and salaries, with an estimated annual return of 12-15%. These findings suggest that asset-based financing not only enhances economic opportunities for women but can also contribute to their physical safety.

Adding to this growing literature on asset-based financing for women, Van Doornik, Fazio, Ramadorai, and Skrastins (2024) examine the impact of access to asset finance on fertility rates in a similar Brazilian context. This study focuses on housing, typically the largest financial asset investment in a household’s life. Using the same consorcio structure, but this time focused on financing houses, they find that access to housing increases fertility by 3.8% on average, with much larger impacts for younger households aged 20-25, who experience a 32% increase in the probability of childbearing. The effects are particularly strong among households in lower-quality housing or with high rental expenses, highlighting the role of housing and financial stability in family planning. 

Related work by Augsburg et al. (2023a) examines how labeled loans can facilitate investment in essential but costly household infrastructure, specifically sanitation facilities like toilets. In their study setting in rural India, the average cost of constructing a toilet represents approximately 50% of a household’s annual income. The authors demonstrate that labeled microcredit for sanitation can help overcome commitment and liquidity constraints, effectively increasing investment in these substantial, welfare-enhancing assets. This complements broader findings on asset-based financing by illustrating how targeted credit products can meet specific household needs, particularly for low-income families, thereby improving living conditions.[3]3. In a companion paper, Augsburg et al. (2023b) explore how gender differences in perceptions and bargaining power within households affect take-up of the sanitation-based loan. They find that women's higher perceived benefits drive loan uptake, while successful investment depends on their lower cost perceptions, underscoring the importance of intra-household dynamics in the sanitation asset investment decision.

Other forms of collateral for microfinance

In the previous papers in this section, the asset that is financed is the same asset that serves as collateral. Of course, this need not be the case: in many contexts, it is possible to finance the purchase of one asset by pledging a different asset as collateral. This is a theme explored by Carney et al. (2022), in the same setting as the earlier work in Jack et al. (2023). Carney et al. run an experiment with Kenyan dairy farmers, in which they randomly offer farmers either a ‘Same-Asset Collateralised Loan’ (SACL) or an ‘Other-Asset Collateralised Loan’ (OACL); the former refers to the asset being financed, whereas the latter relates to some assets that the household already owns. These loans are provided to purchase either a milk can, a cow sprayer, cooking pots, or a large thermos (each having a market value of approximately US$30). The authors find a large and significant increase in the average willingness to pay for a new item under SACL relative to OACL (specifically, a difference of about 15%) – though they find no difference in default rates between the two types of loan. The authors interpret these results in terms of (i) an endowment effect over the assets (such that borrowers are more concerned about the prospect of repossession of assets they already own than assets they are considering purchasing), coupled with (ii) naivete about their likely future attachment to the new asset. The results imply that there is likely to be underinvestment in goods that are difficult to use as collateral.

One response to this problem is for lenders and policymakers to seek out innovative alternative methods for collateralisation. This is a theme explored by Gertler et al. (2024), who test the potential for ‘digital collateralisation’ – in which repayment failures trigger ‘lock-out’ from an asset, rather than physical repossession. Gertler et al. run a field experiment with a Ugandan pay-as-you-go lender, whose existing business model involves such a lock-out technology. Gertler et al. randomly assigned respondents into a control group (who did not receive a loan offer), a ‘Secured’ treatment group (who were offered a loan in return for agreeing to digital collateral) and an ‘Unsecured’ treatment group (who were offered the same loan, but without requirement for collateralisation). Among those agreeing to the ‘Secured’ treatment, the authors randomly informed some that they would receive the loan without the collateral requirement: this ‘Surprise Unsecured’ treatment allows for the separate identification of selection effects from moral hazard effects (following the design in Karlan and Zinman 2009). The authors find that (i) take-up was about six percentage points lower for customers offered the secured loan, (ii) but average repayment increased by 11 percentage points under the secured loans (approximately two-thirds of which was attributable to moral hazard; one-third to adverse selection). As Gertler et al. conclude, “digital collateral increases the share of customers to whom a company can profitably offer loans”.[4]4. See also Alibhai et al. (2022), who use psychometric scoring to substitute for collateral in assessing eligibility for loans of up to US$7,500 in Ethiopia; the authors argue that such scoring highlights “the potential for using innovative technologies to extend entrepreneurial finance to underserved markets”.

Interestingly – and echoing the ‘SACL/OACL’ distinction in Carney et al. (2022) – the prospects for digital collateralisation are not limited to contexts in which the loan finances the lockable asset. Indeed, Gertler et al. study a loan product that provides for payment of school fees – which is collateralised through lock-out through solar-home systems that the households already own. Of course, as in any context in which a borrower provides collateral, lenders and regulators in this space need to be cognisant of potential hardships that defaulting borrowers face – both hardships triggering default and hardships caused by lock-out.[5]5. For example, Waldron and Swindon (2018) describe the humiliation of one Ugandan client who lost access to electricity for non-payment of a PAYGO solar loan. Similarly, Corkery and Silver-Greenberg (2014) describe the consequences of lock-out for a subprime auto borrower in the United States.  This is a point considered by Gertler et al. – who argue, through a theoretical model, that “an intermediate degree of lockout can be welfare maximising”.[6]6. As the authors explain, “Stronger lockout destroys more surplus (i.e. household utility) when it is employed, which can offset the welfare gains of the credit expansion”.

References

Alibhai, S, R Cassidy, M Goldstein, and S Papineni (2022), "Evening the Credit Score? Impact of Psychometric Loan Appraisal for Women Entrepreneurs." World Bank Policy Research Working Paper 10230.

Assunção, J J, E Benmelech, S Fernando and S Silva (2014), “Repossession and the Democratization of Credit”, The Review of Financial Studies 27(9): 2661–2689.

Augsburg, B, B Caeyers, S Giunti, B Malde and S Smets (2023a), “Labeled loans and human capital investments” Journal of Development Economics 162: 103053.

Augsburg, B, B Malde, H Olorenshaw and Z Wahhaj (2023b), “To invest or not to invest in sanitation: The role of intra-household gender differences in perceptions and bargaining power” Journal of Development Economics 162: 103074.

Balboni C, O Bandiera, R Burgess, M Ghatak and A Heil (2022), “Why Do People Stay Poor?”, The Quarterly Journal of Economics 137(2): 785–844.

Banerjee, A, D Karlan and J Zinman (2015a), “Six Randomized Evaluations of Microcredit: Introduction and Further Steps”, American Economic Journal: Applied Economics 7(1): 1–21.

Banerjee, A, E Duflo, N Goldberg, D Karlan, R Osei, W Parienté, J Shapiro, B Thuysbaert and C Udry (2015b), “A multifaceted program causes lasting progress for the very poor: Evidence from six countries”, Science 348(6236): 772–788.

Bandiera, O, R Burgess, N Das, S Gulesci, I Rasul and M Sulaiman (2017), “Labor Markets and Poverty in Village Economies”, The Quarterly Journal of Economics 132(2): 811–870.

Bari, F, K Malik, M Meki, and S Quinn (2024), “Asset-based microfinance for microenterprises: Evidence from Pakistan.” American Economic Review 114(2): 534–574.

Bauchet, J, J Morduch and S Ravi (2015), “Failure vs. displacement: Why an innovative anti-poverty program showed no net impact in South India”, Journal of Development Economics 116: 1-16.

Balboni, C, O Bandiera, R Burgess, M Ghatak and A Heil (2022), “Why Do People Stay Poor?”, The Quarterly Journal of Economics 137(2): 785–844.

Besley, T, S Coate and G Loury (1993), “The Economics of Rotating Savings and Credit Associations”, The American Economic Review 83(4): 792–810.

Besley, T and S Coate (1995), “Group Lending, Repayment Incentives and Social Collateral”, Journal of Development Economics 46(1): 1–18.

Carney, K, M Kremer, X Lin, and G Rao (2022), “The endowment effect and collateralized loans”, National Bureau of Economic Research Working Paper No. 30073.

Corkery M and J Silver-Greenberg (2014), “Miss a Payment? Good Luck Moving That Car”, The New York Times, Available here.

De Mel, S, D McKenzie and C Woodruff (2008), “Returns to Capital in Microenterprises: Evidence from a Field Experiment”, The Quarterly Journal of Economics 123(4): 1329–1372.

De Mel, S, D McKenzie and C Woodruff (2012), “One-Time Transfers of Cash or Capital Have Long-Lasting Effects on Microenterprises in Sri Lanka”, Science 336(6071): 962–966.

Fafchamps, M, D McKenzie, S Quinn and C Woodruff (2014), “Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana”, Journal of Development Economics 106(C): 211–226.

Field, E, R Pande, J Papp and N Rigol (2013), “Does the Classic Microfinance Model Discourage Entrepreneurship among the Poor? Experimental Evidence from India”, American Economic Review 103(6): 2196–2226.

Gertler, P, B Green, and C Wolfram (2024), “Digital collateral”, The Quarterly Journal of Economics 139(3): 1713–1766.

Hussam, R, N Rigol and B N Roth (2022), “Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field”, American Economic Review 112(3): 861–98.

Jack, W, M Kremer, J De Laat and T Suri (2023), “Credit access, selection, and incentives in a market for asset-collateralized loans: Evidence from Kenya”, Review of Economic Studies 90(6): 3153–3185.

Kaboski, J P and R M Townsend (2011), “A structural evaluation of a large‐scale quasi‐experimental microfinance initiative”, Econometrica 79(5), 1357-1406.

Karlan, D and J Zinman (2009), “Observing Unobservables: Identifying Information Asymmetries with a Consumer Credit Field Experiment”, Econometrica 77(6): 1993–2008.

van Doornik, B, D Fazio, T Ramadorai and J Skrastins (2024), “Housing and Fertility”, Working paper.

van Doornik, B, A Gomes, D Schoenherr, and J Skrastins (2024b), “Financial Access and Labor Market Outcomes: evidence from credit lotteries.” American Economic Review 114(6): 1854–1881.

van Doornik, B, D Schoenherr and J Skrastins (2024), “Escaping violent death: Access to credit and female mortality”, Working paper, Banco Central do Brasil.

Waldron D and A M Swindon (2018), “Remote Lockouts: The Dark Side of Pay-as-You-Go Solar?”, CGAP Blog, Available here.