In a nutshell
Evidence from around the world indicates that microcredit does not help to lift poor households out of poverty.
Microcredit does allow low-income households to cope better with risk and to enjoy greater flexibility in how they earn and spend money.
Somewhat paradoxically, to increase microcredit’s ability to reduce poverty, providers may need to be more selective by focusing more on relatively prosperous households with prior business experience.
Recent years have seen an intense debate between microfinance proponents and detractors on whether microcredit can lift people out of poverty. What has been absent from this debate is solid evidence. To fill this gap, a number of research teams across the world started randomised evaluations (large field experiments) to measure rigorously the impact of access to microcredit on borrowers and their households.
Studies were set up in Bosnia and Herzegovina, Ethiopia, India, Mexico, Mongolia, Morocco and the Philippines. Research took place in both urban and rural areas and evaluated both individual-liability and joint-liability (group) loans. Some of the participating microfinance institutions were for-profit organisations while others were non-profits.
Together these studies provide a rigorous body of evidence on the impact of microcredit in a wide variety of settings. The research results (Banerjee et al, 2015b) paint a remarkably consistent picture, which contains four main lessons.
Four lessons on microcredit
Impact on poverty
Across all seven studies, microcredit does not lead to substantial increases in borrowers’ income. It does therefore not help to lift poor households out of poverty. A more formal analysis of the evidence collected in these studies confirms that for a large majority of people, access to microcredit has little impact on household income (Meager, 2016).
A possible explanation is that while microcredit clients overwhelmingly report using loans at least partially for business purposes, many of them also report using part of their loans for consumption.
Another possible explanation is that not all borrowers are natural entrepreneurs. Of those that use microcredit to open or expand a small business, some borrowers are more successful than others. Indeed, the evidence suggests that particular sub-populations – especially relatively well-to-do households that already operate a small-scale business before they access microcredit – appear to be better suited to using microcredit in a profitable way (Angelucci et al, 2015; Meager, 2016).
In contrast, ‘reluctant entrepreneurs’ (Banerjee et al, 2015a), who do not have prior business experience, typically show little impact in terms of business scale and performance.
Impact on wellbeing
Access to microcredit also does not appear to have tangible impacts on borrowers’ wellbeing or the wellbeing of others in their households. For example, three of four studies find no effect on female decision-making power and independence. In six studies, microcredit access does not increase children’s schooling.
Impact on flexibility
On the upside, households with access to microcredit enjoy greater freedom in deciding how they earn and spend money. In Bosnia and Herzegovina, and Morocco, microcredit allows people to change their mix of employment activities, reducing earnings from wage labour and increasing income from self-employment activities.
In the Philippines, it also helps households to insure themselves against income shocks and to manage risk. In Mexico, households with access to microcredit do not need to sell off assets when hit by an income shock.
No harmful effects
Importantly, there is no evidence of systematic harmful effects of access to microcredit. For example, overall stress levels among borrowers are no different from the comparison group in Bosnia and Herzegovina or the Philippines, although male borrowers experience significantly higher levels of stress in the Philippines.
Implications for the microfinance industr
Better and more varied product design
Small changes to product design may influence how people use and benefit from microcredit. For example, the typical microloan repayment begins two weeks after loan disbursement and payment is usually required on an inflexible weekly basis.
This can be an effective strategy to limit default, but it may also limit borrowers’ income growth. In India, granting (some) borrowers a grace period – so that they can build a business before they need to start repaying – leads to higher short-run business investment and long-run profits, but it also increases default rates (Field et al, 2013).
In addition, monthly or seasonal repayment schedules that better reflect borrowers’ income flows can help borrowers to make better use of their loans. Microfinance institutions like Enda Arabe in Tunisia and FINCA in Armenia offer loan products where repayment schedules are matched with expected cash flows (which depend on the seasonality of agricultural products).
Further research is needed to evaluate the impact of such flexible loan products in terms of repayment rates and poverty outcomes.
Moreover, microcredit’s liability structure may be an important determinant of loan take-up and impact. Evidence from Mongolia (Attanasio et al, 2016) indicates that in high-risk environments, individuals take up joint-liability rather than individual-liability loans because joint-liability loans can help them to share risk.
While the continuing trend in the microfinance industry towards liability individualisation may benefit less risk-averse (wealthy) borrowers, it may also gradually exclude poorer and more risk-averse borrowers from the market for financial services.
Better market and product segmentation
Microfinance institutions and their clients may also benefit from more market segmentation. Lenders may consider offering larger, more flexible products to clients who are most likely to perform well, and smaller, less flexible loans to less promising borrowers.
Banerjee et al (2015b) suggest that the recent findings on micro-client heterogeneity should make lenders rethink the extremely non-selective nature of their lending. More effort may be needed to focus on the minority of potentially high-growth entrepreneurs and service these households with larger loans.
A next step would be for microfinance institutions to pilot better ways to help high-performing micro-entrepreneurs become eligible for lending to small and medium-sized enterprises (SMEs). Today, successful and growing clients that need more funding may get stuck: too large for microfinance, but not yet viable clients at traditional lending institutions.
Microfinance institutions could set up arrangements with local banks to transfer such successful clients (for a fee) to a bank so that they can continue their growth trajectory. Likewise, banks with both microfinance and SME departments should ensure that fast-growing micro clients can graduate easily to SME status.
Angelucci, Manuela, Dean Karlan and Jonathan Zinman (2015) ‘Microcredit Impacts: Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco’, American Economic Journal: Applied Economics 7(1): 151-82.
Attanasio, Orazio, Britta Augsburg and Ralph De Haas (2016) ‘Microcredit Contracts, Risk Diversification and Loan Take-up’, EBRD Working Paper No. 189.
Banerjee, Abhijit, Emily Breza, Esther Duflo and Cynthia Kinnan (2015a) ‘Do Credit Constraints Limit Entrepreneurship? Heterogeneity in the Returns to Microfinance’, mimeo.
Banerjee, Abhijit, Dean Karlan and Jonathan Zinman (2015b) ‘Six Randomized Evaluations of Microcredit: Introduction and Further Steps’, American Economic Journal: Applied Economics 7(1): 1-21.
EBRD (2016) Transition for All: Equal Opportunities in an Unequal World – Transition Report 2016-17, European Bank for Reconstruction and Development.
Field, Erica, Rohini Pande, John Papp and Natalia Rigol (2013) ‘Does the Classic Microfinance Model Discourage Entrepreneurship among the Poor? Experimental Evidence from India’, American Economic Review 103(6): 2196-2226.
Meager, Rachael (2016) ‘Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature’, job market paper, MIT.