Financial inclusion is essential to development in an economy. Access to credit, transactions, payments, savings, and insurance contributes to eliminating poverty, and fosters increased employment and economic growth. This broad spectrum of services needs to encapsulate the differing socio-economic circumstances of the population they aim to serve.
Yet for many, financial inclusion remains an ideal rather than a reality. Certain sectors of society, particularly groups with lower social status, are often limited to the informal credit market. The uncertainty in their employment results in a greater need to smooth consumption. This entails maintaining a stable level of spending over time despite fluctuations to manage risks and sustain livelihoods. However, the same demographics are often credit constrained, and thus tend to have low access to formal financial institutions. Credit constrained households are forced to rely on informal sources, like relatives, friends and money-lenders. The informal alternatives are insufficient, unreliable and often very expensive.
Without access to financial institutions, households can have low income, unstable consumption, and heightened economic vulnerability. This in turn can lead to low levels of education and health care, low human capital, low productivity, and ultimately low income. Financial exclusion can thus trap households in a self-reinforcing cycle that formal systems were meant to break.
Microfinancing emerged as an alternative to traditional, formal financial institutions. It is a system that provides financial services to people excluded from traditional banking. In particular, the model enables collateral-free lending to credit constrained people. It is a promise to achieve financial inclusion, particularly for those who have been excluded from the formal banking sector. Microfinance borrowers are also more likely to escape poverty than non-borrowers. Macroeconomic evidence shows that countries with deeper financial inclusion tend to grow faster and reduce income inequality. It is widely recognized as a model that is well established and has delivered success.
However, microfinance still leaves some behind, struggling to completely live up to its promise. It’s useful to consider microfinancing as a principal-agent relationship. The loan-officer (principal) faces the risk of the loan not being repaid and that risk is determined by the borrower (agent) not the lender. Asymmetric information is a defining characteristic in this exchange. The loan officer is unable to observe each potential borrower individually to determine how likely they are to repay the loan. Loan officers need an alternative method: statistical discrimination. This form of reasoning is based on proxy predictors, and is used to decide eligibility. Additionally, microfinance is decentralized. This structure gives loan officers the primary role in deciding which clients will be accepted. In the absence of perfect information, a loan officer’s prediction of an individual’s productivity is the weighted average of proxies (such as gender age and race) and the productivity of workers in the same demographic as the potential borrowers. It can be understood as a shortcut.
However, statistical discrimination allows prejudices to become embedded into the method over time. Loan officers can fill in gaps of knowledge with their own biased assumptions. These assumptions shape who gets access to credit and who doesn’t. If a loan officer determines that the productivity of workers in a certain demographic is low, with a high probability of default, a prospective borrower in the same group is less likely to get a loan, or will pay high interest rates. Potential borrowers are unjustly penalised (or rewarded) for belonging to a group. This is particularly true of caste-based exclusion. The caste system, and the resulting societal fractures are a core facet of Indian cultural identity. Caste-based discrimination is illegal but still occurs implicitly, as there are several proxies for caste identities; names, locations, even diets.
Loan officers and other key frontline service employees assume borrowers from lower caste backgrounds are high risk applicants with high default rates. As a result, they often refuse loans to lower caste or Dalit individuals. Lower caste borrowers are being excluded from accessing microfinance that was established to ensure inclusion of vulnerable populations. To address caste-based exclusion, microfinance institutions need to address the structures that allow discrimination to persist.
Algorithmic credit scores were introduced to reduce statistical discrimination and minimize human bias, aiming to make credit scores more objective. However, these systems remained vulnerable to bias. The prejudices of loan officers who handle access to data entry into the algorithm threatened the efficacy of these tools. Over-generalizations were still used in lending decisions.
High-powered incentives may decrease credit officers’ discriminatory practices. A purely welfare maximizing microfinance institution might use incentive contracts to deter its credit officers from discriminating against customers who belong to vulnerable groups of society, using equal treatment policies. However, these incentives are costly and budgets are limited. Institutions would face trade-offs between fighting discrimination and raising outreach.
Welfare maximization may not imply full eradication of discriminatory practices. An anti-discrimination constraint could be too costly; a strict proportionality rule could hamper women empowerment. Instead, microfinance institutions could use officer bias strategically. If female credit officers are more likely to lend to women, they could hire more female officers on purpose. If a poor woman is preferred over a poor man, the institution achieves women empowerment, and if a poor man is preferred over a richer woman, welfare is maximized. Hiring Dalit officers could, over time, reduce statistical discrimination against lower-caste borrowers. Representation at decision-making levels can counteract the implicit biases that shape lending outcomes, without requiring trade-offs in efficiency or financial sustainability.
Microfinance was built on the idea of empowerment through access. But when the service reinforced social hierarchies, the promise became incomplete. Addressing barriers to financial inclusion is important. It aligns with the themes of transformative service research: the ability to participate fully in society and the existence of options or choices. Financial inclusion is important for economic growth and poverty reduction. Implicit understandings of power and social status are the result of deeply held bias and are hard to shed. Microfinance can fulfil its promise of financial inclusion only when institutions confront structural barriers through policy reform, increased awareness and intentional representation.
Sharmila Bommadevara
