The microfinance movement in India is set to receive another dose of impetus with the Reserve Bank of India’s (RBI) recently released Consultative Document on Regulation of Microfinance in June 2021. Following the Malegam Committee Report, which is a decade old now, the current document looks to reassess and realign the priorities of the sector.
Some of the key regulatory changes proposed in the document takes household income as a critical variable for loan assessment. The definition of microfinance itself is proposed to mean collateral-free loans to households with annual household incomes of up to ₹1,25,000 and ₹2,00,000 for rural and urban areas respectively. The document requires all Regulated Entities to have a board-approved policy for household income assessment. Moreover, it caps loan repayment (principal and interest) for all outstanding loans of the household at 50% of household income. Given the importance accorded to household income, measuring this accurately becomes critical for effective implementation of these norms.
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An elusive figure
Household income, however, is an elusive figure. With a high degree of informality in our economy, income streams, especially for non-salaried workers, tend to be erratic in time and volatile in volume. Low-Income Households (LIHs), who typically form the customer base for Microfinance Institutions (MFIs), often also have seasonal and volatile income flows. An agricultural worker earns the most during the sowing seasons; a land-owning agricultural household sees an income spike during the harvest season; households with migrant workers who migrate to the city for certain months of the year see an income peak during those months; and a flower vendor near a temple sees an income increase during festivals. These highs are also contrasted by lows during certain lean seasons when remunerative work is unavailable (drought), during growing season (before harvest) or general lull times (a tailor, who was busy just before Diwali, receives much lesser orders right after the festival).
Since income for LIHs are seasonal and volatile, there have been attempts to understand their inflows by measuring their expenditure. But, given the rotational debts they avail to fund a consumption expenditure here and a loan repayment obligation there, expenditure also does not truly reflect the household’s income. Moreover, for most LIHs, their expenditure on income-related activity is not separate from their personal expenses. Ask a farmer what their profit was during the last season and you would likely be told the market price they got for the produce. All the input and labour costs that a farmer incurred would be subsumed under general expenses. Therefore, it is difficult to separate the household’s personal expenses from that of their occupational pursuits. Given these complexities, we need to understand and accept that for the bulk of LIHs, household finance is not just personal family finance, but their business finance as well.
In spite of the complexity in assessing household income for a typical microfinance client, creative and cost-effective ways to capture accurate data about household-level cash flows could be devised. Here, we present three ways. First, a structured survey-based approach could be used by Financial Service Providers (FSPs) to assess a household’s expenses, debt position and income from various sources of occupation. However, attention must be paid while designing such a questionnaire so that it captures seasonality and volatility in cash flows, which is an inherent characteristic of the financial lives of LIHs. Second, a template-based approach could be used wherein FSPs could create various templates for different categories of households (as per location, occupation type, family characteristics, etc.). Household templates could be defined based on publicly available data sets that contain State/district-level information about household cash flows and occupation types. These templates could then be used to gauge the household income of a client matching a particular template. Third, FSPs could also form a consortium to collect and maintain household income data through a centralised database. This would allow for uniformity in data collection across all FSPs and, over time, can be used to validate the credibility of any new client’s reported income. Such a database would also enable FSPs to track the changes in household income over time.
It is worth acknowledging that the proposed suggestions to capture household income require time, energy and money on the part of FSPs. Therefore, finding cost-effective yet accurate ways of capturing this information becomes crucial. Technology service providers could play a crucial role in this exercise and create customised digital architecture for FSPs depending on their specific needs. Creating new technology to document and analyse cash flows of LIHs would not only facilitate credit underwriting/decisioning but also innovation in the standard microcredit contracts through customised repayment schedule and risk-based pricing, depending on a household’s cash flows. Eventually, an accurate assessment of household-level incomes would avoid instances of over-indebtedness and ensure long-term stability of the ecosystem.
Priyadarshini Ganesan is a senior research associate. Misha Sharma is practice head with the Household Finance Research Initiative at Dvara Research
