Monday, November 16, 2020

Get by with a little help from my friends (and shopkeepers): Household borrowing in response to Covid 19

by Renuka Sane and Ajay Shah

The lockdown in the early days of the Covid 19 pandemic in India impacted on on economic activity. Between April and August 2020, 18.9 million salaried people lost their jobs, difficulties were faced by migrant labourers, and small and medium businesses. Deshpande (2020) shows that overall employment dropped sharply post-lockdown, with larger drops for women than men. Household incomes were adversely affected. As an example, survey work by Lee, Sahai, Baylis and Greenstone (2020) shows that two months into the lockdown poor and non-migrant workers in Delhi saw a drop of 57% in their incomes, with 9 out of 10 workers reporting that their weekly income had fallen to zero. Bertrand, Krishnan and Schofield (2020) measure the fraction of households who say they are able to survive on their own for a week, and in April that value was 34% in the overall population and 50% or more for below-median household income.

How would households cope with such a shock? Economic theory suggests that households desire consumption smoothing. One mechanism for consumption smoothing is borrowing. For example, there was an increase in household borrowing after demonetisation (Karmarkar and Narayan, 2020; Wadhwa, 2019; Chakraborty and Sane, 2019). This connects to the working of the financial system. While India has made a lot of progress in ownership of a bank account, and increased electronic payments, access to formal credit remains low.

What do we expect about household borrowings?

Borrowing during the Covid crisis is shaped by three factors:

1. Income transfers: The government of India announced a stimulus package worth Rs.1.7 trillion after the lockdown. This included food security measures as well as direct cash transfers to poor households. This may have helped households deal with the immediate crisis.

2. Low demand: As people were at home owing to the lockdown, demand may have been affected. It is also possible that households saw this job loss as permanent, and hence cut back on expenditures in a way they would not have had they seen this as a temporary disruption. This also fits with the view that precautionary savings increase after a deep crisis (Rajadhyaksha, 2020). However, this may be true for households in the higher income distributions, but is unlikely to be the case for those below median income.

3. Supply constraints: India's financial system has faced difficulties since 2012. This has manifested itself as business failure at ILFS and other financial firms, large and small. Credit growth was decelerating prior to the lockdown. The difficulties for the financial sector increased when the Reserve Bank of India announced a moratorium on all loan repayments for three months from March to May 2020, and then extended it for another three months. These moratoriums made it more difficult for financial firms to assess the credit quality of borrowers. Overall bank credit growth was 5.8% in September 2020 compared to 8.1% in September 2019. From 2018 onwards, when certain borrowers faced supply constraints, they would have had to deleverage (repaying old loans while not getting new ones) or default.

The grand question of the field consists of understanding the economic condition of households in India in 2020, in examining how consumption was held up through new kinds of labour supply and through borrowing, and in obtaining insights into these three distinct economic forces that are in play. In this article, we discover some new facts that contribute towards this overall research agenda.

Methodology

We source data from the Consumer Pyramids Household Survey (CPHS) for the months of May, June, July and August from the years 2016 - 2020. The borrowing data comes from the Aspirational India table within CPHS. Using this we ask three questions:

1. Did households have debt outstanding at the time of the survey? This helps us understand the total number of borrowers in the economy.
2. What are the sources from whom households have outstanding borrowings? This tells us whether households borrow from the formal or the informal sector.
3. What is the purpose for which households have outstanding borrowings? This tells us if households are borrowing for consumption expenditure, for consumer durables, or for running their businesses.

CPHS does not provide information on the value of debt outstanding. We are, therefore, not able to analyse the impact on borrowing on an intensive margin. Our analysis is restricted to understanding the proportion of households borrowing from various sources, for various reasons, i.e. on the extensive margin. Household weights for each wave are provided by CPHS -- these are used to get population estimates.

Results: The number of borrowers

Table 1 presents the number and percentage of households having debt outstanding in the months of May - August in each of the five years. The number of borrower households had been consistently increasing till 2019. In May - August 2016, 12% of the population had debt outstanding. This increased to 50% by 2019. The number, however, fell in 2020 to 45% of the population. The fall has been greater in urban regions than rural.

Table 1: Number and share of borrowers in the population
WAVE NATIONAL RURAL URBAN

in million in million in million
May - Aug 2016 34.8
(12.3%)
22.8
(12.0%)
11.9
(13.0%)
May - Aug 2017 81.9
(28.3%)
56.7
(29.1%)
25.1
(26.6%)
May - Aug 2018 136.0
(45.6%)
94.0
(46.8%)
42.0
(43.0%)
May - Aug 2019 154.7
(50.5%)
105.1
(51.1%)
49.6
(49.4%)
May - Aug 2020 141.6
(45.1%)
99.6
(47.2%)
42.0
(40.7%)

Disentangling explanations: Sources of borrowing

Given that a large proportion of households did not have enough to live on for more than a couple of weeks, we would have expected a huge increase in the number of borrower households. In order to investigate the sources of this drop, we begin by analysing the role of the financial system in the household borrowing story by studying the sources of borrowing.

Table 2 presents the percentage of borrower households borrowing from each source. We find that the biggest drop in borrowing is from banks: in 2019, 26% of borrower households had borrowed from banks - this has dropped to 20% in 2020. The proportion of households borrowing from money lenders has also dropped - from 7% in 2019 to 4% in 2020. The drop in households borrowing from banks and money lenders was higher in urban regions than rural regions. There has been a lot of discussion in India about the increased risk aversion of banks. A fall in the number of borrower households may be a result of this phenomenon.

Table 2: Sources of borrowing
SOURCE May - Aug 2019
Rural
May - Aug 2019
Urban
May - Aug 2020
Rural
May - Aug 2020
Urban
Banks 26.6% 25.6% 21.9% 15.3%
Money Lenders 7.1% 7.1% 4.6% 3.4%
Employer 0.5% 1.4% 0.5% 1.0%
Relatives/Friends 14.5% 13.3% 21.1% 27.2%
Shops 52.0% 50.7% 57.6% 49.8%

There has been a concurrent rise in the number of households who have borrowed from friends and family from 14% in 2019 to 21% in rural regions, and from 13% to 27% in urban regions. The sharp increase in the borrowing from friends and family suggests that some smoothing of consumption expenditure is likely to have occurred using informal social networks that play an important role in the economic lives of those in developing countries (Munshi, 2014).

Household borrowing from shops increased in rural India - from 52% to 58%, and fell slightly in urban India. It is interesting to recall that Chakraborty and Sane (2019) had found that between the years 2016 and 2018 (i.e. after demonetisation), the biggest rise in borrowing was from shops, especially by those in the lower income deciles. This seems to be true in the current situation as well, especially in rural regions.

In difficult times, it was not banks, money lenders, and employers that mattered. It was friends and family, and the neighbourhood shops. It appears that non-financial firms and cash flow management by the retail supply chain have been more important than financial firms. The connections from the formal financial system to these shops could then be unusually influential.

Disentangling explanations: Purpose of borrowing

Examining the purpose for which households borrow can tell us something about the demand for credit. Table 3 presents the top five reasons for borrowing in 2020, and compares it with 2019.

Table 3: Purpose of borrowing
SOURCE May - Aug 2019
Rural
May - Aug 2019
Urban
May - Aug 2020
Rural
May - Aug 2020
Urban
Consumption 62.3% 59.8% 70.1% 65.7%
Debt Repayment 7.3% 8.8% 9.1% 11.9%
Housing 7.3% 9.7% 2.4% 4.7%
Durables 5.1% 8.6% 1.6% 4.3%
Investments 6.4% 1.9% 0.4% 0.5%

In May-August 2019, 62% of rural and 60% of urban borrower households had borrowed for reasons of consumption expenditure. In May-August 2020, this had risen to 70% and 66% of rural and urban borrower households respectively. This is consistent with the importance of consumption smoothing, and of many households not having enough resources to survive for more than a few weeks. The increase for reasons of consumption expenditure is higher in urban regions. Urban India was more likely to be affected because of both the Covid infections and the intensity of the lockdown than rural India. Income transfers from the government are also likely to have targeted rural households than urban households.

There has been an increase in borrowing for business and debt-repayment reasons in this period as well. The numbers for rolling over debt went from about 9% to 12% of borrower households in urban regions, and from 7% to 9% of borrower households in rural regions. This suggests that households who would otherwise have serviced debt through business or personal income took recourse to borrowing when those cashflows subsided. A personal insolvency law that is able to provide some relief to debtors and allow for restructuring of the larger loans can help alleviate some of this stress.

The fall in the number of borrower households seems to be driven by the fall in the borrowings for housing, durables purchase and investments. It is also likely that large purchases such as housing and durables are made through bank loans. The fall in the borrowings from banks may be a result of a fall in these large durable purchases.

Conclusion

We study the response of households on borrowings during the 2020 lockdown. We do not have data on the value of debt outstanding. We expected that there would be an increase in the number of households that borrow owing to the disruptions to economic activity. However, it is remarkable, that despite the large shock, overall, there has been a reduction in the number of households that borrow. This fall is driven by fewer households borrowing from banks, and fewer households borrowing for housing, and consumer durables purchases. Households continue to borrow for consumption expenditure, business and debt repayment. The most utilised sources of borrowing are friends and family and shops.

This work suggests many interesting possibilities for downstream research. For example, one can study the differences in borrowing patterns between households with different income and wealth profiles, as well as the correlation between sources and purpose of borrowing. It will also be possible to evaluate whether income transfers from the government led to a fall in the number of borrower households. Similarly, one can ask whether different health outcomes play a role in their borrowing outcomes.

The number of households choosing to borrow has been different from what happened after demonetisation. There may be several reasons for this - the magnitude of the disruption, the length of time for which it lasted, the possibility of more permanent impacts on labour markets among others. This leaves us with interesting research possibilities to understand household behaviour and their interaction with financial markets.

References

Ashwini Deshpande (2020), The Covid-19 Pandemic and Lockdown: First Effects on Gender Gaps in Employment and Domestic Work in India, Working Paper 30, Ashoka University.

Azim Premji University (2019), "State of Working India 2019", Technical Report, Centre for Sustainable Employment.

Kaivan Munshi (2014), "Community Networks and the Process of Development", Journal of Economic Perspectives, 28(4), pp: 49-76.

Kenneth Lee, Harshil Sahai, Patrick Baylis, and Michael Greenstone (2020), "Job Loss and Behavioral Change: The Unprecedented Effects of the India Lockdown in Delhi", Working Paper, EPIC India.

Marianne Bertrand, Kaushik Krishnan, and Heather Schofield (2020), "How are Indian households coping under the COVID-19 lockdown? 8 key findings", Rustandy Centre for Social Sector Innovation, Chicago Booth.

Niranjan Rajadhyaksha (2020), "The covid shock could alter people's financial priorities", Livemint, 5 May 2020.

Sagar Wadhwa (2019), "Impact of demonetization on household consumption in India, Working paper.

Subhamoy Chakraborty and Renuka Sane (2019), "Household debt over time", The Leap Blog, 24 May 2019.

Sudipto Karmarkar and Abhinav Narayanan (2020), "Do households care about cash? Exploring the heterogeneous effects of India's demonetization", Journal of Asian Economics, 69.

Sane is a researcher at the National Institute of Public Finance and Policy, Shah is an independent scholar. We thank four anonymous referees, Kaushik Krishnan, Radhika Pandey and Anjali Sharma for useful comments.

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