Covid impact on household budgets

The Covid crisis has affected many households across the world, and most certainly in India. Triggered by anxiety and worry, many households have significantly changed their purchase, consumption, and savings behaviour. While experts have speculated about the patterns of behaviour shift, there has not been any systematic and rigorous study of these patterns till date. We report the results of a large-scale analysis, which clearly show evidence of behavioural shift.

Using the Consumer Pyramids Household Survey conducted by the Centre for Monitoring Indian Economy, 65,653 households across India were analysed over 2019 and 2020. Sophisticated mathematical models were used to analyse changes in the purchasing behaviour of each household in the sample.

Various economic factors — including income before and during Covid, change in expenditure pattern, whether living in urban or rural areas, and whether they owned a car, house, etc. — pertaining to each household were looked at.

The results showed that 4,100 households (6.2 per cent) in the sample sold their cars during the pandemic, while 1,800 (2.7 per cent) purchased new vehicles. The rest maintained status quo. The model also analysed the difference in purchase behaviour of the same households between a pre-Covid time period and during the Covid, matched for months.

Overall, 64 per cent of the households saw a decrease in their income in this period while almost all of the rest experienced increased income. The decrease in income is understandable because of loss of jobs in many households, especially for those engaged in domestic services, SMEs, and in sectors such as entertainment, tourism, hospitality and transportation industries.

However, it is interesting to see that roughly 36 per cent saw an increase in their income. Those were perhaps working in sectors like IT, healthcare, communication and food distribution, which were in big demand during the Covid times. Perhaps understandably, Maharashtra, with its huge migrant labour force and SMEs, witnessed the highest percentage of people with reduced income (72 per cent), while Karnataka, known for its IT services, witnessed the lowest percentage (46) of people with reduced income.

A comparison between Karnataka and the Rest of India shows the differences in distribution of the workforce, with wage labourers forming only 10 per cent of the workforce in Karnataka while they formed 16 per cent in the Rest of India. Self-employed entrepreneurs, entrepreneurs and white-collar professionals however accounted for 57 per cent of the workforce in Karnataka but only 33 per cent in the Rest of India.

Interestingly, there was no major difference in loss of income between the rural and urban population: the percentage drop was almost the same. However, what was more intriguing was how people planned to cope with the loss in income. Unlike rural households, which appeared more inured to the loss of income, urban households were more likely to sell assets such as their cars to cope.

This shows that rural households are more resilient and/or able to manage income fluctuation effectively. In particular, urban households with only one car were even more likely to sell it to meet the income drop compared to those with multiple cars, thus underscoring the real impact of Covid on urban middle-class households.

Further analyses reveal that a greater percentage of workers across the major categories of workers in Karnataka showed an increase in income compared to those who experienced a decrease in income in post-Covid relative to pre-Covid times. In particular, more entrepreneurs experienced an increase in income compared to those who experienced a decline.

In contrast, most categories of workers experienced a significant decline in income in other parts of India, much more so than those who experienced an increase . The biggest impact was felt by farmers and entrepreneurs, among whom the percentage of those who experienced a decrease in income was almost 100 per cent greater than those who experienced an increase. From these data, it is evident that the impact of Covid was disproportionately felt in some parts of the country compared to others.

Household aspirations

Perhaps, the biggest impact of the Covid may have been on people’s aspirations. A booming stock market and a recovering economy pre-Covid could have fuelled dreams of home ownership and purchase of assets; however, the Covid crisis may have put paid to those aspirations. We compared the households’ aspiration to own a home in the near future in the September-December trimester of 2019 (that is, before Covid struck) and the May-August trimester of 2020 (when Covid was peaking and crippling the economy). As expected, we found a substantial decrease in the aspiration.

However, the effect was more pronounced in rural compared to urban areas. This is surprising because it runs counter to our earlier finding that rural households were more efficient at managing their resources compared to urban ones.

It is possible that urban households were more informed on how well the government was acting to arrest the virus spread and hence felt more confident about their future.

Another possible reason is that there was a steeper drop in real estate prices due to reduced demand in urban areas, which might have presented more opportunities for urban households. This conclusion is borne out by the fact that wealthy individuals — those owning multiple cars and/or whose income increased — had much higher aspiration to buy a house during Covid times, likely because the lower real estate prices offered them an investment opportunity.

White goods

Another significant impact that the Covid may have had on behaviour was in the purchase of white goods (dishwashers, vacuum cleaners, washing machines, etc.). News reports indicate a significant uptick in sales of such goods, presumably due to the inability to use household help. Interestingly, this was confirmed in our analysis. We looked at the households that purchased white goods during the Covid times. We examined how many of these households had expressed an intention to buy a white good in pre-Covid times and how many had expressed no intention to buy, thereby identifying planned versus unplanned or surprise purchases during Covid times. Significantly, over 69 per cent of all white goods purchases during the Covid period were unplanned, suggesting a big impact of the crisis on purchase behaviour.

Households not interested in buying white goods in 2019 actually ended up buying when Covid struck in 2020. Quite interestingly, wealthy households were more likely to purchase white goods despite not intending to do so.

We speculate that this was brought on by the helplessness felt upon losing the household help they relied on, thereby triggering the impulse to splurge on such goods.

Krishnan is Visiting Professor of Marketing, Rathore is Assistant Professor of Operations and Analytics, and Ramanathan is Dean and Principal, Great Lakes Institute of Management, Chennai

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