Economic impact of Covid-19 pandemic to vary in sectors
If the Indian economy were a person, her income in 2020-21 and 2021-22 would be less than what it was in 2019-20. At least, this is what the latest World Bank forecasts tell us. There is enormous, perhaps unprecedented, economic pain ahead. Both policy and politics will have to play an important role to alleviate this. Bad policy can delay, even derail economic revival. Good politics can ensure that the suffering of the masses is minimized. What can be done to ensure this?
A three-part series in these pages looked at the nature of economic challenge facing India in detail. Its main argument was that India needs a demand-side intervention. But Indian economy is both huge and diverse. The policy response will have to be mindful of this diversity. Only then can suitable measures be applied where they are needed. Policy, especially in times of crisis, is also a question of distributing scarce resources among competing needs.
In a democracy, politics influences this process in a big way. This two-part series tries to address these questions. The first part will highlight how the contraction in growth will not be uniform across regions and sectors. The second part will discuss possible avenues of political mobilization.
Earlier this week, the World Bank released its Global Economic Prospects report. It expects India’s gross domestic product (GDP) to contract by 3.2% in 2020-21. There will be a moderate recovery to 3.1% growth in 2021-22. This means that 2021-22 GDP will be less than what it was in 2019-20. To be sure, India is not the only country which will face this predicament. The East Asian region seems to be the only exception. (See Chart 1)
What does a contraction in GDP mean in real life? Incomes will drop. Jobs will be lost. However, the impact of the contraction will vary across sectors, states, even social groups. This knowledge is indispensible for an effective policy intervention.
For example, it can be expected that at least two sectors; agriculture and government, will not see a contraction. In 2019-20, these two sectors had a share of almost 30% in total Gross Value Added (GVA). This means that the economic pain will be far more severe in the rest of the economy.
Let us assume that the growth rate of agriculture and government sectors in the next two years will be the simple average of what they were in the past three years. This comes to 4.1% for agriculture and 9.7% for government.
Using the World Bank’s headline projections of 3.2% contraction in 2020-21 and 3.1% growth in 2021-22, we can calculate the projected growth for rest of the economy. This comes to a 7.2% contraction in 2020-21 and 1.4% growth in 2021-22.
The non-farm, non-government economy contains many sub-sectors. A contraction in each sub-sector will have different impact across states and jobs. For example, the non-farm, non-government sector had a share of 86% in Gross State Value Added (GSVA) for Delhi. This share was 56% for Madhya Pradesh, and only 38% for Arunachal Pradesh. This means that Delhi’s economic pain will be far more severe than Arunachal Pradesh’s. (See Chart 2)
What about employment? A contraction in some sectors can have a bigger impact on jobs than others.
For example, construction had a share of 8% in GVA in 2018-19. But its employment share, according to the 2018-19 Periodic Labour Force Survey (PLFS), was 12%. Financial services, real estate and professional services, on the other hand, had a GVA share of 22% in 2018-19. The employment share of this sector was only 3.4%. This means that construction is a more labour-intensive sector than finance.
So, for an equal value of loss in output, job losses in construction would be far higher than in the financial sector. Bailing out the construction sector can save a lot of jobs, and mostly of the poor.
Supporting finance will probably cushion the not-so-poor, and the sectors which depend on their demand.
A useful way to measure job losses during a contraction is by using what is referred to as employment elasticity of output in economics. It is equal to the change in number of jobs per unit change in economic output. The concept captures the idea that for the same amount of growth, job creation varies across sectors. This also means that job losses during a contraction phase will vary across sectors.
A comparison of year-on-year growth in GVA and jobs in 2018-19 shows that the construction sector and the trade, hotels, transport, storage and communication sector had the highest employment elasticity in the non-farm, non-government sector. (See Chart 3)
Which states or sectors to support is not the only policy question going forward. There will also be a choice in terms of supporting producers and consumers.
For instance, according to the Annual Survey of Industry (ASI) data, Tamil Nadu, Gujarat, and Maharashtra had a share of 45% in value of output of textile and apparel in 2011-12. This output was consumed across the country. An analysis of the 2011-12 Consumption Expenditure Survey shows that Uttar Pradesh and Bihar had a share of 19.9% in total consumer expenditure on clothing and bedding. These two states had a share of just 3.9% in value of output of textile and wearing apparel. If consumer demand does not revive in these two states, supporting textile manufacturers will not yield results. (See Chart 4)
There is a major blind spot for policymakers in tracking consumption trends. India does not have a consumption expenditure survey (CES) after 2011-12. This will be a decade-old in 2021-22. The 2017-18 CES findings were junked by the government.
After the 2008 financial crisis, there was a lot of uncertainty about job losses. India did not have high-frequency employment data. The United Progressive Alliance (UPA) government asked the Labour Bureau to start tracking quarterly employment-unemployment rates.
Today, the PLFS does collect employment data in each quarter of the year. However, the government has not been releasing the quarterly data regularly. The 2018-19 findings were released only earlier this month. It will also be a good idea to start collecting and publishing high-frequency data on consumption. Unless, there is more and better high-frequency data on the state of the economy, policy intervention will continue to be ill-informed. If the surveys show bad news, so be it. We are officially in a recession.