With this chapter we conclude our analysis of the impact of the pandemic on labour markets and households. Most of the data we have drawn on were collected between April 2020 and December 2020. While the pandemic is still raging and the impacts are likely to persist, we believe that the information presented here constitutes a firm basis for designing and implementing policy measures to support workers and households in these extraordinary times. Several policy interventions
5. Falling incomes, rising hunger and indebtedness
Endnotes
1 Thus the household income share of GDP was less than 50 per cent. This divergence between per capita incomes as measured by surveys and as measured in the national income accounts is observed in many countries and has been attributed to differences in definition of the ‘household’
sector, ability of survey to capture all incomes accruing to households and incomes, and the division of corporate earnings into dividends, employee compensation and retained earnings. For example, see various OECD briefs on ‘Growth and economic well-being’ (https://www.oecd.org/sdd/
na/Growth-and-economic-well-being-oecd-01-2021.
pdf) and also Nolan, Roser, and Thewissen (2016)
2 See Appendix Section Two for details on seasonal adjustments.
3 To estimate the cumulative average income in the Covid months only households for which income data is reported in all the Covid months (March 2020 to October 2020) are used. Similarly, for calculating average household income in the pre-Covid period (July 2019 to Feb 2020) only households who report data in all the eight months preceding Covid are used. This is done to eliminate the bias that might be introduced due to attrition and non-response. This is particularly an issue in the lockdown months of April to May 2020, when the survey sample declined by more than half. This results in a sample size of 50,133 households in the March-October 2020 period and 62,194 households in the pre-Covid period.
4 The event study regression incorporates household fixed effects and error terms clustered at the household level. Details of the model can be found in Appendix Section 2.
5 The regression model is (∆y/y)it = α+
β(∆M/M)it + Si + εit where y is monthly household income, M is the number of footfalls in various public areas, S captures state fixed effects, i indexes states and t indexes months.] This exercise allows us to estimate the average percentage loss in
income for a given percentage decline in mobility across states. We find that a 10 per cent decline in mobility was associated with a 7.5 per cent decline in income.
6 The relationship is statistically significant. This can be assessed visually based on the fact that the confidence bands do not overlap with the X axis.
7 The 7th CPC recommended a minimum salary of I18,000 per month (I200 per capita per day). We adjusted this for inflation using rural and urban CPIs and converted it to January 2020 rupees. See https://www.finmin.nic.in/seven-cpc
8 Households are classified into percentiles based on income in July 2018-February 2019 and growth rate in the average monthly seasonally adjusted per capita real income between the periods July 2018-February 2019 and March 2019-October 2019 is calculated. This growth rate is applied to average monthly per capita incomes of each percentile in July 2019-February 2020 to get the counterfactual incomes.
9 This is the increase in the number of people whose consumption will fall below the absolute poverty line of $1.9 per capita per day in International Purchasing Power Parity (PPP) terms (about I2,520 per month for a family of four). https://blogs.
worldbank.org/opendata/updated-estimates- impact-covid-19-global-poverty-effect-new-data
10 Pew defines lower incomes as those earning between $2-$10 per capita in 2011 PPP terms (between I1,280 and I6,400 per capita per month) and middle class as those earning between $10-
$20 2011 PPP (between I6,400 - I12,800 per capita per month). https://www.pewresearch.org/fact- tank/2021/03/18/in-the-pandemic-indias-middle- class-shrinks-and-poverty-spreads-while-china- sees-smaller-changes/
11 https://compass.creditvidya.com/
Sept%202020/Earnings/Earnings%20by%20 value?queryTime=1616954998835
12 https://www.hurun.net/en-US/Info/
Detail?num=LWAS8B997XUP
13 https://www.oxfam.org/en/research/inequality- virus
14 https://cse.azimpremjiuniversity.edu.in/covid19- analysis-of-impact-and-relief-measures/#other_
surveys
15 The Azim Premji University CLIPS is a purposive panel of 2,778 respondents across 12 states in India who were interviewed in April-May (during the lockdown) and subsequently in October- December 2020.
16 This analysis takes into account only those households whose respondents were working in February and who had experienced a loss in food intake during the lockdown, accounting for 29 per cent of the entire sample. Household-level characteristics are regressed on a binary response variable which takes the value 1 if the households food intake recovered to pre-lockdown levels and 0 if the household has not completely recovered.
17 https://thewire.in/rights/hunger-watch-survey- lockdown
18 This could be explained by the nature of the sample. Two-thirds of all those respondents who had received a loan via an SHG were individuals whom we had contacted using the networks of the civil society organisation, Pradan. One of Pradan’s major objectives is to set up viable SHGs in rural areas.
19 https://insights.gaonconnection.com/wp- content/uploads/2020/12/COVID-19-VACCINE- AND-RURAL-INDIA-1-1.pdf
20 Owing to the small size of the urban sample, all analysis here pertains to rural areas.
6. India’s social protection architecture