Notes

The authors thank Anirudha Dutta, Kaushik Krishnan, Sutirtha Sinha Roy, Anurati Tandon, and participants at the Center for Monitoring Indian Economy (CMIE) seminar and the 6th

Turin Center on Emerging Economies Workshop for their comments and suggestions. The findings, interpretations, and conclusions expressed in this chapter are entirely those of the authors. They do not necessarily represent the views of the World Bank. 1. Also in the context of India, Deshpande (2020) examines gender differentials in the impacts of COVID-19. 2. Azam (2012), Berg et al. (2012), Deininger and Liu (2019), Dutta et al. (2012), Imbert and

Papp (2015), and Zimmermann (2020) have examined the impact of the MGNREGA scheme on labor market outcomes among rural workers, especially those who are unskilled and likely to be in casual wage jobs. 3. Order 40-3/2020-DM-I(A) of the Ministry of Home Affairs allowed state governments to lift lockdowns on certain types of activities, including MGNREGA-eligible employment. 4. COVID-19 data of CRC (Coronavirus Resource Center) (dashboard), Center for Systems

Science and Engineering, Johns Hopkins University, Baltimore, https://coronavirus.jhu.edu/. 5. Ministry of Home Affairs order 40–3/2020-DM-I(A). 6. As mentioned in the introduction, this study compares and contrasts the impacts of the shock on three groups: (1) informal wage workers, (2) self-employed, and (3) formal workers.

For brevity, informal wage workers are labeled simply as informal workers, even if, strictly speaking, informality includes both groups (1) and (2). 7. If individuals were unemployed in December 2019, their employment arrangements were not observed in wave 18. This variable is then imputed according to the last time in the previous 10 waves that each individual was employed. If individuals were never previously employed, their December 2019 employment arrangements are imputed based on the next time each individual becomes employed (that is, in wave 19, 20, or 21). If individuals are not employed before or after wave 18, they are treated as unemployed and dropped from the estimation sample. 8. Although the CPHS dataset contains survey weights to adjust for differences in sampling probabilities across the sample, the analysis does not use them because they are designed to be applied to the full CPHS sample and are not appropriate for the balanced panel, which is a subset of the full sample. Hence, the summary statistics presented in table 7.1 may not be representative of the Indian population. For example, the unweighted balanced panel shows an urban bias; the share of rural workers is below 30 percent. This is because the CPHS oversamples urban areas. 9. Because employment is observed at four-month intervals, there is one WAVEt dummy for every fourth month starting in December 2018 and ending in December 2020. 10. There is an important caveat. Because individual-level data are not available on incomes, household per capita income must be used. Household per capita income is classified as informal or formal according to the informal or formal status of the household head.

Household composition is thus ignored. This may underestimate the differential between formal and informal workers. If each household has exactly two working-age members (the head and a nonhead) whose baseline formal (F) or informal (NF) status is observed,