This paper revisits a part of the analysis by Banerjee et al. (2020), in which they examine the consequences of the nation-wide scale up of reforms to the funds management system (e‑FMS) in India’s national workfare programme, using a two-way fixed effects specification. They report a substantial 19 percent reduction in labour expenditures. We exploit the recent literature that highlights the limitations of the TWFE estimator in the presence of staggered roll out and effect a Goodman-Bacon decomposition of the TWFE coefficient, to pinpoint sources of identifying variation. We undertake a detailed examination of subsamples of six constituent and valid DiDs based on timing of treatment that are averaged into the TWFE coefficient to identify heterogeneity in treatment effects. This disaggregated subsample analysis does not support the conclusion of any reductions in MGNREGS labour expenditures, suggesting that the TWFE coefficient based on the full sample is indeed biased.

Authors:

• Deepti Goel
• J.V. Meenakshi
• Zaeen De Souza

Drawing on results from a panel of 2778 workers interviewed during and after the 68-day hard lockdown imposed in India, the following study examines the livelihood impact of the pandemic and the extent of subsequent recovery or lack thereof. Focussing specifically on workers located in the informal economy, the study is a useful addition to the burgeoning body of work on the economic impacts of Covid-19 by providing an insight into the employment and earnings recovery of those located at the margins. These findings are spliced across socio-economic groups to showcase the differential impact of the pandemic on different demographics within the informal sector.

Authors:

• Paaritosh Nath
• Nelson Mandela S
• Aishwarya Gawali

The Covid-19 pandemic has created a need for high frequency employment and income data to gauge the nature and extent of shock and recovery from month to month. Lack of such high frequency household-level data from official sources has forced researchers to rely almost entirely on the Consumer Pyramids Household Survey (CPHS) conducted by the Centre for Monitoring the Indian Economy (CMIE). Recently, the CPHS has been criticised for missing poor and vulnerable households in its sample. In this context, it becomes important to develop a detailed understanding of how comparable CPHS estimates are to other more familiar sources. We examine the comparability of monthly labour income estimates for the pre-pandemic year (2018−19) for CPHS and the Periodic Labour Force Survey (PLFS). Across different methods and assumptions, as well as rural/​urban locations, CPHS mean monthly labour earnings are anywhere between 5 percent to 50 percent higher than corresponding PLFS estimates. In addition to the sampling concerns raised in the literature, we point to differences in the way employment and income are captured in the two surveys as possible causes of these differences. While CPHS estimates are always higher, it should also be emphasized that the two surveys agree on some stylized facts regarding the Indian workforce. An individual earning INR 50,000 per month lies in the top 5 percent of the income distribution in India as per both surveys. Second, both PLFS and CPHS show that half the Indian workforce earns below the recommended National Minimum Wage.

Authors:

• Mrinalini Jha
• Amit Basole