We investigate the impact of the COVID-19 pandemic on income levels, poverty, and inequality in both the immediate aftermath and during the long uneven recovery till December 2021 using high-frequency household survey data from India. We find that the average all-India household income dropped between 30 to 38 percent during the months of the nationwide lockdown of April and May 2020. The subsequent recovery remained incomplete and was unevenly spread over the population even twenty-one months after the start of the pandemic. Households, on average, continued to make 16 to 19 percent lower cumulative income in the post-lockdown period, but have mostly recovered after the second wave in the second half of 2021. Poverty more than doubled during the lockdown and was 50 to 80 percent higher in the post-lockdown period in comparison to the pre-pandemic levels. In the post-second-wave phase, poverty was still slightly higher than in the pre-pandemic period, and any progress in poverty reduction that would have been achieved under normal circumstances over the two years was lost. Inequality too spiked during the lockdown, but returned back to the pre-pandemic levels. Using an event study model we find that the initial shock of the lockdown was more severe for the bottom of the income distribution, but the bottom also experienced a faster recovery. On the other hand, the top end of the distribution experienced smaller declines during the lockdown but they have been slow to recover. The bottom deciles in any period typically constituted households working in contact-intensive, informal, less secure occupations that were hit the hardest during the lockdown, but were quick to recover when the economy opened up. The upper end of the distribution constituted households working in less contact-intensive, formal, secure occupations that were shielded from the sudden shock but were slow to recover.

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.

This case study is of ​“India’s First Fully Solar Powered Village”2 — Dharnai. It is a case of the promises of and challenges facing the realisation of ​“energy democracy” — the idea that distributed renewable energy systems have the potential to democratise the economy and society.

Constitutional measures to ensure fair compensation and livelihood security to the land losing refugees of development processes, overlook the complexity of ​‘public purpose’ — the dominant rationale behind operationalizing ​‘eminent domain’ of the state. Popular perception of public purpose as urbanization muffles the de facto social citizenship around plural values of agricultural landscapes. Ignoring the enduring public purposes served by agrarian landscapes aids in underestimating the longterm welfare impacts on displaced farmers.

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 stylised 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.

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.