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in Azim Premji University
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.
in The Indian Journal of Labour Economics
Historical experience suggests that a sustained rise in per capita incomes and improvement in employment conditions is not attainable without a structural transformation that moves surplus labour from agriculture and other informal economic activities to higher productivity activities in the non-farm economy. In this paper, I analyse India’s performance from a cross-country comparative perspective, estimating the growth semi-elasticity of structural change. Using a cross-country panel regression, I estimate the effectiveness of growth in moving workers away from agricultural and informal activities as compared to other developing countries at similar levels of per capita income. I show that the performance in pulling workers out of agriculture is as expected given its level and growth of GDP per capita, but the same is not true for pulling workers out of the informal sector. I also propose the following five indicators that need to be kept track of when evaluating the growth process: the growth elasticity of employment, the growth semi-elasticity of structural change, the growth of labour productivity in the subsistence sector, the share of the organised sector in total employment and the workforce participation rate. Comparing these indicators across periods, states, regions or countries, allow us to understand which sets of policies have worked better than others to effective improvements in employment conditions. When taken together the indicators allow us to set structural change targets as well as to say whether the current pattern of growth is going to be sufficient to meet those targets.