Studies based on the Consumer Pyramids Household Survey (CPHS) in India have shown that the impact of the nationwide lockdown in 2020 on household incomes was progressive in nature - richer households suffered more. But several media reports as well as purposive surveys carried out during the pandemic suggest that the poor suffered more than the rich. In this paper, we show that the conclusion of progressivity is sensitive to the base period used for comparisons. Previous studies have used the Oct-Dec 2019 quarter as the base for comparison to find that the lockdown was progressive in its impact. We show that ongoing changes in the income distribution in urban India between Oct-Dec 2019 and Jan-Mar 2020 quarters are progressive and hence confound the subsequent regressivity. We find that, if compared to the quarter immediately preceding it, the lockdown was regressive in nature. We also verify this conclusion using another survey, the Periodic Labour Force Survey (PLFS). Our findings have implications for understanding the distributional impact of the Covid-19 lockdown in India.

The COVID-19 pandemic created a need for high-frequency employment and income data. Policy-makers and researchers of developing countries typically have not had access to such data. In India, a new private high-frequency panel dataset has recently emerged as the dataset of choice for analysis of the economic impact of COVID-19. This is the Consumer Pyramids Household Survey (CPHS) conducted by the Centre for Monitoring the Indian Economy (CMIE). But the CPHS has also been criticised for being inadequately representative nationally by missing poor and vulnerable households in its sample. We examine the comparability of monthly labour income estimates for the pre-pandemic year (2018–19) for CPHS and the official 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 and 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 emphasised that the two surveys agree on some stylised facts regarding the Indian workforce. An individual earning ₹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.

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, allows us to understand which sets of policies have worked better than others to effective improvements in employment conditions. And 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.

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.

A typical modern capitalist economy experiences what economists call 'structural transformation', constituting a, declining share of income from agriculture and b, workers dependent on agriculture in the national economy. The share of land owned by large and medium holding families has steadily declined over the last few decades from around 60 per cent to 34 per cent which indicates their declining economic, social and political power in rural areas. State-level comparisons are essential, given the wide variation in historical and geographical conditions in India. The semi-feudal landlords seem to have been replaced by rich middle peasants as the ruling bloc in the agrarian structure of contemporary India. Proletarianization, an important indicator of development of capitalist economy, is reflected in the extent of landlessness. The relative share and absolute number of cultivators in total agricultural workers has decreased in the past one decade.

The Covid-19 pandemic has created unprecedented disruptions in labour markets across the world including loss of employment and decline in incomes. Using panel data from India, we investigate the differential impact of the shock on labour market outcomes for male and female workers. We find that, conditional on being in the workforce prior to the pandemic, women were seven times more likely to lose work during the nationwide lockdown, and conditional on losing work, eleven times more likely to not return to work subsequently, compared to men. Using logit regressions on a sample stratified by gender, we find that daily wage and young workers, whether men or women, were more likely to face job loss. Education shielded male workers from job loss, whereas highly educated female workers were more vulnerable to job loss. Marriage had contrasting effects for men and women, with married women less likely to return to work and married men more likely to return to work. Religion and gender intersect to exacerbate the disproportionate impact, with Muslim women more likely to not return to work, unlike Muslim men for whom we find religion having no significant impact. Finally, for those workers who did return to work, we find that a large share of men in the workforce moved to self-employment or daily wage work, in agriculture, trade or construction. For women, on the other hand, there is limited movement into alternate employment arrangements or industries. This suggests that typical ‘fallback’ options for employment do not exist for women. During such a shock, women are forced to exit the workforce whereas men negotiate across industries and employment arrangements.

The idea that service-sector industries, rather than manufacturing, can drive growth and structural change has caught the imagination of several scholars and policy-makers. For India, the Information Technology (IT) industry is often cited as an example of one such industry. However, empirical evidence for such claims is still weak. This article evaluates the Indian IT industry’s potential for growth. Most studies on the subject focus on the narrower segment of IT-Services in India while we take a holistic view and consider the potential of both software product and services, IT hardware, outsourced/offshored business processes, and activities involved in creation of intellectual property. We evaluate the present position and future prospects of the industry including India’s position in the technology value chain as well as future opportunities for the industry in the context of relocation of global manufacturing value chains and growth of the domestic market. We also discuss the likely impact of technology on jobs in the overall economy, direct and indirect job creation potential of the IT-BPM (Business Process Management) sector and the spill-over effects of technology as an enabler of new business models. Finally, we draw some lessons for effective industrial policy.

This report documents the impact of one year of Covid-19 in India, on jobs, incomes, inequality, and poverty. It also examines the effectiveness of policy measures that have thus far been undertaken to offer relief and support. Finally, it offers some policy suggestions for the near and medium-term future. When the pandemic hit, the Indian economy was already in the most prolonged slowdown in recent decades. On top of this, there were legacy problems such as a slow rate of job creation and lack of political commitment to improving working conditions which trapped a large section of the workforce without access to any employment security or social protection

We analyze findings from a large-scale survey of around 5000 respondents across 12 states of India, conducted during the months of April and May 2020, to study the impact of COVID-19 pandemic containment measures (lockdown) on employment, livelihoods, and food security. Given the predominantly informal nature of employment and critically low investment in State-funded social security nets, the impact, albeit unprecedented in its scale, was not entirely unexpected in its nature. We find that around two-thirds of respondents reported losing employment during the lockdown, and those that continued to be employed witness a sharp decline in earning. Further, with critically low levels of social security net, the loss in employment quickly translated into food and livelihoods insecurity. Almost 80 per cent of households experienced a reduction in food intake, more than 60 per cent did not have enough money for a week's worth of essentials, and a third took a loan to cover expenses during the lockdown. We also use a set of logistic regressions to identify how employment loss and reduction in food intake varied with individual and household-level characteristics. Based on our analysis, we argue that while there is an urgent need to undertake effective measures to support livelihoods and facilitate an economic recovery, we also highlight the necessity to critically evaluate the current development trajectory, whereby decades-long high economic growth has failed to translate into more secure livelihoods for a vast majority of the workforce.

The recently released data from the 2017-2018 Periodic Labour Force Survey have created a controversy regarding the quantity of employment generated in the past few years in India. Estimates ranging from an absolute increase of 23 million to an absolute decline of 15.5 million have been published. In this paper we show that some of the variation in estimates can be explained by the way in which populations are projected based on Census 2011 data. We estimate the change in employment using the cohort-component method of population projection. We show that for men total employment rose but the increase fell far short of the increase in working age population. For women, employment fell. The decline is concentrated among women engaged in part-time or occasional work in agriculture and construction. Keywords

The Indian manufacturing sector has not increased its share in output or employment along expected lines. The aggregate trends in this sector at the 3-digit level of the National Industrial Classification from 1983 to 2017 are investigated here. Using data from the Annual Survey of Industries obtained from the EPWRFITS, it identifies three sub-periods within the overall period: 1988–96, 1996–2006, and 2006–17. A shift-share decomposition is used to show that most of the decline in the labour to capital ratio can be explained by within-industry changes. Finally, industries are analysed with respect to their capacity to deliver job and wage growth.

Despite its weak performance in terms of job creation in recent years, the organised manufacture sector remains vital to employment policy. This paper investigates the aggregate trends in this sector, in employment, output, labour-capital ratio, as well as wage share and wage rates at the three-digit NIC level over a long period from 1983 to 2016 using the Annual Survey of Industries data. We show that three distinct sub-periods can be identified within the overall period. Further, using shift-share decomposition we show that most of the decline in the L/K ratio can be explained by within industry changes. Finally, we analyse industries with respect to their capacity to deliver job growth as well as wage growth.

Reflecting on their experience of using The Economy to teach undergraduate students in India, two teachers of economics discuss the need for a version of the alternative textbook that addresses the needs of students who seek to understand the Indian economy. The possibilities of such a version of the textbook are discussed.

The book Reading Marx in the Information Age: A Media and Communication Studies Perspective on "Capital,"Volume 1, by Christian Fuchs, follows the structure of Karl Marx's first volume, bringing in theoretical concepts and empirical examples from the world of media and communications studies for each chapter. The author ably demonstrates the relevance of Marxian analysis to the Internet economy and the information age. The book will work great as a supplementary text alongside Capital in any course dealing with Marx, whether in economics, sociology, or communication studies.

The agricultural sector has performed worse than the other sectors over the years. The shares of non-agricultural employment and output have increased, while 70% of agricultural households cannot meet their low consumption needs even after diversifi cation of sources of income. An analysis of budgetary p rovisions for the rural economy suggests that the government has not done enough to address some of these well-documented problems, and does not have the required vision to substantially increase rural employment opportunities. A fter the high economic drama of demonetisation and the resulting shocks to the economy, the 2017– 18 budget, the fourth one presented by Finance Minister Arun Jaitley, could be seen as an attempt to regain some credibility for the government and calm the general populace as well as international investors. It has been seen as a “routine” budget but also a “pro-growth” one, promising large increases in public invest ment in social and physical infrastructure and cutting taxes, while being fi scally prudent. Like the previous budget, this one has also been seen as giving a “push” to the rural economy. Is there a vision implicit in the budget commensurate with the challenges that the rural economy is facing. Here, I will speak of one of the biggest challenges, that of generating livelihoods capable of providing regular, living incomes for every household in rural India. Two major prongs can be recognised in the budget to raise rural incomes and create employment opportunities: raising agricultural productivity and creating non-farm employment. This is complemented by provisioning of public services such as health, roads, and housing.