Inequalities in India after COVID Pandemic

Dr. Amarender reddy

The purpose of the present paper is to understand the broad changes in the relationship between state level poverty, inequality, income and purchasing power from the pre-reform to the post-reform period. It investigates the inter- and intra-state purchasing power differentials in rural and urban areas corresponding to the proportion of people thereof in each of the states in a comparable set up and over time from 1987-88 to 2004-05 up to which latest large sample consumer expenditure data are available. To start with, we have tested the robustness between expenditure date (NSSO, GOI) and product data (CSO, GOI) across the states over the same period. The significance of rural urban divide becomes all too important in view of the fact that rural and urban balance is a necessary condition for overall development of any constituent region. We have estimated the Lorenz ratios for both rural and urban areas of the states for the pre- and post-reform period. We have also estimated the share of state population corresponding to all India lowest 30 percentile class (‘poorest of the poor’), middle 50% class (‘vulnerable middle class’) and top 20% class (‘the rich’) in both rural and urban areas of the major states over the last four large sample survey rounds of NSSO-1987-88, 1993-94, 1999-2000 and 2004-05. This has helped us to understand the intra-state disparity between population and their endowment share. It is found that while overall poverty and inequality are marginally on the decline in rural areas, slow reduction in urban poverty and rising urban inequality in almost all the states have marked India’s latest pattern of development under fast rising trade openness and unprecedented foreign exchange reserves. The results are highly significant to warrant urgent steps by all concerned in the event of rising failure of the ‘trickle down’ process in Indian states in recent period. The question of vertical mobility of limited group of people versus horizontal rift across different expenditure classes within a state has come out as a dominant feature of post-reform India where rural urban divergence within each of the states has been intensified. It is conjectured that this is more a government failure rather than market failure, given the fact that market in India is pervasively imperfect across regions. The widespread rise in intense regional disharmonies in recent period across rural areas of the states is a pointer to the statistical significance of our results. Under such intense intra-state spatial disparity, market may fail to deliver the ‘good’ for the ‘excluded’ people because of corruption, administrative inefficiency, informalization, faulty labour laws and low quality infrastructure facilities. There is urgent need to review the existing strategies to tackle rural and urban poverty and inequality syndrome in a simultaneous framework, and not in isolation from each other.

Inequality is a problem which intensifies the plight of the poor people. The persistence of inequality prevents equal sharing of the benefits out of development processes. On many occasions, inequality rises with the increase in growth rate of the economies. Indian economy has witnessed this phenomenon in the post reform periods. There exists considerable income inequality across Indian states. This paper tries to examine the inter-state income inequality in India on the basis of NSSO data and IHDS data. It uses descriptive statistics like mean, median, standard deviation, range, quartiles, percentiles, graphs, and column and bar diagrams to analyse the concerned data. We find that income inequality has been increasing with time. Urban income inequality is dominating rural income inequality. Inter-state variation in income is significantly present in terms of the values of Gini Coefficients and median household income. There exists no definite mechanism to keep the state at the same...

In the first chapter of this dissertation, we create original annual average years of education across the major, present-day states of India from 1951 to 2011 in line with work done by Turner et al.(2007). Drawing from multiple volumes of the decennial censuses, Planning Commission reports, Ministry of Statistics and Programme Implementation databases and historical atlases to account for territorial changes since independence, our data show that the average Indian worker had less than a primary education even as late as 2011. However, this is still significantly more than the years of education of the average Indian, with the disparity growing in later years. We also construct original measures of real output per worker across the same period. Using OLS on differenced data, time fixed effects, as well as dynamic panel estimation, our results show an average return of between 20 and 25% to a year of education. After ensuring that our data show no feedback effects from worker output...

ACADEMIA Letters Inequalities in India after COVID Pandemic A Amarender Reddy, ICAR-Central Research Institute for Dryland Agriculture COVID accelerated use of digital technologies[1], but it also amplified disparities between haves and have-nots in India. Unemployment levels have moved into double digits, at 14.73 per cent for the week ending May 23, according to the Centre for Monitoring Indian Economy [2[, with over 17 per cent unemployment in urban India and nearly 14 per cent in rural India, mostly effecting casual labourer, petty traders and small and micro-enterprises. Many regular salaried workers lost jobs and engaged as informal workers. The rise in the gig economy, contract labour and informality is leading increased vulnerability to future shocks. Many people in backward and rural areas lack basic necessities like internet, power connections and communication tools like phones, laptops and tablets to make use of growing digital technologies for daily use. The isolation and vulnerability of the most vulnerable like old-aged and disabled further increased with the imposition of lockdowns and they remained cutoff even from their relatives and friends within their villages to get even a slightest help. At regional level, over the years the poorest states like Bihar, Odisha, Chhattisgarh, Uttar Pradesh, Jharkhand and Madhya Pradesh remained poor, while richest states like Kerala, Delhi, Haryana, Maharashtra and Gujarat grow faster not only in earnings but also in human development aspects like health and education[3]. The acceleration in gaps between the rich and poor started with the liberalization in 1990s and gained momentum with digitization. The percapita income of the richest state Goa was about 4.5 times that of Bihar in 1980s, but amplified to 10 times now. The percapita income of Goa is Rs.467,998, while in Bihar it is just Rs. 43,822 in 2018-19 [4]. The gaps between top 10% and bottom 20% of population is much more strikingly increased within states. Pattern of intra-state disparities are dynamic across India. Intra-state disparities are more in states like Maharashtra, Karnataka and Telangana, where development is more concentrated around metros like Mumbai, Bangalore and Hyderabad compared to states like Tamil Nadu Academia Letters, June 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: A Amarender Reddy, anugu.amarender.reddy@gmail.com Citation: Reddy, A.A. (2021). Inequalities in India after COVID Pandemic. Academia Letters, Article 1138. https://doi.org/10.20935/AL1138. 1 and Andhra Pradesh, where such high concentration is not evident[5]. Lately, spread effect of metros are visible in backward regions of some states. The percapita income of the backward regions within rich states are higher than some of the bottom states. For example, percapita income of Marathwada and Vidharbha are higher than J&K and Uttarakhand[6]. Again, cumulative effects of high human development in Kerala elevated all districts in the states, the poorest districts of Kerala are better than rich districts in Odisha and Chhattisgarh[7]. Rich states with large urban conglomerates like Maharashtra can cross subsidize poorer hinterlands like Vidharbha region to create more public amenities like roads, markets, schools and hospitals, but poorer states like Chhattisgarh don’t have that wherewithal. The vast tracts of poorer districts in backward states like Odisha, Bihar, Chhattisgarh and Jharkhand remained poorer due to backwash effects as anticipated by Myrdal’s model of growth [8]. Most prominent change in recent years is the increasing disparities in earnings between rural and urban areas. The average urban worker earnings are about 3-8 times more than an average agricultural worker[9]. Given the huge differences in earnings, it is not surprising that people wants to migrate from villages to cities. The wage differentials between rural and urban areas are more in large urbanized states like Maharashtra and Karnataka than predominantly rural states like Jharkhand and Chhattisgarh [10]. The regions at the bottom of the earnings scale are reporting more out-migration crossing beyond state boundaries. Short and temporary rural-urban commuting to near-by towns to work in construction, manufacturing, petty trade activities also increasing. However, the recent COVID exposed miserable work and living conditions of the migrant workers. Many lost employment and some remain unemployed since COVID outbreak last year[11]. In some states anti-migrant or nativist feelings are increasing, for example migration to Mumbai reduced in recent years, while these feelings are not strong in some other states like Kerala, AP and Telangana. Gender discrimination and sexual exploitation was more prevalent in migration sites. Although, in urban centres migrant women are getting regular employment as domestic workers, but their wage rates and working conditions are miserable. For inclusive urban development, more accommodative policies irrespective of gender, religion and nativity have to be promoted. The metros are characterized by overcrowded slums, although they are hope for migrant workers who are desperate to escape poverty in their native places. These are breeding grounds for many communicable diseases including COVID as they are congested with garbage scattered everywhere. Street cats, dogs and goats roam along with impoverished children in small confinements. During the lockdown, unemployment skyrocketed. Public Distribution System (PDS) saved most of the slum dwellers from hunger[12]. The pan-India portability of ration cards Academia Letters, June 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: A Amarender Reddy, anugu.amarender.reddy@gmail.com Citation: Reddy, A.A. (2021). Inequalities in India after COVID Pandemic. Academia Letters, Article 1138. https://doi.org/10.20935/AL1138. 2 under one-nation-one-ration card scheme became handy even for migrant workers. However, it is still not clear, how many migrant workers who lost jobs during COVID came back and reemployed again? Anecdotal evidence shows that still many are unemployed. The migrant workers are treated as secondary citizens in urban areas, as they don’t have voting rights in these slums. These migrant workers stay in urban areas for about 3-4 months, will go back and spend about 3-4 months in villages and then come back[13]. They are politically unimportant, in spite of their significant contribution to local urban economy. Hence, if portability of voting rights to these migrant workers are provided, the local elected bodies will be more accountable to solve problems of migrant workers. Workers should have a choice of voting rights either in their native place or migration-sites. Cities must be accommodative for migrant workers. Rural-urban space is continuum, not binary. There are vast spaces which fall in the category of Rurban. Large number of rural geographic areas acquiring urban characteristics with ever expanding population over the years, all these geographic territories now named as Rurban[14]. Due to low cost of living and working, many industries are moving out of core-urban centres to rurban areas. Hence, there is a need to develop these areas by encouraging housing and other amenities through instruments like land pooling housing societies. These sites needs to be well connected and should have schools, hospitals and community space for social interaction. Similarly, backward rural regions need to be developed through infrastructure like roads, industries, special economic zones etc to reduce overcrowding in cities. How to create more jobs in rural areas should be the top priority for local bodies. As per Periodic Labour Force Survey (PLFS-2018-19) [15] four out of five rural women willing to work are unemployed. Alternative employment opportunities needs to be increased in rural areas, as agriculture alone cannot create jobs. In backward regions where outmigration was more, land pooling cooperative societies needs to be formed to cultivate lands left by migrant workers, as many productive lands of migrant’s workers remained as fallow/uncultivated for many years. Although, MGNREGA[16] is playing major role, it is not an option for skilled labour. Most of the skilled labour did not wants to work as unskilled labour. Modern technologies can solve many problems faced by urban slums. Technologies like Sulabh flush compost toilet, sludge collection and treatment plants, composting pits, collection of grey water and battery-operated vehicles have good potential. Academia Letters, June 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: A Amarender Reddy, anugu.amarender.reddy@gmail.com Citation: Reddy, A.A. (2021). Inequalities in India after COVID Pandemic. Academia Letters, Article 1138. https://doi.org/10.20935/AL1138. 3 References [1] Vargo, D., Zhu, L., Benwell, B., & Yan, Z. (2021). Digital technology use during COVID‐19 pandemic: A rapid review. Human Behavior and Emerging Technologies, 3(1), 13-24. https://doi.org/10.1002/hbe2.242 [2] CMIE(2021) Unemployment Rate in India, https://unemploymentinindia.cmie.com/ [3] NITI Aayog (2020) Sustainable Development Goals Index 2019-20. [4] NSO (2021) First Advance Estimates of National Income 2020-21, National Statistical Office, Ministry of Statistics & Programme Implementation, Government of India. New Delhi http://mospi.nic.in/sites/default/files/press_release/Presss_note_FAE-2020-21_7jan21. pdf [5] Chakraborty, A. (2009). Some normatively relevant aspects of inter-state and intra-state disparities. Economic and Political Weekly, 179-184. https://www.epw.in/journal/2009/ 26-27/inter-and-intra-state-disparities-special-issues-specials/some-normatively [6] Scroll (2016) Dividing Maharashtra: How poor is Vidarbha and how rich is Konkan? https://scroll.in/article/806071/dividing-maharashtra-how-poor-is-vidarbha-and-how-richis-konkan [7] Down to Earth (2001) Poor little rich states https://www.downtoearth.org.in/coverage/ poor-little-rich-states-15616 [8] Gaile, G. L. (1980). The spread-backwash concept. Regional Studies, 14(1), 15-25. https://rsa.tandfonline.com/doi/abs/10.1080/09595238000185021#.YMTiso3is2w [9] GoI (2010) Wage rates In Rural India. Ministry of Labour & Employment, Labour Bureau, Government of India, Shimla. (2008-09) http://labourbureau.gov.in/Wage_Rates_Rural_India_2008_09. pdf [10] World Bank (2014) Urbanization is Narrowing India’s Rural-Urban Wage Gap, Jobs and Development, World Bank Blog, October, 27, 2014. https://blogs.worldbank.org/jobs/ urbanization-narrowing-india-s-rural-urban-wage-gap [11] BusinessStandard (2021) Second coronavirus wave leaves another 7 million people jobless in India, Business Standard, May 3, 2021. https://www.business-standard.com/ article/economy-policy/second-coronavirus-wave-leaves-another-7-million-people-joblessin-india-121050301553_1.html Academia Letters, June 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: A Amarender Reddy, anugu.amarender.reddy@gmail.com Citation: Reddy, A.A. (2021). Inequalities in India after COVID Pandemic. Academia Letters, Article 1138. https://doi.org/10.20935/AL1138. 4 [12] Roy Devesh and Pradhan M (2020) Improving India’s Public Distribution System: What Can We Learn From Covid-19? June 17, 2020 https://a4nh.cgiar.org/2020/06/17/improvingindias-public-distribution-system-what-can-we-learn-from-covid-19/ [13] Migration Data Portal (2021) Migration data relevant for the COVID-19 pandemic, March, 10, 2021, Migration Data Portal The bigger picture https://migrationdataportal. org/themes/migration-data-relevant-covid-19-pandemic [14] GoI (2021) Rurban. https://rurban.gov.in/ [15] PLFS (2019) Periodic Labour Force Survey, Mistry of Statistics and Programme Implementation, Government of India. http://mospi.nic.in/sites/default/files/publication_reports/ Annual_Report_PLFS_2018_19_HL.pdf [16] Reddy, D. N., Reddy, A. A., & Bantilan, M. C. S. (2014). The impact of Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) on rural labor markets and agriculture. India Review, 13(3), 251-273. https://www.tandfonline.com/doi/abs/10. 1080/14736489.2014.937271 Academia Letters, June 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: A Amarender Reddy, anugu.amarender.reddy@gmail.com Citation: Reddy, A.A. (2021). Inequalities in India after COVID Pandemic. Academia Letters, Article 1138. https://doi.org/10.20935/AL1138. 5

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Inequalities in India after COVID Pandemic

Inequalities in India after COVID Pandemic

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