Category: Labour & Employment

Effect of Neo-Liberal Globalisation on Women in Garment Industries: A Third World Perspective
Abstract

Globalisation is a phenomenon that has brought about an effective change in the nature of the economy of the world nations. An inevitable result of this has been industrialisation, from heavy industries like iron and steel to software industries that house recent developments like artificial intelligence. Garment Industries have been a part of this industrial surge and have significantly contributed to the country’s economic growth, involving significant exports. At the heart of this unprecedented growth, an often unsaid and silenced issue remains the labour and lives of millions of women. Women, especially in the so-called third-world countries like Bangladesh, Cambodia, and India, constitute more than 50% of the labour force in the garment sector. However, the gender inequality that persists within these industries exists in the form of a lack of safety standards, wage disparity, stringent maternity benefits and improper compensation packages. World organisations like the International Labour Organisation (ILO) have developed projects to ensure decent working conditions. Still, the resultant effect has been scant due to the lack of gendered understanding of the issues. This research aims to illuminate the consequences of organisational patriarchy in comprehending the issues faced by women in the garment sector and subsequent policy framing. The study is based on the assumption that the inclusion of women andan opportunity to voice their concerns is absent. Therefore, there is a need for a gendered lens in framing policies, their implementation and further monitoring. Thus, this understanding will not only enhance the awareness of the working conditions of women in the garment industry but also be an eye-opener concerning the effect of global policies on the female labour force.

Keywords: Female labour, Globalisation, Gender inequality

Introduction

The second five-year plan (1956-1961) in India had rapid industrialisation as one of its focuses and followed the Mahalanobis model of production of capital goods. This saw capitalism take the

reins of development, which later consolidated with globalisation and took the world economy by storm, making possible the impossible, i.e., connecting the global markets. The transnational phenomenon led to the import of frontier technologies, which are highly capital-intensive (Indira Hirway, 2012). The consequent effect was an improvement in technology and an increase in productivity accompanied by a decrease in employment intensity. This reduction affected the often underrepresented and discriminated group – women. Women’s growth was curtailed by ensuring that patriarchy was perpetuated in the organisational setup as well. The women in the lower strata of the industrial workforce faced the wrath of wage disparity, and the women in the higher echelons had a glass ceiling that prevented them from occupying managerial positions. They also had to endure the double burden of work and household chores, affecting the female labour force participation rate. Garment industries in developing countries like India, Pakistan and Bangladesh have witnessed large-scale employment of women as formal and informal labour to fulfil the needs of international buyers. In this paper, based on secondary research, I would like to traverse the historical aspects that led to globalisation, subsequent policies and the consequential effect it had on women, primarily focusing on export garment industries, which house the highest percentage of employed women.

Industrialisation and women

The post-independence era in India, from 1947 onwards, witnessed industrial growth. The employment of labour also saw a shift from traditional agriculture. “Gender was the primary axis along which industrial labour and the labour force were constituted.” Very few women worked in factories, and the support and protection they received were also poor compared to men. The policies that catered to labour protection, like the Factories Act (1948), The Minimum Wages Act (1948) and The Employees’ State Insurance Act (1948) that paraded gender neutrality were gender blind. The ensuing period of globalisation, which questioned the conservative market nature, also brought about changes in the perception of women’s labour. With the world markets creating an arena for exports, the need for developing countries like India to keep up with the race became necessary, and the viable solution was to employ women from the lower strata of the society, mostly Dalits, in the name of empowering them. They were bait in the corporate hawk culture. The governments also failed to visualise the consequences of liberal markets and the capitalists donning the crown.

As a part of the capitalist world, women were subject to both economic and emotional labour, which affected the female labour force participation rate, and the percentage of women in the informal sector became higher than women in the formal sector. However, there have been a lot of studies on the women employed in the informal sector, and there have also been time-use surveys conducted in this regard. Women’s labour in the formal sector has been consistently neglected due to the common belief that they enjoy the protection of an organised system. This may hold true to some extent, but in the case of garment or textile industries, which are a focus here, women are also the most penalised according to the reports by the International Labour Organisation (ILO).

Ministry of Labour and Employment, India – Statistics on Women Labour

According to the information provided by the Office of Registrar General & Census Commissioner of India in the 2011 Census, the total number of female workers in India is 149.8 million, of which 121.8 and 28.0 million are from rural and urban areas, respectively. Out of the total 149.8 million female workers, 35.9 million females work as cultivators and another 61.5 million as agricultural labourers. Of the remaining female workers, 8.5 million remain in households, and 43.7 million are classified as other workers.

As per Census 2011, the work participation rate for women is 25.51 per cent compared to 25.63 per cent in 2001. The female labour participation rate decreased marginally in 2011 but has seen an improvement from 22.27 per cent in 1991 and 19.67 per cent in 1981. The work participation rate for women in rural areas is 30.02 per cent compared to 15.44 per cent in urban areas.

As far as the organised sector is concerned, in March 2011, women workers constituted 20.5 per cent of total employment in the organised sector in the country, which is higher by 0.1 per cent compared to the preceding year. As per the last Employment Review by the Directorate General of Employment & Training (DGE&T), on 31st March 2011, about 59.54 lakh women workers were employed in the organised sector (public and private). Nearly 32.14 lakh women were employed in the community, social, and personal service sectors.

Feminist Analysis of Existing Laws

The labour laws in India have thoroughly focused on the idea of promoting growth along with social justice in tandem with the efforts of labour unions. However, despite the state and the unions’ consistent efforts, the laws’ ability to improve women’s living and working conditions was negligible, as described in the landmark report published in 1971, “Towards Equality”. It was an eye-opener to the dire circumstances in which women were surviving with patriarchy clawing its way into not just the domestic sphere but the workplace as well. While acts like the Maternity Benefit Act of 1961 offer relief to women on some levels, there is a lack of legal awareness among women workers. This is a contributing factor to their being taken advantage of by employers. An analysis of the allocation of Variable Dearness Allowance of Minimum Wages with effect from October 2022 has no separate mention of women’s labour. The general labour classification in this regard has been Unskilled, Skilled/Clerical, Semi Skilled and Highly skilled are the most probable categories in which women fall under unskilled due to a plethora of reasons. Despite the assumption that the New Labour Code is a relief to women labour across the country, measures must be taken to understand its effective implementation in both the public and private sector organisations.

Women in Garment Industries

The garment industries in South Asian nations like India and Bangladesh have been significant contributors to their economies and increased the employment ratio of women in the labour force. “India’s ready-made garment industry contributes around 16 per cent to total export earnings and is the largest foreign exchange earner in the country” (WTO,2019). Post-1980 saw unprecedented growth of the export industry, and the growth chart statistics show that from $2 million in 1960-61 to $696 million in 1980-81, it then increased sharply to $2,236 million in 1990- 91 and to $4,765 million in 1999-2000. The vast wage disparity was the driving force behind the globalisation of the garment industry. Studies have shown that the hourly wage of Indian

labour is a meagre Rs.8 per hour, whereas a British worker performing the same work received around Rs.420. Thus, the capitalist tendency of the upper class and lower class is synonymous with the imperialist notion of civilised and barbaric groups pushing for cheap labour and higher production of goods.

The onus of cheap labour fell on women, mainly from the marginalised communities who were desperate for jobs that promised a stable source of income. The Indian state also firmly believed that this was a way to empower women and ensure financial freedom. However, the challenges were masked by the rosy nature of the benefits put forth by the employers. The actual reasons for the employment of women, which were different from the portrayed norms, were: i) the common notion that women in the developing regions were meek beings who would barely retort against any kind of discomfort and would succumb to the system, ii) women will not question the wage disparity for they are fed the patriarchal notion of the superiority of men iii) the stable source of income will not let them rise in protest despite the atrocities meted out to them.

Here, I would like to discuss a study conducted in Bangalore, Karnataka, which houses more than 800 garment industries and has the largest workforce of women. The exploitative nature of the employment of women in the garment industry is well documented and needs no elaboration. Briefly, the large majority of women, whether working as skilled tailors or as unskilled helpers, do not get even the legally stipulated minimum wage. Workers are frequently required to work overtime, but since this is set against production targets, they are not paid for overtime work. Insecurity of work is one of the most widely reported problems, as employers frequently terminate a woman’s service just before the completion of five years to avoid payment of gratuity. Harsh production targets, sexual and verbal abuse, lack of maternity and other leave, lack of accident insurance, and absence of toilet and creche facilities are some of the commonly stated and widely known features of female employment in garment manufacture. This misery underpins the production of high fashion garments sold in chic stores in the first world and worn by middle and upper-class women who pay for a single dress at a price that exceeds several times the monthly income of a woman who produces it.

Challenges to women in the garment labour force due to Globalisation

The post-1991 era in India saw a massive difference in the treatment of women as the labour force in industries, especially the textile sector. Female workers typically migrate from rural areas to work in the garment industry to meet their financial needs. Women labour in the garment industry mostly come from households below the poverty line. Therefore, the proposition for the ’empowerment of women’ through employment in these capitalist industries was thought to pave the road for the emancipation of this vulnerable group. However, with the fast fashion industry booming and the convergence of interests among global consumers, there was and still exists a constant need to satiate consumer behaviour consistently. The mass production of goods became inevitable. This had adverse effects in that it created a hostile working environment, and reports suggest that it took a toll on the physical and mental health of women:

i) Impact on physical well-being: The study “Sewing shirts with injured fingers and tears: exploring the experience of female garment workers’ health problems in Bangladesh” found that physical health problems included headaches, eye pain, musculoskeletal pains and fatigue. It further revealed that garment work is also so physically demanding that women cannot work for more than ten years. These findings are consistent with other research, which found that the highest proportion of female workers quit factory work before they reach 40. The workers reported that getting sick and injured was an everyday phenomenon. The doctors thought that women in factories could not work for more than ten years owing to the stressful conditions. This study also described that since the manufacturing units have men as supervisors, it becomes difficult for women to voice their concerns, particularly those related to their menstrual health. Further, this gendered division of labour extends to their home life, where their husbands expect them to fulfil their domestic obligations despite long, physically demanding hours at work.

ii) Impact on mental well-being: The article “Mental Health Status of Female Workers in Private Apparel Manufacturing Industry in Bangalore City, Karnataka, India” steers the discussion in the direction of the importance of mental health awareness and the need for a safe work environment for women in garment factories. Mental health problems, including depression, have become a global health priority, and socially disadvantaged people are more vulnerable to suffering from mental health problems. There is evidence that scarcity of human resources, limited access to, and cost of mental health services are critical issues in most low- and middle-income countries. Separation from their children is an important issue for them. Most had left their children in their home villages, citing lack of time to care for them due to their long work hours and difficulties in paying for their children’s living costs in the city. They work from morning to night and during weekends, with nobody at home to look after their children. They get to leave only a few times a year, and the distance to their villages can be up to 10 hours of travel time. As such, they have no option but to leave their children in their village to live with their grandparents. However, avoiding long working hours is impossible, as they need money to provide for their impoverished families.

To improve the health and well-being of female garment workers, steps should be taken to develop health interventions to meet the needs of this important group of workers who contribute significantly to the country’s economic development.

Way Forward

Although women are at a disadvantage, the involvement of women in decision-making becomes indispensable. A developmental perspective based on male priorities and the male concept of the role of women in a patriarchal society such as ours cannot alleviate the lot of women already inhibited by traditional gender-role expectations. Stakeholder theory advocates that firms bear responsibility for the implications of their actions, and based on this, women come under the category of normative stakeholders to whom the industry has a moral obligation: an obligation of stakeholder fairness. Also, stress has to be placed on including women in the policy-making process, thereby increasing accountability of the framed policies. Illiteracy is a global problem and one of the reasons for the deterioration in the status of women and the feminisation of poverty. Ignorance of their rights- political, social, and economic- leads to the exploitation of women and their inability to converge to form a pressure group. The interface between the grassroots women and the activists must be used to build awareness and sensitise people, both men and women. Involving men who are sensitive to women’s issues is a healthy practice. It would benefit the cause of women if their struggle is seen as a fight for human rights, which it is, and not merely as a gender-based movement.

References
- Ahmed, F. (2004). The rise of the Bangladesh garment industry: globalisation, women workers, and voice. NWSA Journal, 16(2), 34–45. https://doi.org/10.2979/nws.2004.16.2.34
- Unni, J., Bali, N., & Vyas, J. H. (1999). Subcontracted women workers in the global economy: the case of the garment industry in India. http://www.sewaresearch.org/pdf/researches/subcontracted.pdf
- Saha, T. K., Dasgupta, A., Butt, A., & Chattopadhyay, O. (2010). Health status of workers engaged in the small-scale garment industry: How healthy are they? Indian Journal of Community Medicine, 35(1), 179. https://doi.org/10.4103/0970-0218.62584
- Baud, I., & De Bruijne, G. (1993). Gender, small-scale industry, and development policy. https://doi.org/10.3362/9781780442280
- Oonk, G., Overeem, P., Peepercamp, M., & Theuws, M. (2012). Maid in India: Young Dalit women continue to suffer exploitative conditions in India’s garment industry. Social Science Research Network. https://doi.org/10.2139/ssrn.2119816
- Carr, M. (2001). GLOBALISATION AND THE INFORMAL ECONOMY: HOW GLOBAL TRADE AND INVESTMENT IMPACT ON THE WORKING POOR. RePEc: Research Papers in Economics. http://info.worldbank.org/etools/docs/library/76309/dc2002/proceedings/pdfpaper/module6mcmc.pdf
- Hale, A., & Shaw, L. M. (2001). Women workers and the promise of ethical trade in the globalised garment industry: a serious beginning? Antipode, 33(3), 510–530 https://doi.org/10.1111/1467-8330.00196
- Mezzadri, A. (2014). Indian Garment clusters and CSR norms: incompatible agendas at the bottom of the garment commodity chain. Oxford Development Studies, 42(2), 238–258. https://doi.org/10.1080/13600818.2014.885939
- Sharma, L., & Srivastava, M. (2020). A scale to measure organisational stress among women workers in the garment industry. European Journal of Training and Development, 46(9), 820–846. https://doi.org/10.1108/ejtd-04-2019-0060
- Kabeer, N., & Mahmud, S. (2003). Globalisation, gender, and poverty: Bangladeshi women workers in export and local markets. Journal of International Development, 16(1), 93–109 https://doi.org/10.1002/jid.1065
- ANNUAL REPORT 2022-23. (n.d.). In Ministry of Labour and Employment. Ministry of Labour and Employment, Government of India.
Feature Image Credit: www.changealliance.in
November 19, 2023
Social and Economic Aspects of Caste Survey in Bihar

The need for caste census today is because after independence we adopted the top-down development model. It was thought that the development benefits would flow from the upper strata to the lower ones. But this hope has been belied with the well-off capturing most of the benefits, leaving little for the marginalized sections who are lagging behind in development.
————-

The release of the figures of the caste survey in Bihar has immediately led to the heating up of politics in the entire country. There is a demand for conducting a caste survey in many states, including Uttar Pradesh, Madhya Pradesh, Rajasthan and Maharashtra. In Karnataka, the demand is to make public the data of the caste survey conducted in 2015.

Poverty and Population Increase

According to the Bihar survey report, the largest population in the state belongs to the extremely backward class (EBC), constituting about 36 per cent of the total population. While the Report clarifies the situation in Bihar, it does not tell us the situation in the entire country. That would require a national survey. Therefore, now the pressure will increase on the Central Government to conduct and make public the data at the national level. That is the only way the caste composition of the total population can be known. This is required to make policies which can enable equitable shares in employment and education for different sections of the population.

The increase in the proportion of extremely backward classes in the total population of Bihar should have been expected because of the prevailing poverty among them. Those who are poor have more children due to several reasons, like lack of education and awareness. Most importantly, for their social security in old age. The poor do not have savings; hence children constitute their old-age social security. They have more children to ensure at least one child survives till their old age. Also, more children mean more earning hands in the family. As people become more prosperous, people produce fewer children. The economic condition of the middle class and the well-off are relatively better, so they have fewer children, and their population grows less.

The question arises: what is the likely consequence? Upper caste people are worried that since extremely backward castes constitute a higher proportion of the population, their demand for reservation will increase.

Growing Unemployment a Crucial Factor

I believe that if we had given more importance in employment and education to the extremely backward castes from the beginning, today’s situation would not have arisen. Reservation makes no difference if jobs are available in sufficient numbers. Reservation becomes critical when employment generation is weak. When there is a lack of adequate employment, a dispute arises over reservations as to who will get how much employment. At present, due to large unemployment among the educated youth and few available government jobs, the demand for reservations has increased.

The problem has been growing because, after independence, we have adopted the top-down and trickle-down policy. The result has been that the upper sections of society have cornered most of the benefits while the marginalized sections have received very little benefits. Disparities have grown, and so have expectations, thereby raising the level of conflict in society. The use of more advanced technology in every sector has displaced labour and contributed to increasing unemployment. The Agriculture sector, which has the most employment (46%) in our country, has seen increased use of tractors, harvester combines, threshers, potato digging machines, etc., thereby reducing the need for employment and displacing workers. This is also true of manufacturing and services, like banking.

Impact of Government Policies

The government is also fueling this change by promoting the growth of the capital-intensive organized sector at the expense of the unorganized sector (which employs 94% of the workers). For example, the government reduced the tax rates on the corporate sector and rolled out the PLA scheme while cutting allocations to the National Rural Employment Guarantee Scheme. Allocations to education and health sectors have also been kept low and cut, even though both these sectors generate more employment. Due to these policies, most of the investments are being made in big projects, like railway freight corridors, where human labour is being replaced by big machines.

The result is rising inequality, frustration, alienation and sharpening social conflict. Therefore, the parties pursuing social justice politics for the lower classes and the people themselves have been demanding greater reservation for the backward castes according to their proportion in the population. With Bihar’s caste survey becoming public, the demand for conducting such surveys in other states and nationally will become more vociferous. The demand will also arise that the maximum limit of reservation, which is fixed at 50 per cent by the Supreme Court, should be increased. But reservations will be only for a few million jobs while the need is for work for tens of millions. So, the real issue is the generation of enough employment and good education for the children of the poor.

Political Implications

Opponents of caste surveys argue that castes with a lower proportion in the population will start competing to increase their population by bypassing family planning policies. But I don’t accept this. Around the world, as family prosperity increases and education levels rise, people have fewer children. The well-off families with less share in the population are already sending their children abroad for education and employment, which may accelerate.

Bihar’s caste survey data is bound to impact national politics. All political parties would like to use it in their own way, and Mandal-Kamandal politics will intensify in the country. But, the situation for BJP has changed compared to the 1990s since in the last few elections, it has wooed the votes of backward castes. The issue of reservation and demand for an increase in the maximum prescribed reservation limit will intensify. The ruling party will be reluctant, but in view of the electoral arithmetic, it will also not oppose it vociferously. It will hope that the Supreme Court will not agree to increase the limit. Further, it will try to divert the public attention towards issues like Sanatan dharma, terrorism and threats from China-Pakistan.
The lesson is that when socially correct policies are not implemented in a timely manner, social strife and alienation spread, and the nation is forced to implement sub-optimal policies.

This is a translation of the article in Hindi published earlier in Amar Ujala.

October 12, 2023
Right to Work: Feasible and Indispensable for India to be a Truly Civilized and Democratic Nation

Executive Summary of
Report of People’s Commission on Employment and Unemployment
Set up by Desh Bachao Abhiyan

Introduction

When society faces a problem and is unable to resolve it, it implies that something basic is wrong. One needs to look for its basic causes to solve the problem. The causes may lie in the system that has evolved over time and which conditions the dominant social and political thinking in society. The onus of finding the solution and rectifying the problem is on the rulers. Their failure to do so over time implies a lack of motivation/commitment to solve the problem.

All this applies to the issue of employment generation and unemployment in India which has been growing over time and affects the vast majority of the citizens.

The Basic Issue

Gandhi said that India is the only country capable of giving a civilizational alternative. The time has come to take this seriously since unemployment has become a critical issue that needs to be urgently tackled. The issue is multi-dimensional since it is a result of multiple causes and has widespread implications. It impacts the growth of the economy, inequality, poverty, etc. It has a gender dimension and impacts the marginalized sections adversely reflecting a lack of social justice. It is entrenched among the youth. The more educated they are greater the unemployment they face. Consequently, it has political and social implications, like, social relations.

The rapidly growing incomes of the top 1% in the income ladder indicate that the economy has the resources but they are mal-distributed. The rich at the top has created a system that enables them to capture most of the gains from development with little trickling down to the rest.

This Report presents a framework that spells out the causes, consequences, and possible remedies. Further, it looks at the historical process underlying the evolution of policies so as to understand how they can be changed.

If any form of distortion persists over a long period, as unemployment in India, its origins lie in society’s perceptions and priorities. In India, these can be traced to the adoption of state capitalism and persisting feudal tendencies of the elite policy makers who in their own self-interest adopted a trickle-down model of development.

Further, Capitalism has globally taken the form of marketization which promotes `profit maximisation’. But is it then legitimate to keep workers unemployed? It implies loss of output and therefore reduces the size of the economy which leads to a lower level of profits. So, by the logic of individual rationality, the system should create productive employment for all.

The market’s notion of `efficiency’ is status quoist since it seeks to perpetuate the historical injustice in society. `Consumer sovereignty’ implies that individuals should be left free to do whatever they wish. The collectivity should not intervene in their choices no matter how socially detrimental they may be. It promotes the notion that if I have the money I can do what I like. The ratio of incomes is 10,000 times and more between the big businessmen and the poor workers. The market sees nothing wrong in this; in fact, society has come to celebrate it.

Marketization is determining society’s choices through its principles penetrating all aspects of society. One of these principles is the `dollar vote’. The policy makers accept it and prioritize the choices of the well-off over those of the marginalized. The well-off dictate the social judgments of policy makers. Consequently, not only equality is not on the agenda even equity is not.

With marketization stripping off the social aspect of life, individuals become automatons. Their individual distress and situation in life are no one’s or society’s concern. Unemployment becomes just a switching off of a machine. No social concern need to be attached to it. In fact, capitalists welcome unemployment as an efficient’ device to discipline labour and neo-classical economics considers it as natural. Inflation further weakens large numbers of workers as they lose purchasing power.

In essence, whether or not society should aim to give productive employment to all reflects its view of individuals. Society needs to choose what is more important – profits or the welfare of the marginalized majority. The Gandhian view, largely rejected by the Indian elite, was `last person first’ which defined what the priority should be.

[powerkit_button size=”lg” style=”info” block=”true” url=”https://admin.thepeninsula.org.in/wp-content/uploads/2022/10/PCAU-Executive-Summary-of-Report-1.pdf” target=”_blank” nofollow=”false”]
Read the Full Executive Summary
[/powerkit_button]

Disclaimer:

The views represented herein are those of the author and do not necessarily reflect the views of The Peninsula Foundation, its staff, or its trustees.

The report’s executive summary is republished with the permission of the author.

Feature Image:

Economic and Political Weekly

October 29, 2022
Rural Agriculture and the new wave of Migrant Workers to Rural Space

Abstract

Home, belongingness, and identity bring comfort to human existence, but local communities are challenged and become highly volatile by the sudden influx of people from different regions in search of livelihood and survival. Some migrate in their quest to find new opportunities in education, employment, and better living conditions from their home state, but some are displaced due to loss of livelihood, low employment, and lack of safety. This article analyses internal migration toward Tamil Nadu. The migrant population in Tamil Nadu accounts for 18.85 lakh according to the 2011 census, whereas other state migrants account for only 6.2% (Radhakrishnan & Vasanth, 2019). Most migration in the past has been towards the cities for chances of better livelihood and stable jobs. However, migrant workers travelling towards rural areas have been increasingly found working as agricultural labourers. S. Irudaya Rajan, a professor at the Centre for Developmental studies in his work, points out the importance of migrants to this economy as there is a constant outflow of the young population, with reservation wages in this region being high (Radhakrishnan & Vasanth, 2019). A report by the Federation of Tamil Nadu Agricultural Association suggests that over 8,67,582 farmers have stopped agricultural practices, and the market has been taken over by private players who require agricultural labourers (Sreemathi, 2019). This demand can attract migrant workers to rural areas. This article examines the migration pattern in Tamil Nadu to understand the inflow and outflow population, the reasons behind the outflow of farmers from the system and the new wave of migrant workers to rural Tamil Nadu.

Introduction

Millions of people move every year hoping for a better livelihood and future, but the reality may be bitter for some. Nine million people have been migrating annually between states as per the Railway’s data from 2011 to 2016. Around 30% of the Indian population represents the varied level of the migrant population (Migration, 2022). Various factors have contributed to migration. The pull factors which attract people towards the destination include better living conditions, better employment, quality education, absence of violence and high wage rates. The push factors, on the other hand, include the lack of welfare activity, discrimination towards a community, lack of employment and lower wage rates. In both instances, economic ambition occupies the centre space. Thus, it is crucial to form a developmental economy for the residing population and the migrating one. The pull factors usually replace the push factors when the socio-economic condition in the country facilitates good life. People have been moving towards cities, hoping to find better employment and livelihood in the globalised world, making cities the hub for development. In Tamil Nadu, the movement toward the city area was triggered by early industrialisation in the 1980s, when manufacturing capacity accounted for around 26% of its GDP, higher than the national average of 15% (Mahambare & Dhanaraj, 2021). The 1990s liberalisation policy created mobility and development by expanding the economic horizons to telecom, software and banking (Migration to Chennai, 2010). This socio-economic mobility has greatly impacted the state’s rural economy and continues to be one of the few contributors to migration from agriculture. In recent times, farmers are selling out their lands and changing their occupations or working as agricultural labourers. The agricultural sector requires a considerable labour population. This demand for labour forces along with the lockdown during the Covid pandemic has fuelled a new wave of migrant workers in rural areas in south India. However, it is essential to study the causes of the movement of the traditional population from the industry, which helps understand the patterns that need to be avoided.

Migration out of Agriculture

Agriculture has long been a community practice in Tamil societies, but the migration of farmers continues to challenge the status quo. A report by the Federation of Tamil Nadu Agricultural Association mentions that over 8,67,582 farmers have stopped agricultural practices (Sreemathi, 2019). Lower wage rates, discrimination, heavy workload, lack of welfare and crop failure are the main reasons for displacement. Since the agricultural sector is seasonal, the wages are decided by workdays, seasons, and piece/ quantity rate, which leads to an unsteady wage rate based on the season, with fewer or no jobs in some seasons. People, thus, prefer to work non-farm jobs for a steady income throughout the year. Some have been living as labourers for generations on the farm since only a handful of the population possess larger farmlands. A study by Sato Keiko (2011) traces this class difference, farmland size, and the employment status of migrants from a rural village in Madurai. He points out that the village’s upper-class children with larger farms migrated to the city and acquired white-collar jobs. The middle class and the marginal groups, on the other hand, usually landed in blue-collar jobs. Interestingly, he notes that the aspiration to educate and climb the socio-economic ladder has recently been high among the latter (Keiko, 2011). This aspiration leads them to migrate to cities and take up factory jobs, which are comparatively better than being engaged as farm labourers. Educational aspiration attempts to shake the traditional class structure and disparity that exists with it.

Additionally, only 27.1% adolescent population and 24.18% of the youth population were involved as agricultural labourers in 2014 (Sivakumar, 2014). Another reason for the migration is crop failure induced by unpredictable heavy rainfall and drought. Farmers who cannot profit or make ends meet when the crop collapses, end up falling into debt.

More than 85% of people working in the state under MGNREGA are women, higher than the national average of 56% and 28% of the Dalit population (Ramakrishnan, 2017)

The Mahatma Gandhi Rural Employment Guarantee Act (MGNREGA) Scheme has been argued to be one of the major reasons behind the declining numbers of farm labourers When the scheme started, it provided the rural population with a higher income of Rs. 100 compared to farm jobs which offered Rs. 40 a day (In Tamil Nadu labourers, 2010). MGNREGA acts as a precursor for the high wage rate in agriculture as it competes with the scheme to attract more people for work. In 2020, the wage rate for agriculture labourers had increased to Rs. 392, and the notified MGNREGA stood at Rs. 273, which was lower than the farm wage (Aditi, 2021). However, along with steady wages and less workload, MGNREGA has continued to be a source of economic empowerment for women. It enables pathways to formal financial institutions and personal saving habits – “I would be working like a bonded labourer again under any big landowning agriculturist, if there was no Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS)” (Ramakrishnan, 2017). These interviews collected by Ramakrishnan, senior journalist for The Hindu, shed light on the women’s attitude and discontent toward farm jobs. More than 85% of people working in the state under MGNREGA are women, higher than the national average of 56% and 28% of the Dalit population (Ramakrishnan, 2017). These marginal groups are frequently abused and sexually harassed by employers and landowners. In this regard, MGNREGA has provided them with a space to work with dignity. However, some experts like Vijayanand, former Secretary of the Union Minister of Panchayat Raj, opposed the arguments favouring MGNREGA arguing that the scheme did not provide jobs throughout the year and phased out the jobs in accordance with the lean season (Radhakrishnan, 2017).

Involvement of Migrants in Agriculture

Tamil Nadu is a growing economy which renders a stay to 18.85 lakhs migrants, of which 6.2 % are from other states. The origin states of these migrant workers are Bihar, West Bengal, Odisha, Jharkhand, Chattishgarh and states of Northeast India. Some tribal communities migrated from areas with rich mineral resources like the Santal areas of West Bengal, MP and a few other areas in Jharkhand and Chattisgarh because of the prevalent mining practice and dam building. Also, tribal people were displaced because of the settlement of non-tribals in the region and deforestation. In some areas, low human development indicators have led to their displacement. Apart from these push factors, Tamil Nadu has a lot to offer in terms of its higher wage rates, better living conditions and political, religious and social freedom (Sami, Crossin, Jayapathy, Martin, et al., 2016). Once they migrate to Tamil Nadu through contractors, migrants are channelled to Chennai, Coimbatore, Tiruvallur, Tiruppur, Kancheepuram and Chengalpattu to work in manufacturing factories and construction sites. These migrants fill the state’s requirement for 3D jobs, as Dr Irudaya Rajan from the Tiruvananthapuram Centre for Developmental Studies mentioned (Radhakrishnan & Vasanth, 2019). These jobs are mostly dirty, demeaning and dangerous. Since the wage expectation of the state youth is high, the desperation to take up these jobs is low and job positions are thus occupied by migrant workers (Vasanth & Radhakrishnan, 2019).

The Periodic Labour Force Survey (PLFS) data from 2018 to 2020 shows a sharp increase in employment in agriculture from 42.5 % in 2018 -19 to 45.6% in 2019-20 (CMIE, 2021)

In the pre-Covid job market, population movement was constantly moving from rural villages to urban spaces in search of white and blue-collar jobs. However, Covid has shifted the employment market. The Periodic Labour Force Survey (PLFS) data from 2018 to 2020 shows a sharp increase in employment in agriculture from 42.5 % in 2018 -19 to 45.6% in 2019-20 (CMIE, 2021). Most people who changed jobs were formerly employed in construction and manufacturing. While the existing population migrates to cities in search of skilled labour, migrant workers find the farm jobs more appealing. Tamil Nadu provides an average salary of Rs. 392 per day for farm workers, which is higher than the national average of Rs. 348. Most states from which the migrants are displaced have far fewer wage rates; for instance, Jharkhand offered Rs. 258 in 2020 while the wage rate was Rs. 234 in Chhattisgarh (Directorate of Economics and Statistics, 2021). While their movement is unexpected, these migrant populations can contribute to the agricultural sector and rural development, but that cannot be done without good government policies. The GDP contribution of agriculture reduced from 55.3% in the 1950s to 21.8% in the 2000s (Gothoskar, 2021). Most government budgets have little concern for the agricultural economy. Thus, it is essential to implement policies for the existing agrarian population and the migrants. Also, there has been increasing distress caused by the growing movement of the migrants to farmland which continues to be heavily unorganised. Dr Irudaya Rajan, in his interviews, warns that this unexpected surplus labour availability in rural areas cannot accommodate everyone in the existing jobs, which may result in increased poverty and starvation (Nirupama, 2020).

Furthermore, it backfires on the urban economy once industries open up completely and face a shortage of labour (Viswanathan, 2020). To know the current situation of migrant workers, state-level data collection is needed. Tamil Nadu collected the migrant database only once in 2015, following the fall of the Moulivakkam multistorey building. While Thangam Thennarasu, the Tamil Nadu Minister of Industries, mentioned collecting data on migrant workers in a press release, the agriculture sector was not mentioned (Kumar, 2021). Data collection is vital in formulating policies to accommodate the migrant workers in rural economies and avoid unexpected problems.

Measures to be taken

Since the Agricultural sector offers seasonal employment, other sources of organised employment or schemes to assure livelihood during times of distress should be in place. While farming requires work like ploughing and harvesting, which is to be done all year-round, the revenue can be earned only in a particular season. If affected by climate calamities and crop failure, people are most likely to end up in debt. Hence, it is essential to employ migrants during the off-season and distress times in sustainable jobs. Most agricultural products are exported as raw materials or semi-processed to other countries, and therefore, the MSMEs in the rural areas can be focused on enhancing the exporting sector of agriculture.

MOUS between states: The Tamil Nadu government has fewer memorandums of understanding (MOUs) on migrants, with focus mainly placed on Sri Lankan refugees. Thus, signing MOUs with the source state can improve the conditions of migrants and help governments to maintain a database of migrants (Sreelakshmi,2021). The databases can help in formulating policies.

Welfare policies – Rashtriya Swasthya Bima Yojana (RSBY), a central-run health insurance scheme for people working in the unorganised sector and for those below the poverty line, has to be implemented appropriately, and the records should be maintained. Quality schooling for children of migrants working in rural spaces should be provided. Some states have offered regional language subject notebooks and learning kits through MOUs. Tamil Nadu should recruit staff in the favoured language. Further, skill enhancement training for the migrants should be provided.

Conclusion

Agriculture and rural development go hand in hand since 70% of rural households depend upon agriculture for their livelihood. Still, there is an increased pattern of traditional farmers moving out of the business and choosing other industries or being employed as agricultural labourers. This shift, accompanied by the Covid lockdown, has triggered an increased flow of migrant population back to farms as agricultural labourers. Unlike industries, the agricultural sector is unorganised and seasonal, making it highly vulnerable. Thus, it becomes essential to build a safety net for the traditional population and the migrants. These migrants, without proper policies and data entries, can be stranded; lacking identity, rights and political representation. Further, this sudden labour surplus cannot be accommodated immediately, creating a labour shortage in urban areas. It is, thus, important to record migrant workers who return, the sector they are involved in, their security nets and most importantly, their availability in rural agriculture.

Reference

Aditi R. (2021, May 16). MGNREGA workers in Tamil Nadu allege underpayment and wage disparity. The times of India. Retrieved from https://timesofindia.indiatimes.com/city/chennai/mgnrega-workers-in-tamil-nadu-allege-underpayment-and-wage-disparity/articleshow/82673961.cms

Dhanaraj Sowmya & Mahambare Vidya (2021, March 31). Tamil Nadu left Punjab, Bengal far behind. Here’s what it needs to do now. The Print. Retrieved from https://theprint.in/opinion/tamil-nadu-left-punjab-bengal-far-behind-heres-what-it-needs-to-do-now/631213/

Directorate of Economics and Statistics. (2021). Agricultural wages India: 2019 – 20. Ministry of Agriculture & Farmers welfare.

Gothaskar Sujata. (2021, May 12). To Fully Understand the Migrant Worker Crisis, We Need a Larger Perspective. The Wire. Retrieved from https://thewire.in/rights/migrant-worker-crisis-larger-perspective-farm-land-industry

In Tamil Nadu labourers choosing NREGA over farms. (2010, Nov 29). NDTV. Retrieved from https://www.ndtv.com/india-news/in-tamil-nadu-labourers-choosing-nrega-over-farms-440546

Keiko Sato. (2011). Employment structure and Rural-Urban Migration in a Tamil Nadu Village: Focusing on difference by economic class. Southeast Asia Studies. Vol.49. Pg.22-51.

Kumar Vijay. (2021, July 26). Tamil Nadu to create a databank of migrant workers. The Hindu. Retrieved from https://www.thehindu.com/news/national/tamil-nadu/databank-of-migrant-workers-soon-says-tamil-nadu-industries-minister/article35530808.ece

Migration to Chennai follows industrial growth, but quality. (2010, April 13). The Times of India. Retrieved from https://timesofindia.indiatimes.com/city/chennai/migration-to-chennai-follows-industrial-growth-but-quality-of-life/articleshow/5798687.cms

Radhakrishnan V & Vasanth B. A. (2019, September 08). Migrants in Tamil Nadu: case of much ado about nothing? The Hindu. Retrieved from https://www.thehindu.com/news/national/tamil-nadu/migrants-in-tamil-nadu-case-of-much-ado-about-nothing/article29364682.ece

Ramakrishnan T. (2017, February 05). Job scheme, a mixed bag for rural labourers. The Hindu. Retrieved from https://www.thehindu.com/news/national/tamil-nadu/Job-scheme-a-mixed-bag-for-rural-labourers/article17197043.ece

Sami Bernard. Crossin Sebastian, Jayapathy, Martin. P. O. (2016). A survey on Interstate migrants in Tamil Nadu. LISSTAR & Indian Social Institute.

Sivakumar B. (2014, November 02). Most of Tamil Nadu’s adolescents, youth live in rural areas, shows census. The times of India. Retrieved from https://timesofindia.indiatimes.com/city/chennai/most-of-tamil-nadus-adolescents-youth-live-in-rural-areas-shows-census/articleshow/45008956.cms

Sreelakshmi Anjana. (2021, November 07). Distress Migration: A case study KBK districts in Odisha. The Peninsula Foundation. Retrieved from https://admin.thepeninsula.org.in/2021/11/07/distress-migration-a-case-study-of-kbk-districts-in-odisha/

Sreemathi M. (2021, November 23). Migrants now enter agri fields in Nellai. The New Indian Express. Retrieved from https://www.newindianexpress.com/states/tamil-nadu/2021/nov/23/migrants-now-enter-agri-fields-in-nellai-2386930.html

Viswanathan Nirupama. (2020, May 20). We have not factored in Tamil Nadu’s migrant workers in our realm of things: Expert. The new Indian Express. Retrieved from https://www.newindianexpress.com/states/tamil-nadu/2020/may/20/we-have-not-factored-in-tamil-nadus-migrant-workers-in-our-realm-of-things-expert-2145578.html

Vyas Mahesh. (2021, August 09). Migration from factories to farms. Centre for Monitoring Indian Economy. Retrieved from https://www.cmie.com/kommon/bin/sr.php?kall=warticle&dt=20210809122441&msec=850

Feature Image Credits: The News Minute

August 18, 2022
Indian Economy at 75: Trapped in a Borrowed Development Strategy

In 1947, at the time of Independence, India’s socio-economic parameters were similar to those in countries of South East Asia and China. The level of poverty, illiteracy, and inadequacy of health infrastructure was all similar. Since then, these other countries have progressed rapidly leaving India behind in all parameters. ‘Why is it so?’ should be the big question for every Indian citizen in this time of our 75th anniversary celebrations.

Introduction

India at 75 is a mixed bag of development and missed opportunities. The country has achieved much since Independence but a lot remains to be done to become a developed society. The pandemic has exposed India’s deficiencies in stark terms. The uncivilized conditions of living of a vast majority of the citizens became apparent. According to a report by Azim Premji University, 90% of the workers said during the lockdown that they did not have enough savings to buy one week of essentials. This led to the mass migration of millions of people, in trying conditions from cities to the villages, in the hope of access to food and survival.

Generally, technology-related sectors, pharmaceuticals and some producing essentials in the organized sectors have done well in spite of the pandemic. So, a part of the economy is doing well in spite of adversity but incomes of at least 60% of people at the bottom of the income ladder have declined (PRICE Survey, 2022). The great divide between the unorganized and organized parts of the economy is growing. The backdrop to these developments is briefly presented below.

Structure and Growth of the Economy

In 1947, at the time of Independence, India’s socio-economic parameters were similar to those in countries of South East Asia and China. The level of poverty, illiteracy, and inadequacy of health infrastructure was all similar. Since then, these other countries have progressed rapidly leaving India behind in all parameters. So, India has fallen behind relatively in spite of improvements in health services and education, diversification of the economy and development of the industry.

In 1950, agriculture was the dominant sector with a 55% share of GDP which has now dwindled to about 14%. The share of the services sector has grown rapidly and by 1980 it surpassed the share of agriculture and now it is about 55% of GDP. The Indian economy has diversified production `from pins to space ships’.

Agriculture grows at a trend rate of a maximum of 4% per annum while the services sector can grow at even 12% per annum. So, there has been a shift in the economy’s composition from agriculture to services, accelerating the growth rate. The average growth rate of the economy between the 1950s and the 1970s was around 3.5%. In the 1980s and 1990s, it increased to 5.4% due to the shift in the composition. There was no acceleration in the growth rate of the economy in the 1990s compared to the 1980s. This rate again increased in the period after 2003 only to decline in 2008-09 due to the global financial crisis. Subsequently, the rate of growth has fluctuated wildly both due to global events and the policy conundrums in India.

There was the taper tantrum in 2012-13 which cut short the post-global financial crisis recovery. Demonetization in November 2016 adversely impacted growth. That was followed by the structurally flawed GST. These policies administered shocks to the economy. Then came the pandemic in 2020. The economy’s quarterly growth rate had already fallen from 8% in Q4 2017-18 to 3.1% in Q4 2019-20, just before the pandemic hit.

1980-81 marked a turning point. Prior to that, a drought would lead to a negative rate of growth in agriculture and of the economy as a whole. For instance, due to the drought in 1979-80, the economy declined by 6%. But, that was the last one. After that, a decline in agriculture has not resulted in a negative growth rate for the economy. The big drought of 1987-88 saw the economy grow at 3.4%. After 1980-81, the economy experienced a negative growth rate only during the pandemic which severely impacted the services sector, especially the contact services.

Employment and Technology Related Issues

Agriculture employs 45% of the workforce though its share in the economy (14%) has now become marginal. It has been undergoing mechanisation with increased use of tractors, harvester combines, etc., leading to the displacement of labour. Similar is the case in non-agriculture. So, surplus labour is stuck in agriculture leading to massive disguised unemployment.

India is characterized by disguised unemployment and underemployment.Recent data points to growing unemployment among the educated youth. They wait for suitable work. The result is a low labour force participation rate (LFPR) in India (in the mid-40s) compared to similar other countries (60% plus).The gender dimension of unemployment and the low LFPR is worrying with women the worst sufferers.

India’s employment data is suspect. The reason is that in the absence of unemployment allowance, people who lose work have to do some alternative work otherwise they would starve. They drive a rickshaw, push a cart, carry a head load or sell something at the roadside. This gets counted as employment even though they have only a few hours of work and are underemployed. So, India is characterized by disguised unemployment and underemployment.

Recent data points to growing unemployment among the educated youth. They wait for suitable work. The result is a low labour force participation rate (LFPR) in India (in the mid-40s) compared to similar other countries (60% plus). It implies that in India maybe 20% of those who could work have stopped looking for work. No wonder for a few hundred low-grade government jobs, millions of young apply. The gender dimension of unemployment and the low LFPR is worrying with women the worst sufferers.
These aspects of inadequate employment generation are linked to automation and the investment pattern in the economy. New technologies that are now being used in the modern sectors are labour displacing. For instance, earlier in big infrastructure projects like the construction of roads, one could see hundreds of people working but now big machines are used along with a few workers.

Further, the organized sectors get most of the investment so little is left for the unorganized sector. This is especially true for agriculture. Thus, neither the organized sector nor agriculture is generating more work. Consequently, entrants to the job market are mostly forced to join the non-agriculture unorganized sector, which in a sense is the residual sector, where the wages are a fraction of the wages in the organized sector. The unorganized sector also acts as a reserve army of labour keeping organized sector wages in check

Lack of a Living Wage

To boost profits, the organized sector is increasingly, employing contract labour rather than permanent employees. This is true in both the public and private sectors. So, not only the workers in the unorganized sector, even the workers in the organised sector do not earn a living wage. Thus, most workers have little savings to deal with any crisis. They are unable to give their children a proper education and cannot afford proper health facilities. Most of the children drop out of school and can only do menial jobs requiring physical labour. They cannot obtain a better-paying job and will remain poor for the rest of their lives.

The Delhi socio-economic survey of 2018 pointed to the low purchasing power of the majority of Indians. It showed that in Delhi, 90% of households spent less than Rs. 25,000 per month, and 98% spent less than Rs. 50,000 per month. Since Delhi’s per capita income is 2.5 times the all India average, deflating the Delhi figures by this factor will approximately yield all India figures. So, 98 per cent of the families would have spent less than Rs.20,000 per month, and 90 per cent less than Rs.10,000 per month. This effectively implies that 90 per cent of families were poor in 2018, if not extremely poor (implied by the poverty line). During the pandemic, many of them lost incomes and were pauperized and forced to further reduce their consumption.

Unorganized Sector Invisibilized

In the unorganized sector, labour is not organized as a trade union and therefore, is unable to bargain for higher wages, when prices rise. It constitutes 94% of the workforce and has little social security. No other major world economy has such a huge unorganized sector. No wonder when such a large section of the population faces a crisis in their lives, the economy declines, as witnessed during the pandemic. India’s official rate of growth fell more sharply than that of any other G20 country.

The micro sector has 99% of the units and 97.5% of the employment of MSME and is unlike the small and medium sectors. The benefits of policies made for the MSME sector do not accrue to the micro units.

Policymakers largely ignore the unorganized sector. The sudden implementation of the lockdown which put this sector in a deep existential crisis points to that. The micro sector has 99% of the units and 97.5% of the employment of MSME and is unlike the small and medium sectors. The benefits of policies made for the MSME sector do not accrue to the micro units.

Invisibilization of the unorganized sector in the data is at the root of the problem. Data on this sector become available periodically, called the reference years. In between, it is assumed that this sector can be proxied by the organized sector. This could be taken to be correct when there is no shock to the economy and its parameters remain unchanged.

Demonetization and the flawed GST administered big shocks to the economy and undermined the unorganized sector. Its link with the organized sector got disrupted. Thus, the methodology of calculating national income announced in 2015 became invalid.

The implication is that the unorganized sector’s decline since 2016 is not captured in the data. Worse, the growth of the organized sector has been at the expense of the unorganized sector because demand shifted from the latter to the former. It suited the policymakers to continue using the faulty data since that presented a rosy picture of the economy. This also lulled them into believing that they did not need to do anything special to check the decline of the unorganized sector.

Policy Paradigm Shift in 1947

Growing unemployment, weak socio-economic conditions, etc., are not sudden developments. Their root lies in the policy paradigm adopted since independence.
In 1947, the leadership, influenced by the national movement understood that people were not to blame for their problems of poverty, illiteracy and ill-health and could not resolve them on their own. So, it was accepted that in independent India these issues would be dealt with collectively. Therefore, the government was given the responsibility of tackling these issues and given a key role in the economy.

Simultaneously, the leadership, largely belonging to the country’s elite, was enamoured of Western modernity and wanted to copy it to make India an ’advanced country’. The two paths of Western development then available were the free market and Soviet-style central planning. India adopted a mix of the two with the leading role given to the public sector. This path was chosen also for strategic reasons and access to technology which the West was reluctant to supply. But, this choice also led to a dilemma for the Indian elite. It had to ally with the Soviet Union for reasons of defence and access to technology but wanted to be like Western Europe.

Both the chosen paths were based on a top-down approach. The assumption was that there would be a trickle down to those at the bottom. People accepted this proposition believing in the wider good of all. Resources were mobilized and investments were made in the creation of big dams and factories (called temples of modern India) that generated few jobs. They not only displaced many people trickle down was minimal. For instance, education spread but mostly benefitted the well-off.

The Indian economy diversified and grew rapidly. An economy that for 50 years had been growing at about 0.75% grew at about 4% in the 1950s. But, the decline in the death rate led to a spurt in the rate of population growth. So, the per capita income did not show commensurate growth, and poverty persisted. Problems got magnified due to the shortage of food following the drought of 1965-67 and the Wars in 1962 and 1965. The Naxalite movement started in 1967, there was BOP crisis and high inflation in 1972-74 due to the growing energy dependence and the Yom Kippur war. Soon thereafter there was political instability and the imposition of an Emergency in 1975. The country went from crisis to crisis.

Planning failed due to crony capitalism. The prevailing political economy enabled the business community to systematically undermine policies for their narrow ends by fueling the growth of the black economy.

The failure of trickle-down and the cornering of the gains of development by a narrow section of people led to growing inequality and people losing faith in the development process. Different sections of the population realized that they needed a share in power to deliver to their group. Every division in society — caste, region, community, etc. — was exploited. The leadership became short-termist and indulged in competitive populism by promising immediate gains.

The consensus on policies that existed at independence dissipated quickly. Election time promises to get votes were not fulfilled. For instance, PM Morarji Desai said that promises in the Janata Dal manifesto in 1977 were the party’s programme and not the government’s. Such undermining of accountability of the political process has undermined democracy and trust and aggravated alienation.

Black Economy and Policy Failure

The black economy has grown rapidly since the 1950s with political, social and economic ramifications. Even though it is at the root of the major problems confronting the country, most analysts ignore it.

So, the black economy controls politics and to retain power it undermines accountability and weakens democracy.

It undermines elections and strengthens the hold of vested interests on political parties. The compromised leadership of political parties is open to blackmail both by foreign interests and those in power. When in power it is willing to do the bidding of the vested interests. So, the black economy controls politics and to retain power it undermines accountability and weakens democracy.

The black economy controls politics and corrupts it to perpetuate itself. The honest and the idealist soon are corrupted as happened with the leadership that emerged from the anti-corruption JP movement in the mid-1970s. Many of them who gained power in the 1990s was accused of corruption and even prosecuted. Proposals for state funding of elections will only provide additional funds but not help clean up politics.

The black economy can be characterized as ’digging holes and filling them’. It results in two incomes but zero output. There is activity without productivity with investment going to waste. Consequently, the economy grows less than its potential. It has been shown that the economy has been losing 5% growth since the mid-1970s. So, if the black economy had not existed, today the economy could have been 8 times larger and each person would have been that much better off. Thus, development is set back. In 1988, PM Rajiv Gandhi lamented that out of every rupee sent only 15 paisa reaches the ground. P Chidambaram as FM said, `expenditures don’t lead to outcomes’.

The black economy leads to the twin problem of development. First, black incomes being outside the tax net reduce resource availability to the government. If the black incomes currently estimated at above 60% of GDP could be brought into the tax net, the tax/GDP ratio could rise by 24%. This ratio is around 17% now and is one of the lowest in the world. Further, as direct tax collections rise, the regressive indirect taxes could be reduced, lowering inflation.

India’s fiscal crisis would also get resolved. The current public sector deficit of about 14% would become a surplus of 10%. This would eliminate borrowings and reduce the massive interest payments (the largest single item in the revenue budget). It would enable an increase in allocations to public education and health to international levels and to infrastructure and employment generation.

In brief, curbing the black economy would take care of India’s various developmental problems, whether it be lack of trickle-down, poverty, inequality, policy failure, employment generation, inflation and so on. It causes delays in decision-making and a breakdown of trust in society.

Due to various misconceptions about the black economy, many of the steps taken to curb it have been counterproductive, like demonetization. Dozens of committees and commissions have analysed the issues and suggested hundreds of steps to tackle the problem. Many of them have been implemented, like reduction in tax rates and elimination of most controls but the size of the black economy has grown because of a lack of political will.

Policy Paradigm Shift in 1991

Failure of policies led to crisis after crisis in the period leading up to 1990. The blame was put on the policies themselves and not the crony capitalism and black economy that led to their failure. The policies prior to 1990 have been often labelled as socialist. Actually, the mixed economy model was designed to promote capitalism. At best the policies may be labelled as state capitalist and they succeeded in their goal. Private capital accumulated rapidly pre-1990. The Iraq crisis of 1989-90 led to India’s BOP crisis and became the trigger for a paradigm change in policies in favour of capital. The earlier more humane and less unequal path of development was discarded.

Marketization has led to the ’marginalization of the marginals’, greater inequality and a rise in unemployment.

In 1991, a new policy paradigm was ushered in. Namely, ’individuals are responsible for their problems and not the collective’. Under this regime, the government’s role in the economy was scaled back and individuals were expected to go to the market for resolving their problems. This may be characterized as ’marketization’. This brought about a philosophical shift in the thinking of individuals and society.

Marketization has led to the ’marginalization of the marginals’, greater inequality and a rise in unemployment. These policies have promoted ’growth at any cost’ with the cost falling on the marginalized sections and the environment, both of which make poverty more entrenched. So, the pre-existing problems of Indian society have got aggravated in a changed form.

Poverty is defined in terms of the ’social minimum necessary consumption’ which changes with space and time. Marketization has changed the minimum due to the promotion of consumerism and environmental decay imposing heavy health costs.
The highly iniquitous NEP is leading to an unstable development environment. The base of growth has been getting narrower leading to periodic crises. Additionally, policy-induced challenges like demonetization, GST, pandemic and now the war in Ukraine have aggravated the situation. These social and political challenges can only grow over time as divisions in society become sharper.

Weakness in Knowledge Generation

Why does the obvious not happen in India? No one disagrees that poverty, illiteracy and ill health need to be eliminated. In addition to the problems due to the black economy and top-down approach, India has lagged behind in generating socially relevant knowledge to tackle its problems and make society dynamic.

Technology has rapidly changed since the end of the Second World War. It is a moving frontier since newer technologies emerge leading to constant change and the inability of the citizens to cope with it. The advanced technology of the 1950s is intermediate or low technology today.

Literacy needs to be redefined as the ability to absorb the current technology so as to get a decent job. Many routine jobs are likely to disappear soon, like, driver’s jobs as autonomous (self-driving) vehicles appear on the scene. Most banking is already possible through net banking and machines, like, ATMs. Banks themselves are under threat from digital currency.

So, education is no more about the joy of learning and expanding one’s horizon. No wonder, the scientific temper is missing among a large number of the citizens.

India’s weakness in knowledge generation is linked to the low priority given to education and R&D. Learning is based substantially on `rote learning’ which does not enable absorption of knowledge and its further development. So, education is no more about the joy of learning and expanding one’s horizon. No wonder, the scientific temper is missing among a large number of the citizens. Dogmas, misconceptions and irrationalities rule the minds of many and they are easily misled. This is politically, socially and economically a recipe for persisting backwardness.

In spite of policy initiatives regarding education, like, the national education policy in 1968 and 1986, there is deterioration. This is because the milieu of education is all wrong. Policy is in the hands of bureaucrats, politicians or academics with bureaucratized mindsets. So, policies are mechanically framed. Like the idea that ’standards can be achieved via standardization’.

Learning requires democratization. So, institutions need to be freed from the present feudal and bureaucratic control. Presently, institutions treat dissent as a malaise to be eliminated rather than celebrated. Courses are sought to be copied from foreign universities. JNU is told to be like Harvard or Cambridge. This is a contradiction in terms; originality cannot be copied. Courses copied from abroad tend to be based on the societal conditions there and not Indian conditions. Gandhi had said that the Indian education system is alienating and for many it still is.

The best minds mostly go abroad and even if they return, they bring with them an alien framework not suited to India. So, as a society, we need to value ideas, prioritize education and R&D and generate socially relevant knowledge.

Learning is given low priority because ideas are sought to be borrowed from abroad. So, the rulers have little value for institutions that could generate new ideas and inadequate funds are allotted to them. The best minds mostly go abroad and even if they return, they bring with them an alien framework not suited to India. So, as a society, we need to value ideas, prioritize education and R&D and generate socially relevant knowledge.

Conclusion

The growth at any cost strategy has been at the expense of the workers and the environment. This has narrowed the base of growth and led to instability in society — politically, socially and economically.

India is a diverse society and the Indian economy is more complex than any other in the world. This has posed serious challenges to development in the last 75 years but undeniably things are not what they were. The big mistake has been to choose trickle-down policies that have not delivered to a vast number of people who live in uncivilized conditions. Poverty has changed its form and the elite imply that the poor should be grateful for what they have got. They should not focus on growing inequality, especially after 1991, when globalization entered the marketization phase which marginalizes the marginals.

The growth at any cost strategy has been at the expense of the workers and the environment. This has narrowed the base of growth and led to instability in society — politically, socially and economically. The situation has been aggravated by the recent policy mistakes — demonetization, flawed GST and sudden lockdown. The current war in Ukraine is likely to lead to a new global order which will add to the challenges. The answer to ’why does the obvious not happen’ in India is not just economic but societal. Unless that challenge is met, portents are not bright for India at 75.

This paper is based substantially on, `Indian Economy since Independence: Persisting Colonial Disruption’, Vision Books, 2013 and `Indian Economy’s Greatest Crisis: Impact of Coronavirus and the Road Ahead’, Penguin Random House, 2020.

This article was published earlier in Mainstream Weekly.

Feature Image Credit: Financial Express

Other Images: DNA India, news18.com, economictimes, rvcj.com

August 13, 2022
Distress Migration: A case study of KBK districts in Odisha
The former districts of Koraput, Balangir and Kalahandi, also known as KBK districts, were reorganised into 8 districts of Koraput, Malkangiri, Nabarangpur, Rayagada, Balangir, Subarnapur, Kalahandi and Nuapada in 1992. These districts form the South-West part of Odisha comprising the great Deccan Plateau and the Eastern Ghats. These highland districts highly rich in mineral resources, flora and fauna remain as one of the most backward regions in Odisha

Among the different forms of migration, distressed migrants remain the most impoverished and unrecognised. These migrants form the lowest strata of the society; disadvantaged by caste, poverty and structural inequalities. In Odisha, the underdeveloped region of KBK is one among the main sources of distressed migrants. They move to cities in search of employment and better wages, while in cities they are even more disadvantaged due to social, economic and linguistic barriers. Administrative and political apathy over their issues has only enhanced their distress.

This paper attempts to address three questions:
1. What are the characteristics of distressed migrants in KBK district, Odisha?
2. What are the existing policies of the state to curb this form of migration?
3. What form of government intervention is required to address this distress?
The analysis is carried out through a review of published articles, government reports, e-books and newspaper reports.

Defining distress migration

Migration is a multifaceted concept driven by diverse factors. Migration can be internal or international, voluntary or involuntary, temporary or permanent. Depending on the pattern and choice of migration, each migratory trend could be characterised into different forms. Distress migration is one such form of migration.

Involuntary migration is often associated with displacement out of conflict, environmental distress, climatic change etc. That is any sudden threat or event forces people to migrate. However, involuntary migration may also arise out of socio-economic factors such as poverty, food insecurity, lack of employment opportunities, unequal distribution of resources etc. This component of involuntary migration is addressed by the concept of distress migration (Avis, 2017).

To understand distressed rural-urban migration in India, the broad definition used by Mander and Sahgal (2010) in their analysis of rural-urban migration in Delhi can be employed. They have discussed distress migration as:

“Such movement from one’s usual place of residence which is undertaken in conditions where the individual and/or the family perceive that there are no options open to them to survive with dignity, except to migrate. Such distress is usually associated with extreme paucity of alternate economic options, and natural calamities such as floods and drought. But there may also be acute forms of social distress which also spur migration, such as fear of violence and discrimination which is embedded in patriarchy, caste discrimination, and ethnic and religious communal violence” ( Mander and Sahgal, 2010)

In brief, the definition states that distress migration is caused by an array of issues. Environmental disasters, economic deprivation, gender or social oppression, lack of alternate employment opportunities and inability to survive with dignity are mentioned as the main drivers of distress migration (Avis, 2017).

Thus, distress migration is a form of temporary migration driven by environmental and socio-economic factors and not based on an informed or voluntary choice.

Profile of KBK districts

The former districts of Koraput, Balangir and Kalahandi, also known as the KBK districts, were reorganised into 8 districts of Koraput, Malkangiri, Nabarangpur, Rayagada, Balangir, Subarnapur, Kalahandi and Nuapada in 1992. These districts form the South-West part of Odisha comprising the great Deccan Plateau and the Eastern Ghats. These highland districts highly rich in mineral resources, flora and fauna remain as one of the most backward regions in Odisha. The region is termed backward on account of rural backwardness, high poverty rates, low literacy rates, underdeveloped agriculture and poor development of infrastructure and transportation (Directorate of Economics and Statistics, 2021).

The districts are home to primitive tribal communities such as Gonds, Koyas, Kotias etc. dependent on forest produce and subsistence agriculture for a living. KBK region registered a workforce participation rate of 48.06 % in the 2011 census. There was a significant occupation change noticed from the 2011 census. The region witnessed a fall in cultivators from 33% in 2001 to 26.7% in 2011. However, the fall in cultivators was compensated with an increase in agricultural labourers from 44.24 % in 2001 to 48.87% in 2011. Employment in household industries also witnessed a downfall between the period of 2001 to 2011 (Sethy, 2020).

The rise in agricultural labourers has a negative impact on the communities. As agriculture is underdeveloped owing to the arid nature of the region, crop failure, extreme calamities, low net irrigated area and falling government expenditure, these workers are pushed into abject poverty. In search of alternate employment options, these workers migrate to other areas of employment in rural or urban pockets. Such a form of seasonal migration during the lean period in agriculture is a predominant phenomenon in these districts. Their dependence on non-timber forest produce is hindered by the rapid deterioration and deforestation of forests for development projects and mining.

Characteristics of distressed migrants in KBK region
1. Who Are These Distressed Migrants?
In the KBK region, distress migration has been a popular coping strategy during lean periods of agriculture. And this strategy is majorly adapted by disadvantaged and marginalised sections of the region. They are disadvantaged by caste, chronic poverty, landlessness, low levels of literacy and skills, increased dependence on forest and agriculture and debt-ridden (Meher, 2017; Mishra D.K., 2011; Tripathy, 2015, 2021).
1. Why Do They Migrate
Distressed migration in the region is induced by many interlinked factors. One such factor is that the region is highly under-developed in terms of social and economic infrastructure. Such under-development puts the communities at a disadvantage with low levels of literacy and skills. Their dependence on agriculture and forest produce for livelihood rises. However, agriculture is under-developed and forests are subjected to high levels of deforestation. With low levels of income, crop failure and non-availability of alternate employment opportunities, the communities are subjected to absolute levels of poverty, food and employment insecurities (Kujur, 2019).

Landlessness is also identified as one significant push factor. As the region is highly dominated by tribal communities, they are more attached to and dependent on the forest cover. Globalisation and industrialisation resulted in deforestation and encroachment of farmlands for industrial and mining purposes. Eventually, a major proportion of land remains with a smaller group of wealthy people (Mishra D.K., 2011). Relocation and involuntary displacement also result in the loss of their livelihood that is dependent on the local environment (Jaysawal & Saha, 2016).

With falling income, people approach local moneylenders to meet their basic sustenance needs. With low incomes from agriculture and forest produce, families approach these informal creditors to meet emergency needs like marriage, birth and death rituals or medical treatment as well as to meet basic consumption needs with the expectation of cash flow from labour contractors during the lean season. Moneylenders exploit them by charging higher interest rates. Thus, the non-availability of formal credit facilities pushes them into a debt trap and further to adopt migration (KARMI, 2014; Mishra D.K., 2016).

The region is also subject to extreme calamities and drought. Small and marginal farmers, poor in income and land, choose to migrate as they are unable to cope with the regular droughts and climate change. A study on historical analysis of the effect of climate on migration in Western Odisha mentions that the migratory trend saw a rise after the mega drought in 1965. Up until then, large-scale migration from the region was not a phenomenon (Panda, 2017).
1. Channel of Migration
Sardars provide an advance amount and in exchange, the debtor or any family member agrees to work for them for a stipulated period, usually six months. Hence, there exists a form of debt bondage. Large-scale family migration through this system is seen in the KBK region. The major stream of such bonded labour migration is witnessed towards brick kilns in Andhra Pradesh

In the region, seasonal migration occurs through the channels of agents, locally known as Sardars, on a contractual basis. This form of migration is known as Dadan labour migration. The poor migrant labourers are known as Dadan and they are recruited by Sardars, who are usually local people who are familiar with residents in the region (KARMI, 2014). During the period of Nukhai, they go around the villages and contact prospective labourers. These Sardars are the intermediary between the employer and the migrant labourer. Sardars provide an advance amount and in exchange, the debtor or any family member agrees to work for them for a stipulated period, usually six months. Hence, there exists a form of debt bondage. Large-scale family migration through this system is seen in the KBK region. The major stream of such bonded labour migration is witnessed towards brick kilns in Andhra Pradesh. They are also a major source of labour in the areas of construction, handlooms and other forms of informal sector work across South India (Daniels, 2014). The problems they face in the destination are manifold. They are subjected to poor working conditions, poor housing and sanitation facilities and limited access to education and health facilities. They are recognised as cheap labour with limited bargaining power owing to their social, cultural and linguistic exclusion in the destination state. Upon entering the contract their freedom to move and freedom to express is denied (Acharya, 2020).
1. Pull Factors to Migrate
The hope of availability of better job opportunities and wages is the main pull factor. However, upon the analysis of the nature of migration, push factors have a higher weightage in inducing such distress migration. Migration to brick kilns and other informal sectors from the KBK region can be termed as distress migration as in this case, distress is caused mainly by socioeconomic factors. It is not an informed or voluntary choice. Debt migration remains the only coping strategy that they could adopt.

Government intervention to curb such distress
1. Policies Addressing Debt-Bondage Migration:
The first attempt of the state government to address Dadan migration or debt migration is the enactment of the Dadan Labour (Control and Regulation) Act (ORLA) in 1975. The act had provisions for the registration of labourers and agents, ensuring compliance of minimum wages and favourable working conditions and appointing inspection officers and dispute redressal committees (Daniels, 2014). However, the act remained on paper and no evidence of enactment was published until it was repealed in 1979 upon the enactment of the Interstate Migrant Workmen (Regulation of Employment and Conditions of Service) Act, 1979 (Nanda, 2017).

The ISMW act has been criticised to be inadequate and failing to regulate and facilitate safe migration. According to the act, only those interstate migrant workmen who are recruited by licensed agents come under the ambit of the act. However, most agents involved in Dadan migration are not licensed and hence, these workers cannot avail of any of the provisions of the act (Singh, 2020). Though registration of labour contractors is mandatory in the origin state, there is no information about the names of these contractors and hence, further monitoring of the migration process is avoided (NCABL, 2016). Lack of adequate enforcement, under-staffing and poor infrastructure are identified as the reasons for poor implementation of the act in the state (Daniels, 2014).

A positive attempt against distress migration was the Memorandum of Undertaking (MoU) initiated between the labour department of Odisha and Andhra Pradesh to ensure labour welfare measures of migrant workers in Brick Kilns. After the MoU, the state of undivided Andhra Pradesh took up various progressive measures in education, health, housing and PDS for migrant workers in Brick Kilns. ILO necessitated the need for states to enter into inter-state MoUs to effectively address the bonded labour migration. However, no further MoU was signed with other states like Tamil Nadu, Chhattisgarh etc. which are also among the major host states for migrants from the region (NCABL, 2016).

The Bonded Labour System (Abolition) Act enacted in 1976 governs the provisions for identification, rescue and rehabilitation of bonded labourers across the country. The act has its loopholes in implementation. There is no information on whether vigilance committees have been set up in every district or whether the surveys have been periodically conducted or to what extent the act has been functioning in the state (Post News Network, 2019). The centrally sponsored scheme for Rehabilitation of Bonded Labour also has its setbacks. There have been reported cases of delay and denial of financial aid by district officials ( Mishra .S., 2016). In 2016, with restructuring and revamping of the Rehabilitation scheme, rescued workers could only avail the full amount of financial aid with the prosecution of the accused employers. With no database on the employer, the rates of prosecution have been low and the rescued bonded labour do not receive their funds (NACBL, 2016)
1. Ensuring Accessibility of Health Facilities in Destination
The Rashtriya Swasthya Bima Yojana or RSBY launched by the central government in 2008 provides health insurance to BPL families. The scheme incorporates provisions to split smart cards so those migrant workers could avail health insurance in destination states. After signing of the MoU between Andhra Pradesh and Odisha, the two states took steps to spreading awareness among the migrant workers about how to use the smart cards (Inter-State Migrant Workman Act (ISMW), Labour Directorate, n.d.)
1. Ensuring Education of Migrant Workers Children
The state of Odisha has established seasonal hostels to ensure the education of children of migrant workers. The children are enrolled in seasonal hostels during October-June, that is until their parents return home (Odisha Primary Education Programme Authority, n.d.). The state has ensured the education of migrant children at their destination state by sending Odiya textbooks and Odiya teachers to residential schools in Andhra Pradesh (Inter-State Migrant Workman Act (ISMW), Labour Directorate, n.d.).
1. Alternate Employment Opportunities: MGNREGA
Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) was introduced in 2006 to provide guaranteed employment to rural poor with the objective of uplifting them from poverty and restricting distress migration. A study analysing the performance of MGNREGA through secondary sources of data suggests that based on physical criteria of 100 Days of Wage Employment, Person-days generated, ST and Women person-days and financial performance in terms of total expenditure, total wages, average cost and average wage rate per day person, the performance of MGNREGA in KBK districts is better compared to Non- KBK districts. But the region is lagging in rural employability criteria based on average days of employment provided per household and job cards issued (Sahoo et al., 2018). Labour in the region is not interested to work under MGNREGA due to its dismal implementation in the state. Workers complain about the delay in receiving payments and instances of the creation of non-existent workers’ names among MGNREGA’s beneficiaries (KARMI, 2014). Uncertain and low wages make these labourers favour migration to Brick Kilns in hope of better wages (Deep, 2018).
1. Development Policies in KBK Region
The KBK region has a high incidence of poverty owing to regional disparities in development and social exclusion based on caste. The main initiatives implemented by the state government for the upliftment of the KBK region are the Special Area Development Programme, Revised Long Term Action Plan (RLTAP), Biju KBK Plan, Backward Regions Grants Fund, Gopabandhu Gramin Yojana (GGY), Special Central Assistance (SCA) for tribal sub-plan (TSP) areas, Western Odisha Development Council (WODC) and Grants under Article 275(1) of the Constitution. Development projects to reduce poverty and regional disparities are obstructed by economic, social and institutional factors (Mishra, 2020).

The state of Odisha has done positive interventions in the education of migrant children and health facilities of the migrant population. However, the distress migration is still prevalent owing to the social and economic exclusion and debt bondage situations in the region. Land grabbing in the name of development left the tribal communities poor and in distress. Structural inequalities induced by caste discrimination are enhanced with such landlessness.

Policy Recommendations

The state of Odisha has done positive interventions in the education of migrant children and health facilities of the migrant population. However, the distress migration is still prevalent owing to the social and economic exclusion and debt bondage situations in the region. Several initiatives and schemes have been enacted to address distress migration; however, their failure in reducing distress can be linked to dismal governance, poor implementation and misappropriation of schemes.

The state must ensure migration to be safe and a viable coping strategy. From this study it is suggested the state of Odisha follow a multipronged approach to address the distress.

Origin state (Odisha) interventions
- Short Term Interventions:
1. The system of debt bondage should be completely abolished by the proper implementation of legislation. Different loopholes in implementation such as the delay in the release of funds, prosecution of accused and identification and registration of middlemen should be addressed. Apart from the financial aid, the state should intervene in providing a comprehensive livelihood plan for the rescued labourers. Abolishing the bonded labour system is essential to reduce distress and make migration safe.
2. Informal sources of credit should be eliminated and formal credit and microfinance facilities should be made available. Such facilities would reduce the exploitation and prevent the creation of absurd debt. Formal credit provides opportunities for small and marginal farmers to indulge in productive investments. This enables them to cope with extreme climatic changes.
3. Land grabbing in the name of development left the tribal communities poor and in distress. Structural inequalities induced by caste discrimination are enhanced with such landlessness. The provision of land ownership enables the communities to enjoy land-based benefits which further supports them to sustain their livelihood. Ownership of land also provides the indigenous community with a sense of social and economic significance.
- Long term interventions
1. The state should engage in enhancing the skills of the people in the region. Vocational skill training and development schemes can be introduced. This could expand the opportunities available for employment and distribute labour across all the economic sectors.
2. Rural development should be given higher priority. The state of Odisha has already initiated many schemes for the development of the KBK region. However, the state should study the economic and social factors that stagnate the process of development in the region. Chronic poverty, poor infrastructural and rural connectivity and dismal education and health facilities are some of the important areas that require attention.
Host state intervention
1. The host state needs to create a database of migrants entering their state. A statistically significant database on migrants solves a huge array of issues faced by the migrant in the destination state. A comprehensive database helps in identifying and recognising migrants. It also allows for understanding the different characteristics of migrants and the sectors in which they are employed. This would be beneficial for monitoring and ensuring safe and favourable working conditions. A database also helps in ensuring the availability and accessibility of social security and entitlements in host states.
1. Migrant labour is as important to the destination state as it is to the origin state. Both origin and host state should cooperate towards making migration a viable livelihood strategy.
Another important area where both the origin and host state should intervene together is creating awareness among workers about the existing provisions and rights available to them. Access to the same should be made easy.

Conclusion

The highly backward districts of the KBK region remain a major source of distressed migrants. Years of state initiative in reducing distress have had negligible impact. The area remains underdeveloped and migration is the only viable choice of employment. Migration can only be a viable coping strategy for seasonal migrants when the channel of migration is made legal and safe. The major drawback in any initiative attempted to resolve distress is the poor implementation. Administrative apathy, corruption and misappropriation of schemes have stagnated the progress of every initiative.

References
1. Acharya, A. K. (2020). Caste-based migration and exposure to abuse and exploitation: Dadan labour migration in India. Contemporary Social Science, 1-13.
2. Avis, W. R. (2017). Scoping study on defining and measuring distress migration.
3. Bhatta Mishra, R. (2020). Distress migration and employment in indigenous Odisha, India: Evidence from migrant-sending households. World Development, 136, 105047.
4. Daniels, U. (2014). Analytical review of the market, state and civil society response to seasonal migration from Odisha. Studies, stories and a canvas seasonal labour migration and migrant workers from Odisha, 106-115.
5. Deep, S. S. Seasonal Migration and Exclusion: Educational Experiences of children in Brick Kilns. Ideas, Peoples and Inclusive Education in India. National Coalition for Education, India. 2018.
6. Directorate of Economics and Statistics (2021). Odisha Economic Survey 2020-21. Planning and Convergence Department. Government of Odisha. http://www.desOdisha.nic.in/pdf/Odisha%20Economic%20Survey%202020-21-1.pdf
7. Giri, J. (2009). Migration in Koraput: “In Search of a Less Grim Set of Possibilities” A Study in Four Blocks of tribal-dominated Koraput District, Odisha. Society for Promoting Rural Education and Development, Odisha, 1.
8. Inter-State Migrant Workman Act (ISMW) | Labour Directorate. (n.d.). Labour Directorate, Government of Odisha. Retrieved August 10, 2021, from https://labdirodisha.gov.in/?q=node/63%27%3B.
9. Jaysawal, N., & Saha, S. (2018). Impact of displacement on livelihood: a case study of Odisha. Community Development Journal, 53(1), 136-154.j
10. Jena, M. (2018, July 21). Distress migration: land ownership can put a break. The Pioneer. https://www.dailypioneer.com/2018/state-editions/distress-migration-land-ownership-can-put-a-break.html
11. KARMI. (2014). Migration Study Report of Golamunda Block of Kalahandi District of Odisha. Pp.13. Kalahandi Organisation of AgKriculture and Rural Marketing Initiative (KARMI), Kalahandi Odisha.
12. Kujur, R. (2019). Underdevelopment and patterns of labour migration: a reflection from Bolangir district, Odisha. research journal of social sciences, 10(1).
13. Mahapatra, S. K., & Patra, C. (2020). Effect of migration on agricultural growth & development of KBK District of Odisha: A statistical assessment. Journal of Pharmacognosy and Phytochemistry, Sp, 9(2), 162-167.
14. Mander, H., & Sahgal, G. (2010). Internal migration in India: distress and opportunities, a study of internal migrants to vulnerable occupations in Delhi.
15. Meher, S. K. (2017). Distress seasonal migration in rural Odisha A case study of Nuapada District.
16. Mishra, D. K. (2011, April). Behind dispossession: State, land grabbing and agrarian change in rural Odisha. In International conference on global land grabbing(Vol. 6, No. 8).
17. Mishra, D. K. (2016). Seasonal migration from Odisha: a view from the field. Internal migration in contemporary India, 263-290.
18. Mishra, S. (2016, January 13). Rescued migrant workers get raw deal from Govt. The Pioneer. https://www.dailypioneer.com/2016/state-editions/rescued-migrant-workers-get-raw-deal-from-govt.html
19. Mishra, S. (2020). Regional Disparities in Odisha–A Study of the Undivided “Kbk” Districts. Research Journal of Humanities and Social Sciences, 11(4), 261-266.
20. Nanda, S. K. (2017). Labour scenario in Odisha. Odisha Review, 73(10), 20-25.
21. NCABL. (2016). Joint Stakeholders’ Report on Situation of Bonded Labour in India for Submission to United Nations Universal Periodic Review III. NATIONAL COALITION FOR ABOLITION OF BONDED LABOUR (NCABL), Bhubaneswar Odisha.
22. Panda, A. (2017). Climate change, drought and vulnerability: A historical narrative approach to migration from Western Odisha, India. In Climate Change, Vulnerability and Migration(pp. 193-211). Routledge India.
23. Post News Network. (2019, April 30). Elimination of bonded labour calls for cohesive action plan. Odisha News, Odisha Latest News, Odisha Daily – OdishaPOST. https://www.Odishapost.com/elimination-of-bonded-labour-calls-for-cohesive-action-plan/
24. Sahoo, M., Pradhan, L., & Mishra, S. (2018). MGNREGA and Labour Employability-A Comparative Analysis of KBK and Non-KBK Regions of Odisha, India. Indian Journal of Economics and Development, 6(9), 1-8.
25. Sethy, P. (2020). Changing Occupational Structure of Workers in KBK Districts of Odisha. Center for Development Economic, 6(06), 17-28.
26. Singh, V. K. (2020, April 22). Opinion | The ‘nowhere people’ of COVID-19 need better legal safeguards. The Hindu. https://www.thehindu.com/news/national/other-states/the-nowhere-people-of-covid-19-need-better-legal-safeguards/article31400344.ece
27. Tripathy, S. N. (2015). Evaluating the role of micro-finance in mitigating the problems of distress out-migrants: A study in KBK districts of Odisha. The Micro Finance Review, Journal of the Centre for Micro Finance Research.
28. Tripathy, S. N. (2021). Distress Migration Among Ultra-poor Households in Western Odisha. Journal of Land and Rural Studies, 23210249211001975.
Feature Image: Tata Trusts
November 7, 2021
Bonded Labour in India: Prevalent, Yet Overlooked
In 1976, India stood out as the first country in South Asia to enact legislation prohibiting bonded labour. However, the system has not been uprooted owing to the different barriers posed by socio-cultural norms and administrative and legislative incompetency. The country’s most vulnerable and disadvantaged sections of society are at risk of being trapped into such a form of modern slavery. The prevalence of this system over the decades necessitates the need to understand the root causes of the emergence of such bonded labour situations and why it is still prevalent in the country.

Bonded labour in India

The Bonded Labour System Abolition Act (1976) defines a bonded labour system as a relationship evolved out of a debtor-creditor agreement. It is identified as a form of forced labour where the debtor comes into an agreement, oral or written, with the creditor and receives a loan amount in exchange for his labour or that of his family members. The obligation need not just be an economic consideration such as a loan or an advance amount received from the creditor. People also become bonded with social, customary, hereditary or caste obligations and often agree to enter service with no wages or for nominal wages. The labourer finds it difficult to settle the debt amount as the provided wages are too low even to meet their basic sustenance needs. Eventually, they end up in the same form of labour again and again. Thus their choice to join such a system is out of distress or coercion to some extent. They may also be restricted from switching to another job or to ask for the provision of minimum wages given the conditions of the contract and the lack of awareness of their rights.

Indebtedness is identified as a major trigger for people to join as bonded labour, especially migrants from poor rural households. However, the need for money arises out of the existing disadvantages in society that these communities are subjected to. Caste, unequal distribution of resources, increased dependence on agriculture, low levels of education and food insecurity pushes them into such unfree labour choices.

We can identify that this system was prevalent in the country from the pre-colonial era characterised by class hierarchies. Such class hierarchies and high caste exploitations are continuing to function even in this democratic era and consequently, has pushed certain groups of the society to be economically weaker; weak in terms of assets, income and bargaining power. Globalisation and industrialisation have only resulted in the further exclusion of such groups of labour from mainstream jobs. Indebtedness is identified as a major trigger for people to join as bonded labour, especially migrants from poor rural households. However, the need for money arises out of the existing disadvantages in society that these communities are subjected to. Caste, unequal distribution of resources, increased dependence on agriculture, low levels of education and food insecurity pushes them into such unfree labour choices. Owing to these social and economic factors, marginalised communities in the lower strata of the society, especially the women and children, are trapped in such a system.

Over the years, the system of bonded labour has existed and evolved under different names and forms across India. Bonded labour arising out of traditionally accustomed social relations is one of the oldest forms and is still prevalent in the country. For example, the system of “jajamani” wherein the workers receive food grains in exchange for working as barbers and washermen for the upper caste. Labourers in agriculture, seasonal inter and intrastate migrants and child labour in informal sectors of brick kiln, rice mills, quarries, domestic work etc. are the other areas where debt bondage is currently more persistent. There has been a considerable shift from traditional debt bondage relation to aneo-bondage labour system among migrant workers. The former was characterised by an element of patronage amongst the considerable amount of exploitation. However the latter is at a higher tone of exploitation and eliminates patronage relations. This has made employers deny the responsibility of employee’s welfare and the labourers have lost the minimum livelihood security which they had secured under the patronage system. The neo-bondage system is further manipulated by the role of intermediaries.

Thus, with structural transformation in the economy, the system of bonded labour has evolved into a much worse form of exploitation in the country and specifically marginal and backward communities are the main victims of this system.

Interventions to abolish bonded labour

Upon identifying the prevalence and exploitation of bonded labour in the pre-independence era, constitutional provisions prohibiting forced labour were assigned under Article 23. Under the Directive Principle of State Policy, Article 42 and 43 ensured fair and humane working conditions and living wages to workers.

Post-independence, legislation against bonded labour was enacted at a regional level. Orissa, Rajasthan and Kerala were the first states to enact state legislation against bonded labour. In 1954, India ratified the International labour organization (ILO) Convention on forced labour (C029). Despite the constitutional provisions, regional and international interventions in bonded labour, construction and implementation of a uniform law took time.

In 1976, the Bonded Labour System (Abolition) Act was enacted to abolish any form of bonded labour system arising out of debt, customary or hierarchical obligations. In brief, the act has identified and defined bonded labour, provided for extinguishment of past or existing debt, established duties of district magistrate in implementing the provisions of the act, sanctioned the state governments to form a vigilance committee in each district to guide and ensure competent implementation of the act by the magistrate and stated the penal actions against those compelling people into bonded labour. The act was amended in 1985 to bring contract and migrant workers under its ambit.

In 1978, a new centrally sponsored scheme for Rehabilitation of Bonded Labour was enacted to provide financial assistance to the state government for rehabilitating rescued bonded labourers, to conduct surveys, evaluation studies and awareness campaigns across districts. In 2016, the government restructured the scheme. The restructuring involved an increase in the provision of funds to bonded labour for rehabilitation and to states for conducting surveys. Under the restructured scheme, rescued bonded labour is only provided with the full amount of financial assistance after the conviction of the accused and a Bonded Labour Rehabilitation Fund corpus was to be created at every district.

The interplay of caste-based exploitation and subsequent impoverishment in terms of resources and assets combined with underdeveloped rural areas devoid of standard education, health and employment opportunities push marginalised people into bonded labour.

Why and how does the system still sustain?

Many factors contributing to the prevalence of bonded labour continue to prevail despite after years of legislative action to abolish the same. The interplay of caste-based exploitation and subsequent impoverishment in terms of resources and assets combined with underdeveloped rural areas devoid of standard education, health and employment opportunities push marginalised people into bonded labour. Such an environment accompanied by the inept implementation of legislations and schemes further aids in sustaining bonded labour systems.

BLS(A) act 1976 failed to be effectively implemented owing to apathy, corruption, lack of administrative and political will. The vigilance committees were often defunct and working for the employer. The act was criticised on the grounds that it stated only mediocre and minor punitive actions and the rates of prosecution were also low. Moreover, some states remain in denial of accepting the existence of bonded labour. This indifference results in the loss of comprehensive data on bonded labour hindering the further implementation of provisions of the act.

The Central Sector Scheme for Rehabilitation of Bonded Labour also has its loopholes. After the restructuring of the scheme, financial aid is provided only after the accused is convicted and convictions are rare owing to poor implementation of the BLS(A) Act and the absence of a review of cases. Thus, in most cases the rescued labourers do not receive the full financial aid they are entitled to immediately after the rescue. Often, it takes years to receive the full amount or may not even receive any.

The situation is even grave as the rescued labourers have asymmetric knowledge of the rights and entitlements they can avail themselves of. Even when they are fully aware, most of them lack the will to attain these entitlements due to the dismal behaviour of officials and delayed processes.

Moving towards Abolishment

First and foremost, recognition and acceptance of the prevalence of bonded labour should be ensured. Only then the bonded labourers could be identified, rescued and rehabilitated effectively. The collection of comprehensive data is essential for further implementation of the provisions of the legislation. Also apart from the vigilance committee, a new committee composed of the magistrate, members of the marginalised communities, NGO’s and other civil bodies working in the field would enable to get a more comprehensive view of the issues in the sector.

From a long term perspective, there is a need to address the caste induced structural inequalities. One way through which this could be attained is through land redistribution.

Mere financial aid is not sufficient for the rescued labourers to foster a livelihood plan. The Human rights law network suggests the same and recommends a comprehensive rehabilitation package providing for education and job security.

From a long term perspective, there is a need to address the caste induced structural inequalities. One way through which this could be attained is through land redistribution. Apart from this, the government should also focus on skill development and training of rural poor, especially migrants caught up in bonded labour. Varied skills can enhance their employment opportunities and provide more freedom to move towards other areas of work.

References
1. B.L.S., A. (2020, June 30). Telangana: Two Years After Rescue From Bonded Labour, 12 Tribals Receive Compensation. The Wire. https://thewire.in/rights/telangana-bonded-labour-rescue-tribals-compensation
2. Breman, J. (2010). Neo-bondage: A fieldwork-based account. International Labor and Working-Class History, 78(1), 48-62. https://www.jstor.org/stable/40931303
3. Gabra, L. (2021, March 21). Will Bonded Labor in India Ever Come To An End? BORGEN. https://www.borgenmagazine.com/bonded-labor-in-india/
4. Human Rights Law Network. (n.d.). Release and Rehabilitation of Bonded Labour — HRLN. Human Rights Law Network (HRLN). Retrieved August 15, 2021, from https://hrln.org/initiative/release-and-rehabilitation-of-bonded-labour
5. Human Rights Watch. (n.d.). Small Change. Human Rights Watch (HRW). Retrieved August 6, 2021, from https://www.hrw.org/reports/2003/india/India0103-05.htm
6. J, S. (2019, September 15). Rescue of bonded labourers up, convictions rare. Times of India Blog. https://timesofindia.indiatimes.com/blogs/tracking-indian-communities/rescue-of-bonded-labourers-up-convictions-rare/
7. Khan, J. A. (2019, April 30). How effective are the Policies for Rehabilitations of Bonded Labour in India? CBGA India. https://www.cbgaindia.org/blog/effective-policies-rehabilitations-bonded-labour-india/
8. Mantri, G., & Suresh, H. (2020, January 31). The News Minute | Delve. The News Minute. https://www.thenewsminute.com/article/it-s-2020-bonded-labour-still-reality-india-here-s-why-116977.
9. Molfenter, C. (2013). Overcoming bonded labour and slavery in South Asia: the implementation of anti-slavery laws in India since its abolition until today. Südasien-Chronik-South Asia Chronicle, 3, 358-82. https://edoc.hu-berlin.de/bitstream/handle/18452/9122/358.pdf?sequence=1&isAllowed=y
10. Murugesan, D (2018). HANDBOOK ON BONDED LABOUR. NATIONAL HUMAN RIGHTS COMMISSION (NHRC), New Delhi. https://nhrc.nic.in/sites/default/files/Hand_Book_Bonded_Labour_08022019.pdf
11. NCABL. (2016). Joint Stakeholders’ Report on Situation of Bonded Labour in India for Submission to United Nations Universal Periodic Review III. NATIONAL COALITION FOR ABOLITION OF BONDED LABOUR (NCABL), Bhubaneswar Odisha. https://www.upr-info.org/sites/default/files/document/india/session_27_-_may_2017/js34_upr27_ind_e_main.pdf
12. Prasad, K. K. (2015). Use of the Term’Bonded Labour’ is a Must in the Context of India. Anti-Trafficking Review, (5), 162.
13. Sabhapathi, V. (2020, June 11). An Analysis of Bonded Labour System in India. Legal Bites – Law And Beyond. https://www.legalbites.in/bonded-labour-system-in-india/
14. S, B. (2016, April 2). Caught in a vicious cycle of bonded labour. The Hindu. https://www.thehindu.com/news/national/karnataka/caught-in-a-vicious-cycle-of-bonded-labour/article7720754.ece
15. Sethia, S. The Changing Nature of Bonded Labour in India.
16. Srivastava, R. S. (2005). Bonded labour in India: Its incidence and pattern.https://www.ilo.org/wcmsp5/groups/public/—ed_norm/—declaration/documents/publication/wcms_081967.pdf
17. THE BONDED LABOUR SYSTEM (ABOLITION) ACT, 1976. (ACT NO. 19 OF 1976). (India). https://labour.gov.in/sites/default/files/TheBondedLabourSystem(Abolition)Act1976.pdf

Image Credits: starfishasia.com
September 10, 2021
Marginalised among the invisible: The case of female migrant domestic workers
The Pandemic, lockdown, and the chain of events that followed made the country wake up to the state of the most unfortunate group of the labour force; the migrant workers. They have always remained invisible to the development agenda of the government and only the catastrophe of a pandemic could shed light on their woes. Among this invisible workforce, there remains yet another marginalised group of female migrants.

In India, female migration was initially considered insignificant by equating their movement merely as associational or followers of men. However, this has certainly changed in the last decade. Marriage was seen as the central motive behind female migration, though lately more women are seen to enter the labour market post-migration as their labour demand rose in sectors of so-called “female occupations” of domestic work, care-work and certain informal labour requirements in sectors such as in construction, garment work, food services and as coolies and vendors. As family migration from rural to urban abodes saw a rise in the country, both male and female migrants were required to join the labour force to meet their mere subsistence needs. Lack of employment, low income and other economic reasons pushed females, especially from rural areas, to migrate to urban zones of the country (Singh et al., 2015). While in urban areas, the migrants especially females and children are exposed to extreme vulnerabilities with regard to their dismal conditions of work in the informal sector, urban policies are deeply flawed in omitting migrant welfare and the sheer denial of their civil rights and entitlements.

Precarious domestic work and female migrants

Domestic work is often regarded as an invisible and insignificant addition to the social and economic values of a country. The work is increasingly feminised with over 80% of the world’s domestic work occupied by women (International Labour Organisation [ILO], 2013a). And this mirrors the traditional notions of domestic work being a woman’s task. These tasks include traditional housework such as cleaning, cooking, washing clothes or utensils etc. or care-work such as a child or elderly care. Female migrants with low skills, low levels of education and migrating from rural abodes in search of employment form a predominant part of the labour pool. With no recognition and regulation of work, the female domestic workers are subject to unequal power dynamics at the workplace, making their lives precarious in terms of wages, security and wellbeing.

In India, domestic work employment among females saw an upsurge, especially in urban areas. This surge is mainly accounted for by the increasing need for care work given the changing demography, lack of work opportunities in other sectors and the gender constructions moulded by the society (Chandrashekar & Ghosh, 2012). According to the National Sample Survey (NSSO-2011-2012, 68^th round), 39 lakh (3.9 million) people are occupied in domestic work, among which 26 lakh (2.6 million) are females. Micro-level surveys suggest a predominant concentration of female migrants in domestic work, especially in urban areas (Mazumdar et al., 2013). There are two forms of workers: live-in workers, who are accommodated in the household and live-out workers, who return to their respective houses after work and may be involved in work with multiple households. As there is no relevant national data on migrant workers involved in the sector, micro-level surveys or sector-based studies are the only sources in understanding the conditions of these migrants in domestic work. Studies have stated that migrants with low vocational qualifications and often seen as unregulated and undocumented cheap labour, work under low wages for long hours and in dismal working conditions affecting their health and safety. Live-in domestic workers are more prone to the dangers of sexual and physical abuse. Live-out domestic workers migrating to a new city, struggle with the inaccessibility of social security schemes and entitlements. Exploitation by private placement agencies in terms of wages and work conditions is another area among their hassles.

The domestic work arena, already an unregulated and unorganised sector, puts female migrants with low bargaining power on a higher vulnerability scale. The task of identifying domestic work hinders the formulation of a sound regulatory mechanism to confront such vulnerabilities.

Barriers to effective Regulation

Regulating domestic work is impeded by cultural and structural barriers. The traditional notion and disregard of domestic work by women in households is extended to the understanding of paid domestic work as unproductive and hence, making it undervalued. The structural barriers relate to the unusual workplace in private spheres, which makes it difficult in enforcing labour laws and any form of scrutiny against the privacy norms of a household. The informality of work and its complexities aggravates the barriers in regulation. The employment relationship is uncertain as it is without any legal titles of employee and employer, making the relation very personalised and often not under any form of contract or agreement. Even if labour laws are made inclusive of domestic work, implementation and assurance of compliance of these laws in households are challenged until the household is recognised as a ‘workplace’ and the person hiring as an ‘employer’ in the legal framework (Chen, 2011).

Even though these barriers existed, the International Labour Organisation (ILO) convention 2011 attempted in ensuring decent work to domestic workers and this is recognised as the most important landmark in identifying domestic work under a legal framework. ILO defines domestic work as “work performed in or for a household or households” and domestic worker as “any person engaged in a domestic work within an employment relationship”. The convention specified a comprehensive labour standard for domestic workers in areas of their wages, hours of work, occupational safety and health and social security. The convention addressed and standardized the various concerns in the sector regarding child labour, migrant workers, trafficking, live-in domestic labourers and private recruitment agencies (C189 – Domestic Workers Convention, 2011). Even after the completion of 10 years of the convention and 32 ILO member countries enforcing the landmark treaty, India is yet to ratify the convention.

As domestic work remains undefined in the country, no significant statistical standard in estimating domestic workers exist. In the ILO policy brief on “Global and regional estimates of domestic workers” (ILO, 2013b), ambiguous nature of data on domestic workers were noticeable from the widely distributed figures, ranging from 2.5 million estimates from a household survey, 4.5 million workers estimated from official statistics (NSSO 2004-05) to an exaggerated figure of 90 million in news media. This difference in estimation is related to the difference in the identification of domestic work among different establishments (Mahanta & Gupta, 2015). With no clarity in identifying domestic workers inclusive of its peculiarities, these figures could be heavily underestimated too. Being a female migrant in the sector aggravates the problem of estimation as National statistics narrows down female migration patterns merely as associational. And thus failing to understand the true motives behind female migration and the subsequent scale of occupations they reside in (Indu et al, 2012). Macro data narrows down domestic female labour into regular workers based on their duration in employment and disregarding the conditions of low wages and other insecurities, while the temporary and casual nature of work goes unrecognised (Neetha & Indrani, 2020). The informality of work is another area that India has failed to regulate. Labour laws for industrial labour often disregard informal workers. This is evident in the isolation of migrant workers, especially female migrants in domestic work (Poddar & Koshy, 2019).

Lacunae in the legal framework

Domestic work and most feminised occupations, in general, in unorganised sectors, are isolated from the legal framework given their unique characterisation of workplace and employment relationships and not to mention the challenges in recognising their work given the cultural and structural barriers. For female migrants in domestic work or any other informal activity, the situation is similar.

There were certain positive steps in attempting to recognise the domestic workforce in the country. First of such attempts were their inclusion in the Unorganised Workers Social Security Act 2008 which gave hope, but failed to be implemented across different states (Agrawal & Agarwal,2018). Subsequently, the government also set up a task force to recommend a framework for policymaking and after 10 years, in 2019, we see a draft on National policy on domestic work formulated by the government covering their recognition, access to civil rights and social security schemes, skill development, regulating private placement agencies and a grievance redressal system (“National Policy for Domestic Workers”, 2019). Upon the recommendations of the task force, the domestic workers were to be included under the National Health insurance scheme – Rashtriya Bhima Yojana (RSBY). But the limited awareness of the scheme, its functioning and benefits, coupled with corruption reduced the domestic worker’s accessibility of the same (Mahanta & Gupta, 2015). The suggestion of the task force to include domestic worker rights in existing legislations, pertaining to industrial or organised labourers, was widely criticised because it does not adapt to the peculiarities of the feminised domestic work (Poddar & Koshy, 2019). Ensuring minimum wages to the domestic worker through the Minimum Wages Act 1948 with a task-based approach, while ignoring the aspect of personalised nature of employment completely, puts the live-in workers whose tasks are not quantifiable, out of the ambit of the act’s provisions. Similarly, the inclusion of domestic workers in the Sexual Harassment of Women at Workplace Act (2013), Employees’ State Insurance Act (1948) and Unorganized Workers’ Social Security Act 2008 is considered inadequate. Even though such inclusion is appreciated, these legislations fail to cater to the rights of a domestic worker if they are based on organised sector labour standards and without understanding the complexities of the domestic work (Poddar & Koshy, 2019).

Private placement agencies, one of the main recruitment channels of domestic work, remain unregulated. This has led to the rise in exploitation in terms of payment and working conditions. The Delhi government drafted a Delhi Private Placement Agencies (Regulation) Bill in 2012 which was widely rejected by the domestic workers’ unions and groups. The proposed bill was criticised to be ineffective as it does not include the registration of the employers and lacks clarity in the process of inspection of these agencies (Chigateri et al., 2016). A study on one of the frequently travelled migrant routes, which is from Jharkhand to Delhi, reveals that migrants were subjected to conditions of exploitation and forced labour under such placement agencies. Conditions of forced labour are witnessed mainly among live-in domestic workers, who have to work under the agent for the stipulated period. The Inter-State Migrant Workmen’s (Regulation of Employment and Conditions of Service) Act 1978 fails to address this issue as placement agencies relating to domestic work do not come under the ambit of the act. The act considers only those labour contractors who are registered at the origin state. Placement agencies involved in domestic work function through several sub-agents and mostly are unregistered (ILO, 2015)

There were some positive responses from state governments. The state of Tamil Nadu set up the Tamil Nadu domestic workers welfare board. Similarly, Maharashtra set up a domestic worker welfare board under Maharashtra Act (Agrawal & Agarwal, 2018) in 2008 while Kerala adopted a domestic worker bill in 2009. States like Kerala, Karnataka, Andhra Pradesh, Maharashtra, Tamil Nadu, Bihar and Rajasthan have set the minimum wage rate (Madhav, 2010). Neetha and Palriwala (2011) analysed the state legal framework on domestic workers and pointed out the same inadequacies noted over and over again, that is of not recognising the intricacies of domestic work, workplace, its several sub-categories, unregulated placement agencies and its unique employment relation. With no data on domestic workers and at the same time their numbers continuing to increase, these loose legislations and provisions go unnoticed by the workers.

In 2019, with the view to improving compliance and bringing about uniformity of laws, 29 labour laws were consolidated into 4 labour codes: a) code on wages, b) code on industrial relation c) code on social security and d) code on occupational health and working conditions (“Overview of Labour Law Reforms”, n.d.). While the notion was to make the labour laws more transparent and such consolidation was expected to increase the coverage of different workers under the law, these codes remain ambiguous when it comes to certain sectors of informal work. Neetha and Indrani (2020) analyse these codes through a gender lens focusing on domestic and migrant workers. Code on wages does not incorporate private households as an entity hiring employees and thus domestic workers who struggled to attain minimum wages under the previous Minimum wages act (1948) have no mention, leaving them ambiguous. Code on industrial relations dealing with collective bargaining and industrial disputes, do not mention freedom of association in unorganised sectors and curbs the right to strike which has serious implications of registration of domestic workers under trade unions and their right to collective bargaining. Code on social security (CSS) has consolidated the unorganised workers’ social security act 2008, which was the first attempt towards the recognition of domestic workers and the new code puts the functioning of such acts and provisions for the unorganised sector under the discretion of the government, leaving out legislative scrutiny. Hence, there is uncertainty of the efficient functioning of these acts under CSS. Under the code, maternity benefits were applied only to the registered establishment of work. And domestic workers with no recognition of the workplace become ineligible for the same. Code on occupational health and working conditions is also seen to have not recognised the need for laws based on different sectors of work. It has again failed to include private households as a workplace, leaving the conditions of domestic work unregulated. Another failure relates to ignoring the Sexual Harassment of Women at Workplace Act (Prevention, Prohibition and Redressal) 2013, which further leaves out the scrutiny of abuse or exploitation of domestic workers. The fact of being migrants among domestic workers isolates them even further from these labour codes.

The lacunae in existing legislation in recognising domestic work and migrant labour continues to be beset in ambiguities with the new labour reforms.

Present scenario: Covid-19 adding to the vulnerabilities

The onset of the Covid-19 and the resultant lockdowns have led to massive disruptions of normal life resulting in the shocks of sudden unemployment, financial strain and increased burden for workers in the unorganised sector. The migrant workers bore the highest brunt. In such a scenario, female migrants in an unregulated and isolated sphere of work such as domestic workers have been subject to severe distress. The lockdown and reduced mobility left the workers unemployed and without income. Live-in migrants faced increased workload but no change in wages. Even with the slow revival of the economy, they are under threat of being infected or being carriers, given their precarious work and living conditions. Sudden dismissals and financial strain have forced many to the situation of borrowing money for subsistence and eventually ending up in debt. Workers struggle to meet the basic needs of health, food, education of the family with lower income and savings (Sumalatha et al., 2021). With dismal employment relations and working conditions, coupled with the exclusion from the legal framework and social protection, Covid-19 has expanded the existing inequalities.

Government intervention: The need of the hour

Government intervention both in ensuring basic rights and providing for the welfare of the domestic workers have been negligible. The cultural and structural barriers are not the only challenges in regulating domestic work. There is a lack of political will in acknowledging domestic workers and their woes. As they remain scattered and invisible, the domestic workers are not seen as potential vote banks and hence remain without any political influence. The sector which is comprised largely of female migrants is devoid of any political voice and agency in their origin or host states since there are barriers in pursuing their voting rights given the nature of their migration. Their interactions with civic authorities and politicians in the host state are marginal and hence, their issues do not come to the fore (Bureau, 2018). There is a lack of awareness among the migrant workers on their voting rights. They are largely unaware as to who should be approached in the host state to resolve their problems. Even a migrant worker, well aware of his/her political rights and agencies, refrain from pursuing any form of interaction as they have either lost faith in the system or are disillusioned by the long time and effort spent pursuing the cases with no results to show. This highlights the need for effective political inclusion of migrant workers and the generation of political and electoral awareness among them (Bureau, 2018).

Further, identification and protection are the two essentials in creating an inclusive environment for female migrants in domestic work. The feminized nature of domestic work in the country, concentrated predominantly among poor and marginalised migrant workers, need to be recognised as dignified “work” and households they work in as “workplace”. Only separate comprehensive legislation on domestic work can incorporate the varied complexities of the sector, rather than a mere extension of organised sector legislations. Such separate legislation would provide the domestic worker with an identity that can ensure them their rights and entitlements (Sharma & Kunduri, 2015). The legislation should address the working conditions, violations and exploitations, provisions for mobilisation, illegal channels of private placement agencies and establishing basic civil rights from a gender perspective to incorporate the differential experience of females in the sector. Efficient implementation and scrutiny of the same require statistically significant data, the absence of which is another flaw in the system.

Domestic worker’s inaccessibility of social protection is the result of the lack of recognition. Migrant workers in the sector without any identity proof or formal registration are excluded from social protection schemes. Agrawal and Agarwal (2018) suggest setting up an independent welfare board in every district responsible for registering, ensuring availability of social security benefits, conducting dispute resolution, dissemination of information and providing skill development and training for domestic workers. The provision of financial incentives can help in coping with sudden unemployment situations during any form of crisis such as the pandemic. Allowing for the organisation of domestic workers into unions and cooperatives can also be beneficial in attaining social and legal protection. Domestic worker groups such as SEWA and National Domestic Workers Movement (NDWM) in the country have been attending to the woes of the domestic workers by providing a platform for collective bargaining and assertion of rights.

The introduction of the draft on National Policy on Domestic workers can be seen as a positive development, however, the policy still remains in consideration. Vulnerabilities of the domestic workers, exacerbated by the pandemic, highlight the urgent necessity for the ratification of the ILO convention on domestic workers. There is an urgent requirement in increasing the government’s sensitivity towards domestic workers and their precarious existence.

References
1. Agrawal, U., & Agarwal, S. (2018). Social Security for Domestic Workers in India. Socio-Legal Rev., 14, 30
2. Bureau, A. (2018). Political Inclusion of Seasonal Migrant Workers in India: Perceptions, Realities and Challenges.
3. C189 – Domestic Workers Convention, 2011 (No. 189). (n.d.). Retrieved July 15, 2021, from https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C189
4. Chandrasekhar, C. P., & Ghosh, J. (2012, November 12). Changing patterns of domestic work. @businessline. https://www.thehindubusinessline.com/opinion/columns/c-p chandrasekhar/changing-patterns-of-domestic-work/article22985402.ece
5. Chen, M. A. (2011). Recognizing domestic workers, regulating domestic work: Conceptual, measurement, and regulatory challenges. Canadian Journal of Women and the Law, 23(1), 167-184.
6. Chigateri, S., Zaidi, M., & Ghosh, A. (2016). Work Like Any Other, Work Like No Other103. Retrieved July 18, 2021, from http://www.unrisd.org/indiareport-chapter4
7. Chigateri, S. (2021). Labour Law Reforms and Women’s Work in India: Assessing the New Labour Codes From a Gender Lens. Institute of Social Studies Trust.
8. Indu, A., Indrani, M., & Neetha, N. (2012). Gender and migration: Negotiating rights, a women’s movement perspective. Delhi: Centre for Women’s Development Studies.
9. International Labour Organisation (ILO). (2013a). Who are domestic workers? Ilo.Org. https://www.ilo.org/global/docs/WCMS_209773/lang–en/index.htm
10. International Labour Organisation (ILO). (2013b). Global and Regional Estimates on Domestic Workers.
11. International Labour Organisation (ILO), (2015). Indispensable yet unprotected: Working conditions of Indian domestic workers at home and abroad. Retrieved July 19, 2021, from https://www.ilo.org/wcmsp5/groups/public/—ed_norm/—declaration/documents/publication/wcms_378058.pdf
12. Klemm, B., Däubler, W., Beimin, W., Lai, A., Min, H., & Sinha, S. (2011). Protection for Domestic Workers: Challenges and Prospects. Briefing Paper Special Issue, May, Friedrich Ebert Stiftung.
13. Madhav, R. (2010). Legal Recognition of Domestic Work. Labour File, 8, 41.
14. Mahanta, U., & Gupta, I. (2015). Road ahead for domestic workers in India: legal and policy challenges.
15. Mazumdar, I., Neetha, N., & Agnihotri, I. (2013). Migration and gender in India. Economic and Political Weekly, 54-64.
16. National policy for domestic workers. (2019, February 13). Retrieved July 18, 2021, from https://pib.gov.in/Pressreleaseshare.aspx?PRID=1564261
17. Neetha, N. (2004). Making of female breadwinners: Migration and social networking of women domestics in Delhi. Economic and Political Weekly, 1681-1688.
18. Neetha, N., & Palriwala, R. (2011). The absence of state law: Domestic workers in India. Canadian Journal of Women and the Law, 23(1), 97-120.
19. Neetha N., & Indrani, M. (2020, June 01). Crossroads and Boundaries : Labour Migration, Trafficking and Gender. Retrieved July 19, 2021, from https://www.epw.in/journal/2020/20/review-womens-studies/crossroads-and-boundaries.html
20. Overview of Labour Law Reforms (n.d.) Retrieved from https://prsindia.org/billtrack/overview-of-labour-law-reforms#_edn2
21. Poddar, M., & Koshy, A. (2019). Legislating for Domestic’Care’Workers in India-An Alternative Understanding. NUJS L. Rev., 12, 67
22. Shanthi, K. (2006). Female labour migration in India: Insights from NSSO data(Vol. 4, p. 2006). Chennai: Madras School of Economics.
23. Sharma, S., & Kunduri, E. (2015). Of Law, Language, and Labour: Situating the Need for Legislation in Domestic Work. Economic and Political Weekly, 50(28).
24. Singh, N., Keshri, K., & Bhagat, R. B. (2015). Gender dimensions of migration in urban India. In India Migration Report 2015(pp. 200-214). Routledge India.
25. Srivastava, P., & Shukla, P. (2021). Crisis behind closed doors domestic workers’ struggles during the pandemic and beyond. Economic and Political Weekly, 17-21.
26. Sumalatha, B. S., Bhat, L. D., & Chitra, K. P. (2021). Impact of Covid-19 on Informal Sector: A Study of Women Domestic Workers in India. The Indian Economic Journal, 00194662211023845.
Image Credit: ucanews.com
July 22, 2021
Is MGNREGA a Sustainable Employment Option for Migrants?

Covid-19 certainly has kindled a renewed focus on healthcare systems, sanitation, and most importantly, employment in the rural areas of the country. The pandemic has thrown light on the huge inadequacies and challenges of our healthcare structure that the government and the citizens had not foreseen. Millions of skilled and unskilled migrants moved across the country in droves to their hometowns in the absence of income and work and means to sustain their life. Around 30 Million (3 Crore) or 15-20% of the total urban workforce left for their hometowns, accounting for the largest ever reverse migration trend in the country, exclusive of intra-state migration. The World Bank in its report mentioned that a whopping number of 40 million internal migrants were harshly affected by the lockdown. Now that the country is just a few steps from opening up in full, concerns about workers moving back in search of work remain in the air. The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), which has a mixed track record in sustaining the livelihood of people in distress by providing guaranteed employment and considerate wages might be the only way out for the worst of the worst-affected. But, will the scheme be a viable and sustainable employment option for the days and years to come? This article aims to answer the question of efficiency, significance, and sustainability of MGNREGA in rural employment in the country.

What is MGNREGA?

MGNREGA, the world’s largest guarantee work programme, is the legitimised pioneer of the fundamental ‘Right to Work’. The scheme does that by providing a time-bound guarantee of work for 100 days a year, with considerate fixed wages. Workers under the scheme are assigned to agriculture and related capacity building projects thus ensuring sustainable development for all, as advocated by Gandhi. The scheme has reasonable success stories to its credit, all across the country. A study by Parida (2016) at Odisha proves that MGNREGA has played an important role in the agricultural off-season by providing work to the needy, the poor, and the socially marginalised communities. In various villages in Sikkim, families under MGNREGA were more self-reliant and less dependent on government programmes for a livelihood, according to the results of an evaluation conducted by the Tata Institute of Social Sciences (2017).

The Ministry of Finance announced Rs. 40,000 crore fund allocation to MGNREGA on the onset of the fourth phase of lockdown in May, while under the Atmanirbhar Bharat Abhiyan, the government plans in creating jobs for 300 Crore persons, and the national average wages of workers also saw an increase from Rs. 182 per day per person to Rs. 202, with effect from April 1st, 2020. All of these might come off as a huge sigh of relief to the worst affected, but in many states, the scheme wage rates are lower than the minimum wages in the respective states. So, this increase in wages does not hold huge significance in reality.

Unemployment and Work Allocation Concerns

Reverse Migration Trends and Unemployment: Unemployment has always been a perennial problem for a developing country like India, especially in times of crisis. The unemployment rate of the country reached an all-time high of close to 24% in April, while the rate of unemployment is expected to reach 8-8.5% in 2020-21, which may increase owing to the reverse migration trends. According to the Former Chief Statistician of India, rural unemployment is now a double-edged sword, given the impact of different migration trends. The reverse migration trends have altered the demand-supply dynamics in rural India significantly. Areas that previously had negative net migration rates are now expected to experience labour surplus, while the locations that may need workers might lack supply. The trends in reverse migration and its impact on local employment in states are visible, with Uttarakhand topping the charts in both the number of reverse migrants and the unemployment rate at around 22.3% as of September. The state is followed by Tripura at 17.4% and Bihar at 11.9%. Thus a strong correlation can be inferred between the amount of reverse migration and the unemployment rate in a given state.

Putting together numbers of short-term and long-term vulnerable workers gives us a total of about 13 Crore (130 million) workers, who are deeply affected by the Covid-19 crisis.

Another trend that is recognisable from literature is that migration is no longer a one-way street. Seasonal and circular migration continues to grow and take various forms (Conell et.al., 1976). Amongst these, vulnerable circular migrants are termed as the most distressed section of migrants, which include both Short-term seasonal and long-term occupationally vulnerable workers. Srivastava (2020) has estimated the number of 5.9 crore short-duration circular migrant workers in the year 2017-18. In the same study, vulnerable long-term circular migrants have been identified at 6.9 crores in the same period. Putting together numbers of short-term and long-term vulnerable workers gives us a total of about 13 Crore (130 million) workers, who are deeply affected by the Covid-19 crisis.

Work Allocation Concerns: Besides, The Taskforce for Eliminating Poverty constituted by Niti Aayog in the year 2015 (Occasional Paper,2016) has noted that most beneficiaries under the MGNREGS have been on an average get only 50 days of work. This shows that the scheme requires a better mechanism that recommends better targeting of the poorest of the poor and gets them guaranteed work for 100 days. Additionally, if 50-60% of the migrant workers in urban India (2018 above) return to their home destinations, then the scheme has to accommodate between 5.5 – 6.6 crore new workers, which will add 50 – 60% weight on people to be accommodated under the scheme. This exerts additional pressure on the already drying up state funds, which means catering to the huge number of migrants might not be economically sustainable for a long period.

Wages and Work Efficiency under MGNREGA

The wage rate in MGNREGA has been a huge concern for policymakers across India. While the recent increase in wages seemed quite positive at the onset, the wage hike is lesser than the minimum wage rate in certain states. Wage rates in the year 2019 seemed to be on the same trajectory, with the MGNREGA wage hike being lesser than the minimum wages in 33 states. Long payment delays also with meager wages add to the burden on workers under the scheme. Another important loophole in the scheme is the availability of work for such a huge number of workers seeking work under the scheme. In most cases, work is inadequate for such a huge number of workers. The standing committee report on rural development for the year 2012-13 also mentioned a significant decline in annual work completion rates (%). According to the report, work completion rates have taken a deep plunge consecutively in the years after 2011, with work completion rates of 20.25% for the year 2012, and 15.02% for the year ending 2013. Such dismal performances also throw light on the lack of productive allocation of work under the scheme. All of these certainly are results of the weakening of the act.

CONCLUSION

While MGNREGA fails in addressing a lot of important issues, COVID-19 certainly allows it to fit the dynamic changes in employment and work conditions. Making amendments to the act can be the only way out if the act needs to be sustainable in the long term. MGNREGA gives a rights-based framework to migrants seeking skilled and unskilled labour opportunities but lacks in giving enough benefits to the workers. Work under the scheme should be allocated efficiently, as per the project needs. While COVID-19 put a halt to a lot of existing projects, a lot of new projects are on the anvil. Catering to the needs arising on account of the pandemic including sanitation infrastructure building projects and infrastructure and rehabilitation projects can help the scheme diversify its project base, thus increasing employment opportunities to the migrants. Agriculture, the only positive contributor to the GDP of the country should be taken advantage of in the situation. A strong work evaluation setup should be made sure of, that would efficiently track work completion records thus giving opportunities for workers to complete the incomplete projects. This will yield benefits in both completion of a project and increased workdays and consequently increased wages for a worker.

Cash-based transactions can be a game-changer in this scenario. Instead of reliance on Aadhar, the unbanked should be remunerated regularly by the means of cash.

Need for Cash-Based Wage Transfer: While cash crunch and plunging aggregate demand are looming over the country’s economy, MGNREGA can be used as a tool to put money in the hands of the needy. The propensity to consume of a rural worker is way higher than that of an urban employee. Cash-based transactions can be a game-changer in this scenario. Instead of reliance on Aadhar, the unbanked should be remunerated regularly by the means of cash. Bank and Post office ways of remunerating workers surely did have an impact on corruption, but irregular payments and lack of access to formal banking systems are a common testimony among the migrants. Reverse migration is also the beginning of people bringing themselves into the formal cycle of work, with their enrolment under MGNREGA. Tapping the untapped potential and better engagement and benefits to workers under the scheme will largely increase its base and efficiency. If states learn from their past mistakes and amend the working system of the act, then surely it may do wonders in rural employment in the country.

Image Credit; The Quint

October 31, 2020

Forecasting Unemployment Rate during the Pandemic

Forecasting
Forecasting, in simpler terms, is a process of predicting future values of a variable based on past data and other variables that are related to the variable being forecasted. For example, values of future demand for tickets for a particular airline company depend on past sales and the price of its tickets.
Time-series data is used for forecasting purposes. According to Wikipedia ‘A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus, it is a sequence of discrete-time data.’ An example of time series data for monthly airline passengers is given below:

Figure 1

More technically, it is modelled through a stochastic process, Y(t). In a time series data, we are interested in estimating values for Y(t+h) using the information available at time t.
Unemployment rate
Unemployment is the proportion of people in the labour force who are willing and able to work but are unable to find work. It is an indicator of the health of the economy because it provides a timely measure of the state of labour market and hence, overall economic activities. In wake of the impact of Covid-19 on economic activities throughout the world, unemployment rate analysis and forecasts have become paramount in assessing economic conditions.
In India, unemployment rates have been on the higher end in recent times. According to data released by Statistics Ministry, unemployment rate for FY18 was 6.1%, the highest in 45 years. It is no co-incidence that GDP rates have also been declining successively for the past few years. The shock that Covid-19 has given to the economy has only worsened our situation. The unemployment rate rose to 27.1% as a whopping 121.5 million were forced out of work.

Figure 2

Source: CMIE
Methodology
The data used to forecast unemployment rates was sourced from CMIE website, which surveys over 43,000 households to generate monthly estimates since January 2016. The data has 56 monthly observations ranging from January 2016 to August 2020, data before 2016 was not available.
Four popular econometric forecasting models (ARIMA, Naïve, Exponential Smoothing, Holt’s winter method) were used and the best performing model was chosen to forecast unemployment till December 2020.
The forecasting models were programmed in R. The relevant codes are available upon request with the author. The Dicky-Fuller test and the Chow test for structural breaks were conducted using STATA, results of which are presented further in the article.
Before beginning the analysis, I believe that the limitations of the analysis should be mentioned:

The sample size of 56 observations is not sufficient for a thorough analysis, ideally the sample size should have been 2-3 times larger than the available data. Smaller sample sizes lead to skewed forecasting results which are prone to errors.
The unemployment data from CMIE is an estimate and is a secondary source. In India, primary data is only collected once in 3-4 years, thus the forecasting results are only as good as the source of the data.
This is a univariate analysis, an Okun’s law based analysis of Unemployment rate as a function of GDP (output) and past trends would have been more suitable. However, since GDP data is only available quarterly and there are only 56 monthly observations available, it would have rendered the analysis insignificant with only 19 quarterly observations.
Forecasting being based on past trends, is prone to errors. The negative shock provided by Covid-19 to the economies worldwide has made it all the more difficult to forecast. A Bloomberg study analysed over 3,200 forecasts by IMF since 1999 and found that over 93% of the forecasts underestimated or overestimated the results with a mean error of 2 percentage points.

Checking the stationarity of data
In order to model build a model, we need to make sure that the series is stationary. For intuitively checking the stationarity, I plotted the data over time as indicated in Figure 2 above. I also plotted the correlograms (autocorrelations versus time lags) as shown in Figure 8 and 9 in appendix. The plot of data over time indicate varying mean, variance and covariance. The ACF and PACF plot show that autocorrelations function are persistent indefinitely.
We perform the Augmented Dickey Fuller test at 2 lags. Result of the ADF test is shown in Table 1 below. The test statistic is insignificant at 5 per cent and the p-value is 0.1709, which is more than the accepted benchmark of 0.05. We fail to reject the null hypothesis of non-stationarity. We conclude that our series is non-stationary.

Dicky-Fuller test on raw data

Table 1

		—– Interpolated Dickey-Fuller —–
	Test statistic	1% critical value	5% critical value	10%critical value
Z(t)	-2.303	-3.576	-2.928	-2.599

MacKinnon approximate p-value for Z(t) = 0.1709

Converting the non-stationary series into stationary

In order to transform the non-stationary series into stationary, we use differencing method (computing difference between consecutive observations).
We plot the data over time, ACF and PACF again as shown in Figure 5 below and figure 10 and 11 in appendix, respectively. From the figures, we can intuitively say that the transformed series is stationary. Further, we used Augmented Dickey-Fuller tests to ascertain the stationary of our series. Table 2 shows the result of the ADF test. The test statistic is significant at 1,5 and 10 per cent levels and the p-value is less than 0.05. We reject the null hypothesis of non-stationarity of our series. The tests confirm that the series is stationary.

Dicky-Fuller test on first difference data

Table 2

		—– Interpolated Dickey-Fuller —–
	Test statistic	1% critical value	5% critical value	10%critical value
Z(t)	-5.035	-3.576	-2.928	-2.599

MacKinnon approximate p-value for Z(t) = 0.0000

Figure 3

Naïve model
Naïve models are the simplest of forecasting models and provide a benchmark against which other more sophisticated models can be compared. Thus, a Naïve model serves as an ideal model to start any comparative analysis with. In a naive model, the forecasted values are simply the values of the last observation. It is given by
y^{^}_t₊_h_|_t=y_t.
Forecast results from Naïve method are presented below in figure 4 and table1.

Figure 4

Table 1

	Point forecast	Lo 80	High 80	Low 95	High 95
Sept	8.35	4.861900	11.83810	3.0154109	13.68459
Oct	8.35	3.417081	13.28292	0.8057517	15.89425
Nov	8.35	2.308433	14.39157	-0.8897794	17.58978
Dec	8.35	1.373799	15.32620	-2.3191783	19.01918

Box-Jenkins Approach

Identification of ARIMA (p, d, q) model

The data was split into training and testing dataset in 80:20 ratio. The training data was used for estimating the model, while the model was tested on the remaining 20 percent data. This is done in order to forecast the future values of the time series data.
p, d and q in (p, d, q) stand for number of lags, difference and moving average respectively.
The model best fitting the data was (0,1,3) as its Akaike Information Criterion (AIC) was the lowest amongst all the possible combinations of the order of the ARIMA model.
The residuals from Arima model were found to be normally distributed, with a mean of 0.09 and zero correlation. This causes a bias in the estimates. To solve the problem of bias, we will add 0.09 to all forecasts. The ACF and line graph of residuals is attached in the appendix.
After identification and estimation, several diagnostic tests were conducted to check if there were any uncaptured information in the model. Results of the diagnostics tests have been omitted from the article in interest of length.

Forecasting

The model that has been constructed was used to forecast unemployment rates for the next four months. The results are presented below in figure 5 and table 2.

Figure 5

Table 2

	Point forecast	Lo 80	High 80	Low 95	High 95
Sept	9.04	5.978858	11.93987	4.401073	13.51765
Oct	9.77	5.183039	14.1951	2.797671	16.58054
Nov	10.3	5.364191	15.06267	2.797157	17.62971
Dec	10.3	5.280182	15.14668	2.668678	17.75819

Exponential Smoothing method
It is one of the most popular classic forecasting models. It gives more weight to recent values and works best for short term forecasts when there is no trend or seasonality in dataset. The model is given by:
Ŷ_(t+h|t) = ⍺y_(t) + ⍺(1-⍺)y_(t-1) + ⍺(1-⍺)²y_(t-2) + …
with 0<⍺<1
As observed in the model, recent time periods have more weightage in the model and the weightage keeps decreasing exponentially as we go further back in time.
The ⍺ is the smoothing factor here whose value was chosen to be 0.9 since it had the lowest RMSE among all other values.
The forecast results are presented below:

Figure 6

Table 3

	Point forecast	Lo 80	High 80	Low 95	High 95
Sept	8.30	4.739288	11.87260	2.8512134	13.76068
Oct	8.30	3.507498	13.10439	0.9673541	15.64454
Nov	8.30	2.532806	14.07908	-0.5233096	17.13520
Dec	8.30	1.700403	14.91149	-1.7963595	18.40825

Holt Winters’ method
The simple exponential function cannot be used effectively for data with trends. Holt-Winters’ exponential smoothing method is a better suited model for data with trends. This model contains a forecast equation and two smoothing equations. The linear model is given by:
y_t+h= l_t + hb_t
l_t= αy_t + (1-α)l_t-1
b_t = β(l_t-l_t-1)+ (1-β)b_t-1
where, l_t is the level (smoothed value).
h is the number of steps ahead.
b_t is the weighted average of the trend.
Just like the simple exponential smoothing method, l_tshows that it is a weighted average of y_t
The α is the smoothing factor here whose value was chosen to be 0.99 and the β value 0.0025 since they had the lowest RMSE among all other values.
The forecast results are presented below:

Figure 7

Table 4

	Point forecast	Lo 80	High 80	Low 95	High 95
Sept	8.34	4.749288	11.9326	2.84121	13.84
Oct	8.33	3.24	13.4243	0.54541	16.11977
Nov	8.32	2.0800	14.5678	-1.2253	17.87316
Dec	8.31	1.0963	15.53419	-2.725103	19.35565

Evaluation
To compare the models the two parameters chosen are:

Root mean square error (RMSE)
Mean absolute error (MAE)

MAE is a measure of mean error in a set of observations/predictions. RMSE is the square root of the mean of squared differences between prediction and actual observation. RMSE is more useful when large errors are not desirable and MAE is useful otherwise.
RMSE and MAE statistics for all the models are presented below:

	Naive	ARIMA	Exp Smoothing	Holt Winters’
RMSE	2.72	2.24	2.73	2.7
MAE	1.05	1.034	1.06	1.05

From the table it is clear that ARIMA/Box Jenkins method has both the lowest RMSE and MAE among the models under consideration while Exponential smoothing method has the highest MAE and RMSE among all.
Therefore, the unemployment rate forecasts as per the Box Jenkins method for the next four months are:

Sept	9.04
Oct	9.77
Nov	10.3
Dec	10.3

The way ahead?

The unemployment rate is expected to rise in the coming months. This is a bad sign for an economy that is already suffering.
With GDP forecasts getting lower and lower for the current financial year, the govt needs to act quick to mitigate the potential damage.
It is impossible to correctly ascertain the total impact of covid-19 on the economy and the range of the impact, but it is safe to say that we will be seeing the effects for a long time to come in some form or other.
We might see more and more people slip into poverty, depression, increased domestic violence and with potentially long term impact on human development parameters like child mal-nutrition, enrolment rates etc among other things.

Some possible solutions

Expansionary monetary policy: It is a common tool of dealing with high unemployment rate in the short term. Under expansionary monetary policy, the central bank reduces the rate of interest on which it lends money to the banks, subsequently the banks lower their rates which leads to a higher amount of loans being taken by business owners. This extra capital helps businesses to hire more workers and expand production, which in turn reduces unemployment rate.
Expansionary fiscal policy: Under expansionary fiscal policy the government increases its spending, particularly in the infra-structure sector. It spends more money to build dams, roads, bridges, highways etc. This increased spending leads to an increase in employment as these projects require labour.
Expand the scope of NREGS to urban areas permanently and a higher minimum wage for all : NREGS has proved to be really effective in alleviating poverty, improving quality of life and decreasing unemployment rate in rural areas. Given the unprecedented circumstances, the govt can consider expanding its scope to urban areas, so that it could provide employment to the millions of unemployed workers there. This increase in expenditure could also help the govt revive consumer demand, which is essential if we want to help the GDP get back on track.
A stimulus package aimed at putting money into the hands of the poor :

The govt should also consider providing at least a one-time transfer of funds to people just like the US govt did. Such a transfer of putting money directly into the hands of the poor is the most effective way of reviving consumer demand in the economy and many economists around the world have been calling for such a plan to be implemented. There is no better way of increasing consumer expenditure other than putting money into the hands of cash-starved people.

Appendix:

Figure 8