Gist of EPW October Week 2, 2022

The Economic and Political Weekly (EPW) is an important source of study material for IAS, especially for the current affairs segment. In this section, we give you the gist of the EPW magazine every week. The important topics covered in the weekly are analyzed and explained in a simple language, all from a UPSC perspective.

TABLE OF CONTENTS

1. The Periodic Labour Force Survey and the Estimate of the Socio-economic Inequalities- A critical examination
2. Global Food Crisis
3. India’s Textile Sector

1. The Periodic Labour Force Survey and the Estimate of the Socio-economic Inequalities- A critical examination

• Various reforms took place in India in the previous decade and as a part of the statistical reforms in 2014, the system of collecting socio-economic data through the NSSO’s Employment and Unemployment Situation(EUS) surveys underwent crucial changes in terms of methodology and sampling. A task force was constituted by the NITI Aayog in this direction. This task force proposed the Periodic Labour Force Survey.
• The Periodic Labour Force Survey(PLFS) is a new system of data collection on issues pertaining to labour and employment. It replaced the very comprehensive and detailed surveys on the employment and unemployment situation(EUS), also referred to as quinquennial surveys. PLFS was introduced in India in the year 2017.
• The shortcomings of the NSSO’s surveys on the EUS were: 
  • The low frequency of data.
  • Time lag between the collection of data and availability of results. 
• Four yearly rounds of the survey from 2017-18 to 2020-2021 have been conducted under the PLFS system since its inception.
• The outcomes related to labour and employment in the first annual PLFS estimates (2017–18) have been unusual and controversial. It has been criticized on the grounds of the credibility of data and the modified methods
• Unlike the previous household-level sample surveys conducted by the National Sample Survey Office (NSSO) on the employment and unemployment situation (EUS) in India, outcomes of PLFS data indicate aggravated performance of key labour market indicators, like the labour force participation rates (LFPRs), the workforce participation rates (WPRs), and unemployment rates. 
• Recent trends observed in the survey:
  • India witnessed a drastic rise in the unemployment rates and the share of persons of the total working-age population (in the age group of 18–64) who are “Not in Employment, nor in Education, nor in Training” (NEET) between 2011–12 and 2017–18.
  • Though the labour force number saw an increase, a significant decline was experienced in LFPR and WPR along with a surge in NEET and unemployment rates. This was particularly pronounced in the age bracket of 18 to 29 years.
  • In rural areas, the size of the workforce plunged by 7 million from 2011–12 to 2017–18. This was complemented by a dramatic rise in the unemployment and NEET rates by 3.6 and 7.7 respectively.
  • The second annual estimates also showed a similar trend.

For more information on PLFS, read here: Periodic Labour Force Survey 2019-20 Findings | UPSC Notes

History and Significance of EUS Survey:

• The EUS survey is significant in terms of scope and coverage. It is a comprehensive survey that provides detailed statistics not only on employment but also on other dimensions which are crucial for analyzing inclusiveness, socio-economic inequalities, the status of decent work, wages, etc. 
• The initial surveys were irregular and lacked representation of samples across regions, seasons, and communities. However, after the recommendation of the Dantwala Committee in 1968, EUS surveys were conducted on a regular basis- after every 5 years (quinquennial basis). It also overcame inconsistencies and errors.
• Following various conventions and international norms of various organizations like the International Labour Organization conventions and Sustainable Development Goals(SDGs), India’s development approach started focusing on two major dimensions namely poverty reduction and inclusive development. Therefore, the availability of regular, reliable, and robust data on various socio-economic parameters like the unorganized labour and vulnerable sections in the population (for example Scheduled Castes, Scheduled Tribes, and women) became imperative. 
• The quinquennial surveys provided information on these indicators covering inequalities in labour market and status of work. It also covered aspects like poverty, gender inequalities, socio-economic inequalities, and educational inequalities. 

Comparison between EUS and PLFS:

• The three household-level NSSO surveys are considered for comparison including the 68th round (July 2011–June 2012) and two annual surveys under the PLFS scheme conducted during the period of July 2017–June 2018 and July 2018–June 2019.
• All three surveys are similar to each other in terms of methods used to determine and allocate sample sizes across the states and districts in the rural and urban areas. The share of rural and urban households is also the same.
• The only difference is in the criteria for choosing the final households for sampling. This factor creates a fundamental distinction between the two data sets (EUS and PLFS) since it is expected to create a substantial effect on the overall data outcomes, especially in terms of measuring the inequalities.
• Selection Criteria of sample households:
  • First, the Census of India and the Urban Frame Survey (UFS) are utilized to list the first state units (FSUs) identified as villages and urban blocks respectively.
  • These FSUs are further divided into hamlet groups or sub-blocks in rural and urban areas respectively on the basis of the population in the FSU. 
  • The households in each FSU and its hamlet groups/sub-block are divided into 3 second-stage strata (SSS). 
  • Consequently the sample households are identified in the final stage to form the ultimate stage units (USUs). 
• The classification of households by the SSS is the most critical stage in the sampling procedure because it has a direct bearing on the various indicators of inequality.
• The basic rule to be followed during classification is to ensure that the strata are necessarily independent and mutually exclusive subsets of the population. Moreover, heterogeneity and homogeneity within strata guide the establishment of a stratum.
• The PLFS uses the educational backwardness among the members of the household as an indicator in the formation of the SSS. It replaces the previous criteria of using economic affluence in earlier EUS surveys. 
  • Even though there exists a positive correlation between the level of education and the standard of living, education should not replace economic well-being.
  • The determination of sample households should be based on indicators that are directly indicative of the accessibility of households to various assets of livelihoods.
  • Additionally, it should be remembered that better education is only a means to achieving a better standard of living and is not an end
• The criterion adopted in the PLFS is simplistic and does not take into account the actual economic status of the households. 
• In the previous EUS survey system, two separate sets of criteria were adopted for rural and urban areas to determine the relative affluence.
  • For rural areas, the relatively affluent households were identified by using factors associated with richer households, like ownership of the farm, non-farm, and household assets, of a large business or working in a highly remunerative profession, a spacious pucca house in good condition, etc
  • In urban areas, households are categorized across the SSS on the basis of monthly per capita consumer expenditure (MPCE).

Detailed parameters to classify households in rural and urban areas

Source: Economic and Political Weekly

• In the EUS, the period of the survey is divided into four quarters which ensures equal representation of all major seasons. On the other hand, the PLFS provides data on yearly basis. 
• In PLFS “panel data collection” scheme has been introduced for urban areas to estimate the periodic changes in the key labour force indicators on both quarterly as well as annual basis. This is a significant value addition to the PLFS. 
• Though the PLFS ensures a regular supply of data for both rural and urban areas, it has undergone some major omissions in its questionnaire. Some of the omitted queries are access to land owned, cultivated, and irrigated, principal economic activity and occupation of the household, detailed questions on consumer expenditure behaviour, quality of the workforce, domestic duties, and nature of unemployment.

Consistencies/Inconsistencies in data of EUS and PLFS

• Outcomes reveal that various inconsistencies exist in the PLFS data making it incomparable with the data of EUS. Conventionally, household size tends to decline with increasing levels of per capita income in a linear and decipherable way.
• Another category of inconsistency was observed in PLFS 2017–18 and 2018–19 in contrast to EUS 2011–12, which is the percentage share of workers by each stratum within various occupational-cum-skill categories in the rural areas. In the occupational hierarchy, workers belonging to the bottom stratum tend to be highly concentrated in the lower occupations, whereas the middle and upper strata show a relatively greater presence in the middle or higher occupations.

Way Ahead:

• It should appropriately reflect the characteristics of different groups in the population without bias in the selection procedure
• It should provide detailed socio-economic data covering various important aspects of inequality.
• Any comparison between the two data sets based on education and standard of living criteria should be avoided.
• Another approach is to revive the old structure of sampling used in the EUS surveys by rectifying the previous technical errors.
• Thoroughly review the sampling techniques adopted in the PLFS and old detailed queries should be revived.
• Instead of using a single indicator of threshold education, it must be used in an integrated way, with caste and class identities.

2. Global Food Crisis

Context:

• Recently, the heads of Food and Agriculture Organization (FAO), International Monetary Fund (IMF), the World Bank Group, the World Food Programme, and the World Trade Organization issued a joint statement on the global food security and nutrition crisis. 
  • The recent estimates of the FAO shows that 828 million people go to bed hungry each day across the globe.

Factors contributing to the food crisis:

Disruption caused by the pandemic and the ongoing war:

• COVID-19 pandemic and the ongoing war in Europe have resulted in volatile food, energy and fertiliser prices. This along with restrictive trade policies and the disruption of supply chains are adding to the food crisis situation. The price of foodgrains have increased substantially.
• Countries with high dependency on food imports from Russia and Ukraine remain the most affected.

Stagnation and fall in cereal production:

• A major reason for the current global food crisis is the stagnation and fall in cereal production in recent years. 
  • The global cereal production is predicted to decline in 2022, to a level lower than in 2020. 
  • The cereal output is expected to fall across almost all regions, except South America. While production in Asia, North America will stagnate, Europe will register substantial dip in production.

Climate change factor:

• Climate change and growing intensity and frequency of extreme climate events have led to greater uncertainties in the agricultural sector. This affects the production of foodgrains.

Depleting stocks:

• The shrinking foodgrains stock in some countries is a major contributor to the current food crisis. While countries like China and the U.S. have large cereal stocks, countries with high demand are facing depleting stocks.

Recommendations:

• A food import concessional financing facility to help low-income food-importing countries to meet their urgent food needs as suggested by the FAO would be a step in the right direction.
• Countries should focus on increasing their grain stocks to ensure greater food security. Initiatives like the Public distribution system should be further deepened to reach the most-needy sections.

3. India’s Textile Sector

Introduction:

• The textile and apparel sector is one of the largest and oldest sectors in the Indian economy and has emerged as a significant contributor to its growth in recent years. 
• Due to the abundance of raw materials like cotton, wool, silk, and jute, India’s textile industry can produce a wide range of products that are suitable for various market sectors both within India and beyond. 
• Despite showing promising prospects, India’s share in global textile exports is sliding consistently due to a wide range of challenges.
• In India, the fibre orientation is skewed towards cotton, while global sourcing is largely Man Made Fiber (MMF)-based due to changes in global fashion trends. 

Indian Textile Industry:

• Currently, India’s textile sector contributes 2.3% to India’s gross domestic product (GDP), 7% to industrial output, and 12% to its export earnings.
• The sector employs more than 145 million persons (45 million persons directly and around 100 million people indirectly), making it the second largest employment-generating sector next to agriculture in India. 
• Currently, the market size of India’s textile and apparel sector stands at $139.7 billion, with domestic consumption amounting to $106 billion and exports amounting to $33.7 billion. 
• The growth of the Indian textile and apparel sector depends on the availability of raw materials to produce fabric. 
  • Fortunately, the sector has access to a strong production base of a wide range of fibres/yarns from natural sources and synthetic/MMF sources along with low labour costs and well-integrated production facilities.

The Global Textile and Apparel Scenario:

• Indian global exports of textiles and apparel increased from $768 billion in 2015 to $783.8 billion in 2020.
• India witnessed a decline in its global textile and apparel exports from being the second largest in 2015 (a share of 4.84%) to the sixth largest in 2020 (a share of 3.78%).
• Further, both Vietnam and Bangladesh were able to increase their shares in global textile and apparel exports, from 3.55% to 5.18% and 3.69% to 4.77%, respectively. 
• Moreover, the share of the textile sector in India’s overall merchandise exports has slid consistently in recent years, having dropped from as much as 13.7% in FY 2016 to just 10.8% in FY 2020.

Image Source: EPW

Key Challenges:

• The Indian textile sector is currently struggling due to a lack of scale in manufacturing, low level of investments, slow export growth, poor research and development , low credit availability, low market accessibility and poor infrastructure facilities.
• Small scale of operations: The Indian textile sector is highly fragmented and is primarily constituted by micro, small, and medium enterprises (MSMEs), which make up 80% of the industry. 
  • Supply-chain inefficiencies add an extra 6%–7% logistics cost and an interstate transfer delay time of 12–15 days. 
  • Apparel units in India have an average size of 100 machines, which is very less in comparison with China (2,000), Vietnam (1,000), and Bangladesh (500).
• Skewed fibre orientation towards cotton: Amid the changes in the global fashion trends, and given the inherent limitations in the growth of cotton and other natural fibres, the share of MMF has been steadily increasing. 
  • While the share of MMF-based app­arel in overall exports (50%) is steadily growing, the share of MMF in India’s overall textile exports is very low at around 20%. 
• Poor exports: India ranked second among the top global textile and apparel exporters until recently and is now ranked sixth after China, Germany, Bangladesh, Vietnam, and Italy. 
  • Over the years, India has started facing immense competition from economies such as Bangladesh and Vietnam as they have established themselves as apparel manufacturing powerhouses.
• High cost of capital: The cost of capital is relatively high in India, which affects the competitiveness of Indian manufacturers and exporters in the international market.
• Low foreign direct investment inflows: 100% FDI is allowed in the textile sector through the automatic route. Still, India attracts lower FDI compared to its counterparts. 
  • In 2018, FDI in the textiles and apparel sector in India stood at $0.2 billion, which is much lower than its competitors—China ($5 billion), Vietnam ($3 billion), and Bangladesh ($0.4 billion).
• Focus on low value-added product segment: India is essentially a summer-wear player that produces low-value apparel, whereas all high-value products are winter clothing and mainly MMF-based.

The World Fibre Market:

• Fibres/filaments are the basic building blocks of textiles and are derived from either natural or man-made sources.
  • Natural sources include plant-based sources such as jute and cotton and animal-based sources such as silk and wool.
  • Man-made sources include petroleum-based sources such as polyester and nylon, and cellulose-based sources such as viscose and Tencel. 
• The global production and trade in textiles is primarily dominated by two major fibre-based products—cotton and MMF. 
• Competitive fibres such as polyester are lighter and more durable than cotton. Amid the changes in global fashion trends, the demand for MMF textiles is increasing as a substitute for natural fibres.

Technical Textiles

• The global demand for technical textile products has continuously increased due to the demand in end-use industries, such as healthcare, automotive, construction, sports equipment/sportswear, agriculture, and environmental protection. 
• According to a NITI Aayog report, the US is the world’s largest producer and consumer of technical textiles—with a share of 23%—followed by Western Europe (22%), China (13%), and Japan (7%). 
• Since the conventional Indian textile sector has reached a high level of saturation in terms of value addition, innovation, and overall development, the technical textile segment offers a great opportunity to upgrade the sector and make it globally competitive.
  • Technical textiles contribute 0.7% to India’s GDP and account for approximately 13% of India’s total textile and apparel market.
  • With the growth of various related sectors, technical textiles are poised to grow at 18% CAGR during 2018–25. 
• To increase the domestic market size for technical textiles to $40–$50 billion by 2024 at an average growth rate of 15%–20%, a National Technical Textiles Mission (NTTM) was set up in 2020 for a period of four years (2020–21 to 2023–24). 

Government policies on the Textile Sector:

1. Production-linked Incentive Scheme for the Textile Sector

• The government announced the PLI scheme for 10 key sectors with a financial outlay of “Rs.1,45,980 crore” in order to make Indian manufacturers competitive on a global scale, attract investments in core competencies and cutting-edge technologies, ensure efficiencies, create economies of scale, increase exports, and make India an essential component of the global supply chain.
• The textile sector has been allocated Rs.10,683 crore under the scheme  for the incremental production of 40 identified MMF items and 10 technical textile products. 
• The government is focusing on new emerging segments, that is, technical textiles and MMF. 
• By offering financial incentives for an increased turnover for five years, the PLI scheme aims to boost domestic production for both domestic and international sales.
• It also creates numerous job opportunities, especially in the labour-intensive apparel sector. 
• The PLI scheme will also benefit the agriculture sector via a multiplier effect, in turn increasing the incomes of farmers and boosting the welfare of the rural economy.

     2. Focus Product Incentive Scheme

• The FPIS was developed by the government as part of the PLI scheme to remove barriers in the MMF clothing and technical textile segments and allow the textile industry to grow to a size and scale that will allow it to be globally competitive and produce global giants.
• The scheme will concentrate on promoting the 40 identified Harmonized System (HS) lines of MMF apparel and the 10 identified HS lines of technical textiles in order to build 60–70 globally competitive businesses and gain a significant market share in these markets.
• It will provide an incentive of 3%–15% on a stipulated incremental turnover for a period of five years after a one-year gestation period for brownfield investments and a two-year gestation period for greenfield investments.

     3. Mega Investment Textile Parks

• The MITRA programme was introduced in the Union Budget for 2021–22 with the intention of luring sizable investments, creating employment opportunities, and building world-class infrastructure with “plug-and-play” amenities.
• As part of the scheme, seven mega textile parks will be launched in three years. The parks will be set up over 1,000 acres of land with world-class infrastructure. 
• Mega Integrated Textile Region and Apparel (PM MITRA) Parks are expected to have ready-made factory sheds, warehouses, uninterrupted water and power supplies, incubation centres, research and testing labs, and express connectivity to seaports and airports.

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