Glass Ceilings, Second Shifts and Other Ills Indian Women Navigate

The right to work is a fundamental human right, and in India, realising this right for women requires dismantling systemic barriers, addressing biases, and promoting equitable workplace practices.

The right to work is enshrined as a basic human right and a fundamental entitlement that every individual should be able to exercise, regardless of gender. It is premised on the belief that all people have the inherent right to engage in productive employment, earn a livelihood, and contribute meaningfully to society. However, this ideal remains a distant reality for many women in India. Despite constitutional guarantees of equality and significant strides in gender parity, women’s participation in the labour force continues to lag significantly behind that of men. This disparity is not merely a statistical anomaly but a reflection of deep structural, cultural, and systemic barriers that restrict women’s entry into, and advancement within, the job market.

The female labour force participation rate in India reveals significant rural-urban disparities. According to the Periodic Labour Force Survey (PLFS) 2022, rural areas report a higher participation rate of 36.6% compared to just 23.8% in urban regions. Women in rural areas often engage in agricultural or informal sector jobs, contributing to higher participation rates but exposing them to greater economic vulnerability and limited social protections. Additionally, the Ministry of Labour and Employment’s report on Female Labour Utilisation in India highlights that 44.5% of women remain outside the labour force due to personal commitments such as childcare. In comparison, 33.6% prioritise education over employment. In stark contrast, only 0.8% of men are outside the workforce for similar reasons, reflecting deeply entrenched gender expectations that limit women’s professional aspirations.

The impact of gender bias in the workplace

Gender plays a dominant role in shaping an individual’s opportunities within the job market. From recruitment to career advancement, traditional gender roles heavily influence women’s professional trajectories. According to the International Labour Organisation (ILO), around 9.3% of women in India are not in the labour force due to health or age-related reasons, a figure reflecting the toll of balancing professional responsibilities with domestic duties. This “time poverty” not only increases health risks but also limits women’s long-term career prospects.

Workplace discrimination against women is pervasive in both formal and informal sectors. Women are often seen as less competitive and less capable of handling technical or leadership roles. These biases manifest in hiring processes, where employers frequently assume women are unwilling to travel or more likely to take family leave. This results in discriminatory treatment during recruitment and promotions. India’s persistent gender pay gap further reflects these systemic biases. According to the World Economic Forum’s Global Gender Gap Report 2024, Indian women earn on average 20% less than men for comparable work. The wage disparity is particularly pronounced in the unorganised sector, where most Indian women are employed and lack basic legal protections and benefits, exacerbating their economic vulnerability.

Barriers

The “glass ceiling” – an invisible barrier that prevents women from advancing to leadership positions – remains a significant challenge in India. Globally, women hold about 29% of senior management positions, but in India, the figure is lower, at just 19.9%, according to Grant Thornton’s Women in Business 2023 report. Even in sectors where women are qualified and capable, they encounter gender stereotypes and implicit biases that limit their progression. Research by McKinsey & Company shows that women, especially those from marginalised communities, are less likely to be promoted to managerial roles despite equal or superior qualifications compared to their male counterparts.

These systemic biases lead to the “leaky pipeline” phenomenon, where talented women exit the workforce due to frustration over stalled career advancement. In male-dominated sectors like technology and finance, this is particularly acute. Research by the Peterson Institute for International Economics highlights that firms with more women in leadership roles see better financial performance, suggesting that gender diversity is not just an equity issue but also an economic imperative.

Cultural expectations and the ‘second shift’

Cultural expectations in India place a dual burden on women, who are expected to excel in their careers while fulfilling domestic responsibilities. This “second shift” reinforces societal norms that prioritise caregiving roles over professional achievements, leading to chronic time poverty. Women are often forced into part-time work or leave the workforce entirely, which not only undermines their economic independence but perpetuates gender inequality in the labour market.

Despite improvements in educational attainment, these gains have not translated into representation in high-growth sectors like Science, Technology, Engineering, and Mathematics (STEM). The underrepresentation of women in STEM is not due to a lack of ability but rather a consequence of persistent stereotypes, lack of support during formative years, and inadequate opportunities for skill development. According to UNICEF and the World Economic Forum, societal expectations and insufficient mentoring contribute significantly to the skills gap, restricting women’s economic mobility and entrenching gender-based economic stratification.

Shifts

Beyond structural and institutional barriers, safety concerns pose a significant challenge to women’s participation in the workforce. The brutal rape and murder of a doctor in West Bengal in 2023 illustrates the pervasive threat of violence that women face in public and professional spaces. Such incidents deter women from seeking employment and reinforce harmful societal beliefs that view the home as the safest place for women, further limiting their economic opportunities.

Addressing these multifaceted barriers requires an integrated approach involving both policy reforms and cultural shifts. Implementing family-friendly policies, such as paid parental leave, flexible working arrangements, and affordable childcare, can enable women to balance work and family responsibilities. Countries that have adopted such policies have seen higher levels of female labour participation. For example, in the Scandinavian countries, flexible parental leave policies have led to significant improvements in female workforce engagement.

Closing the gender skills gap is another priority. Programmes that offer scholarships for women in STEM, combined with mentorship initiatives, can provide the necessary support for women to succeed in high-demand sectors. Furthermore, promoting inclusive hiring practices like blind recruitment can mitigate unconscious bias, ensuring women have a fair chance at employment and advancement.

The right to work is a fundamental human right, and in India, realising this right for women requires dismantling systemic barriers, addressing biases, and promoting equitable workplace practices. By fostering inclusivity, closing educational and skills gaps, and reforming workplace cultures, we can move closer to a labour market that truly reflects principles of fairness and equality.

Amal Chandra is an author, policy analyst, and columnist. Maneesha A. is a public policy analyst and columnist.

BJP’s Performance in Haryana Is Despite Its Poor Track Record in Improving Employment

The mapping of core issues on which people vote can lead to their sustained centralisation in discourses.

The ruling Bharatiya Janata Party is likely to come back to power for a third time in Haryana. Meanwhile, the Jammu and Kashmir results are indicating a clear victory for the Congress-National Conference alliance, even though the BJP has done considerably well in the valley. 

It’s important in such trend analyses of electoral data to separate the vote-share percentage from the actual seat-share won by parties. For example, while Congress may have a larger vote share in Haryana (versus BJP), the incumbent party may still get a higher seat share to remain in power. 

These results may or may not have a clear bearing on Maharashtra and Jharkhand, but what’s clear is how the mapping of core issues on which people vote and what they experience it continue to lead to a churn of events which require deeper ground analysis. This is even more important, given how exit polls have probably got Haryana wrong.

Beyond regional politics and what’s likely to happen in these three states and one Union territory, next year’s elections in Bihar will also have a strong bearing on explaining the status-quo of the popular mandate. BJP is still the dominant electoral party, despite the 2024 Lok Sabha results. 

At a state level, there is a call for change despite the absence of a better, more suitable alternative in some areas. A common thread in this silent discontent articulated in earlier Lok Sabha and now during assembly polls is the BJP government’s poor track record in creating jobs for those existing or entering the working population.

It also lacks in handling food inflation. Despite a weak economic governance record, the party is continuing to establish dominance in its electoral standing – a third term victory for the BJP in Haryana will reflect faith in the incumbent party which still can win maximum seats, even if it lags in popular vote share.

Still, economic issues matter and have their own bearing on party performance over time. 

The growing inflationary tax has been wrecking household budgets beyond an erroneously high indirect-tax ratio (driven by the imperfections of an extractive GST revenue system) which adversely impacts middle-and low-income households. Here, it is pertinent to bring forth insights from what appears to be a disjointed, unorganised and highly informal employment landscape that needs structural interventions from the nation-state and long-term planning.  

India’s disjointed labour-markets continue to grapple with the structural challenge of unemployment-underemployment, a critical issue highlighted in the Annual Report on the Periodic Labour Force Survey (PLFS) 2023-24. 

The report offers an in-depth look at the state of employment, labour force participation, and the contrasting dynamics between farm and non-farm employment. It sheds light on the broader health of India’s labour market, revealing a complex interplay of rural and urban trends, gender disparities, and the persistent struggle of the country’s youth to secure stable employment. 

More workers seeking employment are turning back to the agricultural sector, working as casual or unpaid workers, in the absence of jobs across urban areas. This might indicate jobs being created in rural areas but what’s happening is a mass reverse trend in people going back to rural areas to work in underpaid, more exploitative agri-space, instead of working in more organised and formal jobs including in manufacturing, industry or construction. This is widening existing informality and inequality amongst workers.

In PLFS 2023-24 data, the unemployment rate for individuals aged 15 years and above in the usual status stands at 3.2%. While this figure may seem relatively low, a closer examination uncovers significant differences between rural and urban areas. In rural India, the unemployment rate for males is 2.7%, whereas urban males face a higher rate of 4.4%. 

The contrast is more pronounced for women and the youth. 

In rural regions, female unemployment is just 2.1%, while urban centres report a much higher 7.1%. This urban-rural divide highlights a critical challenge: urban economies, despite their greater access to non-farm jobs, are struggling to create sufficient employment opportunities for women. For men, rural areas, dominated by farm employment, offer more opportunities, albeit in lower productivity and often underemployment settings.

The labor force participation rate (LFPR) in India paints an equally nuanced picture. For individuals aged 15 and above, the LFPR stands at 60.1%, with rural areas reporting a higher participation rate of 63.7%, compared to just 52.0% in urban regions. This disparity reflects the continued dominance of agriculture in rural employment. 

Agriculture has long served as a key source of jobs, particularly for rural populations, but the sector’s productivity and potential for economic growth remain limited. This high rural participation is driven by necessity rather than opportunity, with much of the workforce engaged in small-scale subsistence farming. 

The seasonal nature of agricultural work often leaves workers underemployed during non-harvest periods, contributing to rural labour market volatility. Farm employment, while providing a safety net for many rural households, often translates to lower income levels and fewer opportunities for skill development.

A significant revelation from the report is the continuance of stark gender disparity in both labour force participation and unemployment. Rural women, with an LFPR of 47.6%, are more engaged in the labour market compared to their urban counterparts, where the LFPR drops to 28%. 

This difference reflects the cultural and economic barriers that prevent women from accessing formal sector jobs in cities. In rural areas, women are predominantly self-employed in agriculture, but this does not necessarily translate to better income or job security. 

The exploitative self-employment nature of rural women’s work, primarily in small-scale farming, limits their participation in more diversified and higher-paying sectors. Access to resources such as credit, land, and market linkages remains limited for women, further entrenching their roles in low-productivity agricultural activities.

Also read: Why Women’s Employment Is a Conundrum in India

Another concerning trend highlighted in the report is the uncomfortably high rate of youth unemployment, especially in urban areas. For individuals aged 15 to 29 years, unemployment rate stands at 10.2%. The rural-urban divide is stark in this demographic as well. Rural male youth-unemployment is 8.7%, while in urban areas, it rises to 12.8%. 

For women, rural female youth unemployment is at 8.2% and a striking 20.1% in urban areas. This high rate of urban female youth-unemployment underscores the mismatch between the education system and the job market. As more young people pursue formal education, particularly in urban areas, the job market has not kept pace in creating opportunities that match their qualifications. Many young women find themselves overeducated and underemployed, struggling to find jobs that meet their skill levels.

Despite these challenges, the report also highlights gradual improvements in labour force participation. In past year, LFPR has seen a slight increase, in rural areas, where the participation of rural males rose to 63.7%. This could be attributed to government initiatives aimed at improving rural employment through self-employment schemes and the promotion of rural industries. Programmes like Atmanirbhar Bharat and Pradhan Mantri Mudra Yojana have sought to encourage entrepreneurship and boost job creation, particularly in non-farm sectors. However, the success of these initiatives remains suboptimal at best. 

The report also touches on the persistent issue of underemployment in India’s labor market. The worker population ratio (WPR) for individuals aged 15 and above saw a slight rise to 57.1% in 2023-24, up from 56.0% the previous year. 

However, this increase does not necessarily indicate full employment. The rise in rural areas can be attributed to agricultural work, where underemployment remains widespread. Seasonal fluctuations in farm work mean that many rural workers are only partially employed for much of the year, contributing to a labour market that is characterised more by subsistence than productive-growth-oriented employment. For women, particularly in rural areas, the WPR also remains low, reflecting the broader issue of underemployment in the agricultural sector.

Youth unemployment remains more acute in urban areas, where young people, especially women, are struggling to find meaningful work. This is not just a reflection of limited job creation but also a sign of the growing disconnects between skills imparted by education system and the industrial needs of the job market. The report highlights the need for better vocational training and education reform to bridge this gap and ensure that young people, particularly in urban areas, are better prepared for the jobs available in the market.

The reliance on agriculture in rural areas limits opportunities for diversification and income growth, while urban areas, despite offering more non-farm employment opportunities, are struggling to provide jobs for women and young people. 

Addressing these issues will require a concerted effort to create more inclusive and dynamic labour markets, ensuring that economic growth benefits all segments of the population. There is a lot more that government intervention can do to correct these structural imbalances, provided there is an explicit will to do so. For now, given how despite core economic issues, an incumbent is winning a third term in one state (Haryana) and is clearly gaining in strength in another (Jammu and Kashmir), credit has to be given to the party cadre and booth workers who helped mobilise electoral strength in favour of the BJP.

Deepanshu Mohan is a Professor of Economics, Dean, IDEAS, and Director, Centre for New Economics Studies. He is a Visiting Professor at London School of Economics and an Academic Visiting Fellow to AMES, University of Oxford.

Why Women’s Employment Is a Conundrum in India

The recent trends in women’s labour force participation rates, showing a stark increase in women’s employment, are puzzling.

The recently released Annual Report of the latest Period Labour Force Survey (2023-24) shows a continuation in the trend of increasing labour force participation rates (LFPR) and worker population ratios (WPRs) and decreasing unemployment rates (UR) observed since the first PLFS conducted in 2017-18.

All India LFPR for those above 15 years of age has increased from 49.8% in 2017-18 to 60.1% in 2023-24, WPR from 46.8% to 58.2% and URs have reduced from 6% to 3.2%.

On the face of it, these are significant changes. But what do they really mean in terms of job creation in the economy and the contribution of employment to people’s living conditions? 

Even a superficial glance at the data show that most of these trends are being driven by women’s employment. Since the 1980s, India had been facing a situation of declining female LFPRs in rural (except for an increase in 2004-05) until a reversal of this trend in 2017-18. In urban areas, the FLFPRs have been more or less stagnant but low, and even here there seems to be an increase in recent years. A number of supply and demand side factors have been discussed in the literature to explain the low and declining FLFPRs rates in India. Women’s increasing participation in higher education and women withdrawing from the labour force as household incomes increase have been common explanations.

Further, cultural norms placing restrictions on women’s mobility, increasing household responsibilities and burden of unpaid domestic and care work have also been discussed. Demand side factors related to fewer women being absorbed in agriculture and unavailability of jobs in the non-farm sector for women is also an important factor.

Clouding all analysis of women’s employment using large scale data such as from the PLFS is the issue of how well and accurately these surveys capture women’s work in the first place. It is accepted that these surveys do generally underestimate women’s work/employment. 

In this context, the recent trends in FLFPRs showing a stark increase in women’s employment are puzzling. Rural FLFPR for women above 15 years of age has increased from 24.6% in 2017-18 to 47.6% in 2023-24, and in urban areas the corresponding increase is from 20.4% to 28%. The male LFPR (15 years and above) has seen a smaller change in this period – from 76.4% in 2017-18 to 80.2% in 2023-24 in rural areas, and 74.5% to 75.6% in urban areas during the same time period. It is obvious from this data that the overall trends in employment are mainly a reflection of the changes in the female LFPRs. There are no changes in the PLFS questionnaire and no known reasons to believe that there has been a sudden improvement in the capturing of women’s work in these surveys (although this could be investigated further) to attribute these trends to comparability issues. Neither are there any major changes in the supply side factors affecting women’s employment in this six-year period. 

Also read: Narrative of Lower Female Labour Participation is False When We Recognise Women’s Work

One aspect from the data which does provide some clue to what might be happening is the corresponding increasing in self-employment. The proportion of women workers in rural areas who are in the self-employed category has increased from 57.7% in 2017-18 to 73.5% in 2023-24. A high proportion (more than half) are in the helper in household enterprise category. Self-employment is high among men as well but does not see a similar increase – 57.8% in rural areas in 2017-18 to 59.4% in 2023-24. In urban areas too the share of self-employment among women workers has gone up, from 34.8%in 2017-18 to 42.3% in 2023-24. Self-employment in India does not usually represent profitable entrepreneurial ventures, rather often is a sign of distress where in the absence of any other gainful employment opportunities people are finding ways to fend for themselves. 

While further analysis is required to unpack this trend of increasing FLFPRs along with a much higher share of self-employment, the sectoral distribution of employment observed also show that at least partly this has to do with distress. The share of agriculture in rural women’s employment is still very high and has also increased from 73.2% in 2017-18 to 76.9%. In urban areas there is no major shift with a large proportion of women workers being in ‘other services’ and in manufacturing. Therefore, this increase in labour force participation is not accompanied by any positive structural change in employment, rather is mostly dependent on agriculture. 

Even when one looks at the earnings from self-employment, women’s earnings are very low and the gender gap remains huge.

Data on earnings is given on a quarterly basis in the PLFS report. For the 2023-24 quarter, highest earning from self-employment (during last 30 days) is reported for the April to June 2024 quarter – Rs 14,564 for rural males and Rs 5,218 for rural females; Rs 23,480 for urban males and Rs 8,492 for urban females. Therefore, women in self-employment earn only about one-third of what men do. The average earning for women in rural areas is much less than one lakh per annum and in urban areas is just about a lakh.

This doesn’t bode very well towards the goal of making ‘lakhpati didis’.

What is clear is that studying short term trends in employment without including a gendered analysis does not make much sense any more. Second, although there is this tremendous increase in women’s work participation rates, there is nothing in the data that show that this is a positive shift towards improving livelihoods. Instead, what it probably indicates is the pressure on women to contribute to household incomes in times of distress. 

Dipa Sinha is a development economist.

Does Household Consumption Data Confirm the Sustenance of Engel’s Law?

The latest household consumer expenditure survey shows a considerable decline in the proportion of food consumption, particularly cereals. Concomitantly, there has also been a rise in the spending on non-essentials. This has been interpreted by many as a progression towards affluence. But is it really the case?

The latest Household Consumption Expenditure Survey (HCES 2023), after a gap of 11 years, shows that the proportion of monthly per capita expenditure (MPCE) spent on food has fallen sharply. When compared with the last survey, 46.4% versus 52.9% earlier in rural areas and 39.2% versus 42.6% earlier in urban areas. Taken along with the rising proportion of expenditure on conveyance and other non-food items, the decline in spending on food is seen by many as the progression of Engel’s law and Bennet’s law.

Engel’s law suggests that as family income increases, the percentage spent on food decreases, even though the total amount of food expenditure increases. Bennett’s law observes that as incomes rise, people eat relatively fewer calorie-dense starchy staple foods and relatively more nutrient-dense meats, oils, sweeteners, fruits, and vegetables.

Put simple, these laws imply that as households experience higher incomes, they gain better access to convenience, with fewer calorie requirements. Thus, the proportion of income spent on food decreases as money spent on other goods or services rises. The summary tables from HCES have convinced many about this observation.

It is well-known that as real incomes rise due to increased economic opportunities, workers from labour-intensive rural areas tend to migrate to urban areas. This phenomenon has been one of the reasons for the structural decline in calorie and cereal consumption since the late 1980s. Therefore, the consumption of basic foods, particularly cereals, is correlated with the quality, or type, of work people engage in.

We test this hypothesis by dissecting item-wise spending shown in the CES.

Over the last 11 years, CES indicates a sharp deceleration in spending on food from 12.2% (two-year CAGR, 2011-12) to 7.9% (46% of MPCE), mainly on cereals, pulses, and sugar, in rural areas. For urban areas, it decelerated from 12.8% (two-year CAGR, 2011-12) to 7.7% in 2022-23 (39% of MPCE).

In the non-food segment, expenditure on conveyance has seen a substantial rise. Components including toilet articles, household consumables, and durables also saw higher growth. On the face of it, these changes in the CES spending pattern indicate a progression typically associated with rising real incomes.

The overall MPCE has during the 11-year period also decelerated: 9.2% for rural at Rs 3,773 and 8.5% for urban at Rs 6,459.

The income test

The first test should be done to see if household incomes have risen.

Derivation of household income from the decelerating private consumption and decline in savings indicate that there has been a sharp deceleration in aggregate real household income growth. It slowed to 3% (four-year CAGR) in FY23, from the peak of 9% in FY10 (based on the 2004-05 GDP series).

According to the Periodic Labour Force Survey (PLFS), which has published data from 2017-18 to 2022-23, the nominal average income for regular, casual, and self-employed workers grew modestly at a rate of 2.2% (five-year CAGR), or -3% in real terms. This contraction in real earnings per worker led to an increase in the number of working persons per household, rising by 3.4%. Consequently, household income saw a mere 5.7% increase, barely keeping pace with the rise in the cost of living.

The PLFS-based estimates – which represent nearly 90% of the working population – do not support the basic condition of the Engel’s law.

Within the top 10%, represented by income taxpayers and salaried workers filing income tax returns, the total assessed salary for workers earning less than Rs 9.5 lakh or 79% of salary earners has declined. For ITR filers above Rs 9.5 lakh, it has increased significantly. Hence, the stronger sales of premium product category is driven by rising income inequality.

The second test would be to look at the spending on education and medical expenditure, which should rise with rising income.

However, the CES data shows a marked decline in the spending proportion on education, clothing, footwear, and medical (non-hospitalisation) categories. This again reflects income constraints rather than bounties.

The decline in the share of spending on education needs to be closely watched as it could impact long-term labour productivity.

Also read: What Is the Health and Education Cess Being Spent On?

Cereal consumption test

Third, the nominal spending on cereals grew by 1.7% in rural areas and 2.7% in urban areas (11-year CAGR). However, when adjusted for CPI inflation, this growth translates into a contraction of 3.5% in rural areas and 2.5% in urban areas.

This looks out of sync because of the divergence between spending patterns and inflation rates.

India’s annual cereal production grew by 2.1%. The real spending on bread, cereals, and pulses, as per the Private Final Consumption Expenditure (PFCE, GDP) over the decade has been higher at 3.8%.

If the real consumption, as per the survey, indeed contracted by 2.5% to 3.5%, there would be a situation of oversupply and a crash in prices. More so recently, the supply impulses improved over the last five years due to stepped-up irrigation, higher reservoir capacity, record production, and export ban, along with a significant rundown in the government buffer under the post-COVID free food distribution programme.

In contrast, cereal inflation remained at 5.2% (11-year CAGR) and 6.1% (five-year CAGR). In FY24, it has averaged at 10.9%.

Also read: India’s Inequality at Historic High; Wealth Concentration Shot up Sharpest Between 2014-5 and 2022-3

Affluence test for rising spending on conveyance

Fourth, the proportion of expenditure on conveyance has increased substantially since 2011-12, in both rural (15.2%, 11-year CAGR) and urban areas (11.3%).

However, such a substantial surge in spending does not match with the a) contraction in passenger kilometres travelled in railways in the last 10 years, and b) collective stagnation in the sales of two-wheelers, buses, and passenger cars versus 10 years ago, notwithstanding the post-COVID recovery (see charts below).

Combining these components, representing the conveyance mode for 99% of the population, the average growth is a meagre 0.8%. Hence, the surge in expenditure towards conveyance is because of the higher cost than rising affluence. In particular, the passenger kilometres travelled by railways at 984 billion in FY24 have contracted by 0.5% per annum over the past decade, while the passenger revenue per kilometre surged by 7.7% per annum (see charts below).

Note: Data for exhibit 33 and 34 sourced from CMIE database.

Overall, this deceleration in the consumption in conveyance signifies rising ruralisation, and a declining incentive for the household to move towards the urban areas amid reduced income opportunities. In fact, the PLFS data shows that the population dependent on agriculture (and that lives in rural areas) has risen to 45.8% in 2022-23 from 42.2% in 2017-18.

Tangible evidence of Engels’ law progression missing

Putting together the falling real household income, the aberration in the reported spending on cereals and food from the CES tables, the decline in real per capita usage of conveyance, and rising ruralisation should ideally translate into higher per capita calorie intake, thereby leading to higher cereal consumption. This is paradoxical to Engel’s law of progression.

There appears to be an inadequate representation of the impact of government subsidies, free food distribution, and other dole-outs, in the imputed MPCE, especially in the rural areas. The fiscal support may have created space for spending on non-food items produced by consumer companies. But it is likely to have remained concentrated on basic products. The premiumization story is thus pivoted on the more affluent income earners that lie outside the surveyed universe of the NSSO.

Dhananjay Sinha is co-head of Equities and head of Research of Strategy and Economics at Systematix Group.

Share of the Working Age Population That Has Work Is Up, but Those Looking for Work Is Down: Data

According to the PLFS data, the unemployment rate among graduates in the age group of 15 years and above has declined. However, the same survey says that a larger number of individuals have opted for self-employment over salaried jobs, raising concerns over the unemployment situation.

New Delhi: The parliament security breach has refocused attention on the unemployment situation in India. While government data portrays a picture of declining joblessness among the youth, private surveys and experts say otherwise.

In such a scenario, it would be interesting to look at two sets of data – the Periodic Labour Force Survey (PLFS) data, for July 2022 to June 2023, and the unemployment data released by the Centre for Monitoring Indian Economy (CMIE).

The National Sample Survey Office launched the PLFS in 2017. The latest survey – the sixth annual report – was released for the period from July 2022 to June 2023.

According to PLFS data, conducted by the Ministry of Statistics & Programme Implementation, the unemployment rate among graduates in the age group of 15 years and above has declined to 13.4% in 2022-23 from 14.9% a year ago.

However, the same survey also showed that more number of people are opting for self-employment over salaried jobs.

The survey does not explicitly indicate the number of graduates who are self-employed, unlike the detailed breakdown provided for the calculation of the WPR.

The worker population ratio (WPR) is the share of the working-age population that has work.

WPR for persons of age 15 years and above has increased to 56% in 2022-23 from 52.9% in 2021-22 and 52.6% in 2020-21.

In rural areas, WPR for graduates among persons of age 15 years and above has increased to 58.3% in 2022-23 from 56.3% in 2021-22, 53.5% in 2020-21, and 50.9% in 2019-20.

In urban areas, WPR for graduates among persons of age 15 years and above has increased to 53.8% in 2022-23 from 51.8% in 2021-22, 50.3% in 2020-21, and 50.4% in 2019-20.

Overall, it increased to 55.8% in 2022-23 from 53.9% in 2021-22, 51.8% in 2020-21, and 50.6% in 2019-20.

In contrast, the labour force participation rate (LFPR)  – the share of population which is looking for work – has seen a downward trend in the past seven years. The decline was witnessed before the COVID-19 pandemic, and the pandemic only exacerbated the situation, CMIE said in June 2023.

In the financial year 2022-23, the overall LFPR was 39.5%, the lowest since only FY17, including the pandemic years, the Hindu reported, citing CMIE data.

Among men, it was 66% and among women, it was 8.7% – both the lowest since at least FY17, the newspaper reported.

So, an increasing share of Indians in the working age were neither employed nor willing to seek employment even after the pandemic, it noted.

In November, Bloomberg noted that economists have come to rely on CMIE data for a better assessment of the labour market as its figures are based on monthly surveys as opposed to government data, which releases country-wide data less frequently. CMIE conducts monthly surveys of more than 44,000 households in urban and rural India

In August, the Indian Express cited a report released by Lokniti-CSDS, saying that 36% of Indians aged 15 to 34 thought that unemployment was the biggest problem facing the country.

When compared to a similar survey conducted in 2016, the proportion of Indians who identified unemployment as the biggest problem increased by 18 percentage points, it said.

Also read: Three Claims of Government Economists About Jobs Put to the Test

Quality of jobs

India’s workforce is not rising in relation to its increasing working-age population, CMIE chief Mahesh Vyas told The Wire in May.

He said that only 40% of people aged 15 years and above offer themselves for work. The rest 60% are dependents.

Vyas said that the quality of jobs in India is very low. “Most jobs pay poorly and are of informal arrangements in the unorganised sectors,” he added.

If we take into account what economist Ajitava Roychowdhury had said, then India’s unemployment rate will be much higher.

In May 2022, he quoted the International Labour Organisation (ILO) to say that only those who are doing “decent” jobs should be marked as employed. “Decent work sums up the aspirations of people in their working lives,” the professor of economics at Jadavpur University had told news agency PTI.

Watch | ‘GDP Data Misleads, Hides Distress of Millions; India Must Focus on Income Growth, Jobs’

More people are opting for self-employment over salaried jobs

Moreover, it’s important to note that as per the latest PLFS survey, a larger number of individuals are self-employed than those in casual labour and the regular salaried class, The Wire reported in October

In urban areas, a relatively higher number of people have salaried jobs as opposed to self-employment, the report said.

In the PLFS report, within the self-employed category, two sub-categories have been established, which include: (i) own account workers and employers, and (ii) unpaid helpers in household enterprises.

“So [the] employer [category] is barely under 2% of the number of people in the economy. In other words, the own account workers’ category is highly driven by people such as cultivators, weavers, potters, who work on their own, in rural areas. And in urban areas, they are the rediwallahs, thelewallahs, kabaadi, barbers, tailors, vendors, and those who work on the street. The share of self-employed [people] in the total workforce is increasing, and within that, most of the increase can be seen among unpaid family labour and own account workers,” Santosh Mehrotra, professor of economics, Centre for Informal Sector and Labour Studies, School of Social Sciences, Jawaharlal Nehru University, had told The Wire.

Unpaid labour – which is categorised as ‘helper in household enterprise’ within self-employment – has increased to 18.3% in 2022-23 from 17.5% in 2021-22 and 17.3% in 2020-21.

He further explained that people are choosing to be self-employed, because of a lack of good work and wages. For example, an educated person would not want to do casual labour jobs.

Do GDP Growth Figures Reflect Robustness or a Statistical Overstatement?

Given the contrastingly frail household income situation, the continued slackness in private capex and deceleration in trade, the divergent core and headline real GDP growth are not trivial. 

The razzmatazz around the robust 7.6% real second quarter (Q2) GDP growth pumped up an adrenaline rush last week, prompting many to up their projections for fiscal year 2024 (FY24) closer to 7%, higher than the RBI’s projection of 6.5%. 

But what is obscured is that consumer companies are facing a supply gridlock as demand falls, inventories pile up, and the festive season demand remained weak. 

Separately, there is extensive evidence of flatness in real incomes of households (76% of the GDP), derived from the RBI’s data on the contraction of financial savings and the Periodic Labour Force Survey (PLFS) report. CMIE says that the unemployment rate has spiked again, to an average of 9.5%. 

Is there a disconnect between the stupendous GDP data and the lugubrious household situation? We think not.

The answer lies in the way GDP is calculated, which is confounded with systemic statistical aberrations. While the headline real GDP growth numbers are disconnected from the ground reality, the removal of the discrepancies shows the true picture. 

Despite the robust headline growth situation as reflected in the 7.4% year-on-year (YoY) gross value added (GVA) growth, employment-intensive agriculture and services – which together contribute 72% of the GVA – saw a major deceleration. 

Robust contribution from manufacturing and construction, which grew by 13.9% and 13.3% YoY respectively, translating into Industry GVA growth of 13.2%, essentially derives from the imputed higher value addition. This was due to low inflation related to the WPI driving up margins and profits of large firms.

On the expenditure side, real GDP growth at 7.6%, however, encloses weak core demand. 

Putting the identified components of the expenditure GDP explained by the components of household and government consumption, capital formation, and net exports, i.e. GDP excluding the unexplained discrepancy portion, the real core GDP stands at 3.0% in Q2 of FY24, following 1.4% in Q1. This is only 40% of the reported GDP of 7.6% in Q2!

Given the contrastingly frail household income situation (a la PLFS 2022-23, RBI’s savings data FY23), the continued slackness in private capex and deceleration in trade, the divergent core (2.3% YoY in the first half of FY24) and headline real GDP growth (7.7%) are not trivial. 

The substantial 71% unexplained portion of the real GDP growth in the first half of FY24 is because the estimated real GVA side, representing the value added from production across all sectors, is 2.7% higher than the real core expenditure GDP. But for the discrepancies, the real GVA plus net indirect taxes should match with the real GDP. 

Why the discrepancy is cause for worry

Normally, discrepancies should not be worrisome if it is a random error, where frequent offsetting positive and negative deviations average at zero. But that isn’t the case. 

The average discrepancy during Q1 FY12 to Q2 FY24 is +0.6% of GDP with a maximum of 5.4% and a minimum of -3.9%; 32 out of 50 quarters saw a positive discrepancy, indicating that real GVA exceeded core GDP in 64% of quarters. And more importantly, it has an inverse correlation of (-)0.63 with the GDP/GVA deflator, which rose to (-)0.76 in the post-pandemic era. 

So, the overestimation of GVA occurred during times of falling inflation or deflation, mainly driven by WPI. Thus, indexed to Q2 FY12, the real GDP in Q2 FY24 is 3.8% (or Rs 1.6 trillion) higher than the core real GDP.

Therefore, the discrepancies capturing a large portion of unaccounted GDP growth are not random and relate to the usage of WPI as a deflator to derive real GVA. Such systemic discrepancies, therefore, pose a challenge to the credibility of the real GDP estimation process.

GVA is the value of output excluding net indirect tax less cost of input consumption valued at purchasers’ prices. Hence, the measured value added would rise sharply with a fall in commodity or raw material prices, as has been the case recently, even with slowing consumption demand, particularly in a scenario where large firms have gained considerable operating margins due to elevated monopolistic power since mid-2016 at the cost of smaller firms. 

Conversely, given the sharp deceleration in estimated household real disposable incomes to 2% YoY in FY23 due to the decline in the quality of jobs, aligning with the decline in savings and slowing consumption and flat real wage earnings in four years (PLFS 2022-23), the real core GDP growth or the demand side does not share the robustness of the output side or estimated GVA growth. 

With the PLFS data also indicating a structural rise in dependence of households on less productive rural (73.3% of the population) and agri (45.8% of workers, 2022-23), the resultant decline in labour productivity and the household income which contributes 76% of GDP is misaligned with the sharp gains in reported productivity of the producing firms represented by real GVA growth.

This feebleness of the household consumption demand combined with the lack of private investment and the recent deceleration in exports has overpowered robust government capex. Hence, the aggregate demand remains significantly weak.

Also Read | Full Text: Pronab Sen Explains Why Data on Which GDP Is Calculated Is A Major Concern

A crucial factor is that the deflator used for deriving the real GVA is highly correlated with the WPI inflation. However, given the dominant contribution of household consumption, core expenditure GDP is also sensitive to CPI inflation. Hence, the sharp decline in WPI inflation (-1.8% in the first half of FY24) and higher CPI inflation (5.5%) also contributed to the rise in divergence between low real core GDP and strong GVA growth. 

Thus, all put together, the GVA side of the national income is susceptible to over-identification of large surviving companies that have gained market share from the non-corporate smaller firms in the manufacturing and services industries. As a corollary, it underrepresents the falling productivity arising from gaining ruralisation. Secondly, it is also susceptible to higher projections of gross value added due to the sharp decline in commodity prices (or underestimation in times of sharp rise in commodity prices).

Contrastingly, growth in real GDP ex discrepancies is a more relatable measure to depict the actual situation of the households’ livelihood, employment and productivity. Also, unlike the GVA estimates, the problem of exclusion and volatility induced by WPI inflation is much lesser in real core GDP.  

Thus, the endogenous growth, depicting the situation of households, in terms of income and consumption capability remains very weak. Behind the robust real headline GDP growth, padded up by overstated GVA is a flattening trend in the core real GDP growth, which is worrisome. 

Whereas the enfeebled demand reflects the structural issues, as the divergence between CPI and WPI inflation eventually narrows it would imply that the reported GDP will also converge towards the lower structural trend in core GDP growth.

Dhananjay Sinha is co-head of Equities and head of Research of Strategy and Economics at Systematix Group.

Full Text: Pronab Sen Explains Why Data on Which GDP Is Calculated Is A Major Concern

In an interview with Karan Thapar, he says the Q1 GDP growth figure of 7.8% is an overestimation and the actual figure is around 6.5%.

India’s former chief statistician and now Chair, Standing Committee on Statistics Pronab Sen says that the data on which India’s GDP is calculated is “a major concern”, and if not corrected soon, India’s GDP growth figures could become “unreliable”. He says he believes the Q1 GDP growth figure of 7.8% is an overestimation. He believes 6.5% is more accurate.

In an interview with Karan Thapar, he explains why he believes the recent Periodic Labour Force Survey finding that 58% of the workforce are self-employed is not an indication of increasing self-entrepreneurship (as claimed by SBI economists) but distress employment.

The following is a transcript of the video interview that was published by The Wire on November 21. It has been edited lightly for style, clarity and syntax.

§

Karan Thapar: Hello and welcome to a special interview for The Wire. India pays a lot of attention to GDP and many people believe it accurately reflects the state of the economy. But does it? We’re going to answer that question by raising two further questions. First of all, how accurately does India calculate its GDP? And secondly, how meaningful is GDP for the vast majority of the country? And remember the vast majority are poor. To answer those questions I’m now joined by India’s former chief statistician and the country director of the International Growth Centre, Pronab Sen. Professor Sen, let’s start with a series of concerns about how India calculates GDP before I broaden the discussion.

Writing in Project Syndicate, Professor Ashoka Modi says that India calculates GDP on the basis of income from production rather than expenditure growth in quarter 1 has been overestimated. He says on the basis of income, it’s 7.8%, and on the basis of expenditure it’s 1.4%. And if you take an average of the two, it’s 4.5%. So, my first question is, does India calculate GDP in a way that exaggerates growth?

Pronab Sen: No. There are three ways of calculating GDP – from production, from incomes, and from expenditures. If you had perfect measurement, the three would give you exactly the same result. The problem is that different countries have data sources which enable them to use one or maybe two at most of these three approaches. In our case, we rely on the production approach because that’s where the data is the best. On income, we have practically no data because there is a very large informal workforce for whom getting income data is practically impossible, when you’re living on an hourly wage. So the income approach is out. The expenditure approach is also fairly difficult to do and there are large measurement errors there. So for us, given our data systems, given what we are able to capture, the production approach is the best. 

So you disagree with professor Modi?

I disagree with him. Now, as far as the expenditure approach is concerned, the fact of the matter is that for all policy purposes, you need the data by the expenditure approach. It’s not good enough for you to know which industries are producing more or less or whatever. It’s very important for you to know what’s happening to savings, what’s happening to investments, what’s happening to consumption and so on. All our policies hinge around that. So the way we do the GDP here is we use the production approach as the benchmark, and then we try to estimate the expenditure approach with whatever data we have – much of which comes from the production approach, by the way. And there will always be a gap, simply because the data on the expenditure approach is limited.

That’s what’s called the discrepancy.

That’s what’s called the discrepancy. And it’s shown under the expenditure approach, so the system says look, we can’t give you the exact expenditure approach information, this is the best approximation we could do. 

Professor Modi’s doubts arise from the fact that the discrepancy he says is very large and therefore he believes that the calculation itself is faulty. You’re now explaining that the way India calculates GDP is the best way for the country given our circumstances…

Given our data systems.

…and therefore you disagree with Professor Modi.

I do indeed.

Now, a second concern about the way we have been recently calculating GDP comes from Arvind Subramanian, the country’s former chief economic advisor and his colleague and co-writer Josh Felman. They say that because the wholesale price index is used as the GDP deflator – a figure which they call a derive number, not measured directly and which fluctuates widely – because of that, they say GDP growth, particularly in the last few quarters, has been exaggerated. They say the truth is and I’m quoting them, “The economy is actually decelerating rapidly but that’s not reflected in the GDP figures.” To what extent do you agree or disagree with this concern?

There is some justice to that. The wholesale price index is not really the appropriate price deflator. What we should be using is what is called the Producer Price Index, which is what the producers are getting. Because remember, the data that we are getting is coming from the production units. So what is of concern is how much are the producers getting for the act of production. We don’t get that. What we are getting is the Wholesale Price Index, which is usually the second point of sale. So what the Wholesale Price Index will contain, over and above what the producer gets, are transport and trade margins. And both of these can vary quite substantially. You know, any movement in the global oil prices and it immediately gets reflected. So, yes, I think there is some justice to it.

So, you would agree with Subramanian and Felman when they say that in the last few quarters – and I don’t know how many ‘few’ is – but they say in the last few quarters growth has been exaggerated?

Well, if we’re making that argument, then the corollary to that is that the WPI is systematically underestimating inflation. That would be the logical [conclusion], that’s how you are overestimating the GDP. If you’re underestimating…

Which means your nominal GDP is wrong.

So, clearly, Felman and Subramanian have in their minds an inflation figure which is much higher. This I suspect is coming from the observation that over the last seven months, the Wholesale Price Index has been in the negative territory. There’s been deflation.

And I think they’ve played around with CPI [Consumer Price Index].

Now when they look at CPI, it’s sub 6%, so it’s come down quite a lot. But the WPI has moved very, very widely. Earlier we had a situation where the WPI was running at about 13-14%, the CPI was about 7-8%, the CPI came down from 7-8% to 5% about and the WPI came down from 14% to minus 8%. So that’s what they’re really talking about.

Which is why the use of the WPI, which fluctuates widely to quote their language, is an unreliable way of deflating GDP or a misleading way. 

No, it is, should we say an approximation. It is, I wouldn’t say entirely inappropriate. The question that really needs to be asked is whether we are measuring the WPI properly. 

What they say in fact it’s a derived figure and is not measured directly and therefore they’re suggesting by the use of those two words that it can’t be accurately measured. 

No, it isn’t actually a derived figure. The WPI is measured directly, there is a basket of goods and these prices are taken from the mandis and the wholesale markets.

And yet you still do believe there’s a measure of correctness in what they have concluded?

Yes, they have. As I said, because it’s not the appropriate price index.

So then let me ask you this. If there’s a measure of correctness, to use your phrase, in what Subramanian and Felman have concluded, that growth over the last few quarters has been exaggerated, it’s actually as they say decelerating rapidly that’s not reflected in the GDP outcome. Yet you disagree with Ashoka Modi. Could not the same logic apply to Ashoka Modi, that he too has spotted – maybe his explanation is wrong – but he too has spotted a higher level of GDP than should be the case?

Yes, but the arguments are a little different – substantially different. Now, you know, as I said, that Ashoka Modi’s case would have been stronger had he not made a statement in his piece saying that we should do what the Americans do, which is take an average of the two. That really is a cop-out. That assumes that the chances of error are equal in the two approaches. They have to be equal for that to work. 

But the point that’s interesting is that you do agree that not just in the last quarter but over the last few quarters, for a variety of different reasons and they’re not the same in the case of Modi or Subramanian, Felman, but you do agree that over the last few quarters GDP has been overestimated. 

I don’t know. That rests critically on whether the WPI is being seriously measured wrongly.

When you say you don’t know, are you saying you suspect but you can’t be certain?

I can’t be certain because if I say the WPI has been measured properly, then there is no argument. But if it hasn’t, then there is. 

So you have grounds to be concerned and worried, but you aren’t certain. 

I am not certain and you know here’s the thing. At what point do you start questioning the WPI? Is it when it goes into negative territories, saying that this does not fit in with… Or when it goes the other way? In which case, when the WPI was running at 14%, somebody would have come and made the statement that India’s GDP is being seriously underestimated. Now, the point is they’re perfectly willing to accept a 14% but not the negative.  

I take your point entirely. The opposite argument could also have been made, a year or so ago, on the basis of the logic Subramanian and Felman present. I take your point. I’ve deliberately spent a lot of time on these two because both of these have been in the news, they’ve created controversy. And I think for the audience, you’ve clarified very much how you view it and maybe also shown why this may be grounds to have concern but there’s no certainty that flows.

Let me add to that. The ground for concern is we need to focus on the price indices. Are we doing it right? Now the fact of the matter is both the WPI and the CPI are today seriously flawed and therefore both of them need to be improved. Both of them need to be looked at. 

A representative photo of vendors on a roadside in India. Credit: Pixabay

And if you don’t approve them, then the flaws inherent in the WPI – just to talk about that – will continue to cause concern about the GDP outcome we get.

This is correct.

Okay, now there is a third reason for concern about the way we calculate GDP and this is one, that to be honest you have mentioned in my interviews several times over the last few years – it’s how we calculate the unorganised sector, which is if you add agriculture 45% of GDP and 80 to 85% of employment, and we do it on the basis of a proxy derived from the organised sector. 

That’s right.

And to my mind that raises two questions. First of all, are we calculating correctly? And secondly, if the unorganised sector is disproportionately affected by something like COVID-19, which did happen, but the proxy for calculating the unorganised sectors derived from the organised sector, then that proxy will end up substantially overestimating the unorganised sector. How much of a weakness are these two concerns?

This is a serious concern and… So, the way the system works is that the initial estimates particularly the quarterly and the first annual estimate we have no information on the unorganised sector. So we use the organised sector proxies, using relationships that have been derived from the past relationship. Then we are expected to get data from the unorganised sector surveys at which point the estimates are corrected. The problem that has cropped up is that it’s been several years since we’ve had an unorganised sector survey.

So they’re very unreliable?

Yeah. So we are way out of whack from reality because we don’t know what the reality is today. 

In which case, at the moment we don’t have a clear or even a good idea of what is the situation in the unorganised sector.

This is right.

Therefore the 7.8% figure for quarter 1 may be quite substantially inaccurate because it’s based upon surveys that are several years old. 

Yeah, in fact, my personal reading on this is that yes it is overestimated. Because what little one has seen from – sort of, what should I say – indirect data, the revival of the unorganised sector begins only sometime in late 2022. And perhaps even early this year. But the corporate sector has been doing extremely well. So the gap between corporate sector performance and the unorganised sector performance, I think grew very substantially post-COVID.

Which means the proxy derived from the corporate sector, the organised sector, is increasingly wrong.

Yes.

Which means then that not for the Ashoka Modi reason, not for the Subramanian and Felman reason, but for this third reason – which is one that you’ve discussed with me several times in the past – that 7.8% figure for GDP growth in quarter 1 is an overestimate?

Yes.

Do you have any sense of how much it’s an overestimate?

That’s difficult to say. You know, the data that I’m relying on is essentially what has been happening to banking sector lending patterns. So bank lending to MSMEs had pretty much gone to zero until early this year – February or March of this year. Then it starts picking up. And the informal sector, only part of the informal sector actually, is able to tap into that. So that was a very clear indication of the weakness among the unorganised sector units and the smaller units of the country, so the MSMEs. The point is we have no information on the alternatives, the informal sources of finance –moneylenders. Whether there was growth there or not, we don’t know. We only infer it when we look at data on unorganised sector production and how much they’ve been getting from the formal sector and then you can derive how much they must be getting from the moneylenders. So on the basis of the finance data, I think it’s pretty clear that the unorganised sector is not doing well. It’s just starting to come out of the woods. 

Now, given that the unorganized sector is pretty close to 45% of the economy and 80 85% of…

Not, not actually. You have to take out about 18%, which is agriculture. So we come down to 27-28%.

But that’s still a very substantial number. In which case, what figure would you personally be happy with if 7.8% looks like an overestimate? Are we talking about a big drop to six or five?

No, we are probably talking about something around around 6, 6.5%, I’d say.

So, we perhaps overestimated by something like 1 or 1.3%? Which is actually quite a substantial overestimation. Then let me end this section of the interview by asking a broad general question. You are a former chief statistician. No one understands better, not just how GDP is calculated but how it should be for it to be accurate. Are you satisfied overall with the way we calculate GDP or do you have a couple of serious concerns?

No, I am as I said, there’s nothing wrong with the way we calculate the GDP. 

It’s the data?

It’s the data. The question is, are we doing enough to update our data sources? And the answer to that is no, we haven’t done enough.

Particularly with reflection to WPI and CPI?

Well, not even that. I think the biggest problem is the unorganised sector data.

What is it that we should be doing that would give us better data that we’re not doing?

Many things. First of all, you know, price indices are very sensitive to what are the products that you have in the basket. That changes. Remember, our production patterns, our consumption patterns, all these change. And if you’re not keeping your basket of commodities up to date, you’re going to be measuring the wrong things.

You’re measuring things people aren’t consuming anymore or at least not in the same amount? 

So that has to be updated on a regular basis, typically 5 years, every 5 years, we should be doing it. We are already 12 years down the line and it hasn’t been changed.

That’s a ridiculous lag! I mean India’s consumption pattern has changed very considerably in the last decade.

Yes, dramatically! 

But these are all reasons why when people doubt the accuracy of GDP calculation you’re giving them good reason to say your doubts are substantial.

Yes, but it is not a problem, as I said, with the methodology. It is a problem with the data.

Absolutely, and I’m glad you made that point. The methodology is good, given the circumstances is probably the best we could have. But the data that methodology uses is in some instances hugely out of date – by 12 years! Therefore, whatever the reason, the conclusion remains the same. Because the data is poor, the calculation of GDP is not that accurate and therefore the concerns you have about the accuracy are huge major concerns.

Yes, they are concerns.

So I can say that these are huge major concerns that you have about the GDP outcome.

This is right and what makes matters worse is that the concerns become even greater with the passage of time. 

In other words with every passing year…

unless the data sources are updated, the concerns will grow. And there will in fact come a point when I’ll be forced to say this is unreliable.

Are we getting close to that point?

We could. We are getting close to 15 years [since data sources have been updated].

So we’re getting very close to a point where you will say our GDP data is unreliable and the GDP outcome therefore is unreliable.

Therefore.

The figure that we boast about is unreliable. Okay, against that background – and I think that’s a very important background that we established for the audience – let me ask you the critical question that lies at the heart of this interview. How accurate or how misleading is GDP growth as a measure of the state of the economy? Before you answer, let me quote what Ashoka Modi wrote in The Hindu on October 30, “GDP is a flawed metric of national economic welfare. It hides inequalities and deflects attention from acute job scarcity, poor education, and health, unlivable cities, a broken down judicial system and environmental damage.” How close to correct is he?

No, he’s completely correct.

Completely?

Of course. I may quibble with his phrasing but in substance, he’s absolutely right. 

So, from a layman’s point of view, when GDP growth or the GDP figure is held up by economists or by politicians as a sign that the country is doing extremely well, you’re saying, hang on, it doesn’t tell us very much it actually hides some very important things like unemployment, job scarcity, like poor education, and health, unlivable cities, and those are very important things to worry about.

Oh, indeed. But there are different measures for that. I mean, look supposing I started talking about the wealth of the country on the basis of educational indicators, you drive me out of the room, right? Saying that look, this is wrong. Why are you doing the opposite? The GDP is very… you know, when economists use the GDP, they’re very clear about what it is: it is the sum total of the income being generated in the country. How it is distributed, it doesn’t say. So, it’s like, if you, Karan, earn Rs 1,000, how you are splitting it between your family members we have no idea. But we do know you have Rs 1,000.

But, you’re saying a very important thing, Professor Sen. You’re saying if the GDP figure on its own is presented as proof that the state of the economy is great, it is not the most revealing and honest way of talking about the economy. It only presents a picture; there are other pictures that need to be woven in alongside before you get the truth. 

Well, it depends on what you mean by the truth. So you have the GDP and think of the GDP as the entire cake. Then you have to ask the question: how is that cake being distributed between the people in this country?

Yes, but you could also ask another question.

What?

The cake on the table looks enticing and the icing looks delectable but there’s an awful lot that’s not on the table that you can’t see and how much is that losing out or missing out in telling the full story? Let’s come to that, because I think that is a major concern and let’s start with quarter one GDP cause it is the most recent figure we have, 7.8%.

Correct.

Now, it coincided with unemployment hovering around 8% and apparently since then unemployment has shot up to over 10%, which Mahesh Vyas tells me is the highest it’s been for 29 months.

That’s right.

Secondly, that 7.8% figure also coincided with the highest demand for NREGA for 10 years, in the months of June, July, August. Now, when you look just at the 7.8% figure on its own, it doesn’t reveal anything about high unemployment, it doesn’t reveal anything about increasing endeavour demand…

Which is why there is employment data, which is there in the public domain. So, I mean let’s take these figures, I mean there is already… what should I say? A battle raging between the CMIE data and the official data. So, you have the Periodic Labour Force Survey data in which the unemploy employment rate has dropped to 3%, the latest footprint, as against the 9% that Mahesh is talking about.

10.05

Or 10.05. So they’re moving in opposite directions. What’s going wrong? And this is where you need to understand the data.

Absolutely. But in that answer is something else that I’m teasing out which I think is important. If you simply judge the economy by which I mean the condition of the people of India cause that’s the important thing by the GDP figure, you’ll never find out that unemployment is pretty close to 10%, you’ll never find out that demand for NREGA in those three critical months had reached. GDP doesn’t reveal it and therefore GDP conveys a sense of satisfaction with growth. 7.8% is very good, but it doesn’t tell you how bad unemployment is and how great the demand for MGNREGA is, which is another sign of distress in the country. 

It is. This is the point, that the GDP is a potential, right? How that potential is translated into well-being depends on how the GDP is distributed. So, I can have a 10% GDP growth but if accruing only to let’s say five families and the rest of the country isn’t getting anything out of it, then it’s of no earthly use?

Which is why if you only take out GDP, you don’t convey the true picture well or the full picture. 

It’s not the full picture which is why when we look at statistics, you have to look at the whole series, that’s why the employment data is….

Absolutely. And my question arises from the fact that quite often politicians – or maybe all the time – politicians and often economists actually present the GDP figure on its own and claim from the high growth rate that it establishes that actually everything’s hunky dory and we’re sailing in high air. But we are not.

No, no, absolutely!

Let me take another illustration to check where the GDP reflects the economic reality, this time confronting the poor, which is the majority of our country. Shortly after the 7.8% quarter 1 figure was made public, we also discovered that 93% of the NREGA allocation for this entire fiscal year had been actually consumed in the first 6 months, which is a clear sign that demand for NREGA is extremely high. 

Well, please remember in the last budget that we had, the finance minister very deliberately underprovided for MGNREGA. There was a 50% drop in the MGNREGA allocation, compared to the past year, right? And that was purely for optics that helped her to show a much lower fiscal deficit.

Representative image of a labourer at work in Rajasthan. Photo: Eric Parker/Flickr CC BY-NC 2.0

So in other words you’re saying that the reason why 93% got consumed in 6 months is not necessarily because demand is exceptionally high….

The provision wasn’t adequate.

That’s a good correction. But let me then give you the second check on that. Shortly, after the 7.8% figure was announced, the prime minister announced that in fact the free food grain scream for 80 crore will be extended not for a year or two but for five years. Now that clearly has to be a sign that there is a desperate need for free food for 800 million people and if that is true, then the 7.8% figure is simply not revealing that because it doesn’t suggest that there is that level of food hunger that you have to give free food to 800 million for 5 years.

Well, no. I don’t think the prime minister is giving free food because 800 million people are starving. I don’t think that’s the case at all.

It’s entirely political?

I think, what it is, is a preemptive political stroke, yes. I mean, simply taken away an important plank that the opposition could have used. He just removed it. 

Okay, so neither of the two that I brought up in the second case of examples to question the fact that GDP reveals the true picture of the economy actually holds. What you’re saying to me is that the allocation for MGNREGA  has been virtually used up in the first 6 months because it was halved

Half of what it should have been.

A full allocation compared to the year before would have been probably utilised only up to 50% and therefore there’d be no questions raised about demand for MGNREGA going up. And similarly, you’re saying, the fact that the prime minister announced free food gain for 800 million people for 5 years is not really a reflection of food hunger and therefore of distress. It’s actually political to seek votes.

Yeah, I suspect so. Now here… In both cases, these were political decisions. The first was the optics of the budget and the second was, you know, taking away a campaigning point from the opposition. But having said that, as I said, there is collaborative data, right? So if you are looking at the issue of whether MGNREGA is important or not, just look at the data of the number of people who have accessed MGNREGA, that has gone up. There’s no question about it. Second is look at the employment data, that has been coming out regularly. Unemployment is down but it is down essentially because people have gone into ‘self-employment’ mostly, and casual work. So we are talking about people who are desperate to do something not because it’s remunerative.

Let’s then come to that because I think that’s another very important way of seeing whether GDP reflects the real reality for the poor of this country which is the majority, by comparing what’s happening to GDP alongside what’s happening to the changing character of employment. Before I come to that, let me just clarify one thing for the audience.

Where you and I – or rather where you disagreed with my questions – was when I said to you that the use of the allocation for MGNREGA in 6 months which was intended for a year does not reflect distress it reflects the cutting of the allocation. Again where you questioned the question I raised was to do with the prime minister making grain available to 800 million for 5 years, that’s political, it’s not a reflection of distress. But where you agree with my question and I’m simply pointing that out to the audience- when I said that the 7.8% figure did not reveal the high unemployment which was 8% of the time has shot up now to over 10% and it didn’t reveal that even in that first quarter, something like 20 million families – which is 100 million people – were dependent NREGA. 

Well, let’s take the employment [data]. What I did say, that is if you use the periodic labour force survey, unemployment actually was 6% and it came down to 3%, not 8% going up to 10%. So we have a fundamental disagreement between two data sources CMIE versus the government. That’s right and that is something that one could discuss but that would become a technical discussion.

And all I’ll say for the sake of the audience is in my conversations with Mahesh Vyas, who is the CEO of CMIE, he has always staunchly and rigorously and vigorously defended his outcome and he has good reasons for questioning the government’s figures.

Let’s come to the government’s figures,  because I think it’s important to ask yourself, does the truth or the extent of the truth that the GDP reflects about the economy also coincide and corroborate with the changing character of employment? Now the recent periodic labour force survey shows that the percentage of people self-employed has increased from 52% in 2017-18 to 58% in 2022-23. This raises the first question: how good is the quality of self-employment? Do these people actually earn a decent, proper wage or are they pakorawallas?

No, even worse. See, by and large, a lot of self-employment is what’s called distress employment. We have nothing to do, we take whatever little we have, we sit on the footpath and we try to sell it. So which is one of the reasons, if you really look at our own historical data on employment, we’ve never had high unemployment. It has always hung around the 2.5-3% mark, where people who didn’t actually have jobs and a secure source of income, they were all self-employed. So self-employment in other countries is treated as essentially entrepreneurship being expressed, which is a good thing. Out here, it’s desperation. So self-employment going up is not a good thing. Now this is where the disagreement over the employment rate becomes important. So when the unemployment rate shoots up, whether we go by CMIE or by PLFS, there is no dispute that it has gone up to 8-10%. Okay, the question that should have been asked is why were these people sitting around without becoming self-employed? Because earlier days, they would have simply been on the street trying to peddle something or the other. It didn’t happen. They declared themselves as unemployed, why?

Because there is MGNREGA? 

No, it wasn’t so much because it was MGNREGA. I think it was hope that this was transient and the jobs would come back. 

And they didn’t want to be seen as unemployed. 

They didn’t want to be seen to be employed. So they were just basically waiting till the jobs came back. What the data is suggesting if you read it properly is that that hope has gone and those people who were sitting out and reporting themselves as unemployed now have become self-employed.

But that self-employment is not really employment as you said, sitting on the street, peddling what they have. For us to consider them as employed when it is desperation is to misuse the word employed. 

No. There’s a very technical definition for employed which is used and this is a definition which is given, this is an international definition which the ILO uses. In fact, not just uses insists that we follow it. So it’s an ILO definition, it’s a global definition, that definition is used in this context for everybody. 

But I discover that even within that category of self-employed, which is now 58% of the workforce, one-third it appears are unpaid workers. Santosh Malhotra has calculated that the number of unpaid workers was 40 million in 2017-18 and it’s more than doubled to 95 million in 2022-23. So out of that category of 58% of the workforce who are self-employed, 95 million actually are unpaid. Now they’re still considered under the system as employed but it’s meaningless to say that someone’s employed and they’re not earning anything!

No, let me again try and explain how this could work. So, supposing I’m a street side vendor. I was sitting there from, let’s say 7 o’clock in the morning till 7 in the evening. Now, my brother who was working, has lost his job. He comes and sits with me and now I open the shop at 6 am and I close at 12 at night. So we are earning something more simply because it’s staying over but the fact of the matter is that this person isn’t really being paid. It’s a part of my collection. A lot of this kind of thing is happening.

Let me put it like this. I’m going back now deliberately to that GDP figure of 7.8% for quarter 1. When you look at that and you say to yourself, ‘Oh the economy is doing brilliantly we’re really growing at a pretty fast rate.’ You aren’t aware that hidden behind that figure is that 58% of your workforce is self-employed, many of whom as you said are sitting on the street peddling what little they have and of that 58%, 95 million actually not even being paid. They’re like the brother in your story. This is where the 7.8% figure if it’s presented to the country is proof that everything is doing well, and we’re sailing, actually, it misleads you. Because when you dig deeper or you go to other statistics, you suddenly discover that 95 million are unpaid. 

Any data can be misused. All data is produced for a purpose. It measures something very specific. If you then start loading all kinds of value connotations to it, that’s not the problem with the data problem, that’s the problem with you.

Absolutely, and that’s really my purpose in this interview – to point out through these questions and your answers that when the GDP data on its own particularly, again that 7.8% figure, is touted as proof that we’re doing and everything is fine, the country is swimming, we’re improving.

The country is swimming, this is correct. The country as an entity.

But many people in that country, up to perhaps 58%.

Maybe more.

Maybe even more self-employed sitting on the pavement, selling what little wares they have…

The other category that PLFS tells you has gone up is casual labour, which is again a distress.

The other thing that’s gone up, is women who are unpaid workers, particularly in rural India. These are wives and sisters joining their husbands and brothers simply to lend a hand but not really earning anything. So the point I’m really making is that the more we tout the GDP growth figure – which politicians do – as proof that everything is fine, the more we actually deceive and mislead ourselves if you don’t add to that picture these other statistics about employment and distress.

Look Karan, who do you mislead? The point is a person who’s become unemployed or who’s had family members who have become unemployed, they are not misled. Who is being misled? It is you and me, who know neither about what’s happening to the country as a whole or to what is happening to the poor. 

Let me put it like this a lot of people who depend upon information from newspapers or television channels where those papers and channels fail to contextualise the information and tell the full story, will be the ones who are misled. They will say, ‘Oh the country is growing at 7.8%, we’re going to be growing at 9% next year, everything is fine, there’s a new horizon opening for us, we will be a superpower.’ The truth is look at other statistics – of employment, of unpaid work, of hunger – and you suddenly discover another story and that is the point I’m trying to make, that the single-minded focus that we have developed in the last eight-nine years on GDP doesn’t tell the full story. It tells a story but it doesn’t tell the full story. 

That is absolutely correct. It doesn’t tell the full story, but it does tell an important story in itself, okay? I don’t think we would feel any better if the GDP growth had been 3.8%. I mean that would be pure schadenfreude, which is saying that the poor are not doing well at all but the rich aren’t doing very well either, right? That’s all it says. Out here what you’re saying, in fact, the poor are doing badly but the rich are doing very well, thank you.

Actually, what I’m saying is this: we need as a country to become aware of the fact that the GDP figure doesn’t tell the full story.

This is correct.

And when you’re talking about the lives of the majority of your countrymen, it actually leaves out their story almost completely.

Yeah, you need different indicators for that. And those are indicators which tell you how the GDP is.

I’ll come to that in a moment’s time because I think that’s a very important next step but let me first put you a rebuttal of the points that you and I are making about the Periodic Labour force Survey figures. They come from economists in the State Bank of India, they say and I’m quoting them, “The increase in the self-employed is wrongly interpreted by labour economists and others as a signal of shrinking employment opportunities.” They add, “India’s labour market is undergoing a deep structural transformation with self entrepreneurship across all echelons and higher educational attainment emerging as key enablers.” Do you agree with that or disagree with that?

I completely disagree with it.

Completely?

Absolutely.

In fact, then let me ask you this. Can you describe the self-employed and remember, within that are 95 million who aren’t earning anything at all… can you describe either of those categories as self-entrepreneurship? 

Oh there is, there are a lot of elements of that, right? Here is the question: the question is not whether India has a very large pool of entrepreneurs at all levels. There are, certainly. So we’ve had a large number of self-employed. The question is that the sudden increase that’s taken place in self-employment is… and remember this has happened in a period of two or two-three3 years. Is that a sign of sudden blossoming of entrepreneurship or is it distress?

What’s your answer?

My answer is distress. Entrepreneurship, budding entrepreneurship is a process. It takes time, it moves, it will move slowly. 

So in other words what the SBI economists called a sign of self-entrepreneurship is actually better understood as a sign of distress. One other thing. The SBI is also that the guy who sits on the pavement selling the wares that he has is an entrepreneur. 

In a sense, he is.

But it’s not entrepreneurship in the sense in which we understand the word large industrialist setting up companies and making profits.

No, no, or a small businessman setting up shop. The question that somebody should ask SBI is, would you lend that guy some money? If you think he’s a budding entrepreneur, the answer would be an emphatic no. 

Okay, let’s then come to what are the statistics and indices that we should be paying attention to if we want to understand what is happening to the majority of our countrymen.

Yeah.

As I keep saying, the majority of our countrymen are poor. Praveen Chakravarti in an article that he wrote for the Indian Express – and I’ll add for the audience he’s a member of the Congress party – said that India – and he was talking both about the country and the opposition bloc – should actually cease to focus on GDP numbers and instead they should look at two things the total number of jobs created in the economy and what he called median income growth. He said these are better measures of what matters to the vast majority of the Indian people. Would you agree? 

True. But the point is that we don’t have data on incomes at all. So you can’t concentrate on the median income growth. 

We probably don’t have good data about the number of jobs created either. 

We have some measure of it, yes, we do.

Good or bad?

Well, again you know, one would have to define the jobs. If you use the jobs in a generic sense of work, I don’t think what he’s saying is correct because that would pick up distress work as well. Where I would agree with him is if he said if he uses the term ‘jobs’ as saying regular paid employment. Not necessarily organised. It’s not necessarily organised but regular paid employment, a regular wage income.

Again, the 58% self-employed were not featured in this category at all cause you couldn’t pick it up. In other words, these may be good categories to look at because they better reflect the condition for the vast majority of our country but we simply don’t have the data. In other words, this is a very sad conclusion. What would reflect the truth about the vast majority of our country, we don’t have the data to find out.

Well, you know income is notoriously difficult to measure, all right? Simply because nobody wants to give that information, for a variety of reasons. Which is why we do collect data on consumption. Consumption data is easier to get and it is a proxy for income with the assumption that how much you spend depends on how much you earn. So using the consumption data is actually the only way we have. It’s a relatively direct proxy, we do know that there’s a relationship issue here which I can elaborate, if you like. But it gives you some sense as to how the distribution in the country is changing between the rich and the poor. The point is typically what happens because the rich have much higher savings consumption distribution is much more equal than income distribution, typically that’s the case. But it does give you a fairly good insight into what’s happening to the poor.

But this would also reflect something else. The increase in inequality that is taking place in the country, it does reflect that right? And if you look simply at what’s happening to car sales at one end and scooter sales at the other, scooters bought by the poor, their rate of growth is very low, cars bought by the rich, their rate of growth is exceeding year by year. That is one way of finding out what is happening to the majority of our country, a minority is doing exceptionally well, thank you very much. The majority is stalling at best or shrinking at worst.

Yes, but you know it’s a little sad when you have to depend on indicators of that kind. We should be getting regular household consumption expenditure data.

This is again where our data lets us down?

Yes.

We need to make an effort to get this data. Why aren’t we making that effort?

Well, we are making the effort. In fact, the data collection is in the field at the moment as we speak.

Okay, so maybe in a year’s time, we will have better data.

Hopefully.

Let me at this point, raise a concern raised by Raghuram Rajan earlier this month in November. He says we need to create 70 million jobs over the next 10 years and he says the rate of growth necessary for this is 8 or 8.5%. In fact, he adds, even at 7.5% growth, only 2/3 of the jobs problem will be solved. Would you agree that if employment is the main concern – and I imagine, for the majority of our country getting a job is probably the difference between life and death? So if employment is the main concern, not growing fast enough to create the jobs we need is the real problem.

It is and if you really read what Raghu is saying, he’s talking about seven million jobs being created in a year. So he’s really talking about educated employment. That’s only a part of the story. These are the jobs we need to generate to employ people who are getting educated.

It doesn’t take into account the jobs we need for everyone else.

Everyone else who is down there, who are in fact the poorest of the poor. The guys who are not finishing their class five.

This 7 million won’t cater for the 58% self-employed.

Correct. If you take those into account, there’s another 5 million of them waiting.

This leads Professor Sen to two last questions before I end the interview and they are questions that I will admit disturb me. If we’re not creating enough jobs not just to meet Raghuram Rajan’s target of 7 million a year, which is questionable whether we can achieve it, but to provide jobs for the 58% that are self-employed. If we’re not creating enough jobs, is there a danger that what we fondly call the demographic dividend could turn into a demographic predicament and perhaps, God forbid, at some point a demographic disaster?

Well, I don’t know whether it should be a demographic disaster. But what it certainly will be is an enormous opportunity lost. The demographic dividend is really about potential and if we miss it, future generations are going to hold us culpable.

But also if we miss it, we will soon become a country where we will be… much older and therefore unable to produce.

You will become a country where a very very large number of self-employed. We will be self-employed, we’ be sitting there.

So, India has a great future for pakorawalas.

And trinkets.

So, in other words, my phrase demographic predicament sounds increasingly correct. 

Well, yes. But as I said, it is a problem, it will be a source of frustration but the most important thing is that it will be such a waste of an opportunity.

Raghuram Rajan. Photo: The Wire

Yes and there’s something else that follows from it. Will we become a developed country? The prime minister has taken to repeating as often as he can that we will be a developed country by 2047. Now by World Bank standards, developed countries I believe have per capita incomes of $13,200. If you want to peg yourself to the United Kingdom, a country’s economy we overtook a couple of months maybe at the most a year ago, we need to have a per capita income of $45,000. Our per capita income at the moment is $2,388. So, given the way things are progressing, will we be a developed country in 2047?

Well, if the definition is $13,200, it’s possible.

But if we want to be at the level of the United Kingdom and America-

No, no, no, no, not even close. But the real question then is what you had raised earlier. Is $13,200 – taking the GDP, dividing by the population… That’s what per capita income means. It doesn’t say anything about how it is distributed. So, if the distribution continues to get worse, then we may well reach $13,200 and still have masses of unemployed people and massive poverty.

You’re making two very important points. First of all, if the prime minister means we’ll be a developed country that we will be at the same per capita income level of the UK or the US or France or Germany, the answer is absolutely flatly no we won’t. But if he says that we can meet the much lower world back standard of $13,200 which is if I’ve got my mathematics correct, just about above a quarter of the UK level, that we can achieve. In other words, by 2047 we’ll be developed by World Bank standards but we’ll still be only a quarter of what the UK is… and by the way, the UK, by then, will have probably shot up to $50,000?

We don’t know.

The other danger is that even at the $13,200 per capita income level if we reach it in 2047, we could still have a situation where hundreds of millions could be self-employed and maybe another 100-odd million still in that dreadful predicament of self-employed but not earning anything. That means that as far as the majority of the country is concerned and I keep saying they’re poor, their future at the moment doesn’t look particularly rosy at all.

Well, no. We don’t know everything depends upon what we do in terms of the way we develop the economy. So, if we have the trend that we have seen over the last five-six years where the bulk of the growth has been concentrated in the organised sector of the economy…

Then this lot are forgotten?

This lot are forgotten and it is getting worse. The gap was increasing and we’ve talked about this before.

But you’re saying a very important thing. If we continue the way we have for the last five-six years, the future for the poor and the majority of the country will remain bleak and dark.

This is correct. So we really need to look at our development strategy.

That is a subject for another day, I promise I will come back to you and talk about that. But the picture I want to leave the audience with is this, and correct me if I’m wrong. Any single-minded focus on GDP as a way of telling the country and the world all’s well, we’re swimming fails to convey the reality faced by the majority of the country who are poor. Secondly, if we don’t change our development strategy but continue the way we have for the last five-six years, then the future for the majority who are poor remains bleak and even if we reach in 2047 the $13,200 per capita income levels and therefore technically qualified to be a developed country, we could still have 58% self-employed. Within which 100 million are actually not earning anything at all.

What a depressing end. Thank you very much indeed!

You’re welcome!

India Cannot Ignore the Tough Questions of Inequality in its Labour Market

An examination of the Periodic Labour Force Survey 2021-22 report shows that the sluggishness of the labour market continues and concerns remain about the quality of jobs.

Measured in terms of gross domestic product (GDP) growth, the Indian economy has been recovering over the past few quarters of the post-pandemic period though concerns remain over the sluggishness of the labour market.

Data from the Centre for Monitoring Indian Economy (CMIE) showed that the unemployment rate continued to be very high at 8.5% as of August 1, 2023. When such a high unemployment rate is combined with a significant decline in labour force participation (one of the lowest in recent times at 35.9% in 2022-23, according to the CMIE), it does not augur well for the prospects of the labour market in a country with an inadequate social safety network. 

Concerns over high unemployment predate the COVID-19 pandemic. The Periodic Labour Force Survey (PLFS) survey 2017-18 suggested an unemployment rate of 6.1% and pandemic-induced disruptions worsened the poor labour market outcome. The evidence is well documented in recent literature.

The factors that impede the recovery of the sluggish labour market require utmost attention. Against this backdrop, the release of the PLFS 2021-22 report allows us to compare labour market dynamics in post-COVID compared with pre-COVID conditions.

The PLFS is conducted in a July-June cycle. Since the last quarter of annual PLFS 2019-20 survey experienced the full force of first wave of pandemic and all the four quarters of 2020-21 survey encompassed both aftermath of the first wave as well as the devastating effects of the second wave (even if there was no national lockdown), the present study compares the employment situation in the PLFS 2018-19 and 2021-22, thereby mitigating any potential biases. 

In a break from the earlier trends reported in both the CMIE and PLFS surveys, the 2021-22 report shows improvements in the employment situation compared to all previous reports of the PLFS, which is a bit unexpected because the pandemic massively disrupted economic and social life. Based on the reports, the labour force participation rate (LFPR, which represents the number of people in the labour force as a percentage of the population) and the worker population ratio (WPR, or the percentage of employed persons in the population) of those aged above 15 years increased from 50.2% to 55.2% and from 47.3% to 52.9%, respectively, between 2018-19 and 2021-22. This reduced the unemployment rate from 5.8% to 4.1%

The primary driver of this increase in participation is female participation. The rise in both LFPR and WPR is more than 8 percentage points for females while they increased marginally by 2-3 percentage points or males. Historically, females have had greater labour market engagement in rural areas than in urban areas and there was no exception to this even after pandemic.

Among those who are 15 years and above, the WPR of rural women (in usual status) has increased significantly by 10.3%, from 25.5% to 35.8 %, with a marginal change in urban employment (from 18.4% to 21.9 %).

Stylised facts about these increases in participation raise the question of the workers’ job locations. 

Where are the workers located?

According to the National Statistical Office, workers are classified under three broad categories – self-employed, regular wage/salaried employee, and casual labour. In urban areas, a significant number of females are in regular salaried jobs. But in rural areas more women fall into the self-employed category. 

In pre-pandemic 2018-19, 59.6% of rural women were in self-employment, of which 21.8% worked as own account workers and the rest (37.9%) as helpers in household enterprises, which were mostly unremunerative (Table 1). In post-pandemic (2021-22), self-employment increased to 67.8% (by more than 8%) for females, with proportionately less increase in own account work and a significant increase in helpers. 

In rural areas, female participation as helpers is primarily as agricultural workers in family firms. Since agricultural activities (being essential items) were mostly permitted during the pandemic, the lockdown did not much affect female participation in farm activities as helpers. An industry-wise classification confirms that female participation in agriculture is not only around 70%; it has increased from 71.1% to 75.9% in post pandemic times while male participation in agriculture reduced from 53.2% to 51%.

Despite the pressure of reverse migration, male workers joined less in agriculture in post-pandemic. In rural areas, males were largely engaged in self-employment activities. Compared to females, within self-employment, there were more male own account workers and few helpers and there were no significant changes in participation in post-pandemic. In urban areas too, the importance of self-employment activities as a source of livelihood grew for both males and females after pandemic. 

Table 1: Percentage distribution of workers in usual status by status in employment

  Self-employment regular wage /salary casual labour
own account workers and employers helper in household enterprise all self-employment
  Rural Female
2018-19 21.8 37.9 59.6 11 29.3
2021-22 25.1 42.7 67.8 8.1 24.1
  Urban Female
2018-19 24.9 9.6 34.5 54.7 10.7
2021-22 26.7 12.7 39.4 50.3 10.3
  Rural Male
2018-19 48.2 9.2 57.4 14.2 28.3
2021-22 47.3 11.3 58.6 14.7 26.8
  Urban Male
2018-19 34.6 4.1 38.7 47.2 14.2
2021-22 35.0 4.6 39.5 46.2 14.3

Source: PLFS rounds.

In the casual workers’ category, participation of both rural males and females has declined, with relatively greater declines observed for females (by 5.2% points compared to 1.5% points decline for males). The decline in female participation in casual work cannot be justified with unavailability of work under Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) since female participation in MGNREGA remains around 55% over 2018-19 and 2021-22 according to official statistics.

The industry wise participation confirms that their participation has largely fall in non-agricultural activities like in construction work it has reduced from 6% to 5.3%, in trade, hotel and restaurant from 4.3% to 3.7%, other service from 9.1% to 6.8% respectively. The increase in household chores because of the pandemic, care responsibilities, competition from jobless males, and the pressure of reverse migration could have been other reasons for this decline.

For males, the industry-wise participation confirms that their participation decline is mainly visible in agriculture 53.2% to 51%, but increase in participation in all other non-agricultural sectors mentioned above. So, the rural dynamics is changing in post COVID time.

It is clear that while females were systematically pushed out from all these non-agricultural activities and greater engagement with farming, males are more in wholesale and retail trade, construction work, manufacturing, transport, communication, and other services which were even if disrupted by pandemic-induced lockdown but males reestablish their foothold. Conversely, for females, the displacement appears to be enduring and irreversible.

Coming to regular salaried jobs, workers’ participation in them fell in both rural and urban areas irrespective of gender because of the lockdown. However, the magnitude of decline was more in urban areas, which have a higher concentration of regular salaried jobs. At the same time, it is noticeable that the magnitude of decline is more for females than males in both rural and urban areas which again reenforces the notion of discouraged worker effect or permanent loss of women from job market. This again can be connected with social security provision under regular jobs.

According to the 2021-22 PLFS report, 62% of regular salaried workers do not have a written job contract, 49.2% of regular wage/salaried employees not eligible for paid leave, 53% are not eligible for any social security benefit. The Covid-19 pandemic exposed the fragility of livelihoods due to the growing informalisation of formal jobs. 

Education and workforce participation

The increase in women’s work participation in rural areas was observed across all education levels after pandemic (Table 2). But the WPR was significantly higher for illiterate women and those who had studied to the primary level, coinciding with the higher participation of females as helpers. The U-shaped relationship, particularly the high WPR at low levels of education, was because those at the lower end of the education spectrum could not afford to remain unemployed. Interestingly, both in rural and urban areas, those with a technical education (diploma/certificate courses) obtained jobs at a greater proportion even after pandemic, which reconfirmed the importance of skilled workers to the service-led growth of India. 

Table 2: WPR across education levels for 15 years above population 

  Not literate Literate, up to primary Middle Secondary Higher secondary Graduate Post graduate and above Diploma/ certificate courses
Rural female 2018-19 30.7 29.8 21 17.2 13.8 18.4 31.5 34.3
2021-22 43.7 43.4 30.6 23.0 20.9 25.7 35.3 50.5
Urban female 2018-19 21.9 20.6 15.9 9.9 9.5 23.1 36.8 34
2021-22 24.0 26.5 18.8 13.8 13.8 25.9 37.4 41.3
Rural male 2018-19 76.5 85.6 74.7 60.5 55.8 69.1 75.4 66.4
2021-22 77.3 86.9 78.3 65.1 61.1 75.6 83.9 76.3
Urban male 2018-19 72.6 80.2 73.1 60.6 52.3 69.5 79.9 73.7
2021-22 72.9 82.9 74.9 61.5 55.8 71.7 80.5 76.2

Source: PLFS rounds. 

Interestingly, for males, there is no significant difference in participation across all education levels between rural and urban areas, and the pandemic did not make any significant difference to this pattern. This can partly be explained by the role of men as breadwinners, which was backed by a long history of masculinity. Contrary to the general perception, the higher workforce participation among less educated workers pointed to a precarious job recovery.

The relatively lower workforce participation among the educated points to higher unemployment among the educated, perhaps due to the lack of jobs that match their expectations. In all likelihood, technology-driven post-pandemic recovery may accentuate this problem. 

Impact on the Disadvantaged 

The Indian labour market is segmented among social and religious groups. Even though Covid-19 was thought to be blind to social stratification, the vulnerability to the virus was not homogeneous in all social and religious groups. Overall, socially disadvantaged communities had a higher WPR than the general category and it increased after the pandemic – by 7.4 percentage points for Scheduled Tribes from 40.6% to 48%, followed by 4 percentage points for Scheduled Castes, 4.2 percentage points for Other Backward Classes, and 3.4 percentage points for the general category. 

Compared urban areas, the work participation of women from disadvantaged communities was much higher in rural areas. After the pandemic, there was a sharp increase in the female WPR in rural areas, particularly among STs (from 28% to 40.1%), followed by SCs (18.9% to 25.7%) and OBCs (18.9% to 25.9%). The male WPR has changed marginally by 3-4 % and remains around 50-55% across social groups in rural and urban areas. Given the low asset base at home, perhaps women from disadvantaged social groups could not afford to wait for better prospects. 

In terms of religion, Christians had the highest WPR (42.6%), followed by Hindus (40.5 %), Sikhs (38.9 %), and Muslims (33.5%) in 2021-22. While there is no significant difference in the male WPR across religions, for females, particularly in rural areas, it varied to a large extent. There was a notable surge of 9.4 percentage points in the WPR within the Christian demographic, closely pursued by a rise of 5.7 percentage points among Muslims and a 4.6 percentage point upturn among Sikhs.Me Occupying the lower rung on the socio-economic hierarchy, individuals hailing from religious minority groups found themselves compelled to eschew unemployment.

Whether we look at the rise in unemployment or precarious employment after pandemic, it points to the sluggishness of the labour market. An examination of the latest PLFS shows that despite some improvements in labour market outcomes, concerns remain about the quality of jobs. The analysis of labour market outcomes in terms of rural and urban, gender, education, and social and religious groups shows that structural questions relating to the segmentation of labour market require urgent attention.

There is higher workforce participation among women, especially from disadvantaged groups. If these structural problems are not addressed, post-pandemic growth will be more unequally shared than earlier. Public policies designed to address concerns about the job market should not only focus on employment generation, which has been low since the pre-pandemic period, but also aim to improve the quality of jobs.  

Namrata Singha Roy teaches Economics at Department of Economics, Christ University, Bengaluru.

A Knotty Affair: India’s Burgeoning Population and Stagnating Middle-Income

The middle class in India is feeling the squeeze because real wages have not risen, but costs are rising.

As per the State of World Population Report published by the United Nations Population Fund, India will soon become the most populous country in the world. At this point, India also has a relatively ‘younger’ working-age population compared to China. Depending upon how one reads the data, this can be a boon or bane, as labour is an important component of growth in national income or gross domestic product (GDP).

If labourers are productive, then their income and the economy grow. Much of the GDP growth that occurred among the emerging Asian economies during the second half of the last century was through increased labour force participation. These countries, for example, China, South Korea, Singapore, Taiwan, and Vietnam, were able to absorb labour from the low-productive agricultural sector to the high-productive manufacturing sectors. Much of the supply of white goods like mobile phones, air conditioners, refrigerators, computers, etc. are manufactured in these countries, thereby making their economy transition from low to middle and high-income economies.

This is why there is a flourishing middle class in these economies. According to Pew Research, the share of Chinese who are in the middle-income group jumped from 3% to 18% during this century, however, the share of Indians middle-income group remain unchanged for the most part. Although, thanks to reforms and the consequential high growth rates in GDP, India was able to reduce poverty – from 40% in 2004 to 10% in 2019 – however, the drop in poverty merely resulted in an increase in the low-income population. Data from the recently published India Consumer Economy 360 survey points towards a fall in income growth for the poor and middle-income households, whereas that of the high-income households surged.

Source: India’s Consumer Economy (ICE) 360 survey

Ergo, although in India the poor are becoming richer, the society is also becoming more unequal, that is, the rich are becoming richer much faster. New World Wealth, a Johannesburg-based company, published a report claiming that India is the second-most unequal country in the world, with millionaires controlling 54% of the wealth. In Japan, the most equal country in the world, millionaires control only 22% of the national wealth. In India, the number of ultra-high-net-worth individuals (with net assets of $30 million or more) has grown by 11% year-on-year in 2021, the highest percentage growth in the Asia-Pacific. 

A reason for unequal income distribution is that most of our labourers are stuck in low-productive sectors. According to the Periodic Labour Force Survey (PLFS) 2021-22, agriculture still remains the largest source of employment, employing 45.5% of the workforce. Construction is at a distant second, employing 12.4%, closely followed by trade, hotel and restaurant, employing 12.1% of the workforce. Now all these sectors require low/semi-skilled labourers, with low productivity. 

India’s labour productivity – economic output per hour of work – is just 12% of the US levels. In purchasing parity terms, GDP per hour worked is $70.68 for the US, in comparison to India’s $8.47, and this cannot be explained by differences in the working population alone. Types of employment, and access to finance and technology matter. For a long time, output per hectare, a common measure of agriculture productivity, remained low in India. For example, in potato farming, the productivity of an Indian farmer is less than half of that of the US, Germany, and the Netherlands. In the case of rice, it is less than half of that of the US and Egypt, and for wheat, it is less than half of that of the UK and Egypt.

India leapfrogged into services without being able to create enough jobs in the manufacturing sector. Even the success stories of the manufacturing sector – Reliance, Godrej, Tata Group, etc – employ a capital-intensive mode of production. For a long, everyone thought labour market reforms such as giving more power to the companies to hire and fire workers will bring in the required change. That did not happen in spite of the Central labour law reforms in 2020.

Instead, over the last five years, there has been an increase in self-employment in low-productive agriculture and the urban informal sector. There are not enough jobs getting created and according to PLFS 2021-22, on the basis of current weekly status unemployment level remained stagnant at 8.8%, without declining much since 2017. High skilled-services sectors such as banks, Information Technology, etc., are not able to absorb workers. In India, according to PLFS 2021-22, only 1.3 % have technical education and only 0.7% have diploma/certificate graduate level in vocational education. Technical knowledge and education are a must for getting a job in the manufacturing or service sectors. On the other hand, a concomitant rise in income inequality is leading to the creation of low-paid and low-productive jobs such as housekeeping, security services, and other gig-type jobs such as Zomato delivery workers

A photo of Swiggy and Zomato delivery workers. Photo: PTI

Rising costs, wages stagnant

A low productive workforce means a lower income, in particular when the informal labour markets are monopsonistic (a higher number of labourers looking for jobs as opposed to employers or aggregators). There has been no significant growth in real wages at the all-India level over the past eight years.

On the contrary, the cost of healthcare and education is rising, most of which has to be borne privately. As per the latest household social consumption data (NSS 75th Round), only 4% of the rural population and 19% of the urban population reported that they had health expenditure coverage. According to the Economic Survey 2022-23, almost half of all medical expense is still borne by the patient themselves. 70% of India’s population who still reside in rural areas has to borrow more (25%) in comparison to their urban counterparts (18%) to meet their healthcare needs, driving an estimated 6 crore Indians into poverty, every year.

The government’s insurance coverage programme Ayushman Bharat, does not cover primary healthcare such as prenatal care, and other common diseases such as influenza and diarrhoea, which form a major part of household expenses on health. Even for the tertiary sector, and if one is lucky to get covered under government insurance coverage, new medicines for terminal illness diseases and surgical procedures remain outside the budget of a majority of the Indian household. For example, each round of chemotherapy and radiation costs more than Rs 1 lakh, whereas a vital organ transplant (liver and kidney) can cost anywhere between Rs 20 and 30 lakh. 

The same applies to quality education. At a time when public spending (Central and state governments taken together) is only 4.5% of GDP, it is not surprising that for a majority of the population, education is delivered by the private sector. Because of the failures of government schools to provide a decent education, studies show even the poor income households prefer sending their kids to private schools. The learning outcomes in government schools deteriorated post-pandemic. The ASER 2022 report flags widening learning gaps. Basic literacy levels of children have taken a big hit, with their reading ability compared with their numerical skills worsening sharply and dropping to pre-2012 level. However, sending kids to private schools cost money. As per a survey conducted by ET Online research, educating a child between the age of three to 17 years costs around Rs 30 lakh; a 4-year BTech or a 3-year BSc costs around Rs 4-20 lakh; and a five-and-half-year MBBS degree can cost up to Rs 1 crore.

No wonder in India, the middle class is getting squeezed.  

Nilanjan Banik is professor, School of Management, Mahindra University. He tweets @banik_nilanjan.

Instead of Withdrawing Food Security, a Minimum Income Guarantee Is Needed

A Minimum Income Guarantee would not just cushion exogenous shocks, but would arrest the process of vulnerability begetting vulnerability.

While the worst of the pandemic is behind us, there has been a decline in general purchasing power amidst inflation. The provisional Consumer Food Price Index (combined for rural and urban) for September 2022 is pegged at 8.6%, a huge increase from 0.68% for September 2021.

The latest Periodic Labour Force Survey (PLFS) shows that simultaneously, the urban unemployment rate by Current Weekly Status still remains high at 7.6% (all ages) and is even higher at 18.9% for the youth (15-29 years). One in every five urban youth is unemployed. These factors signal stagnation and jobless growth.

Given the situation, withdrawal of food security after December 2022 could increase the vulnerability of households. Cash-based assistance is required more than ever, and is feasible with the Direct Benefit Transfer system in place. A Minimum Income Guarantee (MIG) would not just cushion exogenous shocks, but would arrest the process of vulnerability begetting vulnerability. We suggest a transfer which keeps fiscal considerations and efficiency in mind. The proposed transfer would be just sufficient to reduce vulnerability, with no danger of a leftward shift of the labour supply curve.

The proposed transfer is an improvement over the existing three cash transfer schemes in India, all targeting landed farmers, the most significant being PM-KISAN. Launched by the Union government in 2019, it assists landholding owner-cultivator families with Rs 6,000 per year. However, the government further relaxed landholding criteria to include big farmers as well.

The glaring issues with PM-KISAN however threaten to outweigh the advantages of a well-designed MIG. First, PM-KISAN only reaches landholding cultivators and big farmers whose names are entered in land records, excluding the vulnerable and growing segment of tenant farmers and landless labourers. Second, land records are rarely updated and the quality of data varies across states. Third, and the most important, is the very identification of beneficiaries. Instead of visible verifiable data, PM-KISAN considers families as the unit, for which no data exists. When two or more families reside together in a household, as in large rural joint families, the same household receives multiple income grants (including multiple PM-KISAN transfers).

Watch: ‘Latest Growth Data Hides More Than It Reveals, Unemployment Up 3 Times in 6 Years’

Amidst rising fiscal concerns and an estimated fiscal deficit of 10% (Union and states combined), our proposed MIG can replace PM-KISAN as a more targeted and inclusive strategy at comparable cost. We make it more progressive by covering over 3/5 of India’s rural population, including intended beneficiaries of PM-KISAN.

Our proposal covers households as the unit, and the identification of beneficiaries is based on observable, verifiable characteristics using the Socio-Economic Caste Census (SECC-2011). This provides data on 17.97 crore rural households. Of this, we omit 7.07 crore “automatically excluded households” based on 14 parameters of exclusion (39.35% of all rural households). Out of the remaining, we suggest that 15.9 lakh households are “automatically” included as they fulfil any of five parameters of inclusion.

We further include 5.36 crore households with over one (of seven) deprivations: one or less rooms, kuccha walls and roof; no member in the household aged 18-59; female-headed with no adult male member; a differently-abled member with no other able-bodied adult member; SC/ST households; no literate adult above 25; and landless households with major income from manual labour. To be fair and inclusive, we include those with just one deprivation and also those not prosperous enough to be automatically excluded.

Our proposed MIG can thus cover 10.9 crore rural households (60.65% of rural households). We propose that the amount of cash transfer should be directly proportional to the deprivation suffered by households. To enable a fiscally feasible graded cash transfer, we present three scenarios, each introducing a lesser vulnerable category.

From Scenario A (lower coverage) to Scenario C (near universality), we present the option of a graded cash transfer to India’s most vulnerable populations. With each additional inclusion, costs increase. However, the quantum of money to be transferred is kept low, to mitigate any adverse impact on labour supply, but also contribute to reducing vulnerability. Our proposal even in the near-universal case seeks to transfer Rs. 56,900 crore annually. This is much below the cost of PM-KISAN, with an estimated budgeted amount of Rs 68,000 crore, which is regressive, as we noted above. Most importantly, our proposal covers a much wider vulnerable population based on observable criteria at a much lower cost.

Santosh Mehrotra is Professorial Senior Fellow, Nehru Memorial Museum and Library, New Delhi; Anjana Rajagopalan is an independent scholar; Rakesh Ranjan teaches Economics at JK Lakshmipat University, Jaipur.

This article was first published on The India Cable – a premium newsletter from The Wire & Galileo Ideas – and has been republished here. To subscribe to The India Cable, click here.