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It is a national tragedy that women unable to find work are dropping out of the labour force

If labour force survey data are to be believed rural India is in the midst of a gender revolution in which nearly half the women who were in the workforce in 2004-5 had dropped out in 2017-18. The 61st round of the National Sample Survey Office (NSSO) recorded 48.5% rural women above the age of 15 as being employed either as their major activity or as their subsidiary activity — but this number dropped to 23.7% in the recently released report of the Periodic Labour Force Survey (PLFS). Is this part of a massive transformation of rural lifestyles or are our surveys presenting a skewed picture? If this change is real does it offer a cause for worry?

Incremental decline

Before we turn to examining these changes it is important to note that the drop in work participation by rural women is not sudden. The latest data from the PLFS simply continue a trend that was well in place by 2011-12. Worker to population ratio (WPR) for rural women aged 15 and above had dropped from 48.5% in 2004-5 to 35.2% in 2011-12 and then to 23.7% in 2017-18. In contrast the WPR for urban women aged 15 and above declined only mildly changing from 22.7% in 2004-5 to 19.5% in 2011-12 and to 18.2% in 2017-18.

One can view this drop in the rural female WPR both positively and negatively. If rising incomes lead households to decide that women’s time is better spent caring for home and children that is their choice. However if women are unable to find work in a crowded labour market reflecting disguised unemployment that is a national tragedy.

If the WPR is declining due to rising incomes we would expect it to be located in richer households — households with higher monthly per capita expenditure and among women with higher education. A comparison of rural female WPRs between 2004-5 and 2017-18 does not suggest that the decline is located primarily among the privileged sections of the rural population. Between 2004-5 and 2017-18 women’s WPR declined from 30.6% to 16.5% for the poorest expenditure decile and from 31.8% to 19.7% for the richest expenditure decile. More importantly most of the decline in the WPR has taken place among women with low levels of education. For illiterate women the WPR fell from 55% to 29.1% while that for women with secondary education fell from 30.5% to 15.6%.

This broad-based decline with somewhat higher concentration among the least educated and the poorest is consistent with the industries and occupations in which it has occurred. Decomposing the 24.8 percentage point decline in women’s WPR between 2004-5 and 2011-12 the decline in work on family farms and allied activities contributed the most (14.8 percentage points) followed by casual wage labour (8.9 percentage points) and in work on family enterprises in other industries (2.4 percentage points). These were counter-balanced by a 0.7 percentage point increase in regular salaried work and a 0.5 percentage point increase in engagement in public works programmes such as Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA). Most of the decline — 23.1 percentage points out of 24.8 — came from reduced participation in agriculture and allied activities.

Men’s participation in agriculture has also declined. Among men aged 15 and above 56.1% participated in agriculture in 2004-5 while only 39.6% did so in 2017-18. However men were able to pick up work in other industries whereas women reduced their participation in other industries as well as agriculture — resulting in a lower WPR. Therein lies the conundrum for rural women. Mechanisation and land fragmentation have reduced agricultural work opportunities for both men and women. Other work opportunities except for work in public works programmes are not easily open to women. This challenge is particularly severe for rural women with moderate levels of education. A man with class 10 education can be a postal carrier a truck driver or a mechanic; these opportunities are not open to women. Hence it is not surprising that education is associated with a lower WPR for women; in 2016-17 29.1% illiterate women were employed compared to only 16% women with at least secondary education.

Another clue to the decline in women’s work opportunities rather than women’s desire to work is reflected in the fact that women who are counted as being out of labour force are not simply content to be homemakers but often engage in whatever economic activities they find. Women’s work and family responsibilities rarely fit in neat compartments but household responsibilities do not prevent women from working. Many rural women raise chickens as well as children; husk paddy for sale while daal simmers; and sell vegetables in a market while caring for babies.

The NSSO and PLFS survey design relies on two questions. First interviewers assess the primary activity in which respondents spent a majority of their prior year. Then they note down the subsidiary activity in which individuals spent at least 30 days. If individuals are defined as working by either primary or subsidiary criteria they are counted among workers.

This is a categorisation that serves well in cases where agriculture is the primary activity and various agriculture-related tasks can be grouped together to comprise the 30-day threshold. But as demand for agricultural work declines and women engage in diverse activities their work tends to become fragmented. A woman who spends 15 days on her own field during the sowing period 10 days as a construction labourer and 15 days in MGNREGA work should be counted as a worker using the subsidiary status criteria but since none of the activities exceed the 30 days threshold it is quite possible that interviewers do not mark her as being employed. On-going experimental research at the National Council of Applied Economic Research’s National Data Innovation Centre (NCAER-NDIC) suggests a tremendous undercount of women’s work using standard labour force questions particularly in rural areas.

This is not to suggest that fixing the problem of undercount in surveys is the solution to declining WPRs. The undercount is a symptom of the unfulfilled demand for work. Although women try to find whatever work they can they are unable to gain employment at an intensive level that rises above our labour force survey thresholds. This suggests an enormous untapped pool of female workers that should not be ignored.

Possible solutions

Establishment of the Cabinet Committee on Employment and Skill Development is a welcome move by the new government. It is to be hoped that this committee will take the issue of declining female employment as seriously as it does the issue of rising unemployment among the youth. Not all policies need to be gender focussed. One of the most powerful ways in which public policies affect rural women’s participation in non-agricultural work is via development of transportation infrastructure that allows rural women to seek work as sales clerks nurses and factory workers in nearby towns. If the cabinet committee were to focus on multi-sectoral reforms that have a positive impact on women’s work opportunities the potential gender dividend could be far greater than the much celebrated demographic dividend.

Sonalde Desai is Professor of Sociology University of Maryland U.S. and Professor and Centre Director NCAER-National Data Innovation Centre. The views expressed are personal

Rising unemployment must also be seen as a function of rising education and aspirations

The report from the Periodic Labour Force Survey (PLFS) is finally out garnering a lot of attention based on selective reading of tables and spurring partisan debates. In particular the staggering increase in the unemployment rate from 1.7% in 2011-12 to 5.8% in 2017-18 for rural men and from 3.0% to 7.1% for urban men has generated wide ranging hand-wringing. However a more nuanced picture emerges if we are to look beyond the partisan debates to policy implications of the data on employment and unemployment. Three takeaway points from these data are of particular policy relevance.

Three pointers

First while the unemployment rate is a frequently used measure of poor performance of the economy under conditions of rising school and college enrolment it paints an inaccurate picture. Second the reported unemployment rate is dominated by the experience of younger Indians who face higher employment challenges and exhibit greater willingness to wait for the right job than their older peers. Third the unemployment challenge is greatest for people with secondary or higher education and rising education levels inflate unemployment challenges. These three conditions taken together suggest that part of India’s unemployment challenge lies in its success in expanding education while not expanding formal sector jobs.

Comparison of male employment and unemployment data from the National Sample Survey Office’s (NSSO’s) 68th round Employment survey conducted in 2011-12 and the new PLFS of 2017-18 illustrates each of these points. The unemployment rate is calculated by dividing the number of unemployed by the number in the labour forces that is the sum of employed and unemployed. This statistic ignores people who are out of the labour force — students homemakers and the disabled.

Unemployment rate data

As long as the proportion of the population out of the labour force is more or less stable the unemployment rate is a good indicator of the changes in the employment situation. However India has seen massive changes in proportion of individuals enrolled in an educational institution over the past decade. For 15-19-year-old rural men the proportion primarily engaged in studying increased from 64% to 72% between 2011-12 and 2017-18. As a result while the proportion of the population aged 15-19 that is unemployed doubled from 3% to 6.9% the unemployment rate tripled from 9% to 27%. Leaving the numerator (proportion of population unemployed) same while the denominator changes by removing students from the labour force can overstate job losses.

The proportion of the population that is unemployed has increased only slightly for population aged 30 and above but increased substantially for younger men. For rural men (30-34) the proportion of unemployed increased from 1% to 2.3% while that for men (20-24) increased from 4.6% to 16.1%. Much of the increase in male unemployment is located among ages 15-29. It is important to recognise that in a country dominated by informal sector work remaining unemployed is possible only for individuals whose families can survive without their immediate contributions. While a 25-year-old may spend his time diligently applying for a formal sector and be supported by his parents during this period a 30-year-old with a wife and children may have no option but to take any work available to him even if it pays poorly and offers little job security.

Finally the unemployment rate has been traditionally high for men with secondary or higher level of education and this is the segment in which most of the increase in unemployment is located. The unemployment rate for illiterate rural men increased from 0.5 to 1.7 between 2011-12 and 2017-18 but the absolute increase was substantially larger from 3.8 to 10.5 for rural men with at least secondary education. Similar trends are evident for urban men.

This increase in unemployment for educated youth comes at a time when education has expanded substantially. Among rural men (15-29 years) the population with secondary or higher education increased from 43% to 53% between 2011-12 and 2017-18; in urban areas there was a five percentage point increase from 61% to 66%.

These three observations taken together suggest that the roots of India’s present day unemployment challenges lie in its very success. Educational expansion affects the unemployment debate by skewing the unemployment statistics and by creating greater competition for well-paid jobs among a rising population of educated youth. Rising prosperity allows young graduates to wait for well-paying jobs creating an army of educated unemployed before being forced to accept any work frequently returning to family farms or starting small shops.

Recognition of rising unemployment as a function of rising education forces us to grapple with different issues than a simple focus on unemployment statistics. If public policies such as demonetisation are responsible for rising unemployment we would see across-the-board increase in unemployment for all age groups. That this phenomenon is located mainly among the young and well educated reflects a challenge that goes well beyond the temporary slowdown facing India post-demonetisation.

Meeting aspirations

Modern India is an aspirational society. After decades of economic stagnation the 21st century has seen massive growth in aspirations. Parents invest their hearts and souls along with their rising incomes in educating their children. Children hope to make rapid economic progress well beyond the modest gains achieved by their parents’ generation. The unemployment statistics based on PLFS data document the challenges these young people are likely to face.

The Bharatiya Janata Party-led National Democratic Alliance has returned to power with a mandate that allows it the freedom to focus on key challenges facing modern India. Creating jobs for an increasingly educated workforce and ensuring that the new workers are well equipped to enter the labour force are twin challenges that deserve greatest priority. One hopes that leaders of the present government who made their political debut during the student movement in the 1970s will meet this challenge head-on.

Sonalde Desai is Professor University of Maryland and the National Council of Applied Economic Research (NCAER). The views expressed are personal

Asia Society JPMorgan to hold forum on future of work

A FORUM on “Rethinking the Future of Work to Promote Inclusive Growth in Asia Pacific” will hold on June 4 at The Peninsula Manila.

Organized by Asia Society and multinational investment bank JPMorgan the event will form the last leg of the One Step Ahead Series a collaboration that combines the Asia Society’s mission to educate and JPMorgan’s longstanding commitment to investing in communities. Previous forums were hosted in Hong Kong Singapore Mumbai and Beijing.

Panelists for the Manila forum include leaders from the International Labour Organization and the Organization for Economic Co-operation Development as well as McKinsey and Company Associate Partner Boris Van LinkedIn Regional Sales Leader Atul Harkisanka NCAER Director General Dr Shekhar Shah Bainian Vocational School Board Member Sabina Brady and SkillsFuture Singapore Deputy Chief Executive Michael Fung.

They will be joined by local leaders Ayala Corporation Chairman and CEO Jaime Augusto Zobel de Ayala Department of Information and Communications Technology former Undersecretary Monchito Ibrahim and TESDA Deputy Director General Rosanna Urdaneta.

Among the key questions to be tackled are: What challenges and opportunities will the Asia Pacific region face in this age of automation and digitization? How can employers policymakers and educators confront the new socio-economic realities accompanying Asia Pacific’s vast digital transformation?

For the Manila leg key panel sessions will be on the following topics: Unleashing the Potential of Inclusive Growth in the Future of Work: Findings from Research and the Field; The Future of Work Ecosystem — Aligning Supply and Demand for an Inclusive Labor Market; and Reskilling and Upskilling — Reinventing Education and Training for the Workforce of the Future.

Doris Ho Asia Society Philippines Chair said for her part “Technology brings rapid changes to all facets of our life but more so in the area of work. We need to have these discussions on confronting innovations in automation artificial intelligence and digitization so we are prepared to adapt.”

“With the high level discussions at One Step Ahead Manila Forum our aim is to come up with solutions and share best practices on dealing with the challenges of the future of work. Navigating this complexity needs a multi-sectoral multi-level approach” she added.

The value of a river will depend on a unique data-set to construct the water poverty profile and experts who can suggest future correctives say Soumi Roy Chowdhury Devendra B Gupta and Sanjib Pohit

In India the discourse about  new pathways for development hardly focusses on water. The narrative generally centres on two things: First the availability of water and second accessibility to good and safe drinking water. Public policies largely focus on the latter even as the Government launches flagship programmes like Namami Gange and National Rural Drinking Water Programme. 

But to be able to measure societal impact of any given programme it is important to have baseline and endline information.  In this case how river water scarcity or its quality impacts common  households. Specially information on the use of water livelihood aspects and quantifiable aesthetic value of the river are of utmost importance to gauge value.

Such information is however sparse and available only for pilot projects. Further no serious efforts have been made to compile them for better identification of water- stressed regions especially in the Indian context. However efforts are under way to create a data-driven policy-making in our country.

With the launch of the Composite Water Management Index developed by NITI Aayog one gets a sense of the macro picture of the effectiveness of water management across various States. Efforts like these must however be complemented with information linking household welfare understanding livelihood implications of water scarcity and the degree to which it impacts human population. All of these can succinctly bring disparate data sources together. 

Further river basins in India are of different sizes with habitation and livelihood depending on it. Therefore analysing the communities living off the basin is critical to take into account both the physical and socio-demographic factors associated with water scarcity. A water poverty index approach is appropriate for such an analysis as it can monitor both the availability of water as well as the socio-economic factors that hinder the use and access of the same. 

Elsewhere in the world water poverty index a relatively newly introduced policy tool has caught the attention of policy-makers in the realm of water-driven issues. However it is yet to catch the attention of Indian researchers and legislators 

The concept is based on the premise that the lack of adequate water supply in a country can lead to poor health of its population whereas despite its availability it is the user cost of clean water that can drive one to use inadequate and unreliable sources of water supply.

Therefore a country which is water-scarce should encompass understanding of different inter-related components: The availability of internal water resources and external water inflows followed by access to safe water and sanitation in the region. Equally important is to capture the share of regional water use for domestic industrial and agricultural purposes.

Scientific measurements of water quality parameters are equally critical to understanding the role of different kinds of regulatory mechanisms to preserve the water body including biodiversity threats.

Last but not the least the socio-economic ability of availing clean water resources and status of health information constitute the much-needed water data for a comprehensive analysis. The applicability of this kind of measure goes beyond just ranking the regions which is the usual reporting norm but  actually categorising the components. Targetted approach allows diagnosis of the source of water problem and helps identify those policy parameters that need more attention. 

Indeed this is by no means a simple task that can be accomplished easily. An inter-disciplinary team of researchers is needed to understand and analyse the water poverty index of a river basin. More frequently it happens that data is not available at the adequate level in India. For example scanty information is available on water scarcity or how the poor quality of the same impacts the health and developmental goals of the people in various communities across river basins. 

In sum working towards a unique data-set to construct a water poverty measure will require hydrologists who can advise on the water flow and availability scientists who can develop water quality measures and social science researchers who can assess the information on the use and implications of river water usage including health costs economic costs and other socio-demographic linkages. 

(The writers are Associate Fellow and Professors at National Council of Applied Economic Research (NCAER) New Delhi. Views expressed here are personal)