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The trinity of Covid-19 restructuring of Yes Bank and fall in oil prices amidst the current slowdown marks the onset of Spring Fever in India that we would rather not have. The trinity of factors has made a precarious economic situation worse. While the recent sharp drop in oil prices can act as a benign supply shock the Yes Bank shock will dampen demand via the credit and consumption channels negatively impacting consumer sentiments and eroding confidence. Covid-19’s impact is both a negative demand and supply shock. India will also suffer a contagion effect if the shutdown in Italy spreads to the rest of Europe leading to a global financial slowdown. Wednesday’s decision to quarantine India taken after a thorough assessment will isolate India in more ways than one.

The real GDP had fallen from 8.7% in Q3FY18 to 5.6% in Q3FY19. The growth rate stayed virtually unchanged at 5.8% in Q4FY19 before falling to 4.7% in Q3FY20. Gross Fixed Capital Formation exports and imports of goods and services had shown negative growth rates for both Q2 and Q3 in FY20. Consumption expenditure showed higher growth in Q3 but it was government consumption and not private consumption that showed the y-o-y momentum.

On the supply side industrial growth fell from 8.1% in Q4FY18 to 0.1% in Q3FY20. Manufacturing saw a recession in Q2 and Q3FY20. The services sector showed mixed trends but slowed to 3.5% in Q3FY20.

This overall gloomy scenario did have some green shoots. Agricultural growth trended up growing at 3.5% in Q3FY20. Three lead indicators—tourist arrivals aviation passenger traffic and services trade—were bright spots in Q3FY20. Service exports’ y-o-y growth increased from 1.5% in October 2019 to 12.3% in December 2019. Service imports’ y-o-y growth increased from 3.8% to 11.1% in the same period. Tourist arrivals grew at 5.4% in Q3FY20 versus 2.1% in Q3FY19 and 2.3% in Q2FY20. Aviation passenger traffic showed a turnaround in Q3FY20 with domestic and international traffic growing at 4.9% and 3.2% respectively. The Nikkei PMI Manufacturing and Services indices showed positive growth in both January and February 2020. The IIP showed improved y-o-y growth in January 2020. After growing from 2.2% in January 2019 to 7.6% in January 2020 retail inflation showed some moderation in February 2020. Merchandise exports and imports showed positive y-o-y growth in February 2020 after July 2019.

Brent oil price fell from $71.2/bbl in April 2019 to $55/bbl in February 2020. On March 6 2020 it was $45.37 before falling to $34.4 on March 9 and recovered marginally on March 11. Lower crude oil prices will have a benign impact on inflation the fiscal deficit and the trade deficit. Both retail and wholesale fuel price inflation increased in January 2020. The government could pass on the lower prices to invigorate demand or earn more revenue and lower its fiscal deficit. The Yes Bank saga adds to the financial sector’s ongoing turmoil.

As Economics in the Time of COVID-19 (Baldwin and Mauro) points out the virus will act via “cross-border flows of goods services know-how people financial capital foreign direct investment international banking and exchange rates”. If the Sensex’s downward movement and the depreciating exchange rate are any indication India is already caught in a virulent web.

In India Covid-19 will likely affect the precisely the aforementioned green shoots—foreign tourist arrivals aviation passenger traffic and final consumption expenditure. With the government restraining expenditure till mid-March to control fiscal deficit it is unlikely that public administration defence and other services will see sudden improvement in growth. However that may change in Q1FY21. The two largest sub-sectors in the services sector—trade hotels transport communication and services related to broadcasting and financial real estate & professional services—are likely to see further fall in growth. Hence the sector which already saw low growth in Q3FY20 is likely to slow down further.

In manufacturing economies are likely to be affected by what Baldwin and Mauro call a ‘supply-side contagion’. Indian manufacturing and exports and imports of merchandise were already showing negative growth (excepting February 2020). Although its participation in global value chains is limited India imports a significant share of intermediate inputs from China and is bound to get caught up in this contagion effect. Even if China recovers the non-synchronised slowdown of Covid-19 will affect supply chains. Further social distancing in the second half of this month will affect manufacturing activities adversely. In agriculture and allied activities Indian poultry industry and buffalo meat exports are already showing signs of stress.

On the demand side PFCE investment and exports and imports will most likely continue to show muted growth. GFCE may go up depending on the policy route the government decides to take.

In the midst of India’s economic slowdown this trinity of factors has added fuel to the fire. Fuel prices remaining low might come to our rescue but this is just a quick fix; policy must be reassuring and enable long-term economic decisions. An expansionary fiscal policy could invigorate domestic demand. With muted inflation from benign oil prices expansionary monetary policy could help India recover if accompanied by credible structural reforms at the central and state level for long-term recovery and lifting investor sentiment. Difficult times have been shown to spur action in the past and the time is here again. Already the videoconferencing by SAARC countries shows cooperation is possible. India is likely to remain caught in coronavirus’s international contagion till H1FY21. The question is if India can use this spring fever to turn its economy around and emerge stronger.

The writer Bornali Bhandari is Senior fellow NCAER. Views are personal

In our concern with ostensibly declining women’s work participation we have missed out on identifying sectors from which women are excluded and more importantly in which women are included. It may be time for us to count women’s work rather than women workers.

In our concern with ostensibly declining women’s work participation we have missed out on identifying sectors from which women are excluded and more importantly in which women are included. It may be time for us to count women’s work rather than women workers.

India is one of the few countries in the world where women’s work participation rates have fallen sharply — from 29 per cent in 2004-5 to 22 per cent in 2011-12 and to 17 per cent in 2017-18. Both the NDA and UPA governments have found themselves in a hot seat trying to defend economic policies that may have pushed women out of the workforce. Trying to explain whether women are choosing to focus on domestic responsibilities or whether they are pushed out of the workforce has become a minor industry among economists.

Strangely the one explanation we have not looked at is whether the declining quality of economic statistics may account for this trend. Our pride in the statistical system built by PC Mahalanobis is so great that we find it unimaginable that it could fail to provide us with reliable employment data. However as challenges to economic statistics have begun to emerge in such diverse areas as GDP data and consumption expenditure perhaps it is time to consider the unimaginable. Is the decline in women’s labour force participation real or is it a function of the way in which employment data are collected?

The anatomy of the decline in women’s work participation rates shows that it is driven by rural women. In the prime working age group (25-59) urban women’s worker to population ratios (WPR) fell from 28 per cent to 25 per cent between 2004-5 and 2011-12 stagnating at 24 per cent in 2017-18. However compared to these modest changes rural women’s WPR declined sharply from 58 per cent to 48 per cent and to 32 per cent over the same period. Among rural women the largest decline seems to have taken place in women categorised as unpaid family helpers — from 28 per cent in 2004-5 to 12 per cent in 2017-18. This alone accounts for more than half of the decline in women’s WPR. The remaining is largely due to a drop of about 9 percentage points in casual labour. In contrast women counted as focusing solely on domestic duties increased from 21 per cent to 45 per cent.

How do we explain this massive change? Rather than assuming a sudden transformation that has turned Indian women into housewives or an economic catastrophe that has pushed women out of the labour force let us consider the unthinkable — it is the change in our statistical systems that drives these results. The questionnaires through which the National Statistical Office (NSO) collects employment data have not changed but the statistical workforce has and the surveys that performed reasonably well in the hands of seasoned interviewers are too complex for poorly trained contract data collectors.

The National Sample Surveys (NSS) do not have a script that the interviewer reads out. They have schedules that must be completed. The interviewer is trained in concepts to be investigated and then left to fill the schedules to the best of his or her ability. Picture questioning a rural woman busy juggling chapatis and a baby “what was your primary activity over the last year? Is there another activity that you did for at least 30 days?” She thinks for a moment and says “well I looked after this baby and I cooked and had to take care of my mother-in-law when she was sick for a month”. Had the interviewer bothered to probe she might have said and I also took care of a cow and sent my son to sell the milk and worked in my neighbour’s field. An experienced well-trained investigator may know how to probe for this. However with shortage of funds and trained personnel the NSS increasingly relies on contract investigators hired for short periods who lack these skills.

Do we need to return to the days of permanent employees or can we design our surveys to overcome errors committed by relatively inexperienced interviewers? A survey design experiment led by Neerad Deshmukh at the NCAER-National Data Innovation Centre provides an intriguing solution. In this experimental survey interviewers first asked about the primary and secondary activity status of each household member mimicking the NSS structure. They then asked a series of simple questions that included ones like “do you cultivate any land?” If yes “who in your household works on the farm?” Similar questions were asked about livestock ownership and about people caring for the livestock ownership of petty business and individuals working in these enterprises. The results show that the standard NSS-type questions resulted in a WPR of 28 per cent for rural women in the age group 21-59 whereas the detailed activity listing found a WPR of 42 per cent — for the same women. This is an easily implementable module that does not require specialised knowledge on the part of the interviewer.

In our concern with ostensibly declining women’s work participation we have missed out on identifying sectors from which women are excluded and more importantly in which women are included. For rural men ages 25-59 between 2004-5 and 2017-18 casual labour declined by about 6 percentage points. However this decline is counter balanced by regular salaried work which increased by 4 percentage points. Thus it seems likely that men are exchanging precarious employment with higher quality jobs. In contrast women’s casual work has declined by 9 percentage points while their regular salaried work increased by a mere 1 percentage point. Moreover the usual route to success gaining formal education has little impact on women’s ability to obtain paid work. Rural men with a secondary level of education have options like working as a postman driver or mechanic — few such opportunities are open to women. It is not surprising that women with secondary education have only half the work participation rate compared to their uneducated sisters. Thus the focus on employment for women needs to be on creating high quality employment rather than getting preoccupied with declining employment rates.

It may be time for us to return to the recommendations of ‘Shramshakti: Report of National Commission on Self Employed Women and Women in the Informal Sector’ and develop our data collection processes from the lived experiences of women and count women’s work rather than women workers. Without this we run the risks of developing misguided policy responses.

This article first appreared in the print edition on March 17 2020 under the title “Count work not workers.” The writer Sonalde Desai is professor of sociology at University of Maryland and professor and centre director NCAER-National Data Innovation Centre. Views are personal.

Whether we like it or not coronavirus is knocking on the door-and we have two weeks to prepare

It is clear coronavirus is not containable and closing the borders only buys India some time. We must use this time to prepare to manage the virus so that the damage to India’s physical and economic health is minimised. American example provides interesting insights into how fast the virus can spread. As of February 15 there were only 15 diagnosed cases of coronavirus in the United States almost all with known international travel history. On March 1 this number had increased to 76 and to 1762 by March 13. If the US experience of community transmission is transported to India by March 26 we will have over 1500 COVID-19 cases and then it will increase exponentially. Italy reported 1577 cases on March 1; as of March 10 it had 10590 infected people. 

Once the infection begins to spread in India it may move far more rapidly than in the US or Italy. India’s population density is 420 per square kilometre while American population density is only 26. Even when we compare large cities Mumbai’s population density is twice that of New York. Close proximity poor public hygiene and the lack of running water make Indian climate far more hospitable to spreading the virus than the US. There is a saving grace in that flu and other viruses of the same family tend to slow down in hot temperatures and perhaps India can benefit from that. However experience of other hot regions namely Singapore or Australia do not offer reassurance. 

Hence what we have is two weeks at the most one month breathing space to prepare for COVID-19. What should we do? Several mitigation and containment strategies are universal but we also need to consider a number of India specific requirements. First we must prepare for mass testing and focus on availability of testing supplies. Several different tests are available but each requires different collection and analysis procedure. One of the laboratories leading in this field — CoSara Diagnostics — is located in Salt Lake City and in Gujarat. The US has been slow in developing a supply of these tests and the test by CoSara (and its sister company Co-Dignostics) was just approved for use in the US long after its use in Europe. We must learn from South Korea and Australia to work on ensuring adequate testing supplies and involve reputable scientists — including home-grown talent— to find ways of setting up diagnostics centres around the country that are easily reachable.

Moreover India faces several unique challenges. With rampant water shortages more to come in summer months washing hands frequently is feasible only for the rich or those living in water abundant areas. The slum dwellers and service providers like vegetable sellers most at risk of spreading the deadly virus do not have easy access to running water to wash hands for 30 seconds. Investing in manufacturing hand sanitisers and distributing them almost free via the public distribution systems is an option that should be urgently considered. 

Coronavirus spreads when an infected person coughs or sneezes. Most Indians carry a handkerchief to catch the respiratory droplets from cough and sneeze. These handkerchiefs are then tucked into their pockets to spread the contagion. Washing them at riverbanks or hand pumbs may simply lead to more transmission. We must find ways of flooding the market with facial tissue particularly in dense localities and then find a way of collecting and disposing of these tissues. Finally all public transportation systems must be sanitised daily to contain the spread. 

There is one ray of sunshine for India. The World Health Organization estimates that the case fatality rate in China was less than 0.5 per cent for people between ages 10 and 40 and increased to 3.6 per cent for individuals aged 60-69 and to 8 per cent for those aged 70-79. According to the 2011 Census less than 9 per cent of the Indian population is above 60. Policy initiatives that allow older Indians to stay at home from work gives them priority in testing for the virus and ensures their access to sanitisers may help contain fatalities. 

Whether we like it or not coronavirus is knocking on the door. We only have two weeks a month if we are lucky to prepare for its spread. The US wasted this opportunity and is facing spreading virus and economic meltdown. Can India learn from this? As our success with polio vaccination tells us Indian bureaucracy excels when it must tackle challenges in a campaign mode. It is time for the government to activate this resource and make the best use of the borrowed time. 

The writer Sonalde Desai is Professor of Sociology at University of Maryland and Professor and Centre Director at the National Council of Applied Economic Research. Views are personal.

India’s first Land Records and Services Index is a way to gauge States’ relative performance and help improve services on the ground

The National Council of Applied Economic Research (NCAER) recently released India’s first Land Records and Services Index (N-LRSI) 2020 based on data collected over 2019-20 on two aspects of the supply of records — the extent of digitisation of land records and its quality. The first component which aims to assess whether a State has made all its records digitally available to citizens looks at three dimensions — the text (also called the record of rights) the official map associated with a land record (also called cadastral maps) and the property registration process. The second component of the index aims to assess if the data is comprehensive and reliable. Whether ownership details are updated as soon as a sale occurs; the extent of joint ownership; type of land use; size of the plot on the record and on the map and if encumbrances are being recorded (other claims on the property such as mortgages and court cases). All these elements are closely connected to property disputes and to the ease with which transactions can be completed legally recorded and accessed. Madhya Pradesh Odisha Maharashtra Chhattisgarh and Tamil Nadu are the five best-performing States on the index.

One of the unique features of the N-LRSI 2020 is its ability to assess the relative performance of States on various components and sub-components of the index. There is no State/UT that emerges victorious on all the parameters of the index as they are at different stages of progress with regard to the extent of digitisation of records and the registration process. For improved land record management laggard States should extract lessons from the better-performing ones on various parameters which can possibly drive change in State-level policies. While for textual record digitisation Dadra Nagar Haveli Chhattisgarh and Goa appeared to be leading Lakshadweep Madhya Pradesh and Chhattisgarh topped the list for spatial record digitisation. For the registration component Maharashtra emerged as the leader while Jharkhand Odisha and Chhattisgarh were front runners due to quality records.

The fundings will enable States to make efforts in the direction of creating more comprehensive and accurate records by adopting the initiatives that successful States have made. In addition the index brings out certain areas where no State/UT has taken any initiative. Effective integration across departments is one such area. The N-LRSI analysis has brought out the poor synergy across land record departments — revenue department as the custodian of textual records the survey and settlement department managing the spatial records and the registration department.  The N-LRSI design entails a sub-component of updating of ownership (within Quality of Records component) which gauges the extent of integration between registration and textual records — swiftness of the process of updating ownership as the result of registration of a transaction the phenomenon which is commonly known as mutation. The information obtained from all the State/UT sources in this regard revealed that no State/UT has the provision for mutation on the same day as the registration. Moreover there are only seven States/UTs that have the second-best alternative wherein a note indicating the registration appears in the textual record copy. The study also brought out the weak linkage that exists between the revenue department and survey and settlement department. This creates a huge divergence between the land area reported by the textual and spatial record enhancing the chances of legal disputes over the definition of boundaries and extent of a land plot. With such poor inter-departmental synergy aspiring for updated and accurate records will always be a distant goal and States/UTs should strive to undertake necessary actions to have the appropriate systems in place.

With varied recommendations for land record management the N-LRSI 2020 holds immense significance for a number of related factors. It is likely to be a helpful tool to assess the quality of key Government services like PM-Kisan that are dependent on land record details. The efforts by States to improve land record digitisation and quality are expected to increase the chances of accurate identification of PM-Kisan beneficiaries and enhance the scheme’s effectiveness.

The index can be a signalling factor for investors as a clear title is one of the prerequisites for land acquisition that a firm envisions for setting up an industrial unit. An improved quality of land records with accurate information that mirrors the ground reality is expected to provide the necessary push to the underdeveloped mortgage market in India. As per the Committee on Household Finance 2017 mortgages account for only 23 per cent of total liabilities in India. One of the primary reasons for this dismal situation is the inferior quality of land records which are often not updated indicating a high possibility of disputes. Without clear titles it is not possible for banks to give out loans against the land/property. For instance if the record does not get updated to reflect the subdivision of property it cannot be used by the on-ground owner to request for a bank loan. With serious challenges of availability and use of data and information confronting the land policy and governance the index promises to offer a pivotal solution to improve the existing situation.

The writer is Prerna Prabhakar Associate Fellow NCAER

In most sectors share of female employees is low. Where the share is close to 50 per cent there are few women in top roles

The NCAER Skills Report 2018 has emphasised on the importance of female role models to encourage employability and eventual employment. Where are these role models? The numbers from the Periodic Labour Force Survey 2017 inform us that the labour force participation rate (LFPR) of females aged between 15-59 years according to usual status was barely 25.9 per cent and the worker population ratio (WPR) was 23.8 per cent.

Majority of the women (55.3 per cent) aged 15 were employed in the agricultural sector in 2017-18. The corresponding number for men was 39 per cent.

The next top four sectors which employed women in 2017-18 were education (8.3 per cent); retail trade except of motor vehicles and motorcycles (4.6 per cent); manufacture of wearing apparel (3.5 per cent); and activities of households as employers of domestic personnel (3.3 per cent). Together they employed 75 per cent of women aged 15 and above.

In this article we specifically examine the role of female leaders — that is females who fall in Division 1 of National Classification of Occupations 2004 classified as legislators senior officers and managers (henceforth referred to as ‘managers’).

For that reason we leave out agriculture and allied activities and activities of households as employers of domestic personnel from our analysis. While the former sector has its own peculiarities the latter category primarily includes domestic assistants in various capacities with limited scope for managerial roles.

Divisions 2 (Professionals) and 3 (Associate Professionals) are clubbed together in one group referred to as ‘professionals’. Divisions 4 to 9 are clubbed together and referred as ‘workers’ and include clerks; service workers and shop and market sales workers; skilled agriculture and fishery workers; craft and related trades workers; plant and machinery operators and assemblers; and elementary occupations.

The 12 sectors listed in the table account for 76.6 per cent of all female employees outside agriculture and domestic services. The share of female employees amongst all employees is greater than 50 per cent only in the case of the tobacco sector. It is 45-50 per cent for education and human health activities.

Stark difference

However the nature of the sectors differ starkly. Majority of the females engaged in the tobacco sector were ‘workers’ whereas they were ‘professionals’ in the education and health sectors — that is signalling different skills levels.

Within the manufacturing and construction sectors barring the latter the share of female ‘managers’ as a percentage of all managers was approximately equivalent to the share of all female employees as a percentage of all employees.

Majority of the females in these sectors were ‘workers’. The share of ‘professionals’ was the smallest.

In contrast the service sectors show variations. Two sectors namely retail trade and food & service beverage activities behave in a similar manner as the above mentioned manufacturing sectors.

Even though other personal service activities is similar to the other two sectors discussed above the share of female ‘managers’ is lower than the share of female employees signalling presence of glass ceiling in this sector.

There are three sectors — education health and computer programming and consultancy and related activities — where the share of female ‘managers’ is lower than the share of overall female ‘employees’. These sectors are characterised by a very high share of ‘professionals’.

Two sectors namely public administration and financial services fall between the two extremes. The share of female ‘managers’ is the same or higher than the share of female employees.

There is no suggestion of glass ceiling though the share of female employees was overall relatively low compared to health and education. There is a fair share of ‘professionals’ in these sectors.

Double whammy

The numbers suggest a double whammy for women. First in a majority of the sectors the share of female employees is relatively low. And in sectors where the share of female employees is close to 50 per cent glass ceiling seems to be at work.

This is despite the fact that some of these sectors are characterised by a high share of professionals.

Third there is a concentration of women employees in certain sectors — agriculture and education. This is true despite educational attainment.

Majority of the female employees with graduate level education and above were engaged in education (45.6 per cent) and health (10.7 per cent) sectors as opposed to men being engaged in education (17.3 per cent) and retail trade (12 per cent).

To encourage female leadership at work demand side policies have to work along with supply side (education and skilling)

Bornali Bhandari is a Senior Fellow and Ajaya Sahu is a Senior Research Analyst at NCAER. Views are personal.