Editorial 1 : Living in denial about unemployment
Introduction
Citing an RBI report that said that 8 crore jobs have been created in the last 3-4 years, the Prime Minister attempted to counter the narrative of high unemployment which has been bothering the government.
Conflicting reports and statements
- RBI released a Data Manual called The India KLEMS Database. It describes the procedures, methodologies and approaches used in the construction of India KLEMS database version 2024.
- This database covers 27 industries comprising the entire Indian economy and includes measures of Gross Value Added, Gross Value of Output, Labour Employment, Labour Quality, Capital Stock, Capital Composition etc.
- The State Bank of India (SBI) in its report said that total number of jobs created in manufacturing and services is at 8.9 crore during FY14-FY23 and 6.6 crore during FY04-FY14.
- However contrary to the official narrative of massive employment generation, Centre for Monitoring Indian Economy (CMIE), a private data-gathering agency which publishes data on employment and unemployment, reported in July that in June 2024, the unemployment rate had risen to an eight-month high of 9.2% up from 7% in the previous month.
- Public confusion over the extent of unemployment is a result of the differences in the various data bases used.
- Ground reports suggest that unemployment is a major issue and the situation for the educated youth is grim, yet we expect them to be in the vanguard of ‘demographic dividend’.
KLEMS data
- The recently cited KLEMS data uses the official data available from NSSO and PLFS for labour input. So, neither the Prime Minister nor the SBI should present KLEMS data as an independent source of employment data.
- Due to the highly complex structure of the Indian economy and the paucity of reliable data, different sources give widely varying estimates of employment.
- India consists of the organised and the unorganised sectors. The data for the organised sector is available from statutorily published annual data. That is not the case for the unorganised sector, which employs 94% of the labour force.
- For the informal sector data have been collected periodically via the Census every 10 years and the Annual Survey of Unincorporated Sector Enterprises (ASUSE) survey every five years.
- ASUSE survey data in turn depend on data from the Census and the Urban Frame Survey (UFS). There has been no Census since 2011 and UFS data apparently pertain to 2012-17. So, outdated data are being used.
- 2016-2024 was an abnormal period with four shocks to the economy: demonetisation, introduction of GST, the NBFCs crisis, and the COVID-19. These shocks specifically impacted the unorganised sector.
- Due to the shocks, the rural-urban ratio and the ratio of smaller and larger units would have changed. This could give an upward bias to the number of establishments and their employment.
Differences in PLFS and CMIE
- CMIE adopts the International Labour Organization definition and counts only those who get an income from work as employed. Periodic Labour Force Survey (PLFS) counts those who are working even if they do not get an income from it. So, those giving free labour or those who sit in fields but have no work also get counted as employed by PLFS.
- PLFS counts the disguised unemployed and the under-employed. So, as far as PLFS is concerned, almost no one is unemployed, while CMIE tells us how many have simply given up looking for work. That is also unemployment, which the official data do not recognise.
Conclusion
The ground-level situation of unemployment is apparent from the frequent reports about the youth struggling to get work and facing issues in examinations. But the government is in denial. Why not admit the problem and act, lest the growing youth frustration boil over?
Editorial 2 : The promise of parametric insurance
Introduction
2023 was the warmest year on record and of the estimated $250 billion of losses due to disasters in 2023, only $100 billion was insured. The gap in insurance coverage was particularly wide between developed and developing economies.
- With a surge in extreme weather events, the insurance industry needs to enhance disaster resilience by devising a number of alternative methods of coverage.
- The present globally accepted method of disaster risk reduction is to transfer risk through indemnity-based insurance products, which require physical assessment of damage for payouts.
- It becomes difficult to verify the losses when calamities hit large populations and wipe out settlements, especially of the economically disadvantaged communities who have little record of their assets.
Changing Course
- In this context, several insurance products based on the parameters of a weather event are needed.
- In parametric products:
- payments are triggered based on real-time measurements such as rain of more than 100 mm per day for two days in succession, or specific flood levels, and wind speed.
- payments are made regardless of actual loss or physical verification.
- Disaster-prone island countries have largely shifted from the risk retention model and embraced such insurance for climate adaptation. Over time, this has built trust between states and insurers, leading to more reasonable pricing and trigger-payout combinations.
- So far insurers have been offering standardised parametric products only for low frequency, high-impact disasters such as earthquakes, cyclones, and hurricanes. High frequency but low-impact disasters such as landslides, rain, and heat were overlooked, but the consequences of climate change are slowly changing that.
- One of the earliest uses of parametric policies in India was crop insurance. Pradhan Mantri Fasal Bima Yojana is based on verification of loss, while the Restructured Weather Based Crop Insurance Scheme is based on threshold limits, not requiring field verification.
- Over the years, the private insurance industry in India has witnessed a rising number of offers of parametric products, customised for States, corporations, self-help groups, and micro-finance institutions.
- Examples in India:
- Nagaland was the first State to buy a parametric cover for extreme precipitation in 2021. Based on lessons learned, it has tendered for the second improved version by fixing an absolute annual premium, duration and rate-on-line, allowing bidders to compete over lower threshold limits and maximised payouts.
- The Co-operative Milk Marketing Federation in Kerala too has implemented parametric insurance for dairy farmers for lower milk yields due to heat stress to cattle.
Ensuring effective use
- Five factors are essential for governments to ensure effective use of parametric products viz.
- Precise thresholds and proper monitoring mechanisms
- Experience sharing between governments to incorporate lessons learned
- Following the mandatory bidding process for transparent price discovery
- A widespread retail payout dissemination system
- Encouraging premium payment by households in the long term
Conclusion
India is uniquely placed for the use of such products, given that it has the Aadhaar-based payment dissemination system. Given South Asia’s reputation as the world’s most “climate-vulnerable zone”, India and its neighbourhood could consider such products, pool their risks collaboratively, and strike better bargains with the world’s largest insurance companies.
