QUESTION : What do you understand by gig economy? Discuss the advantages and challenges offered by gig economy.





 The New Code On Social Security



 The new Code on Social Security allows a platform worker to be defined by their vulnerability — not their labour, nor the vulnerabilities of platform work.



 A gig is a temporary contract job. It denotes a short-term contract or a freelance work as opposed to a permanent job. The contract employee gets paid once he finishes the work.





  • A gig economy is a free market system in which temporary positions are common and organizations contract with independent workers for short-term engagements.
  • Examples of gig employees in the workforce could include freelancers, independent contractors, project-based workers and temporary or part-time hires.

 Companies like Uber, Ola, Deliveroo have made a huge success with this concept. Even software companies hire gig workers on a project basis.




  • The comfort of working from home – In a modern digitized world, it is easy to work from home with the click of a mouse. It is independent contract work and workers don’t need to go to the office for work but can work remotely from home too.
  • More choices – There is a wide range of choices for the employment seeker as well as the job provider as proximity to work place does not matter here. People also change jobs several times.
  • Experimentation – Gig economy is a reflection of experimentation too.
  • Uncertain business climates – As the nature of jobs is changing (eg. by automation, artificial intelligence), there is no need to hire permanent employees.



  • There is no certainty, stability or job security in gig economy.
  • Workers can be terminated anytime here in a gig economy.
  • Workers do not have a bargaining power in a gig economy.
  • Workers do not get pensions, gratuity, perks etc that is available for full-time workers.
  • There is no basis on which banks and other financial service providers can extend lines of credit when steady income is not assured.
  • The social welfare objectives can be neglected if business and profitable avenues of freelancing are prioritized.
  • It is not accessible for people in many rural areas where internet connectivity and electricity still is a distant dream. Hence they are deprived of this opportunity and this stems up inequality debate again.
  • Confidentiality of documents etc of the workplace is not guaranteed here. When there is a situation where gig worker is potentially working for others as well, including competitors, the employer is wary of what he shares with the gig worker and perennially suspicious.
  • In few work projects where teamwork is essential, gig economy becomes dysfunctional in such a scenario.
  • It is still in a nascent stage in a country like India.



  • Global Gig Economy Index report has ranked India among the top 10 countries.
  • The report says there has been an increase in freelancers in India from 11% in 2018 to 52% in 2019, thanks to various initiatives including Startup India and Skill India.



  • Swiggy workers have been essential during the pandemic.
  • They have faced a continuous dip in pay, where base pay was reduced from ₹35 to ₹10 per delivery order, despite braving against the odds of delivering during Pandemic
  • Stable terms of earning have been a key demand of delivery-persons



  • The three new labour codes passed by Parliament recently acknowledge platform and gig workers as new occupational categories in the making.
  • Defining gig workers is done in a bid to keep India’s young workforce secure as it embraces ‘new kinds of work’, like delivery, in the digital economy.
  • In the Code on Social Security, 2020, platform workers are now eligible for benefits like maternity benefits, life and disability cover, old age protection, provident fund, employment injury benefits, and so on.



  • Platform delivery people can claim benefits, but not labour rights.
  • This distinction makes them beneficiaries of State programmes but does not allow them to go to court to demand better and stable pay, or regulate the algorithms that assign the tasks.
  • This also means that the government or courts cannot pull up platform companies for their choice of pay, or how long they ask people to work.
  • The laws do not see them as future industrial workers.
  • They are now eligible for government benefits but eligibility does not mean that the benefits are guaranteed. Actualising these benefits will depend on the political will at the Central and State government-levels.
  • The language in the Code is open enough to imply that platform companies can be called upon tso contribute either solely or with the government to some of these schemes. But it does not force the companies to contribute towards benefits or be responsible for workplace issues.




 There is a need for the government to step in and implement radical changes in labour laws or implement tax rebates and concessions that can be passed on directly to drivers or delivery partners as health or insurance benefits.


o However, some experts say that this would directly affect prices of service delivered to the end customer.

 With a population of over 1.2 billion, and a majority of them below the age of 35, relying on the “gig economy” is perhaps the only way to create employment for a large semi-skilled and unskilled workforce. Therefore, It is important to hand-hold this sector and help it grow. We need policies and processes that give clarity to the way the sector should function



  • There are no guarantees for better and more stable days for platform workers, even though they are meant to be ‘the future of work’.


QUESTION : India being one of the most flood affected nations in the world requires strong and healthy coordination between Centre and States for long term flood management. Analyse



  • Flood forecasting in India



  • India needs a technically capable workforce that can master ensemble weather and flood forecast models.


  • In India, local agency makes a decision in a flood forecast in a way they merely use the words “Rising” or “Falling” above a water level at a river point.
  • There are many times this happens in India during flood events, when the district administration, municipalities and disaster management authorities receive such forecasts and have to act quickly.


  • Ensemble forecast:
  • In India, there is a form of flood forecast known as the Ensemble forecast that provides a lead time of 7-10 days ahead, with probabilities assigned to different scenarios of water levels and regions of inundation.
  • An example of the probabilities ahead could be something like this: chances of the water level exceeding the danger level is 80%, with likely inundation of a village nearby at 20%.
  • The Ensemble flood forecast certainly helps local administrations with better decision-making and in being better prepared than in a deterministic flood forecast.



  • The India Meteorological Department (IMD) issues meteorological or weather forecasts while the Central Water Commission (CWC) issues flood forecasts at various river points.
  • The end-user agencies are disaster management authorities and local administrations.
  • Therefore, the advancement of flood forecasting depends on how quickly rainfall is estimated and forecast by the IMD and how quickly the CWC integrates the rainfall forecast (also known as Quantitative Precipitation Forecast or QPF) with flood forecast.
  • Thus, the length of time from issuance of the forecast and occurrence of a flood event termed as “lead time” is the most crucial aspect of any flood forecast to enable risk-based decision-making and undertake cost-effective rescue missions by end user agencies.
  • Outdated technologies and a lack of technological parity between multiple agencies and their poor water governance decrease crucial lead time.



  • IMD has about 35 advanced Doppler weather radars to help it with weather forecasting.
  • Compared to point scale rainfall data from rain gauges, Doppler weather radars can measure the likely rainfall directly from the cloud reflectivity over a large area; thus, the lead time can be extended by up to three days.
  • But the advantage of advanced technology becomes infructuous because most flood forecasts at several river points across India are based on outdated statistical methods (of the type gauge-to-gauge correlation and multiple coaxial correlations) that enable a lead time of less than 24 hours.
  • This is contrary to the perception that India’s flood forecast is driven by Google’s most advanced Artificial Intelligence (AI) techniques.
  • These statistical methods fail to capture the hydrological response of river basins between a base station and a forecast station. They cannot be coupled with QPF too.
  • Google AI has adopted the hydrological data and forecast models derived for diverse river basins across the world for training AI to issue flood alerts in India. This bypasses the data deficiencies and shortcomings of forecasts based on statistical methods.
  • Just as the CWC’s technological gap limits the IMD’s technological advancement, the technological limitations of the IMD can also render any advanced infrastructure deployed by CWC infructuous.
  • The limitations of altitude, range, band, density of radars and its extensive maintenance enlarge the forecast error in QPF which would ultimately reflect in the CWC’s flood forecast.
  • Forecasting errors increase and the burden of interpretation shifts to hapless end user agencies. The outcome is an increase in flood risk and disaster.



  • IMD has begun testing and using ensemble models for weather forecast through its 6.8 peta flops supercomputers named Pratyush and Mihir.
  • The forecasting agency has still to catch up with advanced technology and achieve technological parity with the IMD in order to couple ensemble forecasts to its hydrological models.
  • It has to modernise not only the telemetry infrastructure but also raise technological compatibility with river basin-specific hydrological, hydrodynamic and inundation modelling.
  • To meet that objective, it needs a technically capable workforce that is well versed with ensemble models and capable of coupling the same with flood forecast models.
  • It is only then that India can look forward to probabilistic-based flood forecasts with a lead time of more than seven to 10 days and which will place it on par with the developed world.
  • Central Water Commission (CWC) has modernised its flood management system over the years, there are still massive gaps that need to be filled to make it a much more responsive system.
  • Two types of measures are taken for flood protection: Structural (embankments, dams, reservoirs, and natural detention basins), and non-structural (flood forecasting and warning, floodplain zoning).


  • The developed world has shifted from deterministic forecasting towards ensemble weather models that measure uncertainty by causing perturbations in initial conditions, reflecting the different states of the chaotic atmosphere.
  • A study by the National Institute of Technology, Warangal, Telangana shows that it is only recently that India has moved to using hydrological (rainfall-runoff models) capable of being coupled with QPF.
  • The United States which is estimated to have a land area thrice that of India, has about 160 next generation S-band Doppler weather radars with a range of 250-300 km.
  • The United States, the European Union and Japan have already shifted towards Ensemble flood forecasting along with Inundation modelling.
  • India has only recently shifted towards Deterministic forecast i.e. Rising or Falling type forecast per model run.
  • India will need at least an 80-100 S-band dense radar network to cover its entire territory for accurate QPF.



  • On the structural side, the management of reservoirs and dams, maintenance of embankments and data collection on a river’s silt-bearing capacity have to be improved.
  • On the non-structural side, data on river flow and discharge must be enhanced; the installation and maintenance of technical equipment such as gauges have to be expedited.
  • The information on floods is given to the public; it has to be timely, useful and in a non-technical language.
  • An independent evaluation of the flood forecasting system must be put in place to identify the gaps in the system, and ensure that CWC performs its role better than it is doing now.



  • Disaster Preparedness Plan: A comprehensive flood management plan is needed to include Disaster preparedness
  • Integrated Approach: Steps need to be taken for watershed management through an integrated approach. Often these approaches involve both hard engineering solutions and ecologically sustainable soft solutions
  • Prioritising Buffers, Flexibility and Adaptability: This includes reviewing safety criteria of dams and canals, re-building these with higher safety factors, creating new intermediate storages, and introducing dynamic reservoir management.
  • Reducing Disaster Risk Reduction: There is a need for efficient implementation of Sendai Framework for Disaster Risk Reduction, this will reduce the vulnerability of any disaster.
  • Focusing on Urban Flood Management: Keeping in view the fact that the problem of Urban Flooding is becoming more severe and losses are mounting every year.
  • Installation of early warning systems so that losses can be minimised.


 India has a long way to go before mastering ensemble model-based flood forecasting. With integration between multiple flood forecasting agencies, end user agencies can receive probabilistic forecasts, reducing flood hazard across the length and breadth of India.

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