Decoding Inequality In A Digital World

Context: Technological changes amid the pandemic in education and health are worsening inequities.

Example of decentralised data collection: For patients, interoperability can be achieved by decentralising digital storage (say, on smart cards) as France and Taiwan have done.

Background:

• In the context of digital inequities, Virginia Eubanks’ in her book Automating Inequality, alerted how automated decision­making tools exacerbated inequalities, especially by raising the barrier for people to receive services they are entitled to.
• Impact of pandemic on economic inequalities: Economic inequalities have risen –
  • The super­rich have even become richer (the net worth of Adani has increased)
  • The bulk of the Indian population, however, is suffering a huge economic set­ back.
• However, worse than the economic inequalities, well-recognised channels of economic and social mobility — education and health — are getting rejigged, making access to them more inequitable.

Inequality in digital world

• Inequality in education: Privileged getting ahead not necessarily because they are smarter, but because of privileges they enjoy. This is due to the following reasons -
  • Non-universal access to Internet: Penetration at 17% in rural areas and 42% in urban areas.
  • Lack of access to online classes:
    • Only 6% of rural households and 25% of urban households have a computer – NSS 2017.
    • Recent surveys (By NCERT, Azim Premji Foundation, ASER and Oxfam) suggest that between 27% and 60% could not access online classes for a range of reasons: lack of devices, shared devices, inability to buy “data packs”, etc.
  • Connectivity issues: Lack of stable connectivity jeopardises evaluations.
  • Lack of learning environment at home: 25% Indians lived in single-room dwellings in 2017-19.
  • Additional burden on girls: Expectation to contribute to domestic chores if they are at home.
  • Peer learning suffered: Students from non-English schooling lost opportunity for peer learning.
• Inequality in healthcare:
  • Financial issues: India’s low public spending on health (1% of GDP), a high share of ‘out of pocket’ (OOP) health expenditure (60% in India in 2018 vs 10% in the US).
  • Misplaced confidence on app-based infrastructure: Right now the focus is on managing the shortage of essentials (including drugs, hospital beds), whereby developing an app seems like a solution.
    • While the success of this measure is based on private apps like Ola and Zomato, it ignores the fact that they work well because resources (drivers and restaurants) are available for them to allocate effectively.
  • Rising profiteering: Patients are being charged whatever hospitals like, and a black market has developed for scarce services (such as oxygen).
    • While the sensible option would be to clampdown on the handful who indulge in such activities, the focus is more on digital options like making Aadhar mandatory.
  • Digital solution creates additional bureaucracy: Along with the paperwork, patients will have to navigate digi­work. Thus, platform­ and app­based solutions can exclude the poor entirely.
  • Hurdles in vaccination: Booking slot in CoWIN is much harder for those without phones, computers and Internet, reports of techies hogging slots and website available only in English.
• Privacy and data usage issues: Pushing of Digital health ID project creating centralised database overlooks risks especially in light of lack of data privacy law.
  • Health records end up with private entities without consent, may even be weaponised.
  • For E.g. Private insurance companies may use it to deny poor people insurance policy or charge higher premium.

Conclusion:

• Unless health expenditure on basic health services is increased, apps such as Aarogya Setu, Aadhaar and digital health IDs can improve little.
• Need for political, not technocratic solutions: Unless laws against medical malpractices are enforced strictly, digital solutions will obfuscate and distract us from the real problem.