The public policy dilemma: There is indeed tension between lives and livelihoods(Indian Express)

Mains Paper 2:Governance 
Prelims level: Not much 
Mains level: Balancing the conflicts and policy making between various stakeholders 

Context:

• Since the days of Frank Knight, economists have differentiated between the two. 

Differences between risk and uncertainty: 

• Risk has a known probability distribution. 
• For uncertainty, the probability distribution is unknown. 
• COVID-19 makes us confront uncertainty, not risk. 
• For uncertainty, there is a subjective probability distribution, which can, and does, vary from individual to individual.

How do the subjective probability distribution is divided by an individual?

• Through information and experience the individual already possess. 
• There are various rationality assumptions used by economists. They are often violated. 
• Otherwise, behavioural economics wouldn’t have taken off. 

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Availability of data affecting various factors: 

• Because of COVID-19, there is a certain risk of getting infected. Let’s call this the infection rate — total infections divided by the total population. 
• We don’t know this infection rate for India, or for any other country for that matter.
• No country has done universal testing.
• No country has done universal testing for a proper random sample either. 
• The ICMR has told us more than 75 per cent of Indian patients will be asymptomatic. 

Who do we test? 

• Those who show symptoms, those who have been in contact with confirmed patients and those who suffer from severe respiratory diseases. 
• Most countries do something similar. 
• In other words, when we work out an infection rate based on those tested, there is a sampling bias. 
• This isn’t a proper infection rate. 
• The only country where we have had something like a random sample is Iceland. There, the infection rate was 0.8 per cent. 

Death rate:

• There are similar caveats about the death rate. 
• If we mechanically divide number of deaths by the number of confirmed cases for India, we will get a death rate just over 3 per cent. 
• The global figure is a little less than 7 per cent. But neither of these is a death rate for the total population, since only those with severe symptoms are included in infection numbers. 
• Three per cent or seven per cent are over-estimates. 
• In a controlled environment like Diamond Princess, death rate as a ratio of total passengers, and not those infected, was less than 0.4 per cent. 
• The true infection rate and true death rate are not alarming numbers.

What does this have to do with differential subjective probability distributions? 

• There are slices in India’s population pyramid with rural/urban and other spatial differences too. 
• Consider two extreme types. 
• Type A, who are globalised in information access and morbidity. Life expectancy is 80 plus and there are lifestyle diseases like diabetes and high blood pressure. 
• This co-morbidity increases possible death rates and thanks to globalised access to information, certainly increases perceptions about death rates, making them out to be higher than they are. 
• Some of them have fixed incomes, regardless of what happens to lockdown. 
• Therefore, if you think in terms of maximising expected payoffs with a subjective distribution, high probability is attached to loss of life and low probability to loss of livelihood. 

How Type B forms subjective probability?

• Type B, someone whose life expectancy is 60, without a fixed income stream and whose health concerns are tuberculosis and water-borne diseases, not COVID-19. Nor is access to information that globalised. 
• High subjective probability will be attached to loss of livelihood and low probability to death from COVID. Both the types reflect subjective probabilities. 
• Neither is “irrational”. There is tension between the two. 
• Type A would like the lockdown to continue indefinitely, until long tail of the infection curve tapers off, perhaps beyond September. 
• Type B would like lockdown to be eased soon, with necessary restrictions in hotspots. 
• There is indeed tension between lives and livelihood. 
• Even if health outcomes and information access are like Type A, but income is contingent on growth, preferences might mirror Type B.

Balancing differential individual preferences in public policy:

• Type A disproportionately influences policy. This determination of aggregate preferences is a dynamic process. 
• Therefore, sooner or later, Type B contests this and as the lockdown is prolonged and livelihood costs mount, discontent surfaces, as it has across a range of countries. 
• There were also welfare economics notions that pre-dated social choice theory, such as compensation principles of Kaldor, Hicks and Scitovsky. 
• The point can be made using the two stereotypes. Specifically, Type A need to compensate Type B for their losses. 
• To state it starkly, livelihood losses suffered by Type B need to be compensated by government through redistributive measures and this has to be financed by higher taxes imposed on Type A. 
• The right question for the Type A is not whether they want the lockdown to continue, but whether they are willing to pay a COVID-tax to support lockdown extension.

Conclusion: 

• Increasing or decreasing the lockdown decision illustrates the public policy dilemma. 
• Without a revival in growth, tax-paying capacity of Type B is limited and with job losses, some Type As become Type Bs. The choice is starker.

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Prelims Questions:

Q.1)With reference to the CollabCAD, consider the following statements:
1. Atal Innovation Mission (AIM), NITI Aayog and National Informatics Centre (NIC) jointly launched CollabCAD.
2. CollabCAD is a collaborative network, computer enabled software system, providing a total engineering solution from 2D drafting & detailing to 3D product design.
Which of the statements given above is/are correct?
(a) 1 only
(b) 2 only
(c) Both 1 and 2
(d) Neither 1 nor 2

Answer...................

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Mains Questions:
Q.1) What are the major causes of the public policy dilemma?How do balancing differential individual preferences in public policy?