This article deals with how the different sections of a society assign different weightage to the various factors they are faced with in life. In the case of Covid-19, one section of society which is well-off might care more about the possible loss of life while other section might end up attaching more weightage to the loss of livelihood than to the possible loss of life due to infection. The article discusses this issue in detail.

Difference between risk and uncertainty

• Since the days of Frank Knight, economists have differentiated between the two.
• Risk has a known probability distribution.
• For uncertainty, the probability distribution is unknwon.
• 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.

So, how the subjective probability distribution is devised by a person?

• Through information and experience, one already possesses.
• There are various rationality assumptions used by economists. They are often violated.
• Otherwise, behavioural economics wouldn’t have come into existence.
• Typically, given a situation, when your decision doesn’t agree with someone else, you say they are being irrational.
• However, with uncertainty, the problem may not be with rationality assumptions, but with differences in subjective probability distributions.

Lack of data for 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 testing for random sample: 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.
• Sampling bias: 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.
• Data for death rate: There are similar caveats about the death rate.
• If we mechanically divide the 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.

How the lack of data is reflected in subjective probability distribution?

• There are slices in India’s population pyramid with rural/urban and other spatial differences too.
• Consider two extreme types-type A and type B.
• 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.
• The high probability assigned to loss of life: 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 a 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.
• The high probability assigned to loss of livelihood: High subjective probability will be attached to loss of livelihood and low probability to death from COVID.
• Both types reflect subjective probabilities. Neither is “irrational”.
• The tension between the two: Type A would like the lockdown to continue indefinitely, until the 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.

The issues highlighted here can be broadly used in the various scenario where there is uncertainty involved and various stakeholders perceive the probable outcomes in entirely different ways. Various points here can be used to answer the question based on policy making.

Balancing the differential individual preferences in public policy

• One set of individuals imposes its choice on the rest.
• 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 the 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.

A question based on policy formation issues explained here can be framed, for ex. “Risk has a known probability distribution. For uncertainty, the probability distribution is unknown. COVID-19 makes us confront uncertainty, not risk. In this context, there is a debate between saving lives and saving livelihoods. In such a scenario, what can be the most probable solutions that public policy must delve into, in order to maintain the balance between this uncertainty and risk.”

Conclusion

Extending or ending the lockdown decision represent the public policy dilemma. Without a revival in growth, the 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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