Trust in the Era of the AI-Informed Customer

by Maninder Singh Juneja.

A patient grappling with a longstanding eye problem was diagnosed with MGD (Meibomian Gland Dysfunction) by an eye specialist. To make sense of the medical jargon, she photographed her meibography report, the gland scan, and uploaded it to an AI, which confirmed the diagnosis and the line of treatment. A few weeks later, wanting to avoid buying shades of cosmetics she already owned, she uploaded a photograph of her stock and of the items she was about to buy to the same AI. Instead of commenting on the shades, the AI told her the waterproof eyeliner she had used for years was blocking the pores of the gland, like wax in a drain, and making her dry eyes worse. She stopped, and this one change brought immediate relief. The AI connected across domains, unasked. Neither the physician nor the cosmetics counter had connected the two. The ophthalmologist saw the eye. The counter saw the product. AI saw the person. Neither was wrong. Each was trained to look at one domain. Every institution serves its own; AI serves the customer. This is not a failure of individuals but a structural shift with strategic implications. In this article we examine these implications.

Verification in markets

For as long as markets have existed, buyers have had to trust sellers, because there was no way to verify their word, or the effort and cost were too high. The economist George Akerlof spent many days puzzling over the nature of the vegetable retail market in Delhi. In 1970, he offered new insights into the market failure that arises when the buyer cannot verify what the seller knows. He later won the Nobel prize in economics for this work which helps us understand asymmetric information.

The market economy combats asymmetric information using substitutes, brands, professional licensing, statutory audits, regulators, and guarantees. For example, the customer trusts the bank's brand name instead of reading the fine print, or the doctor's medical degree instead of evaluating the diagnosis. Half a century of institutional architecture in financial services is built on this logic.

The use of AI has brought about a foundational shift in trust. AI has made verification in many situations quite feasible. Customers who once accepted substitutes for trust in institutions can now check a claim, compare alternatives, or challenge a recommendation on an inkling of doubt. The barriers to specialist access, cost, time, language, jargon, have all collapsed at once. The result is not an end of trust, but the pillars on which trust rests have changed.

How it impacts the brand

The brand is an informational shortcut, one of Akerlof's substitutes for verification. It stood in for the customer's inability to verify. The bank's or insurer's reputation stood in place of the customer's understanding of provider soundness, and the terms and conditions of the contract. AI changes that, but the picture is complicated, because the brand connotes two things at once: A promise of what we will do for you, and an aspiration of who you become or which tribe you join by choosing us.

The promise of what the brand will do is now more verifiable. The customer's AI checks every promise before purchase, searching the wider internet, cross-referencing user reviews and triangulating from multiple public sources. The bank that claims to be customer-first has its complaint-resolution record extracted from annual reports or instantly summarised from X. The insurer that promises easy claims has its claim-rejection ratio surfaced against peers. Promises that survive verification strengthen the brand. Promises that do not are revealed in seconds.

The aspiration is not in the product, it lives in the customer. People buy Apple products partly because Apple-ness signals something about themselves. Customers bank with a private bank not for any major service benefit but for who else banks there. People ride Royal Enfield partly because owning one says something no specification sheet captures. AI audits the promise. It cannot interpret the tribe.

The split deepens in the AI-to-AI world, where the customer's AI transacts with the institution's AI. The customer only experiences the outcomes, settlement speed, dispute rate, complaint-resolution time and median application-to-approval. It does not watch advertisements. In this perspective, brands need to invest in advertising that bolsters tribal loyalty, but advertising that is supposed to bolster the promise is now less important.

How it impacts labour

Like the brand, the professional is going to be hit by the AI wave. The professional of the old world was, by training, organised around the domain. The cardiologist gave the right answer to the heart in front of her, not to the medicine cabinet or the financial situation at home. The mutual fund agent recommended within his manufacturer's product set, not against the seven existing funds in the customer's portfolio. The AI can see the landscape comprehensively and the professional has to now compete with it.

In most domains the gap between the bottom and the top decile of professionals has been wide. AI compresses the gap from below. The advisor who tests his recommendation with AI before delivering it catches the portability clause he had not considered. The relationship manager who has the model argue the customer's case against his own pitch will close more often. Every word the professional says can now be cross verified; the smartest professionals will go up against 'unsophisticated consumers' with more respect.

In recent research, Brynjolfsson, Li and Raymond (2023) studied 5,179 customer-support agents at a Fortune 500 software firm and found that average productivity rose 14% with access to a generative AI assistant. Within this overall average, novice and low-skilled workers improved by 34%, while experienced and highly skilled workers showed minimal gain. By this reasoning, AI-powered unskilled labour will be tough competition against skilled practitioners.

The pattern is visible in India also. At one NBFC, the productivity of fresh-college LAP underwriters rose 40% with AI assistance, while experienced underwriters showed minimal gain. The bottom of the labour quality distribution comes closer to the top.

As the floor rises, the implications are twofold. First, the customer's worst experience disappears, and with it the customer's reason to switch providers. Second, the professional's competence becomes the table stakes. They have to now provide what AI cannot supply: Trust earned over time, judgement under ambiguity, the willingness to take a customer-friendly call when things are not going well with the business. These are the qualities that live on the aspiration side of the brand, and they are the real moats of professional competence.

When the asymmetry reverses

So far the shift has run one way, AI in the customer's hand against the institution. The same architecture runs the other way too. Institutions have always known what customers did. But AI interactions reveal something deeper, what the customer considered doing. The questions asked, the scenarios tested, the decisions abandoned. These are cognitive traces, and they sit closer to intent than anything an institution has had access to before. If applied to underwriting, pricing or customer acquisition, they create a new informational advantage that did not exist a year ago.

The trust consequence runs deeper than the privacy one. Behaviour is what the customer did once it was done. Intent is what she rehearsed before she was ready to be seen. A customer can absorb the knowledge that her behaviour was logged. When she learns the institution priced her, or declined her, on the strength of her question, the breach is of a different order. The first asymmetry was about information she did not have. This one is about information she did not know she was giving. This may lead the customer to stop being candid with the one tool that was working for her, because she now suspects it is also working for the firms. The independence this piece began with is the first thing she loses when the architecture turns around. We will have to face a new world of consumer protection complexity, going beyond the simpler questions of data privacy.

What then survives in trust? Not the part that rested on the customer's inability to check. What survives is what AI cannot manufacture. Judgement under ambiguity, the call no model will take responsibility for. A relationship proven over time, the banker who backed the customer through a bad cycle and was proved right. The human presence in a hard moment, the advisor who delivers difficult news with care. None of this can be read off a document, so none of it can be verified, and so none of it can be commoditised. The trust that survives is the trust that was never about information in the first place.

The institutions that Akerlof described were built on substitutes for verification. Those substitutes served a purpose. They filled a gap the customer could not fill herself. That gap is closing. What remains when the substitute is no longer needed is the thing the substitute was always standing in for. Genuine expertise, honestly applied, in the customer's interest. The institutions that had that all along have nothing to fear from the informed customer. The institutions that were selling the substitute will find, quietly and permanently, that the customer has stopped calling back.

The scarce asset is the question

If one risk is that the institution reads the customer's question, the other is that she asks a wrong question. While AI can reduce the information asymmetry, friction has not entirely disappeared. It rests on arriving at the right question to ask. Speed and convenience applied to the wrong question produce a confident wrong answer faster. The customer who compares home loans on interest rate alone misses the prepayment clause. The customer who has already decided to switch insurers asks questions that confirm the decision. The informed customer is powerful. The misinformed customer with AI is powerfully wrong.

The question itself is the unclaimed opportunity. No bank will build a question set that surfaces its own prepayment clause weaknesses. No insurer will build a question set that exposes its own claim-rejection ratio. This needs independent actors: non-profits, researchers, consumer bodies, who can make a GitHub for the questions consumers should be asking financial institutions.

What the Boardroom should debate

Every earlier wave of technology was institution first. Computing, internet, mobile was adopted and absorbed by the organisation and then passed onto the customer on their terms. AI exploded in consumers' hand, 100 million users in two months. The Board is now governing businesses where customers will know as much if not more than the organisation.

The first institutional response to AI has been operational, adding chatbots, analytical tools, dashboards, voice bots, service automation, that are good cost-saving initiatives. The strategic question is the viability of the business model itself.

What happens when the customer arrives informed? Which elements of the value proposition survive verification? Which revenue streams depend on customer ignorance or high search costs? Which promises would survive an AI audit? Which parts of the sales process assume an information asymmetry that no longer exists? These are not technology questions for the CTO. They are business-model questions for the Board.

The institutions that emerge stronger will not be those that adopted AI fastest. They will be those whose value remains after verification becomes cheap.

References

Akerlof, George A. (1970). "The Market for 'Lemons': Quality Uncertainty and the Market Mechanism". Quarterly Journal of Economics, 84(3), 488-500. https://www.jstor.org/stable/1879431

Brynjolfsson, Erik, Danielle Li and Lindsey R. Raymond (2023). "Generative AI at Work". NBER Working Paper 31161. https://www.nber.org/papers/w31161

Reuters / Similarweb (2023). ChatGPT user-growth figures. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/

Reserve Bank of India. Annual Report of the Ombudsman Scheme. https://www.rbi.org.in/Scripts/AnnualReportPublications.aspx

Maninder Singh Juneja is a partner at True North. He serves on the boards of Pine Labs, Nivara Home Finance and Integrace, and has previously served on the boards of Niva Bupa Health Insurance, Federal Bank Financial Services and HomeFirst Finance. The author thanks participants at a talk at XKDR Forum for myriad good ideas, and Ajay Shah, Renuka Sane and Aditi Mascarenhas for comments on earlier drafts.

More ammo: Improving resilience against extreme surges in demand

by Ajay Shah.

The Javelin anti-tank guided missile is important for the defence of Ukraine. Under normal times, the production capacity seems to be about 3600 a year. The Ukrainians seem to be using 500 per day, or roughly one missile per kilometre of battlefront per day. The peak load is about 50 times bigger than normal times.

Vershinin, 2022 estimates the Russian army is using 7,176 artillery rounds a day, and argues that these numbers are challenging for the modern Western military manufacturing capacity. He estimates that present US annual artillery production would last for about two weeks of combat in Ukraine. In more recent times there are estimates about Russian use of as much as 60,000 rounds/day.

It may appear that with precision guided weapons, a smaller number of weapons will be required to get the job done. However, precise information about targets is lacking, and the military is reduced to shooting at numerous low probability targets. There are more pathways to target acquisition owing to drones, low earth satellites, night vision, etc., and therefore there are more opportunities to use ammo per unit time. The Ukrainian Armed Forces (UAF) innovated with their new `GIS Art for Artillery' system, where rumoured gains on the delays in the kill loop run from 20 minutes to 30 seconds. As a consequence, modern wars are facing production constraints. As an example, in the small air war in Libya in 2011, the UK and France quickly ran out of precision guided munitions (PGMs).

Such problems with the peak-to-base ratio are not unique to ammo. Consider medical oxygen. The peak load in the delta wave was much bigger than normal times. Alongside this, the bulk of the oxygen production is in the economically advanced peninsula, the biggest demand was in the Hindi heartland, and transporting oxygen is difficult as the refrigerated trucks cannot go at over 25 kph.

Or consider surgical masks and personal protective equipment (PPE). The peak demand during the pandemic perhaps went up by 50 to 100 times when compared with normal times.

Or consider medical education. With students returning from Ukraine, there was a small surge in demand for medical education in India. In a healthy economy, there should be a supply response. In a well functioning society, the resource allocation is not fixed.

Or consider electricity. Electricity demand peaks in the evening, windmills are unreliable, the sun shines in the day and can be obscured by clouds. There is substantial intra-day variation of demand (that is quite predictable), but supply is unpredictable and has a different natural intra-day variation. The puzzle of the energy system lies in dealing with the peak-to-trough ratio.

How should we think about such problems? How does the price system respond to these challenges? Is there market failure? What, if anything, is the role for the state in improving things?

Using more civilian parts

To the extent that commercial, off-the-shelf technologies ("COTS") are utilised, this eases the problem as very large sourcing can be achieved in the normal world of business. This calls for a new kind of design philosophy for military equipment, which emphasises the use of as much as possible of industrial parts that are readily sourced. This leaves a smaller problem of exotic specialised components which require special solutions for surge manufacturing.  

Surge capacity as an option

The right but not the obligation to buy is an option. When the buyer has the right, but not the obligation, to buy 2$\times$ more or 20$\times$ more from the seller, at a preset price, this is an option.

As we know from the field of options, options are always valuable (i.e. they come with a non-zero cost). Being there with excess capacity is not free, for the seller. And, the value of the option goes up when there is more volatility. While financial options loom large in the imagination, the world is full of real options.

Surge capacity in the price system

Prices move, from moment to moment, till supply equals demand. When faced with a shortage, prices go up so as to ration out many prospective buyers. And, equally, those high prices tickle firms into producing more. Vast amounts of patience and intelligence are put in, by buyers and sellers, in order to reduce demand (e.g. by finding substitutes) and increase supply (e.g. by producing in innovative ways). Every surge in prices contains the seeds of its own demise, as buyers establish alternatives, and through the supply surge that follows. Covid vaccines were always going to be a short hot market, and production is now being shut down.

When demand surges or supply drops, the price system sends out signals for firms to produce more through high prices. This tends to be accompanied by a lot of hand-wringing about shortages and high prices. If you think "something should be done", to increase output, you should be happy at what is unleashed by the price system, as there is no force more powerful than high prices, in encouraging buyers to buy less and sellers to produce more.

The market economy is not a bureaucracy; it thinks in all sorts of creative ways. If the price of oxygen is high enough, steel factories will stop making steel and sell oxygen into the public market. If prices go sufficiently high, oxygen cylinders from the Indian peninsula, and from abroad, would be airlifted into the Hindi heartland. The sources of increased supply will always surprise us.

But with the best of effort, mobilising enhanced output is hard and takes time. There is a cost to reallocating the resources of the economy, in order to shift from making widget $x$ to widget $y$. The price system finds this reallocation at the lowest cost to society, at the lowest disruption to society, without harming the incentives for sound behaviour and long-term growth.

Many times, a disruption on the output side is also a disruption of the inputs of the firm. When vast increases in output are required, the inputs (whether physical raw materials or the precise human capital) also become costlier. Both supply and demand curves change in many a surge. Such a combination of factors exacerbates the price rise.

Resilience

The price system will sort things out, in the sense of finding the price at which demand equals supply. It is interesting to go one step further and ask: How big was the supply response, of masks produced per day at its peak divided by masks produced per day before the pandemic? A more resilient economy is one where the price system induces a bigger output surge in a shorter time while requiring a smaller rise in the price.

Alternatively, we can focus on quantities and wonder, under what conditions can very large surges be achieved? We can identify a few sources of resilience.

Complexity in the production process

In a country where many things are produced, and in a country with deep pools of skilled people, there will be more headroom for adaptation. If there is a civilian aircraft industry, it can more easily retool to make military craft. If the world's biggest vaccine manufacturer is in our backyard, it can license a good vaccine from abroad and mass produce it. Tractor factories can become tank factories. For a contrast, a country like Saudi Arabia or Russia has simple structures of production, and the price system has inferior raw material to work with.

A successful software tool
is one that was used to do something
undreamed of by its author.
-- S. C. Johnson

The most important ingredient is the human capital: the managers, the chemists who know multiple routes to get to a given molecule, the creative people who can hack a machine or a software system to do things that were `undreamed of by its author'. Resilience comes from deep pools of these individuals, who are sparked into self-interested action by the price system. It is equally about the raw STEM knowledge, and about the creative thinking of the business folk who see profit opportunities, who imagine new kinds of deals, who innovate. These pools of capacity lie in the private sector. Even when a government-controlled system has the creative people, it does not have the incentives for them to think, take risks, innovate, and solve problems.

Of particular importance are adjacent products and dual-use technologies. A factory that makes vaccines can be the right starting point to rapidly get a factory to make Covid vaccines. Cylindrical engineering products made using special alloys, for civilian applications, can be rapidly retooled to make ammo. The lowest costs for augmenting supply come from the presence of these neighbours to the desired product.

In normal times, the optimal structure of production tends to become monolithic. The market tends to collapse into a small set of firms and techniques of production. Monolithic methods of production are inherently risky. When crises come along, we see the value of more diversified and more eclectic methods of production. Price surges, in a crisis, create profit opportunities for obscure strategies for production, and obscure producers. These occasional bouts of profiteering serve to keep these obscure firms, these option sellers, alive.

Private sector confidence

The private sector will stand ready with option-like capabilities, it will be alert, it will move mountains to produce when there is a price surge, all in search of one outcome: high profit rates in those brief extreme moments. A society that views supernormal profits as unjust, and tries to expropriate these private firms, is a society where private firms will layer risk premia on top of their ordinary market-based responses. In other words, we would require an even bigger price surge to elicit the supply side response when the probability of expropriation of the firms goes up.

Globalisation

High domestic prices incite imports; the productive capacity of the whole world is brought to bear upon the shortage within one country. Covid vaccine manufacturing in India was about an Indian facility that licensed a British vaccine design, and used numerous imported materials. A deep engagement with globalisation also increases resilience by fostering higher human capital of the elite. An inward oriented economy, with barriers to cross-border activities in the laws and in the minds, is likely to be less resilient.

Storage

If more ammo, oxygen or PPE are held in storage, this creates greater resilience. There is no free lunch; this storage has costs in terms of the opportunity cost of capital, the cost of storage and depreciation. Someone has to pay for this.

Production capacity that has an upside

Consider a factory that makes ammo. If the private person has a contract where there are the assembly lines and staff running at 1 shift, but are ready to jump up to 3 shifts, then there is headroom for a 3$\times$ increase in output. Sometimes assembly lines can be designed in a way where additional workers can be added and the line then runs faster. This can potentially create space for another 2$\times$ increase in output.

In the case of oxygen, firms in the field of industrial gases can have additional equipment on standby, through which medical oxygen output can go up on demand.

As with storage, there is no free lunch. The private firm would have to have contracts with skilled workers in order to be able to surge the production on demand, and design a production system with this kind of headroom. As with the `disaster recovery systems' in the world of software, the principal should randomly trigger these provisions every once in a while, and verify that each agent is indeed able to surge output as promised under contract.

Capabilities in government contracting

When there was a sudden requirement to fly students from Warsaw to India, the best pathway lies in the government rapidly running an auction, where global airlines compete to deliver the lowest price. Surge capacity for the state lies in the combination of (a) A capable and innovative private sector and (b) A state that is able to enter into contracts with private persons.

Is there a role for public policy here?

Fighting wars is a service that is produced by the government. The strategic planners in the field come up with a requirements document such as `We need to be able to sustain a war for 3 months where we are using 100 tubes a day'. Establishing this level of surge capacity is required as part of production of the public good of defence.

In the case of health, what is required is a careful counting of deaths owing to Covid-19, and assessing the number of deaths which are attributable to the shortage of medical oxygen. A careful analysis is then required, where the statistical value of a life is compared against the costs to society of higher surge capacity for oxygen. If a certain enhanced surge capacity for oxygen is able to save lives, while spending less than the statistical value of a life, there is market failure, and then there is a case for public policy to think about state action.

The fact that there is a surge in oxygen demand does not necessarily imply that there is market failure. We can envision private hospitals propositioning health insurance companies and to individuals, saying that they have established the following kinds of surge capacity for oxygen. This is not unlike the work that private hospitals do, in order to assure themselves of electricity in the event of a disaster. We should skeptically evaluate whether we want a government to do something.

Consider the field of masks and other personal protective equipment (PPE) at the early stages of the pandemic. When demand went up by 50 to 100 times, prices skyrocketed. Some policy makers were red in the face and barged into the economy, with export bans, with efforts to supplant the managers of private firms and organise production. But the right response was to do precisely nothing. High prices created near-magical responses by the private sector; there was a surge of import and production, and competition drove down prices.

State intervention that harms surge capacity

When the price system gets going, solving the mismatch between supply and demand through high prices, we often get many calls for state intervention into the working of the economy with tools like price limits and ordering private firms to operate in certain ways. It is ironic that the very feature that incites more production and reduces the demand -- high prices -- is what irritates some people.

Firms will earn supernormal profits in a surge. These supernormal profits are the fair return for (a) The hard work to modify production capacity in a short time; (b) The alertness and risk taking when faced with an incipient surge; and (c) The long years of holding option-like capabilities which are not earning high returns in normal times. When a society begrudges these supernormal profits, and uses state power to expropriate firms, the response of firms is to be less alert, take less risk, do less hard work in modifying production capacity and hold less real options, all of which worsens the problem faced by society in responding to the surge.

To commandeer resources, to order private firms, without proper compensation, is expropriation. During the second wave, many private firms were forced to stop their production in order to transfer oxygen to medical applications. If they were not compensated for their lost production, this constitutes expropriation.

Price controls hamper the very process of healing. High prices kick off the modification of the resource allocation in order to produce more and consume less. When policy makers use state coercion to force transactions to take place at artificially low prices, this reduces both responses. The one thing worse than a price that moves rapidly by a lot is one that does not.

There are always eclectic and opportunistic firms that jump into the fray and reap huge profits when a certain situation presents itself. These firms might even earn nothing at normal times, and just provide options to society. When the state interferes with the 'profiteering', their viability is adversely impacted.

State intervention that gets surge capacity at an excessive cost

One path to having the requisite amount of peak ammunition is to build a large number of public sector factories, which are idle in normal times, where the full cost of a factory is paid and the workers do nothing. While this does get the job done, it is an inefficient path; it does not harness the cleverness of private firms to get the same surge capacity at a lower price.

When there is a shortage in the country, it is tempting to ban exports. This appears to augment supply in the country, and bring down prices, in the short term. But it harms the trust of all firms to produce in India and thus harms India's long-term growth.

The Indian state attacked firms who were importing oxygen concentrators at the time of their peak demand [example]. This amplified the required rate of return for doing this important work.

The most damaging state interventions are those that directly control the resource allocation (e.g. forcing factories to close down so as to grab their oxygen), and in violating the rule of law with outright threats to coerce private persons. When the state becomes such a bull in the china shop, it tends to disrupt the complexity and sophistication of the resource allocation of the market economy. This encourages private people to produce less in India.

The discussion here, of unwise state intervention, is related to the problem of supply chain resilience when faced with Chinese exports of APIs to the Indian drugs industry. There also, it is possible to do clumsy things. Bambawale et. al. 2021 show how to do this better, how to go with the grain of the price system.

Going with the grain of the price system for surge capacity in ammunition

In the field of defence, strategic thinking should ideally generate a requirements document such as `We need to be able to sustain a war for 3 months where we are using 100 tubes a day'. Alongside this, there may be a peacetime requirement of 5 tubes a day, i.e. a peak-to-base ratio of $20\times$. This problem would get handed off to defence economics.

The best way for defence economics to solve this problem is to undertake the following kind of contract:

1. To ask for multiple private vendors who add up to a peak capacity of 100 tubes/day while actually running every day in peacetime at one-twentieth this rate;
2. The private firms would find the cost-minimising paths for obtaining this flexibility in production, and they would do this better than a PSU or a government department;
3. Each private firm would be subjected to random fire drills, where they are asked to suddenly up their production by $20\times$ for a period of $n$ days with $n < 90$.

In this procedure, we have fixed the surge capacity and are procuring on the price. Alternatively, the procurement can fix the price of the tube, and ask for bids which promise the highest surge capacity.

Through this, the energy and intelligence of the private sector would be brought to bear on the problem of obtaining surge capacity for the public goods of defence. It is better to have multiple private vendors, rather than one, so as to avoid single points of failure/attack, and to set off the spiral of quality where private firms compete with each other to deliver bigger surge capacity at a lower price.

This requires complexity in government contracting. Government contracting is a critical homeostatic capability that is required by all states, which works poorly in India. This is an important field for research.

Once contracts are in place, state actors must work within the rule of law: they must not not coerce private persons to behave in ways which were not contracted. Once the Indian state has behaved correctly for a few generations, the private sector will become more comfortable, and will require reduced safety factors in their pricing.

I thank Akshay Jaitly, Amrita Agarwal and Pranay Kotasthane for useful conversations.

Implications of free transmission of renewable energy

by Akshay Jaitly and Ajay Shah.

Inter-state electricity transmission

Transporting electricity across long distances requires investments in the transmission system where high voltages are used to minimise losses. An emphasis on renewable electricity generation requires significant new transmission capacity to transport electricity from the natural locations for generation (e.g. Himalayan hydel, or SPV in Rajasthan) to the centres of consumption in the peninsula. In an announcement in December 2021, 23 inter-state transmission system (ISTS) projects have been initiated by the government, at a cost of Rs.159 billion.

As with other elements of the electricity system, investments in transmission would ideally be done through the price system, where the price for transmission is discovered on a market. Once the price system is in motion, present or anticipated high prices would create incentives for investment in transmission. The structure of the Indian electricity market does not permit this: as this announcement of 23 projects shows, we effectively have a centrally planned system where officials control the resource allocation, and only bring in private firms as vendors playing a defined role in a centrally planned system. Transmission investments and prices are largely government controlled, and not discovered through the price system, which always involves misallocation of resources.

In the remainder of this article, we discuss the outlook on ISTS and its implications for renewable energy. To summarise ISTS, it is an electricity grid that runs across the entire country. It connects to end-points who are either generators or users. There is a process, and there are rules and capacity constraints, which determine whether a given person gets on to ISTS. Once a person is physically on ISTS, they are directly buying and selling from others on ISTS; these transactions are immune to the policies of the local discom. There is one constraint: the buyer and seller on ISTS cannot be within the same state.

Special prices for transmission of renewables

The CERC (Sharing of Inter-State Transmission Charges and Losses) Regulations, 2010 had some remarkable clauses: 7(u) and 7(v) established that for a period of three years, solar generation would be charged zero rates for transmission charges or losses. This suggested a world where a solar generator could sell to any buyer in India with no friction from transportation. These zero charges have been expanded and carried forward to cover all renewable energy commissioned till 30 June 2025. For renewable energy projects commissioned prior to 30 June 2025, for a period of 25 years, there will be no charge for transmission. For projects commissioned from 30 June 2025 onwards, the charges come back in gradually, to a level of 100% of the normal charge for projects commissioned after 1 July 2028. This creates a special deal for any renewables project that gets to the finish date by 30 June 2025.

Open access through discoms: In the present legal system, discoms are supposed to give out ‘open access’, where a buyer and seller of electricity are able to privately negotiate transactions, and have guaranteed access to the transportation services of the discom for the transport or electricity within or outside the state. In practice, this de jure situation does not map out into the de facto: many discoms refuse to provide or otherwise impede these services, as they would like to continue overcharging their best customers.

Open access through ISTS: Transmission across the state border through the ISTS seems to offer an increasingly viable way out of this barrier. It appears that when a renewables generator connected to the ISTS network sells to a third party outside the state who is also connected to the ISTS network through a PPA, neither of the two discoms can impede the transaction. This has been possible for a while, but the expansion of ISTS mentioned above will make such transactions more accessible to a wider range of sellers and buyers.

We could thus have generator $A$ in Dahanu (at the north end of Maharashtra) who is unable to sell to a buyer $B$ in Palghar (40 kilometres away), but she would be able to sell to a buyer $C$ who is across the state border in Vapi (at the south end of Gujarat, 70 kilometres away), assuming that connectivity to ISTS exists.

Implications

There are two kinds of ‘free’ in the title of this article. One refers to transportation of electricity without paying for it. Another refers to economic freedom: rational transactions under open access which are impeded and disincentivised within and across states (between a renewables generator and a buyer) and those using ISTS that are seemingly encouraged across the state border. What are the implications of these two kinds of free coming together?

There is no free lunch. When transportation is subsidised for renewables, someone has to pay for this. This can either be an explicit on-budget subsidy, or it can be a within-sector subsidy. In the Indian case, when government-owned transmission utilities undercharge transmission for renewables, this comes with higher prices for fossil fuel generators. Such tax-and-subsidy policies normally require sophisticated public finance analysis, which is not visible, thereby elevating the risk of unanticipated effects.

The ability of renewables generators to frictionlessly transport electricity across state borders is likely to significantly impact upon the distorted pricing being run by discoms. The paying customers (C&I) in any state have a strong incentive to cut the discom out of the transaction and directly buy from any generator. In addition, some C&I customers have ESG equity investors, and need to demonstrate they are using renewable energy. Both imperatives create incentives for C&I customers in each state to find a renewables generator somewhere in India (but not in their own state, where ISTS transactions are absent), and buy directly, thus avoiding the exaggerated prices charged by the discom and freeing themselves from their often unreliable service.

We will have situations where a Gujarat renewables generator will sell to a Maharashtra C&I customer, while at the same time a Maharashtra renewables generator will sell to a Gujarat C&I customer. At an engineering level, transmission between two states would only take place in one direction, and the two streams would get netted out. This would yield the efficient outcome where in each state, buyers and sellers achieve higher economic freedom, and are less controlled by the discom.

Zero or low pricing for transmission of renewables has been around for a while, but earlier there were capacity constraints in inter-state transmission which was holding back this process. The substantial expansion of the ISTS described above would help translate the threat of exit by an increasing number of C&I users into a reality. The rise of ESG investment is also relatively recent. We would hence hazard a guess that these transactions will become more important by 2023 and 2024.

In a recent paper, we argued that the Indian electricity sector in 2021 or 2022 is different from what was seen in the preceding 30 years. While electricity went along a muddled path of non-reform for decades, while private participation only came into the edges of a fundamentally centrally planned system, the stress on the incumbent system is mounting. We are coming to the point where the good old ways are untenable. Inexpensive ISTS, which enables C&I customers to buy cheap renewables from across the state border, adds to this scenario. Other recent developments are also pushing discom finances over the edge [example].

We expect that increased ISTS access will increase economic freedom, and help private investors think more in terms of market opportunities rather than regulatory constraints. But this present moment of the policy configuration will also not be seen as stable, for a 25-year horizon, by private investors. What the state giveth, it can equally take away. All in all, we expect that discom finances will weaken, the ROE in renewables will go up, but the impact upon investment will be somewhat muted owing to fears about the next string of policy actions.

How did courts respond to the pandemic lockdowns: evidence from the NCLT

by Pavithra Manivannan, Susan Thomas and Bhargavi Zaveri-Shah.

Introduction

An important problem of the Indian state is the working of the judiciary, which is hampered by procedural frictions and delays. Several research papers measure the output of the judiciary in terms of number of cases disposed and the elapsed time from start to finish (DAKSH (2016), NALSAR (2016), Regy and Roy (2016), Datta et. al (2017), Tata Trust (2019), Vidhi Centre for Legal Policy (2021)). While recognising that the end objective of a sound judiciary is to decide cases correctly, these practical measures of the output of the judiciary are interesting in capturing what the judicial performance is at any point in time, as well as how it changes from one point to the next.

An example of such an episode is the COVID-19 pandemic. This event disrupted all economic and social processes in India, including the working of courts. Service organisations all over the world responded by building an all-digital workflow. With digital adaptations, many service organisations have matched upon pre-pandemic levels of output and productivity. We analyse the quarterly results of listed non-finance services firms for 2019, 2020 and 2021, for the April-May-June quarter. The total net sales of the firms was Rs.2.87 trillion, Rs.2.2 trillion and Rs.3 trillion. These firms had output in 2021 that was similar to that seen in 2019.

In case of the judiciary, the response included selecting urgent matters for hearing, as well as adopting e-filing and virtual hearings as the norm. How did the judiciary in India fare during the lockdowns that were put in place during the peak of the pandemic, once in 2020 and another in 2021?

Sharma and Zaveri (2020) examined the response of the Indian judiciary during the pandemic. They introduced an important innovation in the literature on court performance, by constructing a data-set of outputs based on cause-lists of the NCLT. They used this data-set to examine the relative output of the NCLT during the first pandemic lockdown (25 March 2020 to 30 June 2020) to the output in the pre-lockdown period in 2020 (1 February 2020 to 24 March 2020).

In this study, we carry this research agenda forward. We argue that a useful quantitative measure of output is the number of cases scheduled per day and cases disposed per day. We use this to examine the extent to which the output of the NCLT changed during their repeated exposure to pandemic triggered lockdown conditions. We examine these for three comparable periods in 2019, 2020 and 2021. In this, we recognise that the NCLT added courtrooms during the pandemic period of 2020, which can influence the NCLT output. We also recognise that the NCLT scheduled hearings only for urgent matters, and that the complexity of the matters scheduled can impact the number of disposals. We introduce a classification scheme of complexity of cases, and examine the extent to which the number of cases disposed responds to metrics of case complexity.

Methodology

As with Sharma and Zaveri (2020), our data-set is constructed from the cause-lists of the NCLT. In this article, we measure the months of March, April and May for 2019, 2020 and 2021. We focus on the same three months in each year for two reasons: One, it controls for any variation that may arise due to seasonal factors, such as court vacations and festivals. Second, India saw the peak of the pandemic in these three months in both 2020 and 2021.

The daily cause-lists for each of these periods are available for 11 out of 15 benches of the NCLT. Our analysis is focused on those benches which consistently published cause-lists during each of these three periods. These were the benches of Cuttack, Jaipur, Kolkota, Mumbai and New Delhi (including the Principal bench). This data-set makes it possible to observe the number of cases scheduled on each day and the number of cases disposed. If the NCLT is viewed as a black box, its performance can be measured by the number of cases disposed. (As stated before, there is a quality dimension, which is not addressed in this quantitative research).

When the systems of the NCLT are augmented, whether by introducing additional courtrooms or technology and technology led processes, we expect a scaling up of the number of cases disposed per courtroom per day. In addition to the per day averages, we focus on hearings scheduled and disposals per courtroom per day to understand the extent to which this took place.

When the pandemic began and only urgent matters were scheduled, there could be a selection bias on the part of both plaintiffs and judges to emphasise important and urgent cases. This could generate an increase or decrease in the complexity of cases which, in turn, could impact the measured output of the court. In order to explore this problem, we construct a measure of complexity of cases. For this, we categorise each hearing under five heads: Insolvency and Bankruptcy Code (IBC), Oppression and Mismanagement (O & M) under the Companies Act (CA), Schemes, Strike off Appeals and Miscellaneous. We classify IBC and O & M matters as Complex and all the others as Simple. This allows us to examine the extent to which the observed changes in output have been influenced by a change in complexity.

Results

Table 1: Average daily NCLT output

Period	Hearings	Disposals
Mar - May 2019	399	65
Mar - May 2020	149	30
Mar - May 2021	255	48

In 2019, NCLT scheduled 399 hearings per day and disposed 65 cases per day. Table 1 shows us that, in 2020, in the aftermath of the first extreme lockdown, the output of NCLT dropped both in terms of scheduled hearings (149) and disposed cases (30). It then partially increased in 2021 (255 hearings per day and 48 disposed cases per day). This demonstrates resilience in the NCLT capacity during the second wave, in 2021.

Some benches of the NCLT had a higher number of courtrooms in 2020 and 2021. For example, the number of courtrooms in New Delhi went from 4 in 2019 to 6 in 2020 and 2021. Similarly, in Mumbai, it increased from 3 in 2019 to 5 in 2020 and 2021. On the other hand, the courtrooms for the Kolkata, Cuttack and Jaipur benches remained constant during all three periods. Some of the increased outcomes in 2021 may be owed to the increased number of courtrooms.

In order to control for this feature, we focus on the average disposals per courtroom per day. Table 2 shows that there was a 66% decline from 2019 to 2020, and then a 50% rise in 2021. The final level – 3 disposals per courtroom per day – was half than seen before the pandemic, but better than during the first wave in 2020. This suggests that the addition of courtrooms alone did not significantly alter the output of the NCLT. Wide-scale adoption of technology such as video-conferencing facilities that enabled the NCLT to operate without exposing the members to the virus is likely to have contributed to these improvements in outcome.

Table 2: NCLT output, measured as the average per courtroom per day

Period	Hearings	Disposals
Mar - May 2019	36	6
Mar - May 2020	10	2
Mar - May 2021	17	3

NCLT hears matters of varying complexity. Time taken to dispose off a complex matter might be higher due to the procedures, technicalities and stages involved. The increased outcome in 2021 could have been achieved by NCLT by merely altering the scheduling proportion of complex v. simple cases. We examine whether such a selection bias contributed to higher disposals in 2021.

Table 3: The role of case complexity

Period	Complex	Complex	Simple	Simple
	Hearings	Disposal	Hearings	Disposal
Mar - May 2019	263	35	126	29
Mar - May 2020	102	13	44	16
Mar - May 2021	199	33	50	14

Table 3 shows that the proportion of complex vs. simple cases scheduled for a day, is greater in 2021 than in the pre-pandemic period 2019. In terms of disposal, in 2019, complex and simple cases disposed were of a similar order of magnitude (35 complex cases a day vs. 29 simple cases per day). In 2021, there is evidence of a greater proportion of complex cases being disposed off: 33 complex cases a day vs. 14 simple cases per day. This shift in the case load, in favour of more complex cases, would mean that the increased output of NCLT in 2021 is not out of scheduling larger fraction of simple cases. But this shift would ordinarily go with a reduction in output per courtroom per day, holding productivity constant.

Discussion

The working of the judiciary has deep ramifications on the working of the economy which depends upon timely and just decisions on disputes. While the ultimate objective is that cases should be decided correctly, there is an emerging literature which emphasises quantitative measures of the output of courts. This is an interesting and important line of questioning, even without bringing in the analysis of the quality of court judgements, because it helps to identify and understand the response of the court to disruptions such as the COVID-19 pandemic.

The evidence here shows that NCLT was disposing 65 cases per day under pre-pandemic conditions. In the worst pandemic conditions in 2020, this output dropped to 30 cases disposed per day. Under similar conditions in 2021, output was higher at 48 cases disposed per day.

Did additional courtrooms that were added in 2020 help explain this rise? When output is measured per courtroom per day, there was a decline in 2020 to 2 cases per courtroom per day from a disposal of 6 cases per courtroom per day in 2019. The output went up to 3 cases disposed per courtroom per day in 2021. This is an improvement in the NCLT output, even if it is still at a level which is half of that seen under pre-pandemic conditions, and resulting productivity gain.

Was the output higher because the case mix emphasised more simple cases? This was not the case. On the contrary, there was a shift in favour of more complex cases. In our evidence, complex cases went up from 55% of disposals in 2019 to 70% in 2021. As these cases would be expected to require more time, this constitutes a partial explanation for the reduced output per courtroom seen in 2021 when compared with 2019.

A third factor is the technology and the digital processes adopted and refined by the NCLT after the strict lockdown imposed in 2020 was lifted. The evidence in our study shows that these new processes yielded the NCLT gains in 2021 when compared with 2020.

The Indian law fraternity is debating whether it would be beneficial to revert to physical functioning of courts as opposed to going further into the video environment (Press Trust of India, 2021). In our data, we see that, NCLT productivity was at 3 disposals per courtroom per day in pandemic environment of 2021, as compared with 6 disposals per courtroom per day in the pre-pandemic environment of 2019. These facts can help shape judgement about future possibilities.

References

DAKSH, Access to Justice Survey, Technical report 2016.

Pratik Datta, Surya Prakash B. S. and Renuka Sane, Understanding judicial delay at the Income Tax Appellate Tribunal in India, NIPFP Working Paper No. 208, October 2017

NALSAR University of Law, A study of court management techniques for improving the efficiency of subordinate courts, Technical report 2016.

Prasanth V. Regy and Shubho Roy, Understanding judicial delays in debt tribunals, NIPFP Working Paper No. 195, April 2017.

Tata Trust 2019, India Justice Report: Ranking states on police, judiciary, prison and legal aid, Technical report 2019.

Vidhi Centre for Legal Policy, The Delhi High Court Roster review: A step towards judicial performance evaluation, Technical report 2021.

Anjali Sharma and Bhargavi Zaveri (2020), Measuring court output in the pandemic: evidence from India’s largest commercial tribunal The LEAP blog, 11 September 2020.

Press Trust of India (2021), Continuance of courts virtually will be a problem’: SC on resuming physical hearing, Business Standard, 8 November 2021 at 

Acknowledgements

Pavithra Manivannan is a Research Associate and Susan Thomas is a Senior Research Fellow, both at XKDR Forum in Mumbai. Bhargavi Zaveri-Shah is a doctoral candidate at the National University of Singapore. We thank Pramod Rao, M. S. Sahoo, Ajay Shah, Anjali Sharma and Diya Uday for comments and suggestions.

Economic stress in Russia

by Ajay Shah.

The Russian economy has faced a series of adverse shocks after the invasion of Ukraine:

• Many de facto restrictions have emerged upon international trade,
• Many foreign companies have chosen to pull out or restrict activities in Russia, spanning non-financial and financial firms,
• Many individuals living in Russia have chosen to emigrate; these are likely to be high skill people.

We may think it is not hard for Russia to absorb these shocks. After all until 1991 it was the USSR, a land of central planning and autarky. We think they will just go back to those ways. However, the recent events are likely to impose substantial costs for the Russian economy.

Russia is no longer a centrally planned economy

It sounds funny, in today's world, to think of officials owning a target for exports, to think of officials making calculations about how much steel will be required in the light of what the five-year plan has envisaged for building railway lines. But that non-market mechanism for thinking and allocating resources did exist in the USSR (as it did in India).

That institutional capacity has been lost after 1991, and it cannot be quickly recreated. Now, Russia is a capitalist economy. The shocks will be dealt with by the price system in its usual ways.

Disruptions in the price system

Within the domain of the price system, trade and FDI have a deep influence upon the structure of production. Every modern economy involves millions of decisions about what to produce and how to produce. These decisions are made in a decentralised way, and millions of contracts are in place that govern the purchases and sales of each firm.

When 10% or 30% of these relationships are disrupted, it adds up to a storm in the economy. Yes, production can be reconfigured in a self-reliant way (and self-reliance will always induce greater poverty), but that takes time. There is a period of extremely volatile prices, of shortages, where every firm is cautiously waiting for the dust to settle before establishing a new set of self-reliant contracts. Millions of negotiations have to take place, to get a new set of production relationships going. There is a learning process where some contracts fall into place, and then prices change, and then once again some contracts are disrupted or renegotiated, and so on.

When the price system is humming, it is a marvel to behold, and when it is disrupted, getting back to normalcy (even the low level normalcy of self-reliance) is hard.

In the case of Russia, foreign goods and foreign technology are particularly important. They are an economy organised around selling natural resources and importing everything else. Hence, cutting off ties to the rest of the world will be particularly painful. Russia is more like Saudi Arabia and less like India in this regard.

Finance is the brain of the economy

Every real sector decision is shaped by finance. To get to the correct decisions in the real sector, we need finance to be operating correctly.

Russian finance is not operating correctly. The Moscow stock exchange was closed down on 25 February. For a month, the economy has not known stock prices. It is difficult for managers to make real sector decisions without the direction that stock prices provide. Conversely, the lack of observation of stock prices induces private decision makers to wait and see.

The credit market is also disrupted. Foreign banks have a position of about $120 billion (about 8 per cent of GDP) and are downgrading or exiting their role in the economy. Many borrower firms have a cashflow crisis owing to fluctuations in the economy, and would default on banks. A large scale banking crisis is likely. These fears, in turn, would hamper the ability of banks to fund real sector firms in rebuilding for a world of self-reliance.

The mind of the firm

In this thinking, it's important to go into the minds of the key persons of Russian firms. They are debating and thinking to themselves: Will I default on debt? What will happen when there is a default? What will input and output prices be a year from now? How can I put my skills to the best use in this environment, so as to buy locally and sell locally and make a profit? How do I address the departures of some of my employees? Should I leave? How much emotional and financial resource should I commit to overcoming this crisis? Do I just wait this out, and there will be a regime change, and we will go back to globalisation?

Many firms will choose to lie low and wait for the storm to end, as opposed to jumping to action in reconfiguring production for a new world of self-reliance. This inaction will increase the short term pain in the economy and increase the time required to get back to a humming economy.

The threat of emergency central planning

While Russia evolved into a market economy in the post-1991 period, in every society, when faced with a war and an economic crisis, there is a greater danger of central planning by the state. For an analogy, think of the behaviour of Indian officials when faced with Covid-19. In a crisis, there is a greater risk of abandoning the price system, of officials giving orders to firms. The lack of rule of law and constitutionalism in Russia implies that there is more of a free hand for officials to behave like this.

To the extent that central planning resurges in Russia, it will make things worse.

Conclusions

There are three levels of bad economic performance.

Economic performance is bad when there is self reliance.

It gets worse when we layer self reliance with central planning.

It is worst when the self reliance and central planning are brought in suddenly.

In steady state, Yes, it is possible to do self-reliance. We know that self-reliance will induce mis-allocation of resources and a low GDP, but it can be done. A sustained estrangement by Russia will taken them back to conditions reminiscent of the old USSR or the self-reliant India of old.

But getting to that (poor) state is itself a difficult task. In the short term, the Russian economy is in even worse shape than the mere self-reliance scenario.

The fact that the USSR was once the prime exponent of central planning and autarky does not mean that it is easy for today's Russia to readily go back to autarky and central planning. Russia now operates in the price system; the institutional capacity for central planning has atrophied and cannot be readily recreated. The sudden difficulties in trade, FDI, and finance, create a very difficult environment for every private firm. Self-reliant structures of production can indeed be created, and they will achieve a low level performance of the economy, but it will take years to get there, to reconstruct the complexity of the modern economy in a self-reliant way. In the short term, there will be a large scale economic collapse.

I have previously argued that freezing central bank assets is not that important. But the rest of the economic sanctions are an imposing barrier, that will likely induce an economic collapse, even without considering the direct cost of waging war.

I am grateful to Alex Etra and Josh Felman for useful discussions.

The industry structure of India's large firms: IT is the biggest industry

by Ajay Shah.

When trade liberalisation took place, roughly 1991-2007, there was large turbulence in the structure of production in India. It's interesting to take stock and wonder: What is the present industry structure of the large firms of India?

The overall output of the country is, of course, made up of both large and small firms. Small firms are illegible, so we know much less about what is going out in the vast informal sector. What we do observe with confidence is the large firms. So, while we recognise that the industry structure of the large firms is not the industry structure of the overall economy, we examine this here.

We look at the 26,040 non-financial firms where data is visible for 2018-19 in the CMIE database. For each firm, we focus on gross value added (GVA) and the wages paid. Our crude estimator of GVA, from the income side, is profit before tax (PBT) + depreciation + wages. All values are nominal.

Industry	No. firms	Wages	GVA	Share in	Share in
		(Rs. Trn.)	(Rs. Trn.)	wages (%)	GVA (%)
Information technology	1117	3.83	5.56	30.92	22.84
Chemicals	1997	0.95	3.44	7.69	14.12
Mining	175	0.64	1.82	5.16	7.49
Transport equipment	899	0.69	1.78	5.60	7.30
Miscellaneous services	4043	1.08	1.37	8.75	5.62
Metals, metal products	1525	0.48	1.34	3.86	5.51
Wholesale, retail trading	4378	0.61	1.19	4.93	4.90
Food, agro-based products	1645	0.40	1.06	3.27	4.37
Machinery	1591	0.51	0.98	4.08	4.03
Electricity generation	640	0.30	0.88	2.41	3.62
Electricity trans., distn.	120	0.47	0.66	3.77	2.69
Consumer goods	645	0.25	0.58	2.02	2.38
Construction materials	416	0.18	0.58	1.43	2.36
Transport services	783	0.45	0.57	3.67	2.36
Ind., infr. construction	2255	0.39	0.54	3.11	2.21
Communication services	153	0.32	0.50	2.55	2.07
Textiles	1111	0.31	0.50	2.48	2.04
Misc. manufacturing	1005	0.14	0.35	1.15	1.44
Div. non-fin. services	692	0.20	0.31	1.58	1.26
Div. manufacturing	82	0.05	0.13	0.40	0.55
Hotels, tourism	564	0.12	0.13	0.93	0.54
Real estate	204	0.03	0.07	0.26	0.29
All non-fin. firms	26040	12.39	24.35	100.00	100.00

IT was the most important industry: with 30.92% of the wages and 22.84% of the GVA. This category includes computer services and IT-enabled services.

There are six big industries, which have atleast 5\% of either wages or GVA, and they are: IT, Chemicals, Mining, Transport equipment, Misc. (non-financial) services, and metals. The fortunes of the economy are now primarily about the fortunes of these six industries, which add up to about 60 per cent of the total.

It's surprising, how little is going on in some labour-intensive industires like textiles which is at 2.48 or 2.04 per cent.

The mean firm size (overall) is Rs.935 million of GVA or about \$12 million. In the case of IT, the mean firm is bigger, at about Rs.4,978 million or \$65 million.

Some will read read this table and jump to calls for industrial policy that favours these six industries. This would work poorly, as all industrial policy does. But this table should influence our thinking on the prioritisation of the public goods that will serve the six most important industries, and most notably IT.

India is in a position of strength in IT. This is inconsistent with the language of weakness in a lot of policy thinking on IT, where we see an emphasis upon national champions and protectionism [example]. It is in India's best interests to favour an open global order for the IT industry, but the Indian state tends to argue for a world of narrow domestic walls.

India's supply chain vulnerability with Chinese APIs: Industrial policy vs. sophisticated policy design

by Gautam Bambawale, Vijay Kelkar, Raghunath Mashelkar, Ganesh Natarajan, Ajit Ranade, Ajay Shah.

India has a remarkable drugs industry. This involves a high dependence upon Chinese manufacturers of `active pharmaceutical ingredients' (APIs). Given the willingness of the Chinese state to behave in unusual ways in economic engagement (e.g. rare earths), there is a certain supply chain risk that is faced by Indian firms.

Should state power be used in addressing this problem? And if so, how should this be done? How do we avoid the long decades of failure in industrial policy, i.e. the experiments with policy pathways where a government picks winners, with a government that claims to know the correct ways in which production should be organised? Today we saw a fascinating article: Drugmakers cry ‘monopoly’ as Modi govt picks 1 firm each to make over 20 key raw materials by Himani Chandna in The Print. This narrates the story of a 1960s style Indian industrial policy intervention played out poorly.

Our book Checkmate China: Winning through strategic patience and accelerated economic growth is forthcoming from Rupa Publications later this year. A paper based on this book has been released in the public domain and summarises our strategic thinking for India about the China question. In the book, we have a treatment of the API question. This text is excerpted ahead. It represents our attempt at learning from 75 years of failure with industrial policy. This approach would have likely avoided the difficulties described in Himani Chandna's article.

Book excerpt: Designing a government intervention to address the supply chain risk faced by Indian firms that import APIs from China

The Indian drugs industry is a heavy user of Active Pharmaceutical Ingredients (APIs) sourced from China. In an environment where we see China as a bad actor in the global economy, where Chinese nationalism can harm counterparties abroad, this presents a risk to the supply chain. It is easy to design Indian economic nationalism which can combat this. However, as with all aspects of industrial policy, such use of state power raises many concerns. It is difficult for a government agency to know whether a certain industry merits subsidies and whether certain firms merit subsidies. There is a long history, in India, of “infant industry” arguments being used for decades, in which some well-connected Indian firms stay infants and continuously collect fiscal subsidies. Similarly, trade barriers in the form of quantity restrictions are prohibited under the WTO and tariffs are harmful and should best be avoided.

Thus, we face a puzzle: How can state intervention be designed, which can make a difference to India’s China problem with the supply of APIs? Given the failures of industrial policy as it was practiced in previous decades, how can this one sharp problem (supply chain risk faced by Indian pharma companies who rely on Chinese producers of APIs) be addressed by state action? How can this state action be done at the minimum fiscal cost, and while imposing the minimum distortions upon the economy? How can the risk of central planning – of officials determining the outcomes of the market-based competitive process – be avoided?

When faced with supply chain risk with a certain API from China, we should not jump to the conclusion that the answer lies in making the API in India. Perhaps the efficient solution is to import the API from a country other than China. Perhaps the efficient solution is to make it in India. Policy makers cannot assume that India has competitive advantage in making the API, when private persons have thus far chosen to not build such factories in India.

The first step in every policy analysis must be a thorough understanding of the behaviour of the private sector assuming there is zero state intervention. When faced with this new supply chain risk, what are Indian drug companies likely to do out of self interest:

1. Customers of these bulk drugs would be conscious about the business risk that they carry. They would watch the rise of nationalism in China with concern.
2. They would increasingly seek to diversify their sourcing. As an example, we are seeing Fortune 500 companies increasingly reduce the share of China in their global production.
3. One important response by the firms will be to buy APIs from countries other than China, e.g. Taiwan or Japan or Brazil. This is perfectly adequate solution, from the viewpoint of an Indian firm, to the threat of Chinese nationalism. Our problems with Chinese nationalism only imply that we should diversify away from China; this does not justify self-reliance.
4. One element of the process of looking for non-China sourcing is higher demand for firms in India that make APIs, which would kick off a supply response. Ordinarily, this market process will work itself out. But it is a difficult and slow journey. A government program can be designed that addresses this problem, which has a few key features: (a) We do not assume that in the long run India will be a successful producer of APIs, but we consider this possible; (b) The intervention is pre-announced and in a few years, liquidates itself; (c) The intervention imposes zero trade barriers upon imports or exports of APIs or drugs with respect to any country.

This proposed intervention would involve the following steps:

• A government agency would identify the top 50 APIs and the quantities $q = (q1, q2, .. q50)$ which are being imported from China.
• We establish the objective of domestic production that comes up to half of the imports from China over a five year period. This suggests escalation of quantities as: $0.1q, 0.2q, 0.3q, 0.4q, 0.5q$ over a period of five years.
• We put out a binding commitment on the part of the state that the government will run procurement restricted to domestic producers only, where there will be purchases over the next five years of these quantities. The government will commit to placing orders with 3 lowest-cost firms that produce in India, in each year’s bidding. The requirement from a bidder should be that production is done in India. Foreign or Indian firms should be permissible, subject to a restriction against firms controlled by the Chinese state e.g. bar a firm where any one member of the board of directors is an employee of the Chinese state or the CCP.
• These commitments about a rising scale of GOI procurement will create incentives for Indian/foreign firms, located in India, to build knowledge and physical capacity to produce APIs at a large scale.
• The government agency has only one objective: to trigger off economies of scale and competition by producers in India. Once the goods are purchased by the Indian government agency, what is it to do with them? Indian firms might not like to buy these APIs at the purchase price, as the purchase price may well be higher than the world price of these APIs. Once the goods are purchased, this agency would run a global auction to sell the same goods off, at the highest possible price. Indian drug companies could potentially choose to buy these goods, but these purchases would be at an import-parity-pricing price. As a consequence, through this program, the Indian government would be drop shipping the goods, purchased in the make-in-India auction to buyers who came into the sell-from-India auction.

This scheme constitutes a promise to buy from Indian firms, at rising quantities over five years, at the lowest prices that Indian firms are able to muster (3 firms for each product in each year). At first, the price in India will be high. Under this proposal, GOI will instantly turn around and sell off the goods at the highest possible price through a global tender. The gap between the two prices will be the fiscal subsidy that is being put down, to spark off API production in India.

At the end of five years, the domestic firms would be on their own. If the theory of change is correct – that there is a fixed cost of building knowledge and facilities to make APIs – then this is the minimum intervention that gets the job done. If the theory of change is incorrect – that India is not actually a good platform for making APIs – then in five years, this fiscal outgo would end, and India would not be a producer of APIs.

There are many strengths of this design:

1. Private persons face no new coercion, other than the coercion implicit in mobilising tax resources which are the source of government spending on this program.
2. There is no tariff; there is no interference in international trade. This program is layered on top of a free trade system.
3. It is a simple and transparent intervention. What it requires is the bureaucratic capability in the Indian state to do procurement: to run these auctions, to buy APIs in India, and to sell the same goods globally, doing high volumes of non-complex commodities. Indian officials are not asked to form a judgement about what APIs are important, about whether an API can efficiently be made in India, about the technology through which an API can be made, about whether public money should be used to build factories to make APIs.
4. There is a lack of fudge factors where there can be lobbying and negotiations.
5. No central planner should ever assume s/he knows the way forward. This design respects the possibility that India might actually have no place in API production. In this case, at the end of this program, there will be no API manufacturing in India. The program would have wasted taxpayer resources, but it would not distort the economy.

However, there are four main difficulties of this design:

1. For the desired impact upon incentives of private firms who should commit themselves to investing in building large scale API production, the private sector would have to believe that the deeds of the government will match the words of the government over the coming five years. If private persons feel that the Indian state cannot be trusted to stay the course for five years, then the incentive impact of the government program would not materialise.
2. The private sector has to feel safe engaging with government procurement; it has to believe that the procurement will be done correctly, that payments will be made on time, that there will be no investigations by agencies.
3. If this works, at the end of five years, Indian API vendors will lobby to not shut this down. Every policy designed to support an infant industry ends up with entrenched infants who like to wield state power in their favour.
4. While the objective of the program should be to foster Indian or foreign firms who choose to produce in India, there is the possibility that this could be skewed to favour Indian firms.

An important change of course by policy in Indian Covid-19 vaccination

by Amrita Agarwal and Ajay Shah.

Strategy for Covid-19 and vaccination

A global race took place on building vaccines for Sars-Cov-2. By late 2020, it became clear that vaccine development was progressing rather well.

With the vaccines in sight, the standard economics knowledge about vaccination came into play. Each vaccinated person reduces the possibility of spread of the disease. While the individual who gets a vaccine is gaining protection, that individual is also imposing a positive externality upon the population. There is a market failure -- a positive externality -- as an individual would tend to under-spend on buying a vaccine. There is a case for state financing, to augment personal expenditure on personal protection, to tip more people over into vaccination. The end goal of vaccination is not to vaccinate everyone, but to change the disease dynamics by achieving herd immunity.

A debate took place in India in 2020 about two alternative pathways to roll out vaccines, on a significant scale.

On one hand was the vision of a centrally planned program, where the government would control everything, and the citizenry would obediently wait for their turn. This involved (a) Using the coercive power of the state to block any vaccination activities in India other than the union government, and (b) Organising a nationwide vaccination program at the union government. This was similar to the vaccination efforts prevalent in many other countries.

An alternative approach involved recognising that in India, state capacity is limited. A centrally planned effort was likely to work out poorly. It was better to harness all the energy available in the country to do more vaccination -- whether it was at a state government, city government, club, association, educational campus, private non-health firm, health care firms, etc. This involved (a) Not using the coercive power of the state to block any other energy in vaccination, alongside (b) Some work on vaccination by state organisations in order to address market failure. An example of this perspective is in an article from 30 November, and this talk, at an NCAER event on 29 December 2020. Shruti Rajagopalan, Mihir Sharma, Naushad Forbes were some of the thinkers who wrote on this.

In the event, decision makers in government chose the first path. There were difficulties [8 March, 5 April]. Using data for 19 April 2021, the New York Times tracker shows India at rank 62 in the world, with 1.2% of the population fully vaccinated, in roughly the league of Malaysia (rank 61) at 1.4% or Bangladesh (rank 64) at 1.0%.

At present, the union government is able to push out 3.5 million doses a day. Looking forward, the rate achieved (by the unreconstructed union government program) is likely to go down:

1. It is likely that the process design used, in any centrally planned union government program, would work for one (hopefully modal) use case, but peter out once we reach out beyond this zone.
2. The present vaccine production for the Indian market [SII, Bharat Biotech] is below the required 100 million doses a month.

By this reasoning, the present run rate, of 3.5 million doses a day, may not be sustainable. If we are to get to half the Indian people fully vaccinated in the coming four months, this requires about 10 million doses a day or 300 million doses a month. We need to get up to 10 million doses/day and we face difficulties in maintaining the present rate of 3.5 million doses/day.

An important change in course

On 19 April, the union government has announced an important change in course. The nature of state coercion will now change as follows:

1. Indian vaccine makers are forced to sell half their output to the union government, at an unspecified price, the remaining half being available for sale to state governments and private persons in India (at a price that must be publicly disclosed),
2. Private firms which perform vaccination services are forced to publicly announce the price at which these services are provided,
3. All providers of vaccination services are forced to supply data to the union government's CoWin IT system, and
4. Private persons and state governments are free to import vaccines.

This is important progress. The union government has stepped back from blocking every other energy in the country in vaccination. State governments, and private persons, will be able to buy/import vaccines and run vaccination programs. Vaccine makers remain in the grip of central planning in the new world, but it is a step forward when half of their output can be sold to state governments and private persons at market-based prices.

Implications

The 19 April decisions harness energy in thousands of organisations all across the country. Some state governments and many private organisations will now be able to embark on vaccination efforts. Each of them would tailor their process designs for local conditions and the practical problems as seen by them. The sum total of resourcing and energy that would go into vaccination, in India, would go up. This would improve the overall progress in conquering the pandemic.

The private sector will surprise us with innovation in business models, billing arrangements, etc. Perhaps some firms will find it easier to deliver the one-dose J&J vaccine in difficult locations. Perhaps telecom companies will call their vast subscriber base and sell vaccination services. Private firms know how to segment the users into a large number of categories, and devise strategies for each of them. This is what a union government, which solves for one use case, is ill suited for.

In the short run, it is hard for SII to drastically change its production. Import of vaccines holds the key. In the world market for vaccines, a buyer asks for a price quotation at a certain quantity. These prices are on the decline. Vaccine supply in India will go up through imports.

There are some concerns about the AZ vaccine with younger persons and particularly with young women. Availability of mRNA vaccines in India would help address the needs of these users.

If India were an AZ vaccine monoculture, there is greater vulnerability to a new strain that is able to breakthrough. The self-organising system will bring diverse vaccines to play into the Indian populace, and generate greater pandemic security.

The second wave will not be the last one. Existing vaccine makers will regularly make booster doses through which people will become safe against new variants. Covid-19 is only one among many infectious diseases which call for sustained large-scale adult vaccination programs. The work done this year, flowing from the 19 April decisions, will matter not just in conquering the second wave. Thousands of organisations in India need to view this as a sustained activity. As an example, it would make sense for every large employer to organise quarterly vaccination camps for their employees and their family members, through which an array of adult vaccines are regularly delivered.

Improvements required in the policy framework

The pathway to elicit better production by private firms does not lie in coercing them with quantity restrictions or dictating terms on issues such as price. Such coercion will bring out reduced output by Indian manufacturers. We learned, in the 1960s and 1970s, that it is impossible for a state organisation to go inside the firm, and discuss elements of the cost function with the firm. It is not the job of the state to be a financial service provider for a firm. There should be market-based engagement with vaccine producers, that is free of coercion, and couched in the language of prices, quantities and foreign competition.

Some vaccines are distinctly less efficacious and/or more dangerous than others. The union government can play a useful role by wielding its coercive power to limit the vaccines that are permitted for use in India to the class of vaccines which have achieved approval in an advanced economy such as the US, UK, Japan or Germany.

The use of coercive power by the union government, to harvest data through CoWin, raises concerns given the absence of legal protections against state access to the data. State surveillance is particularly harmful when it comes to health data, so enhanced state legibility will have unintended consequences. This program of capturing data will exacerbate vaccine hesitancy.

The decisions of 19 April are important and will get India up from 3.5 million doses a day. It is, however, likely that we will not get up to 10 million doses a day. It is useful to think about two distinct problems on the demand side:

The rich

If protecting a family of five costs Rs.5,000 to Rs.10,000, many individuals / employers will spend this much. While a positive externality influences the decision making, the decision will be correct as long as the personal gains from protection exceed the price of the vaccine.

The poor

Many poor families will balk at this magnitude of expenditure. This is where market failure bites, in generating the wrong decision because there is a gap between the gains to society as a whole vs. the gains for the individual. State governments and the union government need to step into this breach. Vaccine vouchers are the precise instrument through which this market failure can be addressed.

Conclusion

Central planning has worked well for Covid-19 vaccination in countries like the US and the UK. These countries intelligently used private sector energy [example] as opposed to many varieties of coercion. But central planning works poorly under conditions of low state capacity. It is better to harness the energy of the self-organising system.

The 19 April announcements make important progress in stepping back from a centrally planned system, in increasing freedom, and in harnessing the power of the self-organising system.

The role of the state lies in addressing market failure. There is a need to use the coercive power of the state to require that vaccines used in India must have achieved approval in an advanced country. Poor people will make better decisions when nudged towards vaccination through the tool of vouchers.

The market for Covid-19 vaccines and the tipping point to herd immunity

by Ajay Shah.

Many firms are developing Covid-19 vaccines. Enormous resources have to be deployed, up front, to develop a vaccine and to build manufacturing capacity. It is likely that many vaccines will get through to approval in mature regulatory regimes. Not all vaccines will work identically for all situations, e.g. some vaccines may work better for an elderly person than others.

It is commonly assumed that the global market size for a Covid-19 vaccine is about 6 billion people. In this article, we argue that this might not be the case. Let's think about the situation in the market once one or more vaccine reaches the market.

The buyers perspective before vaccine sales have commenced

The private gain for an individual from buying a vaccine are shaped by the probability of getting sick when leading an unconstrained life. This is shaped by the extent to which Covid-19 has burned through the communities that the person plans to engage with. As an example, in the slums of Bombay or Delhi, herd immunity has set in. A person living there knows that few people in her circles are now getting sick, and she feels relatively safe. Well known factors such as age and co-morbidities will also shape the threat perception of each person. Therefore, for her, the gains from a vaccine are relatively modest, and the willingness to pay is small.

In each city of the world, there is a different numerical value for the attack rate (the fraction of people who are infectious) and the extent of immunity. The state of the epidemic in Pune is different from that in Bombay. As time passes, each city is inching towards herd immunity, and the passage of time thus diminishes interest in paying for a vaccine. Vaccine IP and manufacturing facilities are wasting assets.

It it were possible to develop a combination of tests that add up to an `immunity passport', then the price of this test and the odds of coming out positive would shape the demand function for the vaccine.

Progress on immunisation and herd immunity

Into this world, let us imagine that the sale of multiple vaccines commences. At first, there would be a rush of demand and high prices. As immunisation progresses, the attack rate would go down and the gains from buying the vaccine would further go down. In places like Bombay and Delhi, where a considerable proportion of the population has already been exposed to the disease, when a modest fraction of the population is vaccinated, this could tip the population over into herd immunity, and the disease could die down.

In such a world, vaccine makers face the prospect of a short hot market. At first, vaccine demand will be high and the factories will not be able to keep pace. Competition will come about and that will exert pressure on prices. In a city like Bombay, with about 20 million people, after (say) 5 million persons buy the vaccine, this may significantly change the threat perception in the eyes of the average individual. Vaccine demand would then decline.

Under such numerical values, the market potential in Bombay is not roughly \$50 $\times$ 20 million people or \$1 billion, but perhaps more like \$25 $\times$ 5 million people or about \$125 million.

All of this reduced revenue potential will go to the first few firms that get 5 million doses into the Bombay market. Competition would exert downward pressure on the price, demand would tail off as herd immunity sets in, and there would be a price crash. The late comers would flood the market with output but would obtain low revenues in return.

The vaccine demand collapse in a simple model and in the real world

We have always known that a vaccine is not just a private good; there is a positive externality. The novel idea of this article is about tipping points.

Consider a simple model in which herd immunity is achieved at 60%. Suppose 50% of the population is already immune and knows it. The first 10% that gets the vaccine tip the system over to $R_0<1$ and then the fires start dying out. Once the fires start dying out, the attack rate goes down, the threat perception changes, and the incentive for private people to buy the vaccine drops a lot. Under these conditions, the positive externality imposed by vaccine purchase by the early vaccine buyers, upon the overall system, is particularly large.

A key factor that drives behaviour in this model is that when a person is immune, she knows it and then has no incentive to buy a vaccine. In the real world, people don't know whether they are immune, and would be more inclined to buy a vaccine just to be safe. In the limit, the veil of ignorance is complete, nobody is able to assess the threat, and everyone wants to buy a vaccine.

In the real world, the veil of ignorance is not complete. At every place, people do have a personal judgement about the threat level based on the extent to which their friends and family are getting sick (or not) per month. Age and co-morbidities will also shape vaccine demand. As a general principle, it is always wise to think that humans are sentient optimising creatures. Individuals have a noisy estimator of the threat that they face and this will shape their willingness to pay for a vaccine.

Wall street tells Main street what to do

These problems feed into the thought process of private firms and shape the commitments of capital to the problems of vaccine development and manufacturing when faced with a novel epidemic. 

Numerous vaccines are under development. The process of vaccine approval is necessarily slow. At present, we generally think that over time, one by one, many of these vaccines will get through to the market. By the reasoning of this article, the first few will get through, within a few months the market will collapse, and all funding will be yanked for other projects. This will be a bit reminiscent of how funding for vaccines against Sars-Cov-1 was abruptly yanked when the funders realised that Sars-Cov-1 had reached $R_0<1$.

The numerical values used here (e.g. 60% for herd immunity, 5 million immunised in Bombay to tip over into herd immunity, $50, etc.) are of course purely illustrative. To translate these ideas into practical calculations requires data on the extent to which immunity has come about. In many places worldwide, there are good estimates of the persons who have antibodies, but there is more to immunity than measured antibodies. In India, the information available about the state of the disease in (say) Bombay is rather poor.

If we take this dynamics of the vaccine market seriously, vaccine makers have an incentive to create such datasets. Alongside the construction of such datasets, there is a need for derivatives trading on underlyings such as the fraction of Bombay residents who have antibodies.

The argument of this article is a special case of the long-standing problems of incentives for vaccine development. An effective pathway for state intervention, and philanthropic capital, lies in offering contracts for R&D and manufacturing which change the incentives of private persons to engage in these activities.

Implications

To the extent that this reasoning is correct, individuals will at first face a vaccine market with high prices and shortages. For many individuals, particularly for low-risk persons, there is a tradeoff between paying more to get the vaccine early versus paying less to get it late or even to not get vaccinated if the pandemic has subsided.

For firms with a vaccine under development, this article paints a winner-takes-all scenario, where the first few vendors who get output on scale will capture all the revenue. To the extent that this reasoning is correct, plodding along to the finish line late will induce low revenues.

For policy makers and philanthropic capital, it is important to avoid a `coronavirus winter', a collapse in coronavirus research of the kind which happened after the SARS epidemic achieved $R_0<1$. There is enormous knowledge, and capable teams, which has been created by the early gold rush of building vaccines against SARS-Cov-2. This knowledge should not be lost. As an example, it would be nice if research groups will publish research papers and release code before they put out the lights. We need to think of the sustainable frameworks, where we achieve a new normal of high R&D into pathogens that can trigger pandemics.