## Thursday, November 19, 2020

### Author: Sudipto Banerjee

Sudipto Banerjee is a researcher at the National Institute of Public Finance and Policy.

## Wednesday, November 18, 2020

### The problem of minimum public shareholding in public sector enterprises

by Sudipto Banerjee, Sarang Moharir, Renuka Sane.

In 2009-10, the government of India increased the minimum public shareholding (MPS) threshold for listed companies from 10% to 25%. The government's rationale for the MPS is that a minimum public float of shares addresses secondary market imperfections like concentration of shares and price manipulation. The Securities and Exchange Board of India (SEBI) has specified several methods that listed firms can use to expedite their MPS compliance. One of the methods is the offer for sale of shares through the stock exchange (OFS-SE). This was introduced in 2012 to facilitate compliance in a broad-based and transparent manner. Prior to the OFS-SE, the government divested shares through OFS by issuing a prospectus. This was a cumbersome and time-consuming process. Since 2012, the government has used the OFS-SE method to undertake disinvestment of CPSEs to meet the MPS threshold.

In 2010, when the Securities Contract (Regulations) Rules were amended [Rule 19A(1)] to increase the MPS threshold from 10% to 25%, listed Central Public Sector Enterprises (CPSEs) were exempted. The government withdrew the exemption in 2014, and set a deadline of August 2017 for compliance with the MPS. This was extended by a year to 2018 and again by two years to 2020. Recently, listed CPSEs got another extension of one year till August 2021. Despite the extensions, 37 CPSEs out of the total 77 listed CPSEs had not met the MPS requirement as on December 31, 2019.

As we approach the August 2021 deadline, we ask if disinvestments through the OFS-SE route have achieved the 25% MPS target. This question is relevant for all disinvestments. We, however, focus on the one's done through OFS-SE as this route was designed to meet the MPS threshold. This study is useful for two reasons. First, it gives us a sense of how much more disinvestment the government has to undertake to meet the MPS. Second, the government's record on meeting the MPS threshold for CPSEs sends a strong signal of its own commitment to the MPS.

### Methodology

We sourced transaction data from BSEPSU. We only consider CPSEs where at least 5% stake was divested through the OFS-SE route between 2012 and 2019. This gives us a sample of 22 CPSEs (with 31 transactions) out of the total 77 CPSEs.

Since OFS-SE is a secondary market transaction, details like name of the purchaser, the number of shares purchased and the final sales price are not available in the public domain. Therefore, we studied each annual report issued in the year of the OFS-SE transaction to document the change in the shareholding pattern of the top ten shareholders. Further, we used these changes to identify the possible purchaser of shares. For example, 5% stake of Power Finance Corporation (PFC) was divested in 2015; LIC's shareholding in PFC increased from 4.81% to 9.08% in the same year. We assume that LIC purchased a stake in the OFS-SE transaction of PFC in 2015.

### Results: other CPSEs as shareholders

Table 1 shows the shareholding of CPSEs that had undergone OFS-SE as of March 2019. Public shareholding contains CPSE shareholding (column 4) i.e., shares held by other CPSEs in these companies. Since CPSEs are themselves government owned, it is useful to evaluate public shareholding after removing their holdings. As an example, National Fertilizers Ltd. has a public shareholding of 25.29% and meets the MPS requirement of 25%. The following CPSEs are listed under the public shareholding category of National Fertilizers Ltd., i.e. LIC (11.31%), NIA (1.76%), GIC (1.48%), Canara Bank (0.69%), OIC (0.29%). The total share of these firms (15.53%) is deducted from the public share of National Fertilizers (25.29%). Public shareholding of National Fertilizers at 9.76% does not meet the MPS threshold. When the share of CPSEs is excluded from the public shareholding category, 13 out of the 22 CPSEs failed to meet the MPS requirement as of March 2019.

Table 1: Shareholding of CPSEs that have undergone OFS-SE (March 2019)
Company Promoters’ share-holding Public share-holding Shareholding of CPSEs (included within public shareholding) Whether MPS requirement is met when share of CPSE is not considered?
BHARAT ELECTRONICS LTD. 55.93% 44.07% LIC (3.61%) Yes
COAL INDIA LTD. 69.26% 30.74% LIC (10.94%), LIFE INSURANCE CORPORATION OF INDIA P & GS FUND (2.18%) No
CONTAINER CORP. OF INDIA LTD. 54.80% 45.20% LIC (3.08%) Yes
ENGINEERS INDIA LTD. 52.00% 48.00% LIC (4%) Yes
HINDUSTAN COPPER LTD. 76.05% 23.95% LIC (12.14%) No
INDIAN OIL CORP. LTD. 51.50% 48.50% ONGC (14.20%), LIC (6.51%), OIL (5.16%), IOC SHARES TRUST (2.48%) No
INDIA TOURISM DEVELOPMENT CORP. LTD. 87.03% 12.97% LIC (3.22%), NIC (0.13%) No
MMTC LTD. 89.93% 10.07% LIC (3.39%), UIC (0.24%), GIC (0.18%), NIA (0.11%) No
MOIL LTD. 65.69% 34.31% LIC NEW ENDOWMENT PLUS BALANCED FUND (7.12%), UIC (1.05%), NIA (0.35%), OIC (0.46%) Yes
NATIONAL ALUMINIUM CO. LTD. 52.00% 48.00% LIC (8.2%), NIC (0.61%) Yes
NATIONAL FERTILIZERS LTD. 74.71% 25.29% LIC (11.31%), NIA (1.76%), GIC (1.48%), CANARA BANK (0.69%), OIC (0.29%) No
NBCC (INDIA) LTD. 65.93% 34.07% LIFE INSURANCE CORPORATION OF INDIA P & GS FUND (6.55%), SBI(0.48%) Yes
NHPC LTD. 73.33% 26.67% LIC (7.31%), PFCL (2.43%), REC (1.75%) No
NLC INDIA LTD. 80.85% 19.15% LIC (3.34%), UTI (0.83%), NIA (0.47%) No
NMDC LTD. 72.28% 27.72% LIC (12.9%), LIC NEW ENDOWMENT PLUS BALANCED FUND (2.03%), SBI (0.38%), NIA (0.34%) No
NTPC LTD. 54.50% 45.50% LIC JEEVAN PLUS NON UNIT FUND (11.51%) Yes
OIL INDIA LTD. 59.57% 40.43% LIFE INSURANCE CORPORATION OF INDIA P & GS FUND (12.19%), IOCL (4.71%), HPCL (2.47%), BPCL (2.47%) No
OIL & NATURAL GAS CORP. LTD. 62.98% 37.02% Yes
RASHTRIYA CHEMICALS AND FERTILIZERS LTD. 75.00% 25.00% LIC (2.07%), NIA (0.60%) No
REC LTD. 52.63% 47.37% LIC (2.30%), CPSE ETF (3.57%) Yes
STATE TRADING CORP.OF INDIA LTD. 90.00% 10.00% LIC (0.91%), NIA (0.89%), OIC (0.07%) No
STEEL AUTHORITY OF INDIA LTD. 75.00% 25.00% LIC (9.60%), LIC MARKET PLUS 1 GROWTH FUND (1.24%), LIFE INSURANCE CORPORATION OF INDIA P & GS FUND (0.63%) No

Source: Company Annual reports

### Results: LIC as shareholder

Table 2 indicates an increase in shareholding of LIC (whose 100% shares are held by the government) in the CPSEs post OFS-SE transactions. As an example, Hindustan Copper went through disinvestment in FY16 and FY17. This lead to a decrease in government shareholding from 89.95% in 2016 to 76.05% in 2017, at the end of the two transactions. Shares of LIC increased from 5.27% at the beginning of FY16 to 12.14% in FY18. Similarly, National Fertilizers was disinvested in FY17, where the government's share decreased from 92.5% to 80%. Shares held by LIC in the company had increased from 4.16% to 11.32% in FY18.

Table 2: OFS-SE transactions and purchases by LIC
Name of entity Year Stake divested LIC's share before disinvestment LIC's share post disinvestment
NMDC FY12 10% 5% 5.54%
RASHTRIYA CHEMICALS AND FERTILIZERS LTD. FY13 12.5% 0.87% 6.45%
NTPC FY13 9.5% 5.91% 7.66%
NALCO FY13 6.09% 3.25% 6.02%
SAIL FY14 5% 6.61% 10.11%
COAL INDIA LTD. FY15 10% 2.10% 7.24%
DREDGING CORP. OF INDIA LTD. FY15 5% 2.99% 5.86%
POWER FINANCE CORP. LTD. FY15 5% 4.81% 9.08%
NHPC FY16 11.36% 3.11% 8.83%
HINDUSTAN COPPER LTD. FY16 7% 5.27% 10.70%
CONTAINER CORP. OF INDIA LTD. FY16 5% 1.03% 3.05%
NBCC FY16 15% 0% 8.11%
HINDUSTAN COPPER LTD. FY17 6.83% 11.14% 14.25%
NATIONAL FERTILIZERS LTD. FY17 15% 4.16% 11.32%
MOIL FY17 10% 3.84% 7.11%
COAL INDIA LTD. FY18 5.19% 8.97% 10.94%

Source: Annual reports

In the sample of 31 transactions concerning the 22 CPSEs selected for our study, the Life Insurance Corporation (LIC) increased its holding in 16 transactions. For six transactions, the top ten shareholders' names were not disclosed in the annual reports. LIC's equity did not change in the remaining nine transactions.

### Conclusion

Out of the total 77 NSE-listed CPSEs, 37 CPSEs had not met the MPS threshold as on December 31, 2019. The government will have to do a lot more to achieve full compliance with the MPS by August 2021. Out of the 22 CPSEs that went through disinvestment by the OFS-SE route, 13 CPSEs do not meet the MPS once we exclude the share of CPSEs. LIC purchased equity in more than 50% of CPSEs in our sample.

One of objectives of disinvestment is to promote public ownership of CPSEs. This also provides an opportunity to citizens to participate in the wealth of CPSEs. The MPS also seeks to widen ownership in listed companies. Under the Securities Contracts (Regulation) Rules and SEBI (Issue of Capital and Listing Disclosure Requirements) Regulations, shareholding of CPSEs and LIC may be considered as public, but their inclusion does not align with the goals of either disinvestment or the MPS. This question also assumes relevance given CAG's recent observation (Para 1.3.2) that disinvestment from one public sector firm to another 'did not change' stake of the government in the disinvested CPSEs. Disinvestment which truly widens CPSE ownership to individuals and institutions outside of the government should be an important goal for policy.

The authors are researchers at the National Institute of Public Finance and Policy. The authors would like to thank Karthik Suresh and Srishti Sharma for useful discussions.

## Monday, November 16, 2020

### Get by with a little help from my friends (and shopkeepers): Household borrowing in response to Covid 19

by Renuka Sane and Ajay Shah

The lockdown in the early days of the Covid 19 pandemic in India impacted on on economic activity. Between April and August 2020, 18.9 million salaried people lost their jobs, difficulties were faced by migrant labourers, and small and medium businesses. Deshpande (2020) shows that overall employment dropped sharply post-lockdown, with larger drops for women than men. Household incomes were adversely affected. As an example, survey work by Lee, Sahai, Baylis and Greenstone (2020) shows that two months into the lockdown poor and non-migrant workers in Delhi saw a drop of 57% in their incomes, with 9 out of 10 workers reporting that their weekly income had fallen to zero. Bertrand, Krishnan and Schofield (2020) measure the fraction of households who say they are able to survive on their own for a week, and in April that value was 34% in the overall population and 50% or more for below-median household income.

How would households cope with such a shock? Economic theory suggests that households desire consumption smoothing. One mechanism for consumption smoothing is borrowing. For example, there was an increase in household borrowing after demonetisation (Karmarkar and Narayan, 2020; Wadhwa, 2019; Chakraborty and Sane, 2019). This connects to the working of the financial system. While India has made a lot of progress in ownership of a bank account, and increased electronic payments, access to formal credit remains low.

### What do we expect about household borrowings?

Borrowing during the Covid crisis is shaped by three factors:

1. Income transfers: The government of India announced a stimulus package worth Rs.1.7 trillion after the lockdown. This included food security measures as well as direct cash transfers to poor households. This may have helped households deal with the immediate crisis.

2. Low demand: As people were at home owing to the lockdown, demand may have been affected. It is also possible that households saw this job loss as permanent, and hence cut back on expenditures in a way they would not have had they seen this as a temporary disruption. This also fits with the view that precautionary savings increase after a deep crisis (Rajadhyaksha, 2020). However, this may be true for households in the higher income distributions, but is unlikely to be the case for those below median income.

3. Supply constraints: India's financial system has faced difficulties since 2012. This has manifested itself as business failure at ILFS and other financial firms, large and small. Credit growth was decelerating prior to the lockdown. The difficulties for the financial sector increased when the Reserve Bank of India announced a moratorium on all loan repayments for three months from March to May 2020, and then extended it for another three months. These moratoriums made it more difficult for financial firms to assess the credit quality of borrowers. Overall bank credit growth was 5.8% in September 2020 compared to 8.1% in September 2019. From 2018 onwards, when certain borrowers faced supply constraints, they would have had to deleverage (repaying old loans while not getting new ones) or default.

The grand question of the field consists of understanding the economic condition of households in India in 2020, in examining how consumption was held up through new kinds of labour supply and through borrowing, and in obtaining insights into these three distinct economic forces that are in play. In this article, we discover some new facts that contribute towards this overall research agenda.

### Methodology

We source data from the Consumer Pyramids Household Survey (CPHS) for the months of May, June, July and August from the years 2016 - 2020. The borrowing data comes from the Aspirational India table within CPHS. Using this we ask three questions:

1. Did households have debt outstanding at the time of the survey? This helps us understand the total number of borrowers in the economy.
2. What are the sources from whom households have outstanding borrowings? This tells us whether households borrow from the formal or the informal sector.
3. What is the purpose for which households have outstanding borrowings? This tells us if households are borrowing for consumption expenditure, for consumer durables, or for running their businesses.

CPHS does not provide information on the value of debt outstanding. We are, therefore, not able to analyse the impact on borrowing on an intensive margin. Our analysis is restricted to understanding the proportion of households borrowing from various sources, for various reasons, i.e. on the extensive margin. Household weights for each wave are provided by CPHS -- these are used to get population estimates.

### Results: The number of borrowers

Table 1 presents the number and percentage of households having debt outstanding in the months of May - August in each of the five years. The number of borrower households had been consistently increasing till 2019. In May - August 2016, 12% of the population had debt outstanding. This increased to 50% by 2019. The number, however, fell in 2020 to 45% of the population. The fall has been greater in urban regions than rural.

Table 1: Number and share of borrowers in the population
WAVE NATIONAL RURAL URBAN

in million in million in million
May - Aug 2016 34.8
(12.3%)
22.8
(12.0%)
11.9
(13.0%)
May - Aug 2017 81.9
(28.3%)
56.7
(29.1%)
25.1
(26.6%)
May - Aug 2018 136.0
(45.6%)
94.0
(46.8%)
42.0
(43.0%)
May - Aug 2019 154.7
(50.5%)
105.1
(51.1%)
49.6
(49.4%)
May - Aug 2020 141.6
(45.1%)
99.6
(47.2%)
42.0
(40.7%)

### Disentangling explanations: Sources of borrowing

Given that a large proportion of households did not have enough to live on for more than a couple of weeks, we would have expected a huge increase in the number of borrower households. In order to investigate the sources of this drop, we begin by analysing the role of the financial system in the household borrowing story by studying the sources of borrowing.

Table 2 presents the percentage of borrower households borrowing from each source. We find that the biggest drop in borrowing is from banks: in 2019, 26% of borrower households had borrowed from banks - this has dropped to 20% in 2020. The proportion of households borrowing from money lenders has also dropped - from 7% in 2019 to 4% in 2020. The drop in households borrowing from banks and money lenders was higher in urban regions than rural regions. There has been a lot of discussion in India about the increased risk aversion of banks. A fall in the number of borrower households may be a result of this phenomenon.

Table 2: Sources of borrowing
SOURCE May - Aug 2019
Rural
May - Aug 2019
Urban
May - Aug 2020
Rural
May - Aug 2020
Urban
Banks 26.6% 25.6% 21.9% 15.3%
Money Lenders 7.1% 7.1% 4.6% 3.4%
Employer 0.5% 1.4% 0.5% 1.0%
Relatives/Friends 14.5% 13.3% 21.1% 27.2%
Shops 52.0% 50.7% 57.6% 49.8%

There has been a concurrent rise in the number of households who have borrowed from friends and family from 14% in 2019 to 21% in rural regions, and from 13% to 27% in urban regions. The sharp increase in the borrowing from friends and family suggests that some smoothing of consumption expenditure is likely to have occurred using informal social networks that play an important role in the economic lives of those in developing countries (Munshi, 2014).

Household borrowing from shops increased in rural India - from 52% to 58%, and fell slightly in urban India. It is interesting to recall that Chakraborty and Sane (2019) had found that between the years 2016 and 2018 (i.e. after demonetisation), the biggest rise in borrowing was from shops, especially by those in the lower income deciles. This seems to be true in the current situation as well, especially in rural regions.

In difficult times, it was not banks, money lenders, and employers that mattered. It was friends and family, and the neighbourhood shops. It appears that non-financial firms and cash flow management by the retail supply chain have been more important than financial firms. The connections from the formal financial system to these shops could then be unusually influential.

### Disentangling explanations: Purpose of borrowing

Examining the purpose for which households borrow can tell us something about the demand for credit. Table 3 presents the top five reasons for borrowing in 2020, and compares it with 2019.

Table 3: Purpose of borrowing
SOURCE May - Aug 2019
Rural
May - Aug 2019
Urban
May - Aug 2020
Rural
May - Aug 2020
Urban
Consumption 62.3% 59.8% 70.1% 65.7%
Debt Repayment 7.3% 8.8% 9.1% 11.9%
Housing 7.3% 9.7% 2.4% 4.7%
Durables 5.1% 8.6% 1.6% 4.3%
Investments 6.4% 1.9% 0.4% 0.5%

In May-August 2019, 62% of rural and 60% of urban borrower households had borrowed for reasons of consumption expenditure. In May-August 2020, this had risen to 70% and 66% of rural and urban borrower households respectively. This is consistent with the importance of consumption smoothing, and of many households not having enough resources to survive for more than a few weeks. The increase for reasons of consumption expenditure is higher in urban regions. Urban India was more likely to be affected because of both the Covid infections and the intensity of the lockdown than rural India. Income transfers from the government are also likely to have targeted rural households than urban households.

There has been an increase in borrowing for business and debt-repayment reasons in this period as well. The numbers for rolling over debt went from about 9% to 12% of borrower households in urban regions, and from 7% to 9% of borrower households in rural regions. This suggests that households who would otherwise have serviced debt through business or personal income took recourse to borrowing when those cashflows subsided. A personal insolvency law that is able to provide some relief to debtors and allow for restructuring of the larger loans can help alleviate some of this stress.

The fall in the number of borrower households seems to be driven by the fall in the borrowings for housing, durables purchase and investments. It is also likely that large purchases such as housing and durables are made through bank loans. The fall in the borrowings from banks may be a result of a fall in these large durable purchases.

### Conclusion

We study the response of households on borrowings during the 2020 lockdown. We do not have data on the value of debt outstanding. We expected that there would be an increase in the number of households that borrow owing to the disruptions to economic activity. However, it is remarkable, that despite the large shock, overall, there has been a reduction in the number of households that borrow. This fall is driven by fewer households borrowing from banks, and fewer households borrowing for housing, and consumer durables purchases. Households continue to borrow for consumption expenditure, business and debt repayment. The most utilised sources of borrowing are friends and family and shops.

This work suggests many interesting possibilities for downstream research. For example, one can study the differences in borrowing patterns between households with different income and wealth profiles, as well as the correlation between sources and purpose of borrowing. It will also be possible to evaluate whether income transfers from the government led to a fall in the number of borrower households. Similarly, one can ask whether different health outcomes play a role in their borrowing outcomes.

The number of households choosing to borrow has been different from what happened after demonetisation. There may be several reasons for this - the magnitude of the disruption, the length of time for which it lasted, the possibility of more permanent impacts on labour markets among others. This leaves us with interesting research possibilities to understand household behaviour and their interaction with financial markets.

### References

Ashwini Deshpande (2020), The Covid-19 Pandemic and Lockdown: First Effects on Gender Gaps in Employment and Domestic Work in India, Working Paper 30, Ashoka University.

Azim Premji University (2019), "State of Working India 2019", Technical Report, Centre for Sustainable Employment.

Kaivan Munshi (2014), "Community Networks and the Process of Development", Journal of Economic Perspectives, 28(4), pp: 49-76.

Kenneth Lee, Harshil Sahai, Patrick Baylis, and Michael Greenstone (2020), "Job Loss and Behavioral Change: The Unprecedented Effects of the India Lockdown in Delhi", Working Paper, EPIC India.

Marianne Bertrand, Kaushik Krishnan, and Heather Schofield (2020), "How are Indian households coping under the COVID-19 lockdown? 8 key findings", Rustandy Centre for Social Sector Innovation, Chicago Booth.

Niranjan Rajadhyaksha (2020), "The covid shock could alter people's financial priorities", Livemint, 5 May 2020.

Sagar Wadhwa (2019), "Impact of demonetization on household consumption in India, Working paper.

Subhamoy Chakraborty and Renuka Sane (2019), "Household debt over time", The Leap Blog, 24 May 2019.

Sudipto Karmarkar and Abhinav Narayanan (2020), "Do households care about cash? Exploring the heterogeneous effects of India's demonetization", Journal of Asian Economics, 69.

Sane is a researcher at the National Institute of Public Finance and Policy, Shah is an independent scholar. We thank four anonymous referees, Kaushik Krishnan, Radhika Pandey and Anjali Sharma for useful comments.

## Position for researchers in public finance and public procurement

The Finance Research Group, Mumbai is an inter-disciplinary group of researchers working in the fields of financial markets, household and firm finance, bankruptcy law, land markets and public finance management and public procurement. In these fields, the group engages in academic and policy oriented research and advocacy.

An indicative list of the project outputs generated by the Finance Research Group is below:

The Finance Research Group is looking for two researchers to work on a project to understand the impact of public finance management and public procurement issues on the private sector:

1. A senior researcher, and
2. A research associate

### Requirements for position of Senior Researcher

As a senior researcher, you will be expected to take a lead on delivering on the project objectives. You will be part of the core group of this project, building a pipeline of research ideas, and executing them. This will mean pursuing independent research as well as supervising and advising team members in their research. The requirements for the role of a senior researcher are:

• You must have 5 plus years of work experience and very strong written and spoken English.
• A background in management/public economics/public policy will be preferred.
• One of the key deliverables of this project is a survey. Having experience with conducting field surveys and managing survey agencies will be desirable.

### Requirements for position of Research Associate

As a research associate, you will work on project deliverables under the supervision of a senior researcher. The requirements for the role of research associate are:

• A background in economics/science/management/data science/ computation/public economics/public policy will be preferred.
• Prior work experience of 1 -- 2 years will be desirable.
• A quantitative/computational orientation will be desirable.

### General requirements

The Finance Research Group functions on free and open source software systems like Linux, Latex, R and others. If appointed, you will be required to learn and use these software systems. You must be willing to adapt to technology, work long hours and deliver quality products within defined timelines.

You must be comfortable in working in an inter disciplinary research environment with people from varying backgrounds such as economics, law, public policy and data science. You must be curious and passionate about research and must be willing to work on independent outputs as well as in teams.

The remuneration offered will be commensurate with your skill and experience and will be comparable with what is found in other research institutions.

### Contact details

Interested candidates must email their resume with the subject line: Application for "Senior Researcher/ Research Associate" at the Finance Research Group, to Ms. Jyoti Manke at careersatFRG@gmail.com by 30th November, 2020.

## Tuesday, October 20, 2020

### Anticipating the Unintended, an India-focused public policy newsletter completes one year

Anticipating the Unintended, an Indian public policy newsletter, completes one year on October 29th. This newsletter is about frameworks, mental models, and key ideas to help readers think about public policy problems in imaginative ways. 78 editions of the newsletter have gone out thus far.

The newsletter aims to bring insights from economic reasoning to contemporary issues in Indian governance. The target audience is anyone interested in knowing why the Indian governments’ many good intentions often end up having suboptimal consequences.

Anticipating the Unintended has two types of editions. Every weekend goes out a review edition which has mini-essays in four sections: a PolicyWTF section on egregious public policies, India Policy Watch: a section on burning public policy issues in India, A Framework A Week section on tools for thinking about public policy, and Matsyanyaaya on foreign policy. The mid-week edition has long-form essays in public policy.

### Some highlight editions:

Subscribe to the newsletter here. To collaborate, write to antiunintended[at]gmail[dot]com

## Wednesday, October 14, 2020

### Introduction

An important idea in medical science is triage. It refers to the process of sorting patients for treatment, depending on the severity of their conditon and the likelihood of recovery. The medical triage process is governed by standard operating procedures (SOPs), which allow limited discretion to doctors and surgeons on the prioritisation of patients for treatment. Courts in India also perform a triage function, and they do this every day. They decide which cases will be scheduled for hearing on any given day and which will be heard on later dates. In a world of infinite court capacity, triage would not matter as much because all cases would come up for hearing in a short period of time. However, in the context of limited court capacity, triage becomes a critical element of the adjudication function. Unlike in the medical profession, in the judicial function, there are no settled rules or SOPs on how courts must triage. Given this, the decisions of courts on prioritising and de-prioritising matters are often the subject of intense scrutiny.

In ordinary times, case-scheduling is within the discretion of the judge and the court registry. While some judges pre-announce the manner in which they will prioritise matters for hearing, others do not. The practice of scheduling is often interrupted by matters that are 'urgent'. Urgent matters are taken up out of turn if the judge is convinced that there will be irreparable harm if the matter were not heard urgently. This makes triaging complex and discretionary enough in normal times.

Triaging becomes more complex in exceptional circumstances when courts are functioning at lesser than their usual capacity, such as, in the ongoing pandemic. Triaging in such exceptional circumstances is different from triaging in normal times. First, the nature of the "exceptional circumstance" might inherently offer some prioritisation. For example, during a pandemic, cases involving questions of public health would, at least intuitively, be more important than cases involving criminal defamation or suits for declaration of title to land. Second, unlike in routine triaging where courts prioritise matters, in exceptional circumstances, triaging is about de-prioritising matters. This makes the triaging decision more complex.

One such exceptional circumstance in the recent period was the announcement of the nationwide lockdown on 24th March. At the start of the lockdown, most Indian courts and tribunals restricted themselves to hearing only "urgent cases" through video conferencing (example, example and example). It is hard to pre-define the categories of cases that courts should consider urgent or non-urgent. Yet, there can be a common principal based framework for making this decision that can be applied depending on the kinds of cases that the court adjudicates. For example, in April 2020, when the courts in UK were functioning with limited capacity, the administrative body responsible for supporting the courts published guidance on Priority 1 cases and Priority 2 cases that the courts will hear. In the absence of such a framework in India, judicial triage in India during the pandemic continues to be done by the judges, the court registry or a combination of the two.

In this article, we ask the question: how did Indian courts perform this triage during the lockdown period? There is anecdotal evidence of inconsistency in practice across courts in determining the urgency of serious matters such as bail. Such evidence is valuable. However, data on patterns and the kind of cases that were heard by a court during the pandemic can shed light on how the courts actually perform this 'triage'. Such data-backed discourse on the prioritisation of cases at courts during the lockdown and otherwise, is currently missing.

We focus our question on the National Company Law Tribunal (NCLT), which is the largest commercial tribunal in India in terms of the number of laws it adjudicates. It adjudicates cases under the Companies Act, the Insolvency and Bankruptcy Code and the Limited Liability Partnership Act. It adjudicates a range of firm-related matters such as shareholder disputes, approvals for corporate actions and mergers and acquistions, proceedings against directors and companies and bankruptcy cases. In a previous article, we demonstrated the impact of the lockdown on the functioning of the NCLT. Using daily cause-lists as a source of our data, we found that there was a 95% drop in the number of cases heard by the NCLT during the lockdown period. With the NCLT functioning at such a low capacity, the question of prioritisation of cases at the NCLT is more critical as most of the cases were not likely to be heard during this period.

### Data and methods

In order to study the prioritisation of cases at the NCLT and their treatment during the lockdown, we drew upon the daily cause-lists published by the NCLT. This data-set is described here.

Our study period spans three months. To identify whether there were any shifts in the composition of cases scheduled for hearing during the lockdown, we divide the study period into three phases: pre-lockdown, lockdown and unlock (Table 1). The pre-lockdown phase allows us to observe the regular functioning of the NCLT. The lockdown and the unlock phases allow us to observe court functioning in the post-Covid world.

Table 1: Study period

Phase Dates Days of data

Pre-lockdown 1st February to 24th March 34
Lockdown 25th March to 31st May 31
Unlock 1st June to 30th June 22

For our analysis, we classify the matters heard by the NCLT into three categories: matters under the Insolvency and Bankruptcy Code, 2016 ("IBC matters"), matters dealing with schemes of compromise and arrangements between shareholders or creditors and companies ("CA Schemes") under the Companies Act, 2013 and other matters under the Companies Act or the Limited Liability Partnership Act, 2008 ("other matters").

For our analysis period, from the NCLT website, we get data for 22 bench-court combinations. We use 18 of these, namely 6 courtrooms of the NCLT bench in New Delhi (including the Principal Bench), 5 courtrooms of the NCLT bench in Mumbai, 2 courtrooms for the bench in Kolkata, and one each for the benches in Bengaluru, Chandigarh, Cuttack, Guwahati and Jaipur. We exclude 4 bench-court combinations, 2 for Chennai, and one each for Allahabad and Kochi due to sparse causelist availability. Ahmedabad bench is excluded as no data is available.

### Prioritization of scheme-related hearings

Table 2 shows the composition of the cases heard by the NCLT across the three phases of our study. Our analysis of the scheduling of cases in the pre-lockdown period shows that the pattern of hearing was being driven by the proportion of matters that were before the court. Since two thirds of the matters before the court were IBC related, the scheduling of hearings also reflected this pattern. Similarly, non-scheme Companies Act matters were getting heard in proportion to such matters being there before the NCLT.

However, we found that during the lockdown there was a sharp decline in the number of IBC cases scheduled for hearing. The share of IBC cases dropped from 68% in the pre-lockdown phase to 11% during lockdown. Even within the Companies Act cases, we found a sharp shift in the mix of prioritisation. In the pre-lockdown phase, the greater focus (22%) was on the Other matters. These comprise of matters such as shareholder disputes, matters involving the approval of corporate actions (such as the reduction of capital), proceedings against directors or the management and the dissolution of companies by striking them off the companies' register of the Registrar of Companies and so on. During the lockdown, CA Scheme-related matters were prioritised, not just above IBC matters but also above Other Companies Act matters. After the lockdown was lifted with effect from 1st June, the prioritisation pattern changed again and we found a near equal distribution of cases heard by the NCLT across these three broad categories.

Table 2: Composition of hearings in the causelist

Share of hearings (in %)

IBC CA Scheme Other matters Total

Pre lock-down 67.8 7.1 21.7 96.6
Lockdown 11.3 57.6 29.7 98.7
Unlock 34.2 32.7 31.1 98.0

The prioritisation of Scheme related matters during the lockdown period was done explicitly through the constitution of special benches in Mumbai and New Delhi for hearing scheme-related matters. This choice could have been driven by the fact that CA schemes are in respect of material corporate actions and are often undisputed. This would make them conducive for quick disposal. However, this does not necessarily mean that they were more urgent than the other two categories of matters. The only other category of matters that were prioritised were cases under Section 252 of the Companies Act. Section 252 of the Companies Act deals with appeals by a company against an order of dissolution passed against it by the Registrar of Companies.

The rationale underyling the prioritisation of cases heard during the lockdown period remains a puzzle. The pandemic and the nearly 10 week nationwide lockdown reportedly increased the financial distress in the economy. On 24th March, the Finance Minister announced the government's proposal to suspend the IBC if the situation did not improve by 3rd April. The IBC is widely perceived as the quickest tool for credit recovery in India. Given this perception, the announcement of a possible suspension of the IBC in March is likely to have accelerated the number of new cases under the IBC after 24th March, 2020.

Finally, on 5th June, 2020, the Central Government promulgated the Insolvency and Bankruptcy Code (Amendment) Ordinance, 2020 ("IBC Suspension Ordinance"), which suspended the operation of the IBC in respect of COVID-related defaults. Simply put, debt defaults committed between 25th March and 24th September could not be used to trigger the IBC. This means that the number of hearings dedicated to the IBC ought to have dropped in the second or third week of June. Our analysis, however, shows that the share of IBC cases heard by the NCLT after 5th June reverted to nearly half its pre-lockdown share.

### Old versus new cases

To understand the question of priortisation of cases better, we analyse the purpose for which IBC matters and CA Schemes were scheduled for hearing during the lockdown period.

Fig.1 is a two dimensional matrix graph that shows: (1) the categories of matters that were scheduled for hearing on the y-axis, and (2) the purpose for which matters were scheduled on the x-axis. On each graph, the red line indicates the start of the lockdown and the green line indicates the end of the lockdown. The number on the top of each graph indicate the average number of hearings that took place in each of the periods viz pre-lockdown, lockdown and unlock.

The graph shows us that in the pre-lockdown period, maximum new admissions were happening under IBC, followed by CA-Other matters. During the lockdown period, new admissions came to a near standstill across all categories of matters. However, in respect of old matters being scheduled for hearing, the prioritisation changed. IBC matters' hearings fell from a daily average of 265 in the pre-lockdown period to 2 during the lockdown. Other Companies Act matters fell from 94 to 16. However, Scheme related hearings continued at be scheduled at close their pre-lockdown levels. In the unlock phase, some new admissions started under IBC as well as Companies Act. There was also some improvement in the number of hearings scheduled for pre-existing IBC cases. However, the prioritisation of Companies Act matters over IBC, a pattern very different from the pre-lockdown phase, continued.

### Puzzles on de-prioritisation of IBC cases

Our finding that the NCLT had nearly stopped scheduling new IBC matters and reduced the number of substantial hearings for pre-existing IBC matters during the lockdown, is worth analysing in the context of the executive actions in respect of the IBC. On 24th March, 2020, the Finance Minister had announced the government's intention to suspend the IBC. However, the precise contours of this suspension were not clear. One would imagine that the threat of a suspension in the near future would incentivise many categories of creditors to file their IBC cases before the suspension. However, the NCLT data shows that this was not the case.

Our analysis suggests that the IBC Suspension Ordinance might have had a pre-mature effect on the composition of cases heard at the NCLT during the lockdown. While the ordinance was promulgated only on 5th June, there is a sharp drop in the IBC cases heard by the NCLT from 24th March onwards, the date on which the potential suspension of the IBC was first announced by the Finance Ministry. It is possible that the announcement might have altered the behaviour of litigants who stopped pursuing IBC existing proceedings or filing new IBC cases due to the uncertainty caused by the announcement. The de-prioritisation of IBC cases during the lockdown period is suggestive of the extent to which the announcement of a possible suspension of the law affected the triage function in case scheduling.

### Conclusion

Our analysis shows that the NCLT used its scarce capacity during the lockdown to dispose of existing, even if less contentious, cases such as the CA schemes. Further, the analysis on new v. old cases indicates that most of the schemes heard during the lockdown were the existing schemes. This is inconsistent with a common understanding of what might constitute an "urgent case". There might have been urgent matters under the IBC. For instance, matters where the resolution plan had been finalised and was awaiting the approval of the NCLT. In such matters, given the possible global impact of the pandemic, it was likely that the resolution plans already finalised might get withdrawn warranting an urgent hearing for the NCLT's approval of the resolution plan.

While our finding is specific to the NCLT, it underscores the need for courts to lay down a principle based approach to triaging in exceptional circumstances when the tribunal is functioning with limited capacity. This framework will need to address two issues: (1) what is an "exceptional circumstance", and (2) what is an "urgent matter" in an exceptional circumstance. This framework can emerge in two possible ways. It could emerge through case-law that acts as precedent or has persuasive value. This is a slow and evolving approach. The other approach is to allow judges to pre-define this framework and publish it. Such a framework will further the cause of the rule of law, transparency and the delivery of justice when courts function with limited capacity during the pandemic.

Central to the triaging problem is also the idea of case management and court administration in normal times. Currently, there is no common framework that informs the average litigant on the manner in which a date will be assigned to her case. Much depends on the court and within the court, the judge to whom the matter is assigned for hearing, the nature of the case, the urgency of the interim relief sought, the existing backlog and the court registry. Exceptional circumstances simply exacerbate the complexity of the judicial triage for courts as resources are even more limited than in ordinary circumstances, but the problem nevertheless exists on a daily basis. In a system constrained by resources, the order of priority assigned to a case has substantive repurcussions for all the stakeholders involved in a case. In the absence of certainty on triaging, the system is vulnerable to abuse. It compels a litigant to rely on the registry, the judge and the lawyer. Resultantly, the system is naturally titled towards litigants who can afford competent lawyers.

Finally, it is common for private organisations that handle work of the scale handled by courts to implement a medium to long term plan outlining the phases in which they will resume full scale functioning. In several jurisdictions, courts have published their medium term strategy to restore full-scale operations (example; example). Given the uncertainty on the time horizons of the pandemic, Indian courts must endeavour to publish their strategy and plan for functioning at full-scale. This is essential for justice delivery and the public confidence in the judiciary's ability and willingness to get back on its feet.

The authors are researchers with the Finance Research Group. They would like to thank Ajay Shah for useful discussions, an anonymous referee for inputs on this article and Rahul Somani for developing the code for constructing the data-set.

## Sunday, September 27, 2020

### 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

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.