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Saturday, July 25, 2020

Announcements: The 7th Juliacon, the flagship conference of the Julia programming language, accessible online & free

1. All JuliaCon Events Are Free and Online: Click here now to register for JuliaCon. JuliaCon continues from now until Friday July 31. The full JuliaCon schedule is available here, and includes 10 workshops and over 100 talks.


3. Julia Computing Virtual Booth: Connect with Julia Computing live by visiting our Virtual Booth Wed July 29 - Fri July 31 from 12:30 pm to 7:30 pm UTC. To be connected, click here and select the Julia Computing Virtual Booth link when it is available during these hours.

4. Live JuliaTeam and JuliaRun Demonstrations from Julia Computing: Register now for a free live demonstration of JuliaTeam and JuliaRun from Julia Computing.

* Wed July 29: 5:45 pm - 6:45 pm UTC
* Thu July 30: 11:30 am - 12:30 pm UTC
* Fri July 31: 7:45 pm - 8:45 pm UTC.

Tuesday, July 21, 2020

Pricing education: An example from Uttar Pradesh

by Bhuvana Anand and Shubho Roy.

School shutdowns across the country have sparked disagreements between parents and schools about fees. Parents filed a plea in the Supreme Court seeking more time to pay schools due to COVID-19. The Supreme Court refused to hear the petition, arguing that it had to be tackled by the executive first. Schools need money to pay their staff, including teachers, and are threatening to cut off access to online classes in case of non-payment (here, here and here). Both parties have approached High Courts in at least 15 states for a ruling (for example, in West Bengal, Madhya Pradesh, and Gujarat).

COVID-19 has only exacerbated an old fight. School fees have been an oft litigated issue in India. Courts have pronounced judgements against capitation fees, profiteering and fee hikes for over two decades (See 1992, 1993, 2002, 2004 and 2019). Several states also enacted laws to regulate school fees. Such price regulations are a poor way of addressing the underlying issue of market failure in school education. The primary reason for fee disputes with parents is that the entry of new schools in India is severely restricted due to a cumbersome regulatory environment.

In this article, we discuss the fee regulation architecture across India. In particular, we focus on the legislative drafting and implementation of the UP Self-Financed Independent Schools (Fee Regulation) Act, 2018.

Regulation of school fees in India

Nine states and union territories in India have stand-alone Acts regulating the collection of fees. These Acts were passed between 2009 and 2019 and mandate a Fee Regulatory Committee to hear fee-related complaints and proposals.

Bihar limits fee hikes to 7% over the previous year’s fee. Schools which wish to increase charges beyond 7%, need to seek approval from a divisional fee regulatory committee. These committees are usually composed of parents, private school representatives, and government officials.

Gujarat law empowers the district fee regulatory committees to determine fees for schools. However, schools charging fees below a specified amount are exempt from the regulation.

Rajasthan and Maharashtra require a school-level committee to approve fee hikes. The school-level is composed of representatives from school management, teachers and parents. If the school-level committee fails to agree on the increase, the school can approach a divisional fee regulatory committee.

The Maharashtra law adds a nuance, missing in Rajasthan. Schools can choose between a block declaration or capped revision. During admission, schools can declare fees for a block of classes (for example, grade 1 to 5). Or, a school can revise fees subject to a cap. Under this option, costs cannot be raised more than 15%, once every two years. In case of unforeseen circumstances, schools may increase fees beyond this cap. But, such increases, above the cap, has to be approved by either 76% of the parents, or the school-level committee. The Act also allows for management and parents (not less than 25% of parents in the affected standard/school) aggrieved by the decision of the school-level Committee to approach the divisional fee regulatory committee.

Other states pass orders and notifications to regulate fees but struggle with implementation. In Delhi, districts are supposed to set up Fee Anomaly Committees. But these are either not constituted or defunct, so parents raise their complaints to the Directorate of Education (Agarwal et al. 2019).

Uttar Pradesh

Uttar Pradesh enacted the UP Self-Financed Independent Schools (Fee Regulation) Act, 2018 to control fees for schools. Sadly, the law suffers from two drafting problems: a contradiction in fee fixation provisions, and the lack of clear instructions on the price index to use.

Section 3(1) of the law lays down the heads which the school can take into consideration. It is the governing principle of the law on fixing school fees. It reads:

"A recognised School shall determine its fee structure under subsections (1) and (2) of Section 4 … commensurate to, inter alia, meeting its operational expenses, providing for augmentation of facilities and expansion of infrastructure and for providing facilities to the students, to generate reasonable surplus to be utilised for development of educational purposes including establishment of a new branch or a new school under the management of the same eligible educational entity;"

The law is refreshingly pro-school in this provision. It recognises that a fee increase is not just to meet operational expenses. Schools have to augment facilities, expand infrastructure (build new classrooms, maybe a swimming pool), provide facilities to students. The law also recognises that a school must be allowed to generate some reasonable surplus and may wish to expand by opening new branches or schools. It is a surprisingly frank and forward-looking recognition of the myriad expenses that a school faces. In India, where price controls laws rarely recognise the costs that the provider has to undertake, the provision stands out as one recognising the genuine needs of the school.

Section 3(1) states that the process of determining fees is laid down in Sections 4(1) and 4(2).

Section 4(1) completely undermines the approach of Section 3(1). It reads:

A recognised school may revise its fee annually for its existing students by itself for each grade/class/level of school equivalent to average percentage per capita increase of monthly salary of teaching staff of previous year, but the fee increase shall not exceed latest available yearly percentage increase in consumer price index [CPI] + five per cent of the fee realised from the student;”

(emphasis added)

Gone are the grounds recognised in Section 3. Section 4(1) reduces all those grounds to only one: teacher salaries. No more can schools increase fees to pay for expanding infrastructure, providing facilities, opening branches or generating a surplus. The only amount that the school can raise fees is the increase in salaries. If a school does not increase teacher salaries but wants to build a new auditorium, it is out of luck. Buying a new computer lab? Section 4(1) will not allow you to raise money for it. School’s financial reserves are low? Section 4(1) has no solution for management. The only criteria for school fees increase are teacher salaries. All the good ideas in Section 3 have been washed away by the restrictions in Section 4.

The pegging of fee increases to teacher salary is indicative of a deeper problem in Indian education: teacher interest domination. Too often, laws designed for education end up protecting teachers. For example, the only performance measure that the Right to Education Act enforces in the parent legislation itself is the teacher-to-student ratio, even when the evidence of the effectiveness of this measure is weak. This one measure is baked into the Parliamentary law. All other performance measures are left to be decided by the government through subordinate legislation.

The second problem in the UP Act arises from the formula under Section 4(1). The law provides a cap on the fee hike. Fee hikes have to be less than CPI + 5% per year. The drafters have left out defining CPI. In India, there are two bodies which provide five types of consumer price indexes. The Labour Bureau publishes two indexes, (CPI industrial workers and rural workers) and the Ministry of Statistics publishes three indexes (Rural, Urban, and Combined). Narrowing down to the applicable index is not the end of your problems. These indexes are published monthly, while the fee increase is supposed to happen once a year. The law is silent about which CPI to use and how to convert the monthly numbers into a yearly value. Predictably, different district fee regulatory committees have come up with different values for the maximum fee hike. Gautam Budh Nagar calculated the maximum fee hike as 7.88% (5+2.88), and Varanasi calculated the same as 8.71% (5+3.71).

This problem could have been solved by clearly cross-referencing to the specific index that the schools should use. Since the value of the index does not change across districts, there is no need for each district committee to decide the CPI. This function could have been done at the state level itself and saved schools from the confusion.

Conclusion

Why is the UP law drafted so poorly? The underlying reason is that the legislators have misidentified the problem. The best way to regulate prices is through a market mechanism. Parents should have a wide choice of schools at different price points. Sadly, we do not have that in India. Regulatory burdens imposed on schools reduce the supply of private schools. State governments control the availability of land in urban areas, mandate minimum salaries for teachers, and impose many requirements on schools through state school laws and the Right to Education Act. The consequence of these laws is two-fold: it raises the costs of running a school and makes it difficult to set up new schools. In turn, the existing schools raise fees. The entry barrier to new schools ensures that they do not face any competitive pressure to reduce fees.

Instead of encouraging competition in private schooling, the laws put administrative controls over the fee setting mechanism. An administrative price-setting usually misprices the fees. A government committee is in no better position in deciding what the price of education in a school should be. Even the legislature is unable to articulate any principles by which such committees should determine fees. The U.P. law starts with a wide range of costs that a school may incur. But when it comes to the implementation clause, it narrows down to just teacher salaries.

The price control laws are trying to solve a problem which should not exist in the first place. It would be much better if we tried to identify and dismantle the entry barriers to setting up low-cost private schools in the first place and encourage competition.

References

Does Class Size Matter?, Ronald G. Ehrenberg, Dominic J. Brewer, Adam Gamoran and J. Douglas Willms, Scientific American, November 2001.

How are private school fees regulated?, Ritika Agarwal, Atreyi Bhaumik, Adit Shankar and Anindya Tomar, in Anatomy of K-12 Governance in India, Centre for Civil Society, October 2019.


Bhuvana Anand is a researcher at Centre for Civil Society and Shubho Roy is a researcher at the University of Chicago. The authors thank Tarini Sudhakar at Centre for Civil Society for research support.

Sunday, July 12, 2020

Response to the Consultation Whitepaper on 'Strategy for National Open Digital Ecosystems (NODEs)'

by Rishab Bailey, Harleen Kaur, Faiza Rahman, Renuka Sane.

The Ministry of Electronics and IT, Government of India (MeitY) had sought public comments on a Consultation Whitepaper (CW) titled a "Strategy for National Open Digital Ecosystems (NODEs)" earlier this year. NODEs are defined as:

open and secure delivery platforms, anchored by transparent governance mechanisms, which enable a community of partners to unlock solutions and thereby transform social outcomes.

The NODES framework will allow the opening up, and sharing of personal and non-personal data held in various sectors (such as healthcare, agriculture, and skills development). Each NODE will consist of infrastructure developed and operated by the government. The private sector will utilise the common infrastructure and data to provide solutions to the public. Per the CW, this will enable greater intra-government and public-private coordination and create efficiency gains. This framework will promote access to innovative e-governance and other services for citizens while enabling robust governance processes to be implemented.

We wrote a detailed response to MeitY. In our submission, we make suggestions on four key issues with the CW:

  • Role of the state: The CW needs to demonstrate clarity on the need for government intervention on the scale proposed. The market failures that require State intervention must be identified on a sectoral basis.
  • Centralisation of governance and technical systems: The CW envisages establishing monolithic, stack-based digital systems in a variety of sectors. The government would be responsible for establishing and operating the technology infrastructure as well as the governance of such systems. However, excessive centralisation can reduce competition, innovation, and produce unsecure systems.
  • Alignment with the existing government policies: The CW needs to consider existing government policies on the adoption of open source software (OSS), open APIs and open standards. Further, the CW needs to account for existing open data and e-governance related initiatives in the identified sectors, and how these would interact with the NODEs framework.
  • Preserving and protecting Constitutional norms: The CW needs to ensure the protection of fundamental rights, democratic accountability, and transparency in the creation and regulation of NODEs. Further, it also needs to account for the federal division of competencies enshrined in the Constitution.

This article summarises our comments and suggestions on the above mentioned issues.

Role of the State

The CW adopts a 'solutionist' approach, in that it does not undertake sufficient analysis of the circumstances and problems in each sector. For instance, the CW identifies two market failure in the skills sector: (i) information asymmetry amongst the stakeholders, and (ii) a lack of trust in the information that is available. It proposes a Talent (Skilling and Job) NODE as a one-stop solution to connect employers, job seekers, counsellors and skilling institutes. Instead of the approach undertaken by the CW, one should consider if private entities can or are already innovating to bridge the information asymmetry and trust issues in the sector, and what policies could provide an environment where such information asymmetry may be reduced. If the problem in the skills sector is a lack of trust, it is unclear why this cannot be solved by interventions such as certification standards.

As a general rule, the State should be involved with building technological systems only for essential state activities (Kelkar and Shah, 2019). It is therefore critical to differentiate between sectors where the State has a legitimate role (say in the provision of its welfare and statutory functions), from sectors where private sector solutions could suffice. For example, the State could have a role in providing access to Public Distribution System (PDS), but need not be a player in building a platform for access to rail reservations.

The responsible ministry should analyse if the NODE is serving welfare or other essential function of government. In case there is no such element, the government should not use its finances on creating infrastructure for such a NODE. Such an approach would promote innovation, prevent the emergence of a state-centric technological mono-culture, and allow the private sector to respond appropriately to requirements of any particular sector. Entities would not be forced to build on top of state-mandated infrastructure, which may not always be necessary or appropriate.

In the context of the NODEs framework, the State should primarily have three roles:

  1. Open up data: The government must focus on building databases and providing access to the public, in a non-discriminatory manner. The benefits of enabling free flows of information are well known. That said, it is important to keep in mind the need to ensure non-discriminatory access to ensure data quality, and to prevent against privacy and other downstream harms. For instance, the Delhi government recently shared locations for COVID-19 relief centers on Google maps, thereby giving Google a competitive edge over other mapping solutions. We believe that an appropriate approach would involve the Delhi government making the relevant information open. This can be done by providing the geo-tagged locations on its open data governance website. Methods to embed this data in third-party apps and services could be provided to enable non-discriminatory access. Similarly, the benefits of opening up railways related data, which is currently monopolised by the IRCTC can enable the provision of customised travel solutions. Greater linkages could be formed with private players in the hospitality and tourism sectors, leading to mutual benefits to the railways as well as the private sector and consumers.
  2. Implement regulatory frameworks: The government should institute regulatory processes and norms based on the need to protect and promote fundamental rights and correct market failures. Interventions must be designed to (a) promote effective competition and the maintenance of a level playing field, (b) avoid function creep, (c) protect and promote fundamental rights, (d) ensure appropriate apportioning of functions, obligations and responsibilities/liabilities.
  3. Ensure democratic accountability: It is now well-established that "code is law" (Lessig, 1999). This makes it imperative for the government to establish systems of democratic accountability, transparency and openness in the creation and regulation of public digital systems. Transparency and accountability measures should be implemented both at the conceptualisation stage as well as thereafter. This should involve:
    • An open and transparent consultation process in the design of the NODEs, similar to the the Report of the Financial Sector Legislative Reforms Commission recommendations for regulation making.

    • A cost-benefit analysis that takes into account the economic costs and benefits of operationalising a NODE within a sector. This would also allow for suitable alternative approaches to be explored.

    • Integration of principles of participatory and democratic governance into the implementation and operation phases. This would promote citizen-centric governance, particularly in the context of privatisation of regulatory functions. For example, the National Payments Corporation of India (NPCI) functions as a quasi-regulatory agency due to the scope of its powers, functions, and de-facto regulatory monopoly. However, being a private entity, it has not been brought under the purview of the Right to Information Act, 2005. This limits citizen engagement with governance processes.

    • Mechanisms to enable allocation of responsibilities and coordination between government entities at different levels (local, state, and central). This is especially important when dealing with common issues (such as tagging of data sets, instituting grievance redress mechanisms, etc.) without usurping constitutional and statutory functions.

Centralisation of governance and technical systems

Enabling the government to pick technological winners and losers or enabling a technical monoculture would decrease innovation and competition. It is well-recognised that centralisation can lead to increased security concerns. One must also be wary of unintended consequences of even the best planned regulation in the technology space. Technology moves too fast and has multiple possible future use cases. Over-regulation or excessive centralisation could have negative effects on expected outcomes.

In cases where the government is required to create digital systems, these must be federated and decentralised to the extent possible. The creation of monolithic technical architectures, which are often de facto mandatory, must be avoided. For instance, the creation of a centralised identification system -Aadhaar- which was thereafter mandated for use across different sectors has caused various problems ranging from exclusions, intrusions into privacy rights of citizens and inhibiting innovation (i.e. such a system is preferred over other possible forms of identification that could suffice in any particular use-case). Implementing a centralised system of 'public infrastructure' may therefore not be necessary and may in fact reduce competition and civil liberties protections.

Instead, the focus of the government should be on enabling the private sector to develop relevant platforms and technologies that compete with one another on a level playing field, albeit with due consideration for regulatory, human rights and other problems that may arise in any given context. Such a system would also promote greater security. The use of federated databases, enabling the development of alternative technical solutions to be built on data, etc., would mean that problems associated with having a single source of truth or a single source of failure can be avoided.

Alignment with existing government policies

The CW proposes principles of open and interoperable delivery platforms. There are two concerns in the manner in which these are described in the CW.

  1. The CW does not refer to existing government policies on the use of OSS in e-Governance projects. Various policies specifically deal with the issue at hand (for example, National Policy on IT, 2012, the policy on Adoption of Open Source Software for Government of India, and the policy on Open Standards for e-Governance).

  2. The scope of the word 'open' as used in the CW is vague and appears to confuse concepts of "open access" and "open source". The CW suggests that each NODE will require a different degree of openness to adhere to specific objectives, context, or mitigate potential risks. This approach can dilute existing policies (mentioned above) that contain clear definitions and mandates on the use of open source solutions by the government.

It is imperative that the NODEs framework build on and strengthen existing government commitments towards the use of OSS solutions. This will unlock the benefits of OSS/Open APIs/open standards such as enhanced security and verifiability, no vendor lock-in, etc.

Preserving and promoting constitutional safeguards

The creation of NODEs platforms would significantly impact fundamental rights. We envisage three instances where the NODES environment needs to be careful about preserving constitutional safeguards.

  1. Right to equality, right to life, and personal liberty: Digitisation at the scale contemplated by the CW may lead to concerns about access to services and possible exclusions therefrom. Ensuring rights protection may be particularly important in the context of the use of AI-based solutions and possible discrimination that may arise as a result. The understanding of what amounts to discrimination must be evolved by each NODE distinctly and will depend on the sector.

  2. Right to privacy: Each of the NODEs will invariably result in the collection and processing of personal data and non-personal data by both government and private entities. The collection and use of personal data by different state entities must necessarily satisfy the tests laid down by the Supreme Court in the Puttaswamy decisions (2017 and 2018). Similarly, principles relating to the use of data by the private sector as laid down in the context of the Aadhaar judgment (Puttaswamy, 2018) must also be adhered to. Due regard must also be given to (the developing) regulatory frameworks concerning personal and non-personal data.

  3. Federal structure: The NODEs framework must also consider the impact on the division of subject matter competencies under the Constitution. One could envisage benefits arising from NODEs in areas such as agriculture, judicial services, healthcare, etc. However, these sectors fall under the State List in the Seventh Schedule to the Constitution. Implementation of NODEs in these sectors should not result in de facto centralisation of federated competencies. Instead, mechanisms to ensure coordination and cooperation between different levels of government must be considered.

We, therefore, recommend that each NODE be backed by an appropriate statute, to the extent possible. This would ensure greater democratic deliberations, prevent excessive and arbitrary executive action, set out the rights of citizens and private entities, and clarify the scope/ limits of any particular project. Providing statutory backing would also limit mission creep, while delineating rights and obligations and governance processes. For instance, despite its various faults, the statutory mandate provided to the Unique Identification Authority of India and the restrictions on data sharing in the Aadhaar Act have proven invaluable in ensuring that biometric and other data is not made freely available for non-Aadhaar purposes by the public sector, including for instance, in criminal investigations. In contrast, projects such as FASTags (which aims to digitise highway toll systems) are being gradually expanded with plans to integrate the system with criminal tracking networks, amongst others.

Conclusion

The CW provides a basic overview of the concept of a NODE and identifies certain sectors in which such a system could lead to gains (such as the skills and health sectors). For various reasons outlined in our submission, our recommendation is to not proceed with implementing the NODEs framework in the manner currently outlined in the CW. We believe that the CW should be seen as an exploratory document. Greater clarity is required on the need for interventions on the scale envisaged in the document, particularly in view of the proposed centralised, stack-based approach. The NODEs framework should consider the need for openness at lower layers of the stack (infrastructural layers), adhere to existing government policies on the use of OSS, Open APIs and Open Standards, and consider policy developments concerning the regulation of personal and non-personal data. The CW should also ensure greater transparency and democratic accountability of governance frameworks and the processes for the creation of a NODE.

References

Bailey et al, 2020: Rishab Bailey, Vrinda Bhandari, Smriti Parsheera and Faiza Rahman, Comments on the draft Personal Data Protection Bill, 2019: Part I, LEAP blog, 2020.

Centre for Digital Built Britain, 2018: A Bolton, M Enzer, J Schooling et al, The Gemini Principles: Guiding values for the national digital twin and information management framework, Centre for Digital Built Britain and Digital Framework Task Group, 2018.

FICCI & KPMG, 2014: FICCI and KPMG, Skilling India: A look back at the progress, challenges and the way forward, 2014.

Kelkar and Shah, 2019: Vijay Kelkar and Ajay Shah, In service of the republic: The art and science of economic policy, Penguin Allen Lane, 2019.

Lessig, 1999: Lawrence Lessig, Code and other laws of cyberspace, Basic Books, 1999.

Puttaswamy, 2018: Justice K.S. Puttaswamy v. Union of India (Aadhaar case), 2019 (1) SCC 1.

Leblanc, 2020: David Leblanc, E-participation: A quick overview of recent qualitative trends, DESA Working Paper No. 163, United Nations Department of Economic and Social Affairs, 2020.

Michealson, 2017: Rosa Michealson, Is Agile the answer? The case of UK universal credit, in Grand Successes and Failures in IT - Public and Private Sector, IFIP Advances in Information and Communication Technology, Springer, 2017.

Ministry of Rural Development, 2013:Ajeevika skills guidelines, Ministry of Rural Development, Government of India, 2013.

Puttaswamy, 2017: Justice K.S. Puttaswamy v. Union of India (Right to privacy case), 2017 (10) SCC 1.

Raghavan and Singh, 2020: Malavika Raghavan and Anubhutie Singh, Building safe consumer data infrastructure in India: Account aggregators in the financial sector - Part I, Dvara Research, 2020.

Steinberg and Castro, 2017: Michael Steinberg and Daniel Castro, The state of open data portals in Latin America, Centre for Data Innovation, 2017.

Zambrano, Lohanto and Cedac, 2009: Raul Zambrano, Ken Lohanto and Pauline Cedac, E-governance and citizen participation in West Africa: Challenges and opportunities, The Panos Institute, West Africa and the United Nations Development Programme, 2009.


The authors are researchers at NIPFP.

Tuesday, July 07, 2020

The five paths of disinvestment in India

by Sudipto Banerjee, Renuka Sane and Srishti Sharma.

Privatisation of Central Public Sector Enterprises (CPSEs) in India has typically been done in one of the following ways: in the early years government equity was sold through an auction to financial investors, while since 2004, the popular method has been public offer. Strategic sales, where control of the public sector is transferred to private entities have been very few, concentrated in the 1999-2004 period. As a result, sale of government shareholding in India is referred to as disinvestment and not privatisation.

In recent years the methods used for disinvestment include: a) Public offer, b) Buybacks, c) Sale to employees, d) Exchange traded funds (ETFs), and e) CPSE to CPSE sale. Buybacks and ETFs are new ways of divesting minority stake. As we study the trajectory of disinvestment in India, it is important to understand the relative magnitudes involved in each transaction. There are two metrics that are important - first, the amount of resources raised and second, the change in government equity through these methods. The latter is especially important as disinvestment has great potential to improve economic efficiency by reducing government control. By focusing only on resources raised as an outcome, we end up ignoring the more important economic rationale for undertaking disinvestment.

In this article, we describe the methods adopted for disinvestment of CPSEs since FY2015. We use the BSEPSU disinvestment database and individual annual reports of firms to arrive at the magnitudes of disinvestment. We use two measures:

  • Disinvestment proceeds and shares sold. The proceeds are the amount realised through the sale process. Shares sold is the ratio of the number of government shares sold by the total equity of the firm.
  • Change in government equity. This is the difference between the share of government in total equity of the firm before and after the disinvestment transaction.

Disinvestment methods

Table 1 provides an overview of disinvestment by the government in the last 6 years. It shows the number of transactions, the number of CPSEs, the disinvestment proceeds, % of total shares sold and the change in government equity post the transaction.


Table 1: Disinvestment from FY15 to FY20
Methods of disinvestment Number of
transactions
Number of CPSEs
Disinvestment proceeds (INR million)
Average % of
shares sold
Average change in % of govt equity post
disinvestment
1 PUBLIC OFFER 37 32 984,054 10 10
2 BUYBACK 36 23 403,549 8.34 0.64
3 SALE TO EMPLOYEES 21 15 9,379 0.138 0.138
4 EXCHANGE TRADED FUND* 10 18 989,490 1.09# 1.09#
5 CPSE TO CPSE SALE 8 8 667,119 77.15 77.15
Source: BSEPSU database and authors' calculation based on annual reports

* There were a total of 10 tranches of ETFs in this period. Each tranche contains a basket of firms. If the disinvestment in each firm that was part of an ETF tranche is considered separately then we would have 126 ETF transactions instead of 10. The average change in government equity for ETFs is therefore calculated across these 126 transactions, and not the 10 tranches

The government of India disinvested its stake in 50 CPSEs and raised a total of INR 3,053 billion using five methods: public offer, buy back, CPSE to CPSE sale, exchange traded funds and sale to employees. On an average, the government sold 7.28% of total shares and the average reduction in government equity has been around 5.84%. The sum total of the number of CPSEs in column 2 does not match with the total number of 50 unique CPSEs because some CPSEs adopted multiple methods across years. Public offer was the most used method with 32 firms and 37 transactions. The second most popular method was buyback with 36 transactions. The maximum revenue was raised through ETFs followed by public offer. The maximum share of sales and change in government equity was through CPSE to CPSE transfers. There is some missing data on % shares sold for buyback and ETF transactions as annual reports for FY20 is not published yet (indicated by #).

Figure 1 below shows the yearly distribution of amount raised and % reduction in equity across various methods from FY15 to FY20. The significant increase in proceeds in FY18 and FY19 is driven by ETFs and CPSE to CPSE sales. Besides CPSE to CPSE sales, the average % reduction in government equity remained low and constant across all years. We next study the five methods in detail and understand the extent of disinvestment in each method.


Public offer

Public offer has been the most common method of disinvestment. Since FY 2015, there have been 37 public offer transactions including 21 offer for sale (OFS) transactions. The public offer route is considered as a transparent way of offloading government shares and aims to encourage public participation. In several public offer transactions, the Life Insurance Corporation (LIC), whose shares are fully owned by the central government, has bought majority of the shares. Some of these transactions include:

  1. In 2014, LIC bought 5.94% stake in Bharat Heavy Electricals Ltd (BHEL) for INR 26,850 million, increasing its stake in BHEL to 14.99%.
  2. In 2015, LIC bought shares worth INR 70,000 million INR in the public offer of Coal India Ltd. This was equivalent to one-third of the public offer.
  3. In the same year, it bought nearly 86% of the shares on offer of the Indian Oil Corporation paying over INR 80,000 million.
  4. In 2016, LIC bought 59% of shares offered in NTPC stake sale worth $730 million. Thus, LIC spent approximately INR 29,000 million.
  5. In 2017, LIC bought shares worth around INR 80,000 million in the disinvestment of General Insurance Corporation of India and again bought shares worth INR 65,000 million in the IPO of New India Assurance Company.
  6. In March 2018, LIC subscribed 70% of shares in the IPO of Hindustan Aeronautics Ltd, paying INR 29000 million.
  7. Between November 22, 2019 and February 27, 2020, LIC acquired 59.49 lakh shares worth INR 1,770 million, or 2.38 % stake, in RITES though an offer-for-sale (OFS).

LIC spent roughly INR 381,620 million on the transactions listed above. This constitutes 38.7% of the disinvestment proceeds raised through the public offer method in the period of our study.

Buyback

Buyback is a process where a company purchases its shares from its existing shareholders. This helps a company to restructure capital and increase the underlying value of shares. The company is required to extinguish the bought back shares. The government has used buyback in the past as a method of disinvestment. However in 2016, buyback was made compulsory for CPSEs who met the prescribed threshold of net worth and cash reserves.

A company is under an obligation to provide a buyback offer to all existing shareholders. In such a case, reduction in the total equity is higher than the reduction in government shares which may lead to an increase in % of government equity post buyback. However, if a CPSE is wholly owned by the government, total number of shares will be reduced (extinguished) by the same number of shares bought back. Hence, there will be no change in % of equity held by the government post buyback.

Table 2 presents the impact of buyback transactions on government shareholding. Since 2015, 23 CPSEs have bought back shares from the government raising INR 403,549 million. It is important to note that % shares sold for three buyback transactions in FY20 is unavailable since annual report for the year is not published yet (indicated by *). Out of total 36 buyback transactions, 9 transactions led to an increase in government equity. In 11 transactions, where CPSE was wholly owned by the government, there was no change in government holding. The remaining 16 transactions recorded an average reduction of 1.19% in government equity. In column (2) the count of individual number of CPSEs do not match with the total number of CPSEs because same 8 CPSEs recorded increase in equity in one year while decrease in another (indicated by **).


Table 2: Summary of buyback transactions
Transaction type Number of transactions No. of CPSEs Total disinvestment proceeds(INR million) Average % of shares sold Average change in % of govt equity post buyback
Reduction in government holding 16 12 244,947 7.63 (1.19)
Increase in government holding 9 9 83590.7 2.31 0.16
No change in government holding 11 7 75,011 15.55* 0
Total 36 23** 40,3549 8.34 (0.64)
Source : Authors' calculation based on annual reports

Sale to employees

As part of its disinvestment strategy, the government has often reserved a certain quantity of its shares for offer to the CPSE employees. Usually these shares are offered at a discount. Such transactions are expected to incentivise the employees and create dispersed shareholding. In the last six years, there have been 21 such transactions across 15 firms from which the government raised a total of INR 9,379 million. On an average, the % of shares sold to the employees is around 0.14%. Almost half of the proceeds from this method comes from two transactions in FY17 by Indian Oil Corporation Ltd. and NTPC Ltd. In May 2016, government sold 0.29% of the total shares of Indian Oil Corporation Ltd. to its employees raising INR 2,624 million. Pursuant to the 5% OFS stake in February 2016, NTPC offered to sell 2.06 crores equity shares of government to the employees at a discount rate of 5%. 85% of the shares were subscribed by around 10,800 eligible employees and government raised approximately INR 2,037 million.

Exchange traded funds (ETFs)

ETF is a pool of stocks that reflects the composition of an index, like S&P BSE SENSEX. This method has been frequently used for disinvestment in the recent past, where the government sells shareholding in select CPSEs to a fund house which owns the ETF. The ETF fund manager first formulates the scheme and offers to the public for subscription by way of a new fund offer (NFO). The subscription proceeds are used to purchase the shares of constituent companies in similar composition and weights based on the underlying index. Shares are usually sold at a discount to the scheme and the fund manager in turn creates and allots units of the scheme, to the investors. Once the NFO closes, the units are listed on the exchanges.

The government has launched two ETFs, namely, CPSE ETF and Bharat-22 ETF. CPSE ETF was launched in 2014. It contains stock of 11 listed CPSEs and follows the NIFTY CPSE index. In 2017, Bharat-22 ETF was created. This comprises of 16 CPSEs, 3 public sector banks and 3 private company stocks held by Specified Undertaking of the Unit Trust of India (SUUTI). The underlying index is the S&P Bharat 22 index. From FY15 to FY20, there were six tranches of CPSE ETF and four tranches of Bharat-22 ETF transactions which raised INR 989,490 million.

Table 3 lists each ETF tranche from FY15 to FY20 and provides details on allotment date, number of constituent CPSEs, amount raised by government and average reduction in % of government equity post each tranche. It is important to note that the average % reduction in government equity for three ETF transactions in FY20 is unavailable since annual report for the year is not published yet (indicated by *NA).


Table 3: Summary of ETF tranches from FY15 to FY20
ETF Name ETF tranche No. of constituent CPSEs Allotment date of ETF units Average % reduction in government equity Amount realised (in INR million)
CPSE ETF FURTHER FUND OFFER 1 10 28/01/2017 0.98 59999.9
CPSE ETF FURTHER FUND OFFER 2 10 25/03/2017 0.39 24999.9
CPSE ETF FURTHER FUND OFFER 3 11 07/12/2018 2.88 170000
CPSE ETF FURTHER FUND OFFER 4 11 29/03/2019 1.22 93500.7
CPSE ETF FURTHER FUND OFFER 5 10 26/07/2019 NA* 100003.9
CPSE ETF FURTHER FUND OFFER 6 10 07/02/2020 NA* 165000
BHARAT 22-ETF NEW FUND OFFER 16 24/11/2017 0.93 145000
BHARAT 22-ETF FURTHER FUND OFFER 1 16 29/06/2018 0.58 83252.6
BHARAT 22-ETF TAP OFFER 16 22/02/2019 0.92 104045.9
BHARAT 22-ETF FURTHER FUND OFFER 2 16 10/10/2019 NA* 43688
Source : Author's calculation based on annual reports

While aggregate proceeds from ETF may have been high, the average reduction in government equity has been low.

CPSE to CPSE sale

Under this method, government transfers its shares in one CPSE to another CPSE. There have been eight such transactions in the last six years which raised a total of approximately INR 667,119 million. The details of each of the transaction is given in table 4. Except REC Limited, the entire government shareholding was transferred to another CPSE. In case of REC Limited, government still holds 0.25% shares. Post these sales, the firms became subsidiaries of the buyer CPSE firms, but continue to remain government companies as defined under section 2(45) of the Companies Act, 2013.


Table 4: CPSE to CPSE sales from FY15 to FY20
CPSE Date of transaction Buyer's Name % of shares sold Amount realised (in INR million)
HINDUSTAN PETROLEUM CORPN. LTD. 31/01/2018 OIL & NATURAL GAS CORP.LTD. 51.11 369,150
H S C C (INDIA) LTD. 06/11/2018 NBCC (INDIA) LTD. 100 2,850
DREDGING CORPN. OF INDIA LTD. 09/03/2019 CONSORTIUM OF FOUR PORTS 73.47 10,491
R E C LTD. 28/03/2019 POWER FINANCE CORP.LTD. 52.63 145,000
KAMARAJAR PORT LTD. 27/03/2020 CHENNAI PORT TRUST 66.67 23,830
NORTH EASTERN ELECTRIC POWER CORPN. LTD. 27/03/2020 NTPC LTD. 100 40,000
T H D C INDIA LTD. 27/03/2020 NTPC LTD. 74.49 750,00
NATIONAL PROJECTS CONSTRUCTION CORPN. LTD. 26/04/2020 WAPCOS LTD. 98.89 798
Source : BSEPSU disinvestment database

The CPSE to CPSE sale transactions constituted around 22% of total disinvestment proceeds in the last six years. While technically, the government may have divested 77% shareholding in these CPSEs (as shown in Table 1), it did not bring any change in government ownership of these firms.

Conclusion

There has been a huge increase in disinvestment proceeds in the recent years. However, reduction in government equity in the CPSEs has not witnessed much growth. About 5.19% of disinvestment proceeds came from buyback transactions that led to an increase (or no change) in government equity and 21.8% came from CPSE to CPSE sale transactions that led to no change in government ownership. While 32.2% of proceeds came from public offer, almost 39% of these were actually purchased by LIC. Thus, purchases by LIC accounted for 12.49% of the total proceeds which also imply no change in government ownership. Finally, around 32.4% came from ETFs which, on an average reduced government equity by 1.09%. These considerations become central issues for any research on disinvestment and its impact.


The authors are researchers at NIPFP. We thank Karthik Suresh and Sarang Moharir for useful comments.

Friday, July 03, 2020

Legal and regulatory framework for laboratory testing in India: A case study for Covid-19

By Harleen Kaur, Ameya Paleja, and Siddhartha Srivastava.

Testing is central to understanding the spread of the SARS-CoV-2 virus at an individual & population level and designing suitable interventions (Shah, 2020). As of June 23, 2020, India has the fourth-largest number of SARS-CoV-2 cases worldwide. This is despite having conducted only 119 tests per million people. In comparison, the United States and Russia, countries with more cases than India have conducted 1518 and 2074 tests per million respectively. While India has somewhat improved its testing rate since the early stages of the SARS-CoV-2 pandemic (21 per million on April 24), we are still unable to test in adequate numbers. In this blog, we study the reasons behind insufficient testing rates in India by reviewing the legal environment for regulating medical testing.

The Indian diagnostics industry is dominated by the private sector. The legal framework for regulation of private labs is set up under the Clinical Establishments Act, 2010. The issues of non-standardisation of service quality and supplier-induced demands are prevalent in the industry (Competition Commission of India, 2018). Therefore, these labs have been functioning under market-led and self-imposed norms. The government did not depend on this regulatory framework during the SARS-CoV-2 pandemic. Instead, it granted unchecked discretionary power to the Indian Council of Medical Research (ICMR) to regulate the testing strategy. Under the regulatory framework set up by the ICMR, the private lab network is not being utilised optimally for SARS-CoV-2 testing. For instance, the private sector accounts for about 70% of the health care market in India. As of June 22, 2020, only 27% of all labs approved for SARS-CoV-2 testing in India are private labs. In this article, we argue that; i) the private labs are governed by a weak regulatory framework that has allowed market failure to persist in the diagnostics sector in India, and ii) the testing strategy mandated by the ICMR for SARS-CoV-2 pandemic has led to poor outcomes with respect to the participation of private labs. Hence, there is an immediate requirement for reviewing the powers of ICMR for managing the testing strategy and a long term requirement for rethinking the present regulatory framework for labs.

Concerns about market failure in the field of medical testing

A market failure occurs when the free market is unable to obtain efficient economic outcomes. Of the four types of market failures, viz; externalities, asymmetric information, market power, and public goods, the diagnostics sector in India is seen to be affected primarily by information asymmetry. Information asymmetry or information inequality occurs when one party such as a physician possesses much greater information than the other, a patient (Arrow, 1963). During a pandemic, testing becomes a crucial part of a nation's public health strategy and hence, the public goods element of market failure also comes into play. For instance, testing data is a public good in as much as it is useful to understand the spread of the disease in an area that helps the government to design public health policies, and sharing of such data by the government affects behavioral changes in individuals.

As a result of information asymmetry, the field of medical testing in India faces the recurring issue of quality control and standardisation of services. For instance, practices such as hiring unqualified professionals, using sub-standard equipment, and proxy digital signatures have become prevalent in the industry in the absence of effective regulation. In extreme cases, there have been instances of private labs disbursing 300-400 diagnostic reports within a matter of hours, often without conducting any testing at all.

The free market does not solve the issues of market failure on its own and requires state intervention. This can be done through effective regulation of the market either by itself or through State coercion. We now study the existing regulatory framework for labs in India and its limitations.

Regulation of diagnostic labs

Health care is a state subject under the Indian Constitution. This means that in the usual course of events, states have exclusive powers to make laws concerning different aspects of health care such as diagnostic laboratories. Article 249 of the Constitution provides exceptional powers to the union government to make laws on state subjects in the national interest. For such matters, the states retain the power to accept or reject the union law. The Clinical Establishment Act has been passed by the union government under this provision and 11 states have enacted it as of now. However, there are two difficulties with the law which have created a gap between aspiration and outcome. First, under our constitutional arrangement the Clinical Establishments Act is only applicable to those states that choose to adopt it, and only 11 states have adopted this law. Second, the law has serious difficulties in design and implementation.

In the 11 states where the Act is present and implemented, the regulatory function is limited to granting registration to labs and maintaining a register of clinical establishments. The labs interact with the regulatory authority only at the time of registration when they submit evidence of having complied with the prescribed standards for registration to the regulatory authority. Once a permanent registration is granted, there is no mechanism to review the functioning of the labs or provide grievance redressal to patients under the Act. If a person starts a lab without registration, the maximum punishment under the law is a monetary penalty of rupees five lakhs.

Other than the Clinical Establishments Act, private labs have to comply with the standard regulatory requirements under the state Shops and Establishments Act (relating to hours of work, cleanliness, holidays, etc.) and obtain registration under the provisions of the Biomedical Waste Management Rules, 2016. Additionally, diagnostic kits and reagents used by labs are defined as 'drugs' under the Drugs and Cosmetics Act, 1940, and therefore have to be approved by the Central Drugs Standard Control Organisation (CDSCO).

We see that there is effectively no legal framework for regulating private labs in India. The labs only comply with allied regulatory requirements such as disposal requirements for biomedical waste and approval of diagnostic kits under the Drugs and Cosmetics Act. Given this regime, two mechanisms namely accreditation and public procurement have sought to fill the regulatory void in the diagnostics industry.

Alternative methods of regulation

In the absence of an overarching law that assures the quality of clinical establishments, private labs have turned to voluntary accreditation for establishing credibility in the vast diagnostics market. Accreditation of labs is not mandatory in India. The National Accreditation Board for Testing and Calibration Laboratories (NABL), an autonomous body under the Quality Council of India, prescribes accreditation criteria for various kinds of labs. Of the estimated 100,000-110,000 labs present in India, around 4000 have NABL accreditation. Some labs prefer obtaining certifications from international accreditation bodies in addition to obtaining NABL accreditation. Accreditation helps in assuring the quality of labs to the public as well as the government.

The second method to ensure quality standards and avoid market failure is public procurement. The government has dealt with the absence of a regulatory framework in the past by using contractual mandates to avail the services of private labs. The standards expected from these labs are contractually specified by the government while entering into public-private partnership (PPP) agreements for diagnostics. For instance, the union government under the National Health Mission (NHM) has a Free Diagnostics Services Initiative which contains detailed requirements from diagnostic/pathology labs. NABL accreditation is one of the common requirements for private labs to participate in such government programmes.

To compensate for weak regulation under the Clinical Establishments Act, voluntary accreditation by the NABL and public procurement through PPP agreements have acted as alternative strategies for regulation. These alternatives help in reducing information asymmetry and assuring the quality of services to the public and could have played an important part in the regulation of the labs for SARS-CoV-2. Yet, we find that the government strategy for medical labs for SARS-CoV-2 is based on a command-and-control approach under ICMR.

Regulation of medical labs for SARS-CoV-2

Under the existing regulatory framework, private labs did not have to follow any criteria or adhere to any standards before starting a new/novel test, such as the SARS-CoV-2 test. This means that patients would have been able to get SARS-CoV-2 tests done in any private lab offering the test using reagents/test kits approved by the CDSCO and having a valid bio-waste and other local licenses.

The lack of a regulatory framework led to confusion regarding the role of private labs in the response to the SARS-CoV-2 pandemic. As a result, the government set up an emergency regulatory framework for the SARS-CoV-2 crisis using provisions of the Epidemic Diseases Act, 1987, and the Disaster Management Act, 2005. Using these laws, it appointed the Indian Council of Medical Research (ICMR) as the apex decision-making body for India's diagnostic testing strategy through the MoHFW (see notifications here and here).

The Epidemics Act authorises the state governments to take exceptional measures and prescribe regulations to contain the spread of a dangerous epidemic disease. It lists a set of basic subjects for which regulations may be made such as travel restrictions, examination and quarantine of suspected cases, and inspections of any ship or vessel leaving or arriving at any port of call. The role of the union government under this law is limited to managing epidemic diseases at ports.

The Disaster Management Act contains an administrative framework for disaster management. Section 6 of the Act sets up the National Disaster Management Authority (NDMA) as a nodal body for disaster management. Any directions issued by the NDMA and the union government must be followed by the Union Ministries, State Governments and State Disaster Management Authorities. The SARS-CoV-2 pandemic has been notified as a disaster under this Act. Under this, the government has passed various directives on different aspects of the SARS-CoV-2 response using the umbrella clauses of this legislation such as section 6(2)(i) (The NDMA may lay down the policies, plans and guidelines for disaster management) and Section 10(2)(l) (The National Executive Committee may lay down guidelines or give directions to union ministries, state governments and state authorities for responding to the disaster) have been invoked to respond to the SARS-CoV-2 crisis.

Using the powers granted to it by the government, the ICMR has placed severe restrictions on private labs to test for SARS-CoV-2. These restrictions include requiring approvals from ICMR for lab facilities, commercial testing kits, and cost-capping for testing. We now study the ICMR decisions on testing strategy in detail to understand its role in the testing outcomes for SARS-CoV-2.

The role of the ICMR

The ICMR has been responsible for the regulation of public labs under a 2012 scheme called the Viral Research and Diagnostic Laboratories (VRDL) network under the MoHFW. The scheme was initiated to increase government capacity for the timely detection of emerging/re-emerging viral diseases. The VRDL labs were exclusively responsible for testing in the initial phase of the SARS-CoV-2 pandemic in India.

The initial advisories issued by the ICMR contained no mention of private labs and focused only on directing public labs to undertake SARS-CoV-2 testing. At the time, some state governments explicitly banned private labs from testing as per their regulations issued under section 2 of the Epidemic Diseases Act, 1897. For instance, the Delhi Epidemic Diseases COVID-19 Regulations, 2020 and the Bihar Epidemic Diseases COVID-19 Regulations, 2020 contain the following provision on testing of potential SARS-CoV-2 cases by private laboratories:

"No private laboratory has been authorised to take samples for COVID-19 in the State. All such samples will be collected as per the guidelines of the Government of India..."

Subsequently, the ICMR issued guidelines for private labs to undertake SARS-CoV-2 testing on March 21, 2020. Since then, the ICMR has been responsible for approving private labs to test for SARS-CoV-2. The ICMR conducts checks on the capability of private labs to test for SARS-CoV-2 and updates the list of approved private labs regularly. It also issues detailed guidelines for other aspects of testing such as procurement of reagents, evaluation of commercial testing kits, etc. In doing so, it has usurped the regulatory functions of existing statutory regulators such as the CDSCO, as well as voluntary bodies like the NABL. For instance, while diagnostic kits for SARS-CoV-2 are considered "drugs" and should be approved by the CDSCO, they also require validation by the ICMR. Similarly, NABL approved private labs are required to get a mandatory clearance from ICMR for SARS-CoV-2 testing. This means that while NABL has accredited 278 labs for RT PCR RNA testing for SARS-CoV-2, the ICMR has approved 258 of these labs for testing as of June 21, 2020. The ICMR does not document the rationale or process of performing these regulatory functions. The Epidemics Act and the Disaster Management Act do not require the ICMR to adhere to minimum standards of accountability, transparency, and public engagement. The invocation of these laws to empower the ICMR means that there is no coherent or intellectually defensible framework for reviewing the ICMR's actions during the pandemic except that the basic rule of law principles are followed by it.

Building state capacity for regulation is a gradual process that requires backing by a comprehensive legal framework (Roy et al, 2018). ICMR was abruptly thrust into a role for which it did not have the required organisational or procedural capacity. Hence, it compensated for the lack of a regulatory framework by issuing strict command and control orders. We see that after being appointed as the government regulator for the testing strategy for SARS-CoV-2, the ICMR barred all private labs from testing unless approved by it. Given that the labs are already approved by NABL, the rationale for re-approval for testing of private labs by ICMR was never shared. Additionally, ICMR started regulation of reagents, test-kits and costs of tests. This has had an adverse impact on the testing outcomes as seen below.

Implications of regulation of private labs by ICMR

ICMR has been responsible for advising on the SARS-CoV-2 testing strategy for the country. The restrictive policies by the ICMR have led to the inaccessibility of the tests for a vast population. As a result, various courts in India are being involved to challenge such policies.

In April, the Supreme court heard the issue of cost-capping of lab testing for SARS-CoV-2 by ICMR and ordered that the tests shall be free for persons falling under government schemes such as Ayushman Bharat or any other category of economically weaker section of the society as notified by the government. The ICMR cost-cap of INR 4500 per test for private labs was not examined by the court in this petition, but it emphasised on the need for affordable tests to the population.

The Delhi high court reviewed the cost fixed by ICMR for the procurement of rapid testing kits in April. It held that the costs at which ICMR procured the kits had an unduly high profit-margin for the vendors and ordered the cost per kits to be reduced from INR 600 to INR 400. Furthermore, the kits procured by ICMR were later found to be faulty. The court criticised the government and ICMR for low testing of SARS-CoV-2 cases in another order dated June 18, 2020. It ordered the government to review ICMR policies on labs such as the protocol for sample collection, approval of labs, data sharing by labs, and costs per test through an existing government committee.

The Gujarat high court is monitoring the state response to SARS-CoV-2 under a suo-motu writ petition. Under this petition, in an order dated May 29, 2020, the court modified the ICMR guidelines on testing for different categories of patients as it found the patient categories to be non-exhaustive. The court has also decided to review the rationale behind the ICMR SARS-CoV-2 testing strategy.

The ICMR has been criticised for its advisories on the evolving SARS-CoV-2 testing strategy by experts. For instance, its restrictions on the usage of RT-PCR and rapid antigen testing are seen to be unreasonable as the testing capacity has been increasing over time. Additionally, the issue of lack of transparency in sharing testing data and its regulatory procedure makes ICMR decisions difficult to understand and implement.

The ICMR policies regarding the testing strategy for SARS-CoV-2 are restrictive for private labs. This is indicative of a trust-deficit between ICMR and the labs. The ICMR regulatory strategy to reduce this trust-deficit is to micromanage every aspect of testing sought to be done by the private labs. This has led to lower participation of such labs in testing for SARS-CoV-2 and issues of unavailability of tests to the public.

Conclusion

The bulk of the health care services in India are provided by the private sector despite the presence of public health care facilities (Hooda, 2015). Recognising the growth and demand of the private sector, the policy framework in health has gradually shifted from the government providing health care services to being a financier of these services (Patnaik et. al, 2018). Recently, the Indian government conceded before the Supreme Court that the testing capacity of the public sector for SARS-CoV-2 is insufficient.

In this article, we studied the regulatory framework with respect to medical laboratories in India. We find that in the regular course of events, the Clinical Establishment Act, 2010, and the rules thereunder are responsible for such regulation. Issues with the adoption and implementation of this Act leave the sector effectively unregulated. Despite the presence of some alternative methods of regulation, the regulatory gap in the diagnostic sector persists. Therefore, there is a need for a comprehensive law to deal with the market failure of information asymmetry and public goods. However, the enactment of such a law is a long-term deliberative process and should not be attempted in the face of a pandemic.

For SARS-CoV-2 testing, the government has deviated from the existing course of minimal intervention in regulating private labs to regulating every aspect of testing through the ICMR. Government laboratories set up under the VRDL framework were initially the exclusive bodies allowed to test for SARS-CoV-2. While private labs have now been allowed to test for SARS-CoV-2, they are still heavily regulated by the ICMR. The rationale for this approach has not been provided. We believe such an approach is unsuitable for managing the SARS-CoV-2 pandemic. Using the broad powers given to it, the ICMR has reduced the capacity for testing in India by introducing prescriptive testing guidelines, licensing requirements, and cost-capping. This has resulted in non-utilisation of a bulk of the testing capacity for SARS-CoV-2 in India so far. Therefore, we suggest that the power given to the ICMR for SARS-CoV-2 regulation be minimised by specifically disallowing any duplication of regulatory functions already being performed by bodies such as CDSCO and NABL. Further, for the powers delegated to ICMR for regulating the testing strategy, due process requirements such as documenting the rationale, public consultation, sharing of public data should be mandated by the government to increase the accountability of ICMR.

References and further reading:

Arrow, 1963: Kenneth J. Arrow, Uncertainty and the welfare economics of medical care The American Economic Review, December 1963.

Nandraj, 2012: Sunil Nandraj, Unregulated and Unaccountable: Private Health Providers, Economic and Political Weekly, January, 2012.

Srinivasan, 2013: Sandhya Srinivasan, Clinical Establishments Act, 2010 Regulation and the Medical Profession, Economic and Political Weekly, 19 January, 2013.

Hooda, 2015: Shailendra Kumar Hooda, Private Sector in Health Care Delivery Market in India: Structure, Growth and Implications, Institute for Studies in Industrial Development, Working Paper 185, December, 2015.

Patnaik et. al, 2018: Ila Patnaik, Shubho Roy, and Ajay Shah, The rise of government funded health insurance in India, NIPFP Working Paper Series, No. 231, 21 May 2018.

Roy et al, 2018: Shubho Roy, Ajay Shah, B. N. Srikrishna, and Somasekhar Sundaresan, Building State capacity for regulation in India NIPFP Working Paper Series, No. 237, 3 August, 2018.

Competition Commission of India, 2018, Policy Note: Making markets work for affordable health care, Competition Commission of India, October, 2018.

Kelkar and Shah, 2019: Vijay Kelkar and Ajay Shah, In service of the Republic: The Art and Science of Economic Policy, Penguin Allen lane, December 2019.

Shah, 2020: Ajay Shah, More testing: From concept to implementation, The Leap Blog, 06 April, 2020.


Ameya Paleja is a molecular biologist and science blogger based in Hyderabad. Harleen and Siddhartha are researchers at NIPFP. The authors are thankful to Ajay Shah, Renuka Sane, Amrita Agarwal, Smriti Parsheera, Shubho Roy, Anand Prakash, Arjun Sinha, and three anonymous referees for their valuable comments.

Wednesday, July 01, 2020

Covid-19 and Corporate India

by Aakriti Mathur and Rajeswari Sengupta.

India is dealing with a massive shock in the form of the Covid-19 pandemic. The first case was reported in India on 30 January, 2020. By middle of March the disease had begun spreading rapidly across the country. To prevent the spread of the virus the Indian government announced a nationwide lockdown on 24 March. The pandemic and the lockdown affected nearly all firms and sectors of the economy; however, there are likely to be significant heterogeneities.

We propose a novel approach to identify firms that may have had greater exposure to the pandemic even before it assumed serious proportions in India, by virtue of, for example, their connections to other affected countries, among others. These firms may have fared worse when the lockdown was announced. We also examine the pre-pandemic balance sheet characteristics that may have worsened the impact of the lockdown on some firms compared to the others.

Analysing earnings call reports

We propose the use of earnings call transcripts as an important source of information for gauging a firm's fundamental exposure to the pandemic.

Earnings calls typically follow the presentation of a firm's quarterly results. These calls are attended by senior management of the firm (for example, the CEO, CFO, MD, etc), who present short prepared remarks, and then open the floor to questions from analysts. This implies that the calls are more spontaneous as compared to say the firm's annual report, because the senior management answers questions on the fly from the audience. These reports therefore convey not just fundamental financial information, but also analysts' and managers' opinion about the firm (Borochin et al., 2018).

A significant part of the literature focuses on the tone and sentiment of these reports and their implications for stock market returns, trading volumes (Frankel et al., 1999; Bushee et al., 2003, 2004; Brown et al., 2004), and options pricing (Borochin et al., 2018). Our work closely relates to two recent papers, Hassan et al (2020) and Ramelli and Wagner (2020). Both these studies use the information contained in earnings call transcripts. Hassan et al (2020) focus on globally listed firms, and study whether firms that were more exposed to previous disease outbreaks such as SARS and MERS were better prepared for the 2020 pandemic, and therefore had higher equity returns, than those who were not. Ramelli and Wagner (2020) analyse characteristics of US firms that explain both their stock market performance between January and March 2020 and their discussions of Covid-19 in the earnings transcripts.

Unlike these papers, we use the information in earnings call reports, to measure fundamental exposure of Indian firms to the pandemic. We are interested in using this information to study the equity market performance of the firms around the largest, most stringent lockdown announced in the world (at the time). Our analysis complements earlier work by Sane and Sharma (2020) who calculated the liquidity cover of listed firms in India in the face of large revenue shocks during the pandemic and Bansal et al. (2020) who also examine variations in the market valuation of firms on account of firm-specific characteristics during the pandemic. In this work, we take a more holistic view of firm-level vulnerabilities, examining the fundamental exposure to the pandemic, as well as the role of financial flexibilities, including liquidity. We also focus on one specific event -- the 24 March lockdown -- in order to obtain greater precision.

We focus on earnings calls conducted by firms in January and February 2020, when the case load was still low in India but the pandemic had begun spreading in other countries. These are calls discussing the income statements of October-December, 2019 (Q3 FY20) and January-March, 2020 (Q4 FY20) respectively, of the Indian financial year.

When India reported its first case of the Covid-19 pandemic on 30 January, 2020, close to 7,700 people had been infected all over the world, the majority being in China. Other countries such as the US, Australia, Germany, Japan, South Korea, UAE and HongKong had started reporting Covid-19 cases. By end February, it had morphed into a full blown public health crisis. The total number of infections globally had risen to more than 83,000, with a death toll of more than 2,800. While the disease was spreading rapidly in countries such as Italy, South Korea, France, the US and Iran, these were still early days of the pandemic in India which had less than 10 confirmed cases.

Focusing on the call reports of Jan-Feb 2020 enables us to analyse the firms' exposure to the pandemic at a time when the disease was still at a nascent stage in India unlike say March when the spread of the pandemic had begun affecting most firms. It also allows for easier identification of firm exposure because it is not muddled by domestic policy interventions. For example, there were only 13 Covid-19 related notifications issued by the Indian government in February, compared to 266 in March, as listed by PRS Legislative Research. Hence, from March onwards, the stock market performance of all firms was likely to be affected by these interventions over and above firm-specific concerns around the disease itself.

We start with a sample of the largest listed firms on the Nifty500 index of the National Stock Exchange (NSE) of India. Of the 500 firms in the index, we have access to the call reports of 196 firms in January-February 2020, and of 90 firms in April-May 2020.

Which firms had exposure to Covid-19 in Jan-Feb 2020?

We interpret the number of times a firm mentioned Covid-19 related words in its call reports as an indicator of its exposure to the pandemic. Accordingly, we count the number of times Covid-19 and related words (such as "coronavirus", "pandemic", "ncov", "sarscov", "epidemic" etc) are mentioned in the quarterly earnings call reports of the 196 firms in January-February 2020, and also of the 90 firms in April-May 2020 for the sake of comparison. We briefly summarise our findings below.

  • Only one-third of the firms in our Jan-Feb sample mention Covid-19 or related words. The average number of times these words are mentioned is three. Only three of the firms discussing the pandemic are in the financial services sector.
  • All 90 firms in the Apr-May 2020 sample mention Covid-19 or related words, demonstrating the extensive spread of the disease by this time. The average occurrence of the words per report is ten times higher, close to 31. This reflects our earlier concern that from March onwards, all firms had become exposed to the pandemic.
  • Even in Jan-Feb 2020, there were sector-wise heterogeneities in Covid-19 discussions, as shown in figure 1 below.
  • The occurrences of Covid-19 related words were higher in those sectors which presumably have more fundamental exposure to the pandemic, for example in the form of supply-chains with China or other early-affected countries. Some of these sectors are pharmaceuticals, consumer goods, automobile, chemicals etc.
  • Firms in health care services, financial services, media and entertainment, power and telecom industries either did not mention or mentioned much less pandemic related words during this period in their call reports. With the possible exception of health care, these sectors were likely to be affected due to indirect exposure to the pandemic.

Figure 1: Sector-wise occurrences of Covid-19 related words in Jan-Feb call reports

We also look at the firms that mentioned "supply", "demand" and "uncertainty" related words in context of the Covid-19 discussion in their call reports. These are likely to be the most common channels of disruption faced by the firms during the pandemic. In results not reported here we find that firms in sectors with higher than average mentions of Covid-19 related words also had higher than average mentions of "supply" related words in the sentences where Covid-19 was discussed. For firms in the services sector, mentions of "demand" related words in the context of the disease were higher. For all the sectors having higher than average mentions of Covid-19 related words, we also find significantly higher mentions of "uncertainty" related words in the context of the pandemic.

This preliminary analysis gives us an idea of which firms and sectors had greater exposure to the pandemic as early as Jan-Feb 2020 when the disease still hadn't spread in India.

For subsequent analysis, we consider the firms that mention Covid-19 in Jan-Feb 2020 as our "treated" sample and those that did not discuss the pandemic as our "control" sample. A relevant question to ask is how similar are the "treated" and "control" samples in terms of their key balance sheet characteristics. Using annual data from the pre-pandemic period (ending in March 2019) from the Prowess database of CMIE, we compare the two sets of firms in size, age, profit, foreign exchange earnings, inventories, cash balances etc. For ease of comparison, we drop the three firms that are in the financial services sector and that mentioned pandemic related words in the Jan-Feb call reports. As shown in table 1 below, we do not find any major difference between these two groups of firms, except that the "treated" firms are on average older and hold higher inventories than the "control" group firms.

Table 1: Summary statistics for non-financial firms: Data as of March 31, 2019
No. of firms with no COVID mentions in Jan-Feb 2020No. of firms with COVID mentions in Jan-Feb 2020
9660
VariableMean of firms with no COVID mentionsMean of firms with COVID mentions
Age3543.7
Log Size11.1911.13
Leverage (Debt/Assets) 0.150.16
PBDITA/Total Sales0.260.23
FX Earnings/Total income0.300.27
Cash and Bank balance/Total Assets0.070.05
Trade Receivables/Total Assets0.140.13
Inventories/Total Assets0.090.12
Operating Expenses/Total Income0.760.78

What kind of exposure did firms have to the pandemic in Jan-Feb, 2020?

We next analyse the context within which the firms discussed the pandemic in their call reports, for example, references to supply-chains, demand disruptions, or uncertainty due to the pandemic and so on. This will give us a sense of the kind of exposure the firms may have had to the pandemic in the early part of 2020.

We use the techniques applied in Mathur and Sengupta (2019). For every firm's call report, we first isolate the sentences that contain Covid-19 related words. There are 176 sentences in total for the Jan-Feb 2020 reports. Then, we create a word cloud with the most frequently occurring words in these sentences, after stripping out stop-words and other uninformative words.

The word cloud for Q3 FY19-20 reports is shown in figure 2. The size of each word is directly proportional to its frequency in the sentences. We plot the 50 most frequently occurring words. All the coronavirus related words, which are the most common words in these sentences by construction, are not plotted here for ease of comprehension.

  • The words "china" and "impact" occur most frequently indicating that firms were talking about the origin of the coronovirus disease and its effect.
  • We also see words related to areas where the impact of the pandemic was potentially anticipated or the expected transmission channels of the disease such as "earnings", "shipping", "pharma", "macro", "supply chain", "trade", "logistics", "imports", "demand", "supply", "prices" etc.

Figure 2: Word clouds of sentences with Covid-19 related words in Jan-Feb call reports

Which firms were more affected by the 24 March lockdown announcement?

The 24 March lockdown in India was regarded as one of the most severe lockdowns in the world, based on data from the Oxford COVID-19 Government Response Tracker. All transport services, except those for essential personnel, were suspended, in addition to all educational, commercial, and private establishments (see here). The lockdown affected all sectors of the Indian economy. The stock market reacted negatively overall. This is not surprising, since stock prices reflect changes in expected future cash flows and/or discount rates. However it is possible that some firms were more affected than the others depending on their exposure to the pandemic as well as pre-pandemic characteristics.

To measure the differential, cross-sectional responses of firms to the lockdown announcement, we use high-frequency stock market data and an event study methodology. We have two main hypotheses.

Our primary hypothesis is that firms that were more exposed to the pandemic and mentioned Covid-19 in their earnings call reports in Jan-Feb 2020 (the "treated" group) fared worse than the "control" group when the lockdown was announced.

  • If investors believe that firms who discussed Covid-19 and its implications for their businesses early on in the year are more exposed to the virus, for example due to supply chains with China, or factories in badly-affected countries like Italy, then they would revise their expectations of future profitability downwards in response to the lockdown. Therefore, we would see that treated firms as a whole perform worse than control firms.
  • If investors believe that early discussions of the pandemic implied that these firms were better prepared to weather the storm, then their returns would be better than those that seemed to have been caught "off-guard". We hypothesise that the former is likelier than the latter, since it is not clear how firms could have unilaterally prepared for the over-arching extent of the shock (such as to demand disruptions) just a couple of months in advance.

Our second hypothesis is that low-profitability firms with higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses should have witnessed lower stock market returns when the lockdown was announced, compared to more domestically oriented firms which were more profitable, were holding lower inventories, had lower dependence on trade credit and also lower operating expenses.

Estimation strategy

We use a difference-in-difference strategy to estimate the impact of the lockdown event on firms' stock market returns. We consider all "treated" firms as one group by using a dummy ("Covid dummy"). Our dependent variable is the cumulative abnormal stock market returns (CARs) for each firm over a window of (-1, +2) days around the lockdown event, i.e. between 23 March (Monday) and 26 March (Thursday). To obtain these abnormal returns, we estimate a market model (i.e. controlling for movements in the Nifty50 index), as shown in the equation below, over a period of 81 days prior to March 24. More specifically our window starts 91 days prior to the lockdown and stops 11 days before the lockdown. The model specification is:

$$\text{Daily firm returns}_{firm,t} = \alpha + \beta~\text{Daily Nifty50 returns}_{t} + \epsilon$$

The advantage of using a tight window around the event is that it better accounts for anticipation effects and other confounding factors. We use a cross-sectional ordinary least squares regression shown in the equation below, to regress the firm-specific CARs on the "Covid dummy" and on a host of balance sheet variables. Among the regressors, of particular interest is the "Covid dummy" which tells us the difference in CARs between the "treated" and the "control" firms. Other regressors include the balance sheet variables shown in table 1 above as well as dummy variables for the sectors that the firms belong to. Firm level annual balance sheet variables are as of March 31, 2019.

$$\text{CARs around event window}_{firm} = \alpha_{sector} + \beta~\text{Covid Dummy}_{firm} + \\ \log(age)_{firm} + \log(size)_{firm} + Controls_{firm} + \epsilon $$

Results

We summarise our results in Figure 3. In panel (a) we plot the results from our baseline model (model 1) which includes only age and size of firms, and the sector dummies, over and above the Covid dummy. We also plot the results from models 2 to 6 where in addition to the Covid dummy, age, size, and sectors, we sequentially add the regressors of interest: profit, FX earnings, inventories, operating expenses, and trade receivables. We also investigate the role of cash, leverage, borrowing composition, and tangible assets. Here we only report results that are significant at 90% confidence interval. We list our main findings below.

  • In all our specifications, the stock returns of "treated" firms, i.e. those that mentioned Covid-19 in their call reports early on in 2020, significantly underperform (at the 90% confidence level or more) the "control" firms. On average, returns of the "treated" firms are roughly 3.5 percentage points lower.
  • We find that the equity returns of more profitable firms outperformed those of less profitable ones by 9 percentage points (model 3). Higher profitability implies higher ability to withstand large revenue shortfalls.
  • Firms with a higher share of foreign exchange earnings in their total income performed worse. They were likely to be more affected due to supply and demand disruptions in the rest of the world.
  • Firms with high inventories saw 24 percentage points lower returns. High share of inventories in total assets might make it difficult for firms to get rid of their inventories once an economywide lockdown is announced yet they would have had to incur the costs of maintaining these inventories which makes them worse off than firms with lower share of inventories (Banerjee et al., 2020).
  • Firms with higher pre-pandemic trade credit reliance saw significantly lower abnormal returns. This is likely because in a broad-based crisis such as this one, credit markets are likely to freeze along both extensive and intensive margins. Thus, rolling over existing trade credit as well as obtaining new supply of trade credit would be difficult. (Banerjee et al., 2020).
  • Firms with higher pre-pandemic operating expenses also fared worse once the lockdown was announced. Operating expenses are typically short term expenses. In absence of steady revenues in a lockdown, firms would depend on credit from the financial system to meet these expenses. During a crisis if the financial system is unwilling to offer short term credit (Sengupta and Vardhan, 2020), then these firms are likely to witness lower stock returns.

In figure 3, panel (b), we plot the coefficients on the sector dummies from the baseline model 1, with only age and size included as controls. Automobiles is the benchmark sector. We find that stock returns of more consumer facing sectors (textiles, media and entertainment) and those that rely on supply chains (metals, and oil and gas) did particularly badly when the lockdown was announced. On the other hand, healthcare services in particular outperformed as compared to automobiles.

Figure 3, panel (A): Explaining cumulative abnormal returns around first lockdown (24 March, 2020)

Figure 3, panel (B): Sector dummies from baseline regression

In a nutshell, we find that when the nationwide lockdown was announced on 24 March, firms who mentioned Covid-19 in their earnings calls in early-2020 and hence were more exposed to the pandemic, fared worse than firms who did not discuss the pandemic. This result holds when we account for the sectors and key balance sheet characteristics of the firms.

As discussed in Fahlenbrach et al.(2020), less financially flexible firms are less able to withstand large negative shocks to their revenues, which translates to worse equity market performance. Lower cash, lower profitability, lower diversification in earnings (e.g. higher reliance on foreign exchange revenues) or in borrowing sources (e.g. higher reliance on trade credit) can all be considered indicators of low financial flexibility. In other words, firms that had lower financial flexibility in the pre-pandemic period were worse affected when the lockdown was announced.

We further find that controlling for mentions of "supply" and "demand" related words (not shown here) in the firms' call reports -- which may account for the nature of their exposure to the pandemic -- does not change the results qualitatively, and makes them stronger in some specifications.

Firms with more cash holdings reported higher returns on average around the lockdown announcement, but this effect is not significant (hence, not reported here). In further tests, we find some evidence of non-linearities. Firms with above-median cash holdings significantly outperform their counterpart.

Conclusion

Using the informational content of earnings call reports of some of the largest, non-financial firms in India we throw light on the firms and sectors that may have been more exposed to the pandemic as early as January and February 2020 when as per the official statistics, the disease had still not spread in India. We find that these firms were also worse affected by the announcement of a nationwide lockdown in March compared to firms that were presumably less exposed to the pandemic early on.

Our results highlight the kind of firms that are likely to be more affected when a crisis such as the ongoing one hits the economy. Firms with lower profits, higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses fared worse on the stock market when the lockdown was announced.

References

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Aakriti Mathur is a PhD candidate at The Graduate Institute (IHEID), Geneva. Rajeswari Sengupta is an Assistant Professor of Economics at IGIDR, Mumbai.