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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%
Business 12.9% 9.1% 19.2% 8.2%
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.

Wednesday, November 11, 2020

Announcements

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.