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Thursday, April 11, 2019

The Indian bankruptcy reform: old defaults vs. recent ones

by Renuka Sane and Rajeswari Sengupta.

The Insolvency and Bankruptcy Code (IBC) was enacted in 2016. At the time, there were a large number of zombie firms in the Indian landscape, where the first default had taken place a while ago. This motivates a question: Does the IBC perform differently when confronted with a recent default, compared with the outcomes obtained when dealing with legacy problems? In what form does the difference manifest itself?

Our prior is that old cases would fare worse, for two reasons. First, there is a selection bias. The defaulting firms who were relatively stronger, are more likely to have found a solution through successful private negotiations. The survivors are likely to be the zombie firms who would be in bad shape. Second, a firm that defaults is a melting ice cube. Every day, value is destroyed. The very fact that the default in these old cases took place a while ago suggests that substantial value destruction may have taken place. In a sound bankruptcy process, the firm is brought to the insolvency resolution process rapidly, which increases the chances of rescuing a viable business under a new ownership and balance sheet. These old cases did not have access to a sound bankruptcy process, until now.

If such a phenomenon is at work, the unconditional performance of the IBC (example, example, example) will tend to be understated. The correct measure of how well the IBC works should then be seen by restricting the analysis to recent defaults only.

In this article, we show some early small sample evidence, where IBC outcomes of old and new defaults are compared. We look at two metrics: the final outcome (liquidation or resolution), and in the class of firms that did go through a resolution, the time taken and the recovery rate. We find that a larger proportion of the old, legacy cases were liquidated. Median recovery rates were lower for the old cases that did go through a resolution compared to the more recent cases. However, there was no difference in time taken to complete the IRP process.

Institutional setting

We do not have access to the date of default. This information is not disclosed by either the National Company Law Tribunal (NCLT) or the Insolvency and Bankruptcy Board of India (IBBI). We identify old cases as those where resolution began under the Sick Industrial Companies Act, 1985 (SICA) and have remained pending at the Board for Industrial and Financial Reconstruction (BIFR).

With the repeal of SICA, the Eighth Schedule of IBC explicitly provides for the abatement of the existing SICA cases pending at BIFR and Appellate Authority for Industrial and Financial Reconstruction (AAIFR), with an option to re-initiate them as new cases under IBC.

Data

We use two sources of data. The first comes from the IGIDR Finance Research Group's (FRG) database on bankruptcy. The dataset contains all unique debtor firms from January 1, 2017 till December 31, 2018. This provides us a macro picture on the number of liquidations and resolutions of BIFR and non-BIFR firms.

The data on resolutions is sourced from the IBBI which has obtained the information from resolution professionals. In this databse we see 82 firms where resolution plans have been approved by the NCLT as of December 31, 2018. This is the most recent data available as of now. We remove the outliers from our sample, where the realisation amount exceeds the total amount of claims filed. This gives us a dataset with 72 cases. An interesting feature of this data is that the BIFR cases are smaller, with a mean liquidation value of Rs.1.75 billion, as compared with the mean liquidation value of Rs.6.25 billion for the non-BIFR firms.

Q1: Do we see more liquidations of BIFR firms?

According to the FRG database, there were 1000 ongoing cases of bankruptcy. 304 firms had been liquidated while 79 had undergone resolution. Table 1 provides a break-up of BIFR and non-BIFR firms depending on their resolution status.

Table 1: Comparison of BIFR & non-BIFR cases: Resolution status

Legacy (BIFR) cases Fresh (non-BIFR) cases
Liquidation 220 84
Resolution 28 51

This suggests that most of the firms that had been in BIFR and that came to IBC, went into liquidation. This is not surprising as substantial value destruction would have taken place just by being in the process for many years, and it is highly unlikely that these firms had any value as a going concern.

Q2: Do we see a difference in time and recovery rate of the
approved resolutions?

Using the information from the IBBI dataset, we calculate the average recovery rates and the average time taken for the legacy (BIFR) and recent (non-BIFR) cases, as shown in Table 2. The recovery rate is estimated as the ratio of the realisable amount and admitted claims of the respective classes of creditors (FCs and OCs). This is not calculated on a net present value (NPV) basis and hence needs to be interpreted accordingly. We do not have access to information on realisable amount on an NPV basis.

Table 2: Comparison of BIFR & non-BIFR cases: Time and Recovery

Legacy (BIFR) cases Fresh (non-BIFR) cases
Number 24 48
Mean time to resolve (days) 311 311
Median time to resolve (days) 282 282

Mean recovery rate (%) 46.23 47.63
Median recovery rate (%) 32.69 42.77

Mean recovery rate of FCs (%) 45.57 49.01
Median recovery rate of FCs (%) 35.70 42.75
Mean recovery rate of OCs (%) 36.04 35.44
Median recovery rate of OCs (%) 11.92 19.84

The main feature of these results is that the sample means and the medians of the two groups are rather alike on a number of parameters. The average and median time taken to resolution is the same for the two groups. The mean recovery rate is around 47% for both groups. There is, however, almost a 10 percentage point difference in the median recovery rate - once again not surprising given that BIFR firms are likely to have seen significant value destruction before they came to the IBC. The same pattern is repeated when one looks at recovery rates of financial and operational creditors between the two groups of firms.

Conclusion

Our prior was that the legacy cases have been festering for long and hence have experienced prolonged value depreciation, and would therefore see different outcomes than fresh defaults. We find the difference in the form of more liquidations of the legacy firms, and significant differences in liquidation value, and median recovery rates. We, however, do not find any difference in the time taken to complete the insolvency resolution process. This could be because the legacy cases which were in worse shape went into liquidation and those that remained in IRP were not very different in nature compared to the recent defaults.

This also motivates thoughts for further research. Will this result hold up with stronger datasets that maybe visible in a year or two? This kind of analysis, conducted on a bigger dataset spanning a few years post IBC, is likely to give a more realistic picture of the performance of IBC as opposed to analyses which view the legacy and recent cases from the same perspective.

 

The authors are researchers at NIPFP and IGIDR respectively.

Tuesday, April 09, 2019

Delays in deposit insurance

by Karan Gulati, Shubho Roy, Renuka Sane.

In late 2017, the government introduced the Financial Resolution and Deposit Insurance (FRDI) Bill which proposed a new resolution framework for banks and financial firms. It planned an overhaul of the present system operated jointly by the Reserve Bank of India (RBI) and the Deposit Insurance and Credit Guarantee Corporation (DICGC), and introduce a modern Resolution Corporation with more extensive powers to regulate and resolve banks.

The bill faced resistance in Parliament. The opposition stated that the bill risked the solvency of public sector banks and accused the government of putting public money at risk. Some argued that the current system of deposit insurance would be taken away by the proposed FRDI law. The Bill was said to have been “designed to punish small depositors for the sins of defaulters, corrupt bank managers and political masterminds”. The bill was withdrawn in 2018. As a result, the DICGC has remained the insurer for bank deposits.

In this article, we attempt to measure how the DICGC has fared in processing bank failures and settling claims of depositors.

Present system

Today, the DICGC acts as a pay-box. Under its eponymous law of 1961, the DICGC insures the deposits of banks created by parliamentary (central) legislation, private banks, and eligible co-operative banks. Eligible co-operative banks are a subset of co-operative banks where the state legislature has empowered the RBI to exercise some regulatory oversight. Under the 1961 Act, if a bank insured by DICGC is wound up or has its license cancelled by the RBI, every depositor is entitled to insurance of up to Rupees 100,000.

The law envisages a quick payout to reduce inconvenience to depositors due to a bank failure. Within three months of being appointed, every liquidator of a bank must provide to DICGC, a list of depositors with the amount due to each one of them (S.17(1)). The DICGC, then makes the insurance payout. The law (S. 17 (2)) requires DICGC to pay claims within two months of receiving the list from the liquidator.

Data

We source data from annual reports of the DICGC and RBI notifications concerning the cancellation of license/ de-registration of 127 banks from 2013 to 2018. From these, we note the year of payout under the DICGC Act, number of depositors, and the amount of claims settled. We were able to trace RBI notifications for 115 banks, which accounted for 95% of all depositors, and 99% of the amount of payouts. We measure the time taken between the RBI notification (which we assume to be analogous with the exit of the bank), the disbursement of the payments; and the opportunity cost of the amount of claims at 8% and 20% per annum.

The hand-collected data set is accessible here.

Overall performance

Bank failures are especially disruptive for depositors. Banks work on the promise of providing deposits callable at par. A bank makes a promise to allow the depositor to withdraw their money within one banking day. This gives confidence to the depositor to use banks for their daily needs rather than hoarding cash. Though there have been no commercial bank failures in India in the recent past, this hides a deep and persistent problem of co-operative bank failures in India.

Cases of Bank Failures handled by the DICGC from 2013 to 2018
Year Number of Payouts Depositors Claims (Rupees million)
2013-14 51 96590 1030.93
2014-15 30 185901 3212.89
2015-16 17 90792 471.44
2016-17 10 35215 586.37
2017-18 19 56173 435.19
Total 127 464671 5736.82

As the table above shows, over the last five years, 25 co-operative banks have received payouts by DICGC each year on an average. More than 400,000 depositors had to use the deposit insurance scheme. These are not small numbers. Their experience of the deposit insurance payout will be the basis of the trust they will repose in the banking system, the regulator, and the deposit insurance scheme.

Time taken to disburse payments

Under the DICGC Act, the liquidator is supposed to provide a list of claimants to DICGC within three months of her appointment. However, a major cause of delay for the payouts is that the claims list is not received from the liquidator within the stipulated time limit. This may be because cases filed against the liquidator are in court, appeals by the bank are pending before the Ministry of Finance (Appellate Authority), or clarifications are required about the claims list.

The process of disbursement of payments needs to be expedient so that depositors do not suffer undue losses and maximum value is derived from the failed institution. To measure the effectiveness of DICGC we calculate the opportunity cost of the delay in payments.

Money today is worth more than money tomorrow. Since the depositors are unable to access their money, which was promised to be to be callable at par, they face a loss. Depositors have three choices (i) find an alternate source/borrow, (ii) forgo consumption or, (iii) forgo investment. The time taken by DICGC for the disbursement of the due amount imposes a cost on the depositors. The standard measure for delayed payment is the opportunity cost of the money. It is calculated by discounting the amount with a discount rate over the time by which it was delayed. For example, if the depositor is owed Rs. 100; and the insurance payout delay is 1 year; and the discount rate is 10%, then a payout of Rs. 100 at the end of the year is effectively a payout of only Rs. 91. If the payout is delayed by 2 years; effective payout is Rs. 82.64.

We use two rates of discounting to measure the opportunity cost: 8% and 20%. 8% is slightly above the risk-free rate of return and can be considered as the minimum opportunity cost of money. However, if you are poor or an individual, it is almost impossible to borrow at this rate. So we choose another realistic rate of 20%. This is the rate at which the government requires buyers to compensate MSMEs for delayed payments (S. 16, MSMED Act).

As the graph above shows, it took an average of 2.10 years for the disbursement of the amount by DICGC. This is five times the statutory limit of five months. Similarly, the median time taken is more than four times the statutory limit. The pay-out process took over 5 years in almost 1/3rd of the cases of failures. A reading of the Annual Reports of DICGC shows that in two cases of bank failure, it took over 14 years for the insurance claims to be disbursed.

As of 20th March 2019, there were another 25 bank failures pending before DICGC, where depositors have been waiting for an average of 6.57 years. Two of these banks were de-registered 20 years ago and DICGC is yet to receive the claims list from the liquidator.

Year-wise metrics of delay in disbursement
Year Average Time Taken (years) Claims (million) Opportunity Cost at 8% (million) Opportunity Cost at 20% (million)
2013–14 1.16 1030.93 930.18 814.21
2014–15 2.31 3212.89 2642.44 2030.18
2015–16 1.35 471.44 410.30 342.16
2016–17 2.16 586.37 496.86 399.19
2017–18 3.86 435.20 322.56 216.32
Total 2.10 5736.83 4802.34 3802.05

As Table 2 shows, opportunity costs have risen over the years. At a conservative discount rate of 8%, depositors in 2017-18 effectively got only Rs. 322.56 million, for claims of Rs. 435.2 million, a 26% loss. This is due to the increasing delays in processing payouts. In 2013-14, the loss rate was only 10% of the value of insurance. However, 8% is an optimistic rate. At a realistic rate of 20%, depositors lost more than 50% of the value of payouts in 2017-18. At the realistic rate, depositors have lost over one third the value of their insurance in the past five years due to delays.

Proposed System

One of the reasons for the delay is due to the fact that that DICGC itself does not have the power to obtain the list of depositors in a bank. It has to depend on the liquidator to provide the list. This explains a significant portion of the delays. As shown here, in a number of cases the DICGC is waiting for the liquidator to provide the list of insurance claimants.

The FRDI Bill solves this problem through two measures:

  1. Allowing the Resolution Corporation to take over the management of a bank at risk before it stops banking activities and is bankrupt.
  2. Combining the function of the liquidator/receiver and insurer in the same agency.

The Bill proposed a mechanism for an early warning system for banks at risk of failure. Banks would be classified into five categories ranging from low to critical. If a bank was classified in higher risk categories it would have to formulate its resolution plan which would include information about depositors [S.44]. The bill also allowed the Resolution Corporation to take over the management of banks at critical risk (S. 46) which is before bankruptcy (unlike the present system). This would give the Resolution Corporation the power to get into the books of the bank before it failed and consequently give it more time to make a list of insurance claimants without delay to depositors.

This is unlike the present system where the DICGC enters the process only after the bank has been declared insolvent by the RBI and is unable to pay its dues (see here, here and here). By coming in before a bank has failed, the Resolution Corporation would have the ability to analyse its operational statements, books of records, and list of depositors entitled to payouts in the event of such a failure.

Another cause of the delay is that the liquidator and DICGC are independent of each other. The liquidator is appointed after a Bank has been ordered to be wound up, and is not part of the DICGC. The DICGC hence has no authority in preparing the list of eligible claimants and has to depend on the liquidator to provide it. In contrast, the proposed Resolution Corporation would have acted as the liquidator and insurer for a co-operative bank in the event of a failure (S. 62). As such, the corporation would not have to depend on an external party to prepare a list of claimants.

Conclusion

The process under the current regime is slow. It takes close to two years after the cancellation of license for the disbursement of claims. Since most co-operative banks service poorer clients, their failure hurts people who are not in a position to forgo their deposits for long periods of time. Today, without a framework for bankruptcy and orderly resolution for financial firms, India faces the risk that if a large private sector bank goes bankrupt, the depositors could be stuck for years before getting their money back.

The focus of the deposit insurance corporation needs to shift from payouts after default, into the problem of identifying weak banks and stopping them. Many countries have developed specialised resolution regimes for various categories of financial firms. The Financial Stability Board recommends operational independence as a key attribute of resolution regimes. In several jurisdictions, including the USA, Canada, Malaysia, Mexico, Japan, Korea, etc., these are in the form of separate institutions with resolution powers.

References

A welcome retreat: withdrawing the FRDI Bill, The Hindu, 10th August 2018.

FRDI Bill: Panacea for banking sector set for quiet burial after PNB scam?, Business Standard, Archis Mohan, 22nd February 2018.

Movement on the law for the Resolution Corporation, by Suyash Rai in The Leap Blog.

Raghuram Rajan and Ajay Shah. New directions in Indian financial sector policy. In Priya Basu, editor, India’s financial sector: Recent reforms, future challenges, chapter 4, pages 54–87. Macmillan, 2005.

The demise of Rupee Cooperative Bank: A malady, by Radhika Pandey and Sumathi Chandrashekaran in The Leap Blog.

The Financial Resolution and Deposit Insurance Bill 2016, Department of Economic Affairs.

Won’t let Centre pass FRDI Bill in Parliament: Abhishek Banerjee, The Indian Express, Express News Service, 26th December 2017.

 

Shubho Roy and Renuka Sane are researchers at the National Institute of Public Finance and Policy and Karan Gulati is a law student at Symbiosis Law School, Noida.

Saturday, March 30, 2019

Delays in liquidated and resolved firms: Visualisation of an output measure of the Indian bankruptcy reform

by Geetika Palta, Anjali Sharma, Susan Thomas.

The ultimate objective of the Indian bankruptcy reform is to get up to plausible recovery rates and change the behaviour of borrowers. The key tool for achieving these objectives is reducing the delay. In the existing literature, we know that there are large delays, particularly for large firms (Bhatia et. al. 2019, Felman et. al. 2019, Shah 2018). The most important proximate objective of the Indian bankruptcy reform is to reduce delays in the bankruptcy process (Shah and Thomas, 2018).

A great deal of the focus so far has been upon the average value of the delay. It is, however, important to look at the full distribution of the delay, and not just the sample mean. Box-and-whisker plots are a nice visualisation tool through which we can see more than just the sample mean. In this article, we (a) Construct a visualisation of a key output measure for the Indian bankruptcy reform : a box-and-whisker plot for the delay associated with Resolved and Liquidated firms; and (b) Argue that this output measure is likely to get worse in coming days.

The overall distribution of the delay


Sometimes, it is argued that the right way to measure the delay is to exclude certain elements of the delay, which is not correct seen from first principles. The fundamental fact about distressed firms is that every day of delay reduces recovery rates and hampers economic dynamism. For an analogy, a sick animal is unproductive and suffers, regardless of whether the vet takes the weekend off or not.

We work with two years of data about cases that have concluded and exited from the IRP. These are obtained from the IBBI website. In this data, 1383 cases embarked into the Insolvency Resolution Process (IRP) from January 2017 to December 2018. Of these, 79 concluded with an accepted resolution plan and 304 cases that concluded with the firm being put into liquidation. This yields the following box-and-whisker plots:

Figure 1: Box-whisker plots for the delay of IBC cases, under three buckets (Ongoing, Liquidated or Resolved)

Let's start at the right column (for resolved cases). The bottom pane shows that 79 firms were resolved. The upper pane depicts the range of values for these firms. The black horizontal line is the median, and the box is drawn from the 25th to the 75th percentile values for the delay. The dots show the most extreme values. A key finding here is that the median resolved case took more than 270 days (the horizontal red line).

When we look at the liquidated cases, things are slightly better. The black line -- the median delay -- is close to 270 days. It still says that half of liquidated cases took more time than the legal limit of 270 days. A little under 25 percent of the liquidation cases reached their conclusion in 180 days, while very few of the resolved cases concluded within 180 days. But more than 50 percent of the liquidation cases concluded within the 270 days limit, while a little more than 25 percent of the resolved cases were done by this time.

These two pictures -- the box-and-whisker plots for resolved and liquidated cases -- are a nice visualisation of a key output measure of the Indian bankruptcy reform. The trouble is, so far, we have seen only 79 + 304 cases reach the conclusion. These statistical estimates are censored: the cases that have finished are likely to be the ones where the IBC fared relatively well. The bulk of the action is in the Ongoing cases, and there are over 900 of them. For these, the median delay is already in the region of 270 days.

The box-and-whisker plots for Liquidated and Resolved cases, which is the output measure of the Indian bankruptcy reform, will be modified in the future based on cases emerging out of the Ongoing bucket. Very crudely, we may conjecture that if all the ongoing cases finish tomorrow, the 25th and 50th and 75th percentile values of the overall distribution will be much like those seen as of today with the Liquidated and Resolved cases. But this is an over-optimistic scenario. In fact, cases will only trickle out in the future with higher delays, cases where the median delay has already reached about 270 days. Therefore, as cases emerge out of the Ongoing bucket in the future, the box-and-whisker plots for Liquidated and Resolved cases are going to get worse.

How might the output measure evolve in the future?


The most interesting question before us is: In the future, when Ongoing cases trickle out into completion, how will the output measures (the box-and-whisker plots of Resolved and Liquidated cases) shape up?

To help visualise what comes next, we create the box-and-whisker plots for the Ongoing cases by quarter, from Q1 (Jan to Mar) 2017 to Q4 (Oct to Dec) 2018 in Figure 2. The $x$ axis shows the quarter in which cases were admitted into the IRP. The top pane of the graph shows the box-and-whisker plot for the days in IRP for the Ongoing cases only (those which have not concluded as of Dec 2018) and the bottom pane shows the number of firms.

The graph for the number of firms shows that a large fraction of the Ongoing cases have started their IRP in the last three quarters of 2018 -- between April to December 2018. Among these three quarters, there is a near split of about 33%-33%-33%, between cases that have spent more than 270 days, between 180 and 270 days, and below 180 days.

Figure 2: Delays associated with ongoing cases, organised by quarter

To some extent, these results are mechanically driven by the facts of time. But the results are remarkable nonetheless. As an example, the (few) pending cases from Q1 2017 have already spent over 700 days of delay! When these cases complete, they will push the outcome measures in an adverse direction.

On the other hand, a good number of cases are in 2018 where, so far, the delay that has been clocked is relatively low. If, hypothetically, the Indian bankruptcy reform suddenly works better, then a slew of cases can complete, and then the outcome measure may even improve.

Conclusions


  1. The box-and-whisker plot of the delay (measured in calendar days) for Resolved and Liquidated firms is a nice visualisation of a key output of the Indian bankruptcy reform.

  2. It shows a gloomy picture, where over half of the delays are worse than the outer limit in the law of 270 days.

  3. Looking into the future, based on the delays already incurred with Ongoing cases, the output measure is likely to get worse.

References

 

Time to resolve insolvencies in India, Surbhi Bhatia, Manish Singh, Bhargavi Zaveri, The Leap Blog, 11 March 2019.

The RBI-12 cases under the IBC by Josh Felman, Varun Marwah, Anjali Sharma, 2019 (forthcoming).

Sequencing issues in building jurisprudence: the problems of large bankruptcy cases, Ajay Shah, The Leap Blog, 7 July 2018.

The Indian bankruptcy reform: The state of the art, 2018, Ajay Shah, Susan Thomas, The Leap Blog, 22 December 2018.


The authors are researchers at the Indira Gandhi Institute for Development Research.

Friday, March 29, 2019

Announcements

Researchers interested in the field of land and access to finance at Mumbai/Hyderabad

Number of positions: 2

The Finance Research Group (FRG) at the Indira Gandhi Institute of Development Research (IGIDR) is looking to hire researchers for a short-duration project in the field of land and access to finance.

The specific project involves understanding the linkages between the distribution of welfare benefits and land record systems in India. The duration of the project is four months, with a possibility of extension if the candidate is found suitable and is interested in working on the broader land research agenda at FRG.

Researchers at IGIDR have been working in the field of land and access to finance for over two years now. Examples of the work done include:

  1. Gausia Shaikh and Diya Uday, Rethinking urban land records: A case study of Mumbai (November 2018)
  2. Sudha Narayanan, Gausia Shaikh, Diya Uday and Bhargavi Zaveri, Do digitised land records mirror reality? (June 2018)
  3. Sudha Narayanan, Gausia Shaikh, Diya Uday, and Bhargavi Zaveri, Report on the implementation of the Digital Land Records Modernisation Program in the state of Maharashtra (November 2017)
  4. Bhargavi Zaveri, Distortions in the Indian land collateral market (February 2017)
  5. K. P. Krishnan, Venkatesh Panchapagesan, and Madalasa Venkataraman, Distortions in land markets and their implications to credit generation in India (January 2016)

We are looking for candidates with a background in development economics, preferably having experience in conducting surveys and analysing survey data. The candidates should have demonstrated ability for analytical thinking, strong qualitative and quantitative skills and average writing skills.

Familiarity with free and open source software like Linux, LaTeX, R and Python is also desirable. While previous experience in these softwares is not essential, the person will be required to learn and use them for their work.

Job description

The researchers will be part of a team spread across Mumbai and Hyderabad, that studies the linkages between the distribution of welfare benefits and land records administration.

The researchers will have to perform the following tasks:

  • Support research in economics, public policy and law;
  • Carry out field surveys;
  • Build, manage and analyse datasets related to land;
  • Contribute towards writing the project report;
  • Contribute towards organising workshops and roundtables to disseminate research.

How to apply

If you meet the above criteria and are interested in this position, please email your resume and covering letter to: careersatFRG@gmail.com.

The subject line of your email should be "Telangana recruitment".

The last date for submitting the application is 7th April, 2019.

About IGIDR

IGIDR is an advanced research institute established and fully funded by the Reserve Bank of India for carrying out research on development issues from a multi-disciplinary point of view. For more information, please see here.

Estimates of household debt in India

by Subhamoy Chakraborty and Renuka Sane.

Credit plays an important role both in consumption smoothing and in fostering entrepreneurship. In this article, we present some facts about household borrowing from a new household survey for the period May - August 2018.

Data

We source data from Consumer Pyramids Household Survey (CPHS), a pan-India panel household survey of about 170,000 households carried out by the Centre for Monitoring Indian Economy. The survey captures data on household demographics which includes member-wise characteristics, household amenities such as access to water and electricity, household income and expenses and household assets and borrowing by households. For the purpose of sampling, CPHS creates one or more Homogeneous Regions (HR) for each state from a set of neighbouring districts that have a similar agro-climatic condition, urbanisation levels and female literacy. There are a total of 102 HRs in the CPHS database.

The data is captured three times a year (known as a Wave or Round). One wave is completed over four months. The information on incomes and expenditure is provided for all months, while the information on assets, liabilities, and member characteristics is as of the month of the survey. For example, Wave 1 in 2018 would consist of January, February, March, April 2018, followed by Wave 2 that would include May, June, July, August 2018 and Wave 3 would have data on September, October, November and December 2018. If the household was visited in April 2018 in Wave 1, the income and expenditure details would be collected for the previous four months. However, member characteristics, assets and borrowing would be as of April 2018.

The data on sources and purpose of borrowing comes from the Household Amenities, Assets and Liabilities database within CPHS. This is our primary source of data for understanding credit access. Households are asked questions on their borrowing status (Yes/No) across multiple sources and purposes. The questions are:

  1. Borrowings from SOURCE for any purpose: The question is, "Does the household have any outstanding credit from a particular SOURCE?". Here 'SOURCE' includes Banks, NBFCs, Credit Card, Money lenders, Relatives or friends, Employer, Shops, Self Help Groups, Micro Finance Institutes and Chit funds. Debts not belonging to any of these sources are classified as 'Other Sources'.
  2. Borrowings from any source for PURPOSE: The question is "Does the household have any outstanding credit for a particular PURPOSE?". Here 'PURPOSE' includes Consumption expenditure, Purchase of consumer durables, Health expenses, Education, Marriage, Housing, Business, Investing in financial assets, Repaying other debts, vehicles. All other purposes are classified under 'Other Purposes'.
  3. Borrowings from SOURCE for PURPOSE: Households are also asked borrowing status for different combinations of source and purpose (eg. Borrowings from BANK for HOUSING.)

It is pertinent to note that the database only provides us information on whether the household has accessed credit from a particular source for a specific purpose. It does not provide information on the amount of credit outstanding.

We use Wave 2 (May - August), 2018 for our analysis. The sample from the Household Amenities, Assets and Liabilities database includes 1,49,160 households as about 23,000 households had to be dropped in this particular wave owing to non-response. We use household weights to get population estimates for share of households having debt and the distribution of debt across different sources and purposes. We further merge this with the previous wave of the Household Income and Expenses database. This is to ensure that data on household income pertains to the time period before the respondent answers the question on debt. Upon merging with Household Income and Expenses database the total number of households in the sample reduces to 1,47,403. These households are then used to arrive at estimates of how sources and purposes of borrowing vary across income deciles for borrower households.

Q1: How many households borrow?

Table 1 describes the proportion of households with debt outstanding in Wave 2, 2018. Around 46% (137 million) of households in India reported having debt outstanding between this time period. 46% of rural households reported having debt as compared to 44% of households in urban areas.

Table 1: Number and Share of Borrower Households
Region NO.of HH
(millions)
HH (%)
National 137.7 45.74
Rural 91.9 46.53
Urban 45.8 44.24

Q2: Where are the borrower households located?

Figure 1 presents the share of households across India, calculated at an HR level, who say they have credit outstanding from any source for any purpose. The Southern and Eastern states such as Telangana (90% of households) followed by AP (80%), Odisha (77%), WB (72%), Tamil Nadu (65%) and Kerala (60%) have a larger proportion of households who claim to have debt outstanding than the Northern States, with J&K being an exception at 90%. Regions represented in white are not covered in CPHS and so data is not available for them.

Q3: Where do households borrow from?

We have classified the different sources of borrowing into three groups :

  • Formal sources: Any borrowing from banks, NBFCs and credit card debt.
  • Informal sources: Any borrowing from money lenders, relatives and friends, employers, shops or sources classified as "other" by CPHS.
  • Semi-formal sources: Any borrowing from self help groups,
    micro finance institutions and chit-funds

13% of all households have borrowed from formal sources whereas 32% have borrowed from informal sources and 6.6% from semi-formal sources. There is a significant share of households who have borrowed exclusively from one of the three sources, for instance, 9% of all households have borrowed only from formal sources, 27% only from informal sources and 3.6% households have borrowed only from semi-formal sources. Around 3% households have borrowed from both formal and informal sources, 2% have borrowed from both informal and semi-formal sources and only 0.5% have borrowed from both formal and semi-formal sources. A small share of 0.3% households have borrowed from all three sources.

Q4: Why do households borrow?

Figure 2 presents the different purposes for which households borrow. A household may borrow for more than one purpose and therefore the share does not represent borrowing exclusively for that purpose. Borrowing for consumption expenditure far exceeds other reasons, with 22.5% of all households having borrowed for the same. The next major reasons for borrowing are housing (5%) and business (4.6%). Around 4% of all households report having borrowed for repaying existing debts. There are a lot of anecdotes about households borrowing to deal with health shocks. In this data, however, less than 2% of households reporting having borrowed for medical purposes.

Figure 2: Borrowing Purposes: May-Aug 18

Q5: What is the relation between income and borrowing?

Figure 3 plots the overall share and sources of borrowing in each income decile. 44% of all households in the lowest income decile have borrowed compared to 40% in the top income decile. Borrowing share is maximum for the fourth income decile households at 50%. Sources of borrowing shows a wide variation across income groups. 36% percent of households in the lowest income decile have borrowed from informal sources while only 20% households in the highest income decile have borrowed from informal sources. Share of informal sources for borrowing peaks for the fourth decile at 38% and declines gradually. Share of formal sources for borrowing shows a steady rise with income. 7% households in the lowest income decile have borrowed from formal sources while 23% in the highest income decile have borrowed from formal sources. The highest income decile is the only group with more households borrowing from formal sources (23%) compared to informal sources (20%).

Figure 3: Sources and Income

Figure 4 presents the variation in the purpose of borrowing across income deciles. Although consumption remains the single largest reason for borrowing across incomes, it varies significantly. 28.6% of all households in the lowest decile have borrowed for consumption as compared to 13% amongst the highest income decile. Borrowing for business varies less, ranging from 2.7% (decile 1) to 6.5% in decile 10. The line Any Purpose is the same as Overall Borrowing in Figure 3, representing the share of household having debt in each income decile.

Figure 4: Purposes and Income

Conclusion

In this article we have established some basic facts about household borrowings in India. These are:

  • Around 46% (137.7 million) of households in India reported having debt outstanding between May - August 2018. The share of rural households (46.5%) is larger than urban households (44%).
  • There is a wide variation in borrowing patters across India. In Southern and Eastern states, between 60-90% of households have borrowed. In the Northern states, this number is around 20-40% of households.
  • 32% of all households borrow from the informal sector, 13% from the formal sector, and 6.6% from the semi-formal sector. The data shows a significant reliance on informal sources for the purpose of borrowing.
  • Consumption expenditure is the most important purpose for
    which households borrow. This is followed by housing and business.
  • Almost 36% of all households in the lowest income decile have borrowed from the informal sector. This declines to 20% of all households in the top income decile.
  • The share of borrowing for consumption expenditure declines from about 29% of all households at the lowest income decile to about 13% at the top income decile.

 

The authors are researchers at the National Institute of Public Finance and Policy. We thank Ajay Shah for useful comments.

Monday, March 11, 2019

Time to resolve insolvencies in India

by Surbhi Bhatia, Manish Singh, and Bhargavi Zaveri.

Since the enactment of the Insolvency and Bankruptcy Code (IBC) 2016, studies undertaken to estimate the insolvency resolution time have provided varying estimates. As part of the World Bank's 'Ease of Doing Business' outcomes 2018, the estimate for time taken to resolve insolvencies in India is approximately 4.3 years. Felman et al. (2018) survey the 12 large cases referred for resolution under the IBC by the Reserve Bank of India in 2017, and find that while the larger cases have been in resolution for more than 500 days, the smaller cases are also taking up to 350 days from the date of admission by the National Company Law Tribunal (NCLT). Shah and Thomas (2018) present a survivor function on the cases admitted at the NCLT and find that at the end of 270 days, there is an 80% probability of a case still ongoing. We build on this approach to estimate the time taken in insolvency resolution processes triggered by different kinds of litigants and before different benches of the NCLT.

Our findings have three direct implications. First, estimation of survival function using case level data provides an empirical methodology for measuring time taken to resolve cases. Second, the probability of case completion within a given timeframe, thus computed, allows stakeholders to plan their affairs and resources appropriately. For example, a probability estimate of an insolvency case seeing an outcome within a certain timeframe offers valuable information to a creditor on making a strategic choice of settlement or pursuing resolution. Third, for policymakers, our findings offer insights into the manner in which the eco-system of stakeholders under the IBC, is evolving over time.

We find that the probability of seeing an outcome within 180 days from the date of admission is less than 5%. However, it picks up once the 180 day deadline is passed. Within 270 days, the chances of case closure are between 10 to 30% depending on the bench and case characteristics (e.g., creditor type). We observe high closure rate just past the 270 day period. Within 360 days of admission, the probability of seeing an outcome is significantly higher (30 to 70%). Quicker outcomes (liquidation or resolution) are observed for resolution proceedings triggered by the debtors themselves. Similarly, proceedings triggered before some benches result in resolutions speedier than those before some others.

Data

The IBC provides for a linear process for corporate insolvency resolution beginning with the filing of an insolvency petition before the NCLT. Once a petition is filed, the tribunal can admit or reject it. If it is admitted, a creditors' committee is constituted and a timeline of 180 days is provided for the submission of a resolution plan. If no resolution plan is submitted, the NCLT is required to pass an order for the liquidation of the firm. The timeline of 180 days is extendable to 270 days (in exceptional circumstances), with the approval of the NCLT. These two sets of timelines provide a natural setting for conducting the analysis. We track all the cases that are admitted by the NCLT from the date of their admission until the date of the order of the NCLT either approving a resolution plan or directing the liquidation of the debtor.

For our analysis, we use the Finance Research Group Insolvency Dataset on cases filed before the NCLT. We combine this with data on outcomes of cases published by the Insolvency and Bankruptcy Board of India (IBBI) for admitted insolvency petitions. The result is a dataset of all the insolvency petitions admitted by the NCLT. Our study period extends from December 1, 2016, until June 30, 2018. The data provides information on case ID, the bench at which the case was filed, who filed, type of creditor, the date on which the insolvency petition was admitted, and the date on which the final order was passed (resolution or liquidation). We compile this data for eight out of the nine benches (Ahmedabad, Bangalore, Chandigarh, Chennai, Hyderabad, Mumbai, New Delhi and Kolkata) of the NCLT that were functioning during the study period. Information on Guwahati bench is not recorded in the dataset.

Our final sample consists of 761 admitted cases. Table 1 shows the time taken by the closed cases, as on June 30, 2018. It is observed that 24 cases across all benches were closed within the first 180 days of their admission. 73 additional cases got closed between 180 and 270 days of their admission. If we extend the timeline by another 90 days, 62 more cases get closed. As on 30th June 2018, we find 76 cases which are ongoing for more than a year.

Table 1: Time taken by insolvency cases during the study period
Time taken (in days) Cases closed Cases ongoing
< 180 24 314
181-270 73 199
271-360 62 76
> 360 9 -

Table 2 shows details of the cases admitted during the study period, across the four benches of NCLT with the highest workload. It outlines the number of cases admitted together with the outcome (whether liquidated or resolved). To reduce the upward bias, for the closed cases, we also show the median time taken to reach the final outcome. We find that the median time varies between 200 and 280 days depending on the bench. While summary statistics of this nature are useful, they do not offer any insight on the probability of a case having an outcome within a given timeframe.

Table 2: Details of cases admitted during the study period
Bench No. of insolvency
petitions admitted
Resolution (a) Liquidation (b) Closed
(a+b)
Median time to reach
outcome
Mumbai 204 8 32 40 279
New
Delhi
181 4 10 14 267
Chennai 94 4 27 31 213
Ahmedabad 77 1 19 20 203

Methodology

In the past, survival analyses have been used to understand judicial delays in tribunals (Datta et al. (2017)). While the same principle could potentially be applied to understand judicial delays for cases under IBC, there is a critical difference between cases before the NCLT under the IBC and cases before other quasi-judicial tribunals. This distinction stems from the resolution process being led entirely by the creditors and other stakeholders with limited touchpoints with the judiciary. For this reason, case completion within the timeline of 180 or 270 days, cannot be attributed to judicial delays alone. In the absence of more data on the time spent in litigation during the different phases in a resolution process, a survival model cannot be directly applied to analyse judicial delays under the IBC. Therefore, applying the survival model contextually to the IBC will yield findings on the duration that the entire resolution process takes.

From the date on which an insolvency petition is admitted, we track the case up until 30th June 2018. For the purpose of this study, we define the event as case completion, resulting in resolution or liquidation. The event variable is equal to 1 if the case got closed within the study period and 0 otherwise (still ongoing). The dependent variable is the duration for which a case remains open. For cases which saw a definite outcome, the duration is calculated as the difference between the admission and the outcome date. For ongoing cases, the duration is the difference between admission and analysis date (30th June 2018).

We assume that the dependent variable duration follows a continuous probability distribution d(t). In this case, the probability that the duration will be less than t days will be:
\[ D(t) = Prob(T \leq t) = \int_{0}^{t} d(s)ds \]

where "T" denotes the duration of the insolvency petition. To estimate how long the cases stay unresolved in our sample,
we calculate the survival probability S(t) using non-parametric (Kaplan-Meier) estimation:

\[ S(t) = Prob(T \geq t) = 1 - D(t) \]

Findings

Figure 1 shows the probability of survival (case ongoing) for the top four benches of the NCLT (based on the number of admitted cases). The X-axis shows the number of days that a case takes from the date of admission until the outcome. The Y-axis shows the probability of the case continuing up to a given number of days. The black line, based on all observations, indicates that on average:

  • The probability of case completion within 180 days is less than 5%.
  • The probability of case completion within 270 days is 22%.
  • The probability of case completion within 360 days is 45%.

Comparing across benches, we see that the survival curves for Mumbai and Delhi lie above the national average, thereby indicating a much lower probability of a case closing at either of the benches. At the end of 270 days, the probability of case closure at Ahmedabad and Chennai bench moves upto 30% while it stays at 14% and 8% for Mumbai and Delhi respectively. Within a year's time, the outcome probability is 60%, 41%, 36%, and 21% for Chennai, Ahmedabad, Mumbai, and Delhi respectively.

Table 3 shows the estimated survival probability (with 95% confidence interval) at various reference points (180, 270 and 360 days). A narrow confidence interval suggests that the observed survival probability is very close to the estimated one with minor deviations. For 180 days, confidence intervals are narrow for all benches, suggesting near zero probability of case completion. Moving to 270 days, the variations widen. The survival probability drops considerably for Chennai and Ahmedabad, but the confidence intervals widen. This points towards increased fluctuations in the duration of completed cases. We find a similar trend across all benches as we move to 360 days.

Table 3: Probability of case ongoing beyond the benchmarked timelines
(Note: The estimates within brackets show the 95% confidence interval.)
Full sample Mumbai New Delhi Chennai Ahmedabad
T > 180 days0.95300.98560.98920.94050.9191
(0.9347-0.9716)(0.9659-1)(0.9685-1)(0.8913-0.9925)(0.8536-0.9898)
T > 270 days0.78180.85670.91610.72110.6981
(0.7437-0.8218)(0.7956-0.9225)(0.8577-0.9784)(0.6221-0.8359)(0.5828-0.8362)
T > 360 days0.56270.64100.79610.39830.5943
(0.5096-0.6214)(0.5483-0.7494)(0.6979-0.9080)(0.265-0.5986)(0.4666-0.7569)

For the reasons explained above, the probability estimation cannot be attributed to the judiciary alone as the resolution process is driven by the creditors' committees. The actual time until the outcome will depend on various factors such as the complexity of the case, the size of the debtor, the composition and number of creditors on the creditors' committees, and the propensity of the creditors or the debtor to litigate. Firm characteristics also differ across jurisdictions. So variation in duration across benches should not be interpreted as the lack of judicial capacity. The lower probability of a faster outcome before the Delhi and Mumbai benches of the NCLT may be related to the complexity of the cases handled by these benches.

The inference made above is corroborated by Figure 2 and Table 4, which depicts the probability of case outcomes for different type of litigants. Cases which are filed by corporate debtors are more likely to see earlier outcomes, relative to cases filed by creditors. The probability of insolvency triggered by a debtor seeing an outcome within a year of admission of the petition is 70%. On the other hand, where the petition is filed by an operational or financial creditor, this probability drops to 40%.

Table 4: Probability of case ongoing beyond the benchmarked timelines (based on litigant type)
(Note: The estimates within brackets show the 95% confidence interval.)
            Full sample Corporate debtor Financial creditor Operational Creditor
T > 180 days0.95300.94690.96550.9421
(0.9347-0.9716)(0.9063-0.9892)(0.9406-0.9910)(0.9092-0.9761)
T > 270 days0.78180.68740.83410.7869
(0.7437-0.8218)(0.6038-0.7825)(0.7797-0.8923)(0.7257-0.8532)
T > 360 days0.56270.34700.63430.6330
(0.5096-0.6214)(0.2573-0.4678)(0.5428-0.7412)(0.5546-0.7225)

Conclusion

By analysing the data relating to cases that have undergone the IBC process in its entirety, we put forward a new approach to understand time to insolvency resolution. Using survival analysis, we estimate the probability of a case ongoing across different reference points. The methodology is robust to censoring. Case dropouts on account of reasons such as settlement or withdrawal will simply modify the probability distribution of duration. Since we don't have censored observations at the time, our point-in-time estimate is just the empirical distribution of the duration.

This analysis can also be extended. With a more detailed break-up of case-level data, it is possible to use this framework to estimate the time taken across different phases of the resolution process. This will be suggestive in bringing into focus the bottlenecks in the process. A time-varying analysis can also be used to evaluate institutional performance. If the probability of timely resolution consistently increases, this implies that the institutional eco-system is evolving in the right direction. This measure will be especially important for tribunals which are set-up with a speedy disposal in mind.

References

The RBI-12 cases under the IBC by Felman J, Marwah V and Sharma A, Working paper on file with the authors, 2018.

The Indian bankruptcy reform: The state of the art, 2018 by Shah A and Thomas S, 22 December 2018, The LEAP blog.

Understanding judicial delay at the income tax appellate tribunal in India by Datta P, B.S. Prakash S and Sane R, Working Paper 208. National Institute of Public Finance and Policy, 2017.

 

The authors are researchers at Finance Research Group at IGIDR. They acknowledge useful discussions with Ajay Shah, Susan Thomas and Anjali Sharma.

Thursday, March 07, 2019

Announcements

Researchers in land and property rights

Location: New Delhi

The National Institute of Public Finance and Policy (NIPFP) is looking to hire researchers in the field of land and property rights, on a full-time basis.

The Land Policy group at NIPFP, aspires to understand regulatory and governance issues in the field and contribute to the policy process. While our aspirations extend to a broad array of issues, at present, our work focuses on land administration, land market efficiencies regulatory reform, property rights and contract enforcement.

Some examples of our work in this field include:

  1. Ajay Shah, Anirudh Burman, Devendra Damle, Itishree Rana, and Suyash Rai, The Digital India Land Record Modernisation Program
  2. Anirudh Burman and Devendra Damle, How well is India's land record digitisation programme doing: Findings from Rajasthan
  3. Jai Vipra, Analysing The Odisha Land Rights to Slum Dwellers Act, 2017
  4. Jai Vipra, Working Titles: Property Rights for Slum Policy in India
  5. Shubho Roy, Runs on real estate companies?
  6. Ajay Shah, Should policy makers favour home ownership?
  7. Ajay Shah, The resource curse of land ownership
  8. Ajay Shah, Uncomfortable times in real estate in store?
  9. Ila Patnaik, Problems of land acquisition by the State

Essential requirements

We are looking for candidates with a background in law, economics, sociology or public policy. The candidates should demonstrate the ability for analytical thinking, strong qualitative and quantitative skills, and excellent writing skills. Two years of work experience is desirable.

The office functions on free and open source softwares like Linux, LaTeX, R and Python. While previous experience in these softwares is not essential, the person will be required to learn and use them for their work.

Job description

The researchers will work together to build expertise in the domain of land and property rights. The team will have to perform the following tasks:

  • Support research in law, economics and public policy,
  • Work with government institutions, including government departments, regulators, etc.
  • Carry out field surveys,
  • Build, manage and analyse large datasets related to land and property rights,
  • Write articles, blogs and research papers,
  • Organise workshops, conferences and roundtables to disseminate research.

Remuneration

The candidate will be offered a competitive salary depending on his/her qualifications and experience.

How to apply

If you meet the above criteria and are interested in this position, please email your resume and covering letter to:

lepg-recruitment@nipfp.org.in

The subject line of your email should be "Land recruitment"

The last date for submitting the application is 31st March 2019

About NIPFP

NIPFP is a centre for research in public economics and policy. Founded in 1976, the institute undertakes research, policy advocacy and capacity building in areas related to public economics. For more information, please see here.