## 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
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 days 0.9530 0.9856 0.9892 0.9405 0.9191 (0.9347-0.9716) (0.9659-1) (0.9685-1) (0.8913-0.9925) (0.8536-0.9898) T > 270 days 0.7818 0.8567 0.9161 0.7211 0.6981 (0.7437-0.8218) (0.7956-0.9225) (0.8577-0.9784) (0.6221-0.8359) (0.5828-0.8362) T > 360 days 0.5627 0.6410 0.7961 0.3983 0.5943 (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 days 0.9530 0.9469 0.9655 0.9421 (0.9347-0.9716) (0.9063-0.9892) (0.9406-0.9910) (0.9092-0.9761) T > 270 days 0.7818 0.6874 0.8341 0.7869 (0.7437-0.8218) (0.6038-0.7825) (0.7797-0.8923) (0.7257-0.8532) T > 360 days 0.5627 0.3470 0.6343 0.6330 (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

### 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

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.

## Friday, March 01, 2019

### The geography of firms and firm formation in India

by Surbhi Bhatia, Manish Singh, Susan Thomas.

We look at the geographical location of firms in the figure below. The map on the left shows the stock of the firms in India, with all firms as on 31st Dec 2015 while the map on the right shows the flow of new firms that were added in the year ended 31st Dec 2016. All values are expressed per unit population.

The graphs are built using the list of all active companies registered with the Registrar of Companies on 31st Dec 2015 and on 31st Dec 2016. This is from the Ministry of Corporate Affairs (MCA) website. This data contains the unique identification number of the firm, firm name, current status (active or not), type of firm (public, private, LLP, etc.), authorised and paid-up capital, date of registration, office address, sector, and ownership details.

Most Indian firm research uses the CMIE firm database, which has deep information about 50,000 firms. The MCA dataset shows limited information about all limited liability firms. The headcount of the stock of firms on 31st Dec 2015 was 1,022,174 and the new firms registered in 2016 were 111,274 in number.

What do these maps tell us? Let us start at the left map. This is on somewhat expected lines. The highest values are found in Maharashtra and in West Bengal (where there are many ancient firms). For the rest, there is strength in the West: from Punjab/Haryana to Rajasthan to Gujarat to Maharashtra to the Southern peninsula. This is generally the region of prosperity in India, in the common preconception.

The map on the right, with new firm creation in 2016, diverges from this picture in striking ways. Five states dominate: Haryana, Maharashtra, Karnataka, Andhra Pradesh and Tamil Nadu. The NCT of Delhi shows a high concentration of new firms.

In our prior, Gujarat and Rajasthan might have fared well, but they do not. We may have expected Uttar Pradesh to be weak, but it is similar to Gujarat. West Bengal has a very low density of new firms, showing that conditions there for firm formation have deteriorated when compared with the past.

The authors are researchers at IGIDR. They acknowledge useful discussions with Anjali Sharma.