A gap has opened up between the performance of listed and unlisted companies

by Ajay Shah.

The performance of listed firms has been poor

We start at an index of the net sales (i.e. the top line) of listed companies. This is computed at the quarterly frequency, using the quarterly disclosures which are mandatory for listed companies. As has been our standard procedure when thinking about macroeconomics, we exclude the financial firms and the oil firms. Our methods for constructing indexes (Dua et. al., 2013) are based on obtaining growth estimates from overlapping consecutive-quarter panels.

Figure 1: Index of net sales of listed non-finance non-oil firms (Nominal, seasonally adjusted)

The big fact that is visible here is that the index went up by 4x from 1999 to 2008, and after that it went up by 2x from 2008 till 2019. This doubling in the recent 11 years corresponds to an average nominal growth rate of 6.5%, which is pretty poor.

Figure 2: Index of operating profit of listed non-finance non-oil firms (Nominal, seasonally adjusted)

A similar picture is visible with the index of operating profit, for the same firms. The index went up by about 4x from 1999 to 2006, and has scored a doubling from 2006 to 2019, if we ignore the big decline in the Jul-Aug-Sep 2019 quarter owing to the unusual events for some telecom companies. This recent doubling in 13 years corresponds to an average nominal growth rate of 5.4%, which is also pretty poor.

In this article we wonder: While the listed firms have fared poorly, how different was the performance of unlisted firms?

Do we expect a significant difference between listed and unlisted firms?

Large firms such as Hyundai Motor, IBM India, L G Electronics India, Nokia Solutions & Networks India, Reliance Corporate IT Park, Toyota Kirloskar Motor, etc. are unlisted companies that are observed in the data. At first, we do not expect to see a significant difference between the overall average performance of listed and unlisted companies.

We expect that macroeconomic fluctuations impact upon both listed and unlisted companies, and that the overall growth of both groups should be roughly equal. The growth of all listed companies represents the performance of a diversified portfolio, as does the growth of all unlisted companies.

The traditional concept in India has been that after a firm reaches a certain level of maturity, an IPO takes place. The better and larger firms become listed companies. Listed companies have better access to capital, as the cost of capital goes down. So we have expected that a greater extent of investment may take place in listed companies. By this reasoning, we expect a somewhat higher growth rate for listed companies.

The performance of listed versus unlisted companies

Unlisted companies only have annual frequency disclosures, so we switch from quarterly results to the annual report. The CMIE firm database sees about 50,000 companies for at least one year. We construct indexes of the net sales of all the non-finance non-oil firms, both listed and unlisted. Our methods for constructing indexes are based on obtaining growth estimates from overlapping consecutive-year panels.

Figure 3: The index of net sales (nominal), log scale

The figure above shows the long time-series of the index of net sales. Two curves are shown: Listed companies and unlisted companies. The y axis is in log scale.

The two lines look the same till 2012. Until 2012, the diversification story worked: listed companies were a diversified portfolio, the unlisted companies were a diversified portfolio, but when the portfolio sales growth was calculated, the firm-specific or industry-specific fluctuations tended to cancel out and the overall performance was essentially the same.

But from 2012 on, the two groups have diverged substantially. The listed companies (the red line) have generated weak growth: about 50 per cent (nominal) in about 6 years. The unlisted companies have generated much stronger growth: about a doubling (nominal) in 6 years.

A similar difference is seen with the operating profit also.

Figure 4: The index of operating profit (nominal), log scale

Here also, we see remarkably similar performance between the two groups all the way till 2012. After that, the listed companies have delivered mediocre growth in performance: a total growth of 26% in 6 years. The blue line, for unlisted companies, has delivered total growth of 136% in 6 years.

The investment behaviour of the two groups has also diverged

We measure investment at the firm level using the percentage change, year on year, of the Net Fixed Assets.

Figure 5: Year-on-year growth of net fixed assets (nominal)

We see a striking phenomenon here. Listed companies invested at a bigger rate than unlisted companies in the early years, the red line and the blue lines becoming roughly equal from 1997 to 2009. After that, the blue line has always been above the red line.

There is an investment slump in the listed companies, but this is less the case with unlisted companies.

Is this just an artifact induced by the measurement process?

The CMIE database is now pretty large, it has about 50,000 firms observed for atleast one year. A large number of unlisted companies are observed. This was not the case in earlier years. An alternative hypothesis can, then, be proposed: Perhaps unlisted firms were always more dynamic, but they had a tiny presence in the CMIE database, and what has changed in recent years is not the dynamism of unlisted firms but the coverage of the CMIE database.

In order to examine this, we look at the magnitudes of listed and unlisted firms in the CMIE database.

Figure 6: Share in the count of firms observed by CMIE

Figure 7: Share in the balance sheet size of firms observed by CMIE

The figures above show the share of listed firms in the overall CMIE database. While it is true that the coverage of unlisted firms has improved greatly, it is not as if unlisted firms were absent in earlier years. Well before the recent shift, of greater dynamism by unlisted firms, a good chunk of the firms in the CMIE database were unlisted firms. The estimates for earlier years, for unlisted firms, are based on a strong dataset. This suggests that the phenomenon that we have uncovered is not merely an artifact of changes in measurement by CMIE.

Why might this be happening?

We have discovered that after 2012, fixed investment, revenue growth and operating profit growth are weaker for listed companies when compared with unlisted companies. Why might this be the case? We may conjecture that there are three explanations at work.

The gains in liquidity of shares from listing have declined. There was a time when listing at NSE and BSE gave a quantum leap in the liquidity of the stock, while the shares of unlisted companies were quite illiquid. In the last decade, however, the working of the exchanges has faced many difficulties, and alongside this there are greater opportunities to obtain liquidity through OTC transactions in shares. Through this, the gap between the liquidity of listed vs. unlisted has gone down.

Private equity has become a major source of capital. Private equity investors have become much more important in financing firms, and large ticket investments are now possible while the firm stays entirely private.

The burden of listing has gone up. The compliance burden imposed upon a firm by exchanges and by SEBI has become greater. The penalties meted out for non-compliance have become greater. The unpredictability in the behaviour of enforcement has become greater. For many a firm, staying unlisted is a way of avoiding the hazards of engaging with more state actors.

Implications

1. When we see the remarkably weak operating performance of listed companies, we should be cautious before concluding that the overall performance of the Indian economy is weak. Listed companies are faring unusually poorly, and their performance constitutes an under-estimate of overall performance.
   In this sense, these findings undermine the claim of Dua et. al. 2013, which argued that you could construct a good output proxy for India by utilising the quarterly results of listed firms.
   As a consequence of these findings, in our business cycle measurement work (Pandey, Patnaik, Shah 2019), the only application of the net sales growth of listed companies is in identifying the macroeconomic measures which are leading vs. coincident. The performance of listed companies is not, in itself, utilised in constructing business cycle measures, as it has a downward bias in the post-2012 period.
2. In response to the frictions imposed by the Indian state upon private persons, there is a desire to exit from business plans that induce interfacing with the Indian state. As an example, from 2007 onwards, a good deal of trading in Nifty and the Rupee -- the two largest financial products -- has left India, in response to weaknesses of financial regulation, capital controls and taxation in India.
   The phenomenon identified in this article may constitute an element of this exit: private persons are responding to the regulatory environment of SEBI and the exchanges by avoiding listing. This reflects the impact of the policy environment upon the gains from listing (what liquidity do we obtain on the exchange?) and the costs of listing (what additional burden of regulation and enforcement do we face as a consequence of being listed?).
3. For investors, there is merit in looking beyond listed equities in order to obtain the tail wind of high growth of operating profit.

Bibliography

Dua et. al. 2013, A better output proxy for the Indian economy, The Leap Blog, 21 July 2013.

Radhika Pandey, Ila Patnaik and Ajay Shah, 2019, Measuring business cycle conditions in India, NIPFP Working Paper No. 269, May 2019.

Acknowledgments 

I thank Pramod Sinha, who implemented the ideas of this article as R programs.




The author is a researcher at the National Institute for Public Finance and Policy, New Delhi.

Credit stress in large Indian firms

by Ajay Shah and Pramod Sinha.

We in India are used to thinking about banks and NPAs. We infer the state of difficulty of the banks, and indirectly of their borrowers, by using data from banks. There are many advantages in looking directly at the state of credit stress in the large non-financial firms, and identifying the firms where there is high credit stress:

1. Do not rely on bank information. This evidence is not filtered through the difficulties of banking regulation. Whether a bank classified Kingfisher Airlines as an NPA or not, we can see credit stress in the financial statements of Kingfisher Airlines.
2. Look beyond banks. Banks are not the only financial lenders to the non-financial firms. As an example, bank-centric thinking is not useful in understanding runs on mutual funds. When there is stress in a borrower, this impacts not just on banks but on all lenders. Pulling together information about stressed borrowers helps us see the difficulties of lenders, on a financial system scale, and not just in banks.
3. Micro-prudential considerations. The stressed firms face likely defaults. The debt of such firms is likely to be worth less than book value. Under sound micro-prudential regulation, banks and other lenders should mark down these assets, even if no default has taken place. The extent of stress, as seen here, gives us insights into the fragility of banks and other lenders.
4. The bankruptcy process, the distressed debt industry. There is a new world opening up in India, of distressed firm transactions and the bankruptcy process. We will see the empirical contours of this industry, and the bankruptcy process, by examining the state of credit stress in the non-financial firms.
5. A drag on growth. A firm that is in a state of credit stress is likely to face difficulties meeting payments to creditors. It might often be liquidity constrained, and may struggle to obtain cash to pay its suppliers. The mind space of the leadership of such a firm is likely to be absorbed in the struggle for survival. Such firms are unlikely to fare well on growth through increasing the resources utilised or through increased productivity. To understand what is coming in Indian macroeconomics, we should look at the non-financial firms and their balance sheet difficulties.

Identifying stressed firms

The interest cover ratio is defined as PBIT/interest. If a firm has to make interest payments of 100, and if its profit before interest and taxes is 150, then its interest cover ratio ("ICR") is 1.5. Such a firm has the 100 required to pay interest in the year, but there may be a task in terms of juggling the dates on which interest has to be paid versus the dates on which the business produces cash. And, such a firm is left with just 50 after paying interest, which can be used for debt repayment and the regular capital expenditures required for the upkeep of the business.

A good thumb rule which identifies a firm in a state of stress at time $t$ is: The firm has ICR$ < 1.5$ in year $t$ and in year $t-1$. This avoids the false positive of a firm which only hits ICR$<1.5$ for one year.

A `stressed firm', by this definition, is not necessarily one that has defaulted (and is thus eligible for the bankruptcy code), and it is not necessarily one that is classified as a non-performing asset by RBI's rules of recognition. We would, however, suggest that a firm with two consecutive years at an ICR of below 1.5 is under stress, has an enlarged risk of default, and has a management team that is absorbed in dealing with this stress.

Methodology

We study all the non-financial firms in the CMIE database. At each year, we isolate the firms which are observed for two consecutive years. Some additional sanity checks are applied. Through this, we are able to construct two sets at each point in time: The set of all firms observed and the subset of this, which is the stressed firms.

Here are some counts of the firms in the two sets.

Year	Total	Stressed
2014-15	9,289	3,674
2015-16	9,208	3,702
2016-17	6,687	2,573

In the table above, the total number of firms (9,289) for 2014-15 is the number
of non-financial firms that are observed, and pass some sanity checks, in both 2013-14 and 2014-15. Of these, 3,674 were stressed. The last year that we utilise here -- 2016-17 -- has fewer firms when compared with the years prior to
it, where information for a larger number of firms has trickled into the CMIE database. In this last year, we see 2,573 non-financial firms in the database, where the ICR was worse than 1.5 in both 2015-16 and 2016-17.

Conditions in 2016-17

Parameter	Value (Rs. Tln)
Balance sheet size
Stressed firms	29.79
All firms	77.24
Bank borrowing
Stressed firms	8.87
All firms	14.94
Total borrowing
Stressed firms	15.58
All firms	27.59

This shows that the sum of the balance sheet size for all the 6,687 firms for 2016-17 was Rs.77.24 trillion. Of this, Rs.29.79 trillion was in the 2,573 stressed firms.

Totally, borrowing of Rs.27.59 trillion was visible. This is small when compared with the total assets of these firms of Rs.77.24 trillion. Of this borrowing, Rs.15.58 trillion was in the stressed firms.

Finally, we are able to see Rs.14.94 trillion of borrowing from banks, in this sample of 6,687 firms, in 2016-17. Of this, Rs.8.87 trillion was in the stressed firms.

How has credit stress evolved over time?

We are able to do these calculations for all years from 1998-99 onwards. We will express the time-series evidence using confusingly similar graphs, all of which produce important stylised facts for our understanding of the economy.

The share of bank debt to stressed firms, in the total bank debt seen in the sample firms

The health of banks is related to the health of their borrowers. Hence, in the graph above, we compare the sum of bank credit to stressed firms (in our sample) against the sum of bank credit to all firms (in our sample).

The business cycle is clearly visible here. In the last tough downturn, 2000-2003, this ratio was at about 50%. That is, about half of the bank borrowing seen in the CMIE database was in stressed firms.

This ratio dropped all the way to about 15% in 2007. It climbed steadily thereafter and is now at about 60%. There is a tiny gain in 2016-17 when compared with the previous year.

In the last recession, this measure improved through the recovery of the economy. Firm exit took place through the sluggish traditional ways. When the bankruptcy reform resolves or liquidates a large volume of stressed firms, this will deliver improvements in this measure. To the extent that the bankruptcy reform works, we may expect the next recovery to proceed faster than the last one, where it took five years of a powerful expansion to get from about 50% to about 15%.

We apply this same thinking to total borrowing -- instead of just bank borrowing:

The share of total borrowing by stressed firms, in the sum of borrowing seen by all sample firms

The last business cycle downturn got to values of above 50%, there was a great decline to about 20%, and then it has risen to about 55%, with a slight improvement in 2016-17.

Implications

There is considerable balance sheet stress. In the latest year, the aggregate balance sheet size of the stressed firms -- observed in the CMIE database -- was Rs.30 trillion. The stressed firms had Rs.15.6 trillion in borrowings of which Rs.9 trillion was from banks. This has implications in other parts of finance, beyond banking.

Bank debt in stressed firms is about 60% of total bank debt seen in the sample. Similarly, the borrowing by stressed firms is about 55% of all borrowing in the sample. Under sound micro-prudential regulation, asset-based lenders would mark down these assets based on the price at which the loan/bond could be sold on the market.

In the conventional wisdom, there is about Rs.10 trillion of bad debt on the balance sheet of banks. Our analysis shows that in the 6,687 large non-financial firms, where Rs.15 trillion of bank debt is located, we see 2,573 stressed firms with Rs.9 trillion of bank debt. The 2,573 stressed firms that we see in this sample, alone, account for 11.4% of the overall bank debt ("non-food credit") in the economy.

The stressed firms are about 40% of the overall corporate balance sheet. These firms are likely to fare poorly in investing or in productivity growth, and are thus a drag upon overall economic growth.

It is likely that many of these stressed firms will be sold, or go into the bankruptcy process. There is a substantial task ahead, in terms of resolving these firms and paying for the losses experienced. These 2,573 firms are the happy hunting ground for this new industry. This process of resolution is central to India's economic recovery.

There is much value in understanding the balance sheet stress in the economy using such methods. We obtain insights into difficulties of the financial system going beyond a bank-centric view, we get a view of the new distressed debt industry, and we get insights into the drag on GDP growth that the stressed firms represent.



The authors are researchers at NIPFP.

Invoice financing in India: TReDS and way forward

by Sudipto Banerjee and Vishal Trehan.

Introduction

Medium, small and micro enterprises (MSMEs) operate on tight margins and need immediate settlement of invoices to avoid shortage of working capital. However, due to the poor bargaining capacity of MSMEs, their working capital often remains blocked in receivables as they work on an unfavourable credit cycle for goods and services supplied to corporate buyers. This problem is exacerbated due to the existence of a huge funding gap for MSMEs. In order to bridge the gap between invoice date and its due date, invoice discounting emerged as a financing solution for entities which are unable to access funding options such as short term credit and working capital loans. Under invoice discounting, the seller, instead of waiting for the payment to be made by the buyer, gets a certain percentage of the invoice amount from the financier in advance. The seller pays a fee to the financier for this discounting service. Once the invoice amount is received by the seller from the buyer, it repays the amount to the financier.

However, adoption of invoice discounting as a financing mechanism in India has not been as expected. This may be due to several reasons. First, the bargaining capacity is skewed in favour of corporate buyers who express reservations while accepting assignments of receivables made in favour of financiers. Second, it is difficult for financiers to establish the credit rating of MSMEs due to information asymmetry. This, coupled with the absence of pledgable collaterals increases the credit exposure of a financier. Third, the discounting landscape is still dominated by banks and there are very few specialised discounting entities. Finally, there is a lack of awareness among MSMEs about discounting services, especially in non-urban locations. For example, even though many MSMEs are exporters, they lack information about export factoring.


In 2014, the RBI observed that there is a need for institutional setup to boost discounting in India and for this purpose conceptualised an electronic exchange for invoice discounting known as trade receivable electronic discounting system or TReDS. This post looks at the TReDS platform critically in order to assess whether this fintech solution has been able to address specific issues related to invoice discounting in India. Further, we explore the developments around invoice discounting in the context of new technologies and examine whether their adoption holds any merit. It must be noted that the invoice discounting problem space is quite broad and TReDS, a technology based solution, must be seen as a solution for specific problems in the invoice discounting space in India.

Specifically, TReDS seeks to address the problem of information asymmetry and the consequent high rates offered by financiers. Also, it is envisaged to reduce the time taken for sellers to receive payments. TReDS, however, was not conceptualised to address other persistent issues related to invoice financing. For example, although TReDS operates on the concept of 'no rescourse to seller', it is not a solution to the problems arising out of the bargaining power of buyers.

A digital platform to boost invoice discounting

In 2009, SIDBI, in collaboration with NSE set up the first e-discounting platform for MSME receivables. This was based on the lines of the Mexican NAFIN model. However, this was a closed single financier model and therefore, had limited scale of operation. To overcome these limitations, in 2014, RBI released a concept paper to set up a full fledged electronic exchange for invoice discounting. This was followed by TReDS is essentially an online electronic institutional mechanism for facilitating the financing of trade receivables of MSMEs through multiple financiers. The platform enables discounting of invoices of MSME sellers against large corporates including government departments and PSUs, through an auction mechanism, to ensure prompt realization of trade receivables at competitive market rates.

• In the TReDS ecosystem, sellers, buyers and financiers can come on board by executing a one time agreement with the platform. This reduces the documentation cost for sellers who have to execute a separate agreement everytime there is a discounting transaction with a different financier.
  
• After executing the agreement, once the seller provides goods or services to the buyer and after acceptance by the buyer, the invoice is uploaded on the platform. This can be uploaded either by a buyer or a seller. Once the invoice is accepted by the buyer, it is converted into a factoring unit, a nomenclature used for invoices on the platform. Subsequently, an electronic auction involving bidding for the factoring unit takes place on the platform.
• Once a bid is accepted by the seller, the amount is credited to the account of the seller either on T+1 or T+2 basis depending on the cut-off time. This financier's account is auto-debited through the National Automated Clearing House (NACH) mandate. Instructions are sent electronically by the platform to the parties. On the due date of the invoice, the bank account of the buyer is auto debited and the amount is credited to the account of the financier.

As mentioned previously, the TReDS platform aims to address certain specific aspects of MSME financing. The current invoice financing system is riddled with an asymmetric flow of credit information. Financiers are not always aware of the financial condition of MSME suppliers due to limited publicly available information. Due to this, screening costs incurred by a financier go up for discounting an invoice of a MSME supplier. TReDS ensures easier access to invoice discounting at better rates for MSME suppliers due to the following reasons:

• Financing on the TReDS platform is done on the credit rating of the corporate buyers, hence, financiers need to define the credit limit of buyers and not sellers. This reduces the due diligence cost for financiers and in turn lowers the cost of discounting for sellers.
• TReDS operates on the model of without recourse to the seller which means that the financier can recover the invoice amount only from the buyer.
• Outside TReDS, MSME sellers negotiate with individual banks and NBFCs who may not offer them competitive rates for the reasons discussed above. The average interest rate on working capital loans is 12% as compared to 8-10% on TReDS. TReDS allows multiple financiers to participate and bid for invoices - this is expected to provide better rates to MSME sellers. As described previously, the entire transaction happens digitally on the platform in an efficient and transparent manner.

Establishing the genuineness of an invoice is another challenge that TReDS addresses. Once the seller provides goods or services to the buyer and they are accepted, the invoice is uploaded on the platform. The bidding by financiers start only after the uploaded invoice is accepted by the buyer. However, the issue of double discounting of invoices was not addressed in the original implementation of TReDS. This was addressed through a blockchain implementation recently.

Key issues

The most critical problem in the invoice financing space in India, and consequently in the TReDS setup, is related to the obligation on buyers to repay on time. TReDS too follows the requirement of the Micro, Small And Meduim Enterprises Development Act, 2006 (MSMED Act, 2006) which imposes an obligation on buyers to settle the invoice amount within 45 days. Hence, in the TReDS ecosystem, the buyer has to pay the factored invoice amount to the financier within 45 days from the date of acceptance of bid by the seller. This requirement of adhering to time bound payment, which is otherwise mostly flouted outside TReDS, can cause reluctance on the part of buyeres to sign up. Presently, MSME suppliers facing competition from other players are inclined to accept higher volumes of trade credit on less favourable collection terms.

Interactions with practitioners in the MSME financing segment revealed that at times, sellers also avoid disclosing their MSME status so that the buyer is not deterred by the applicability of MSMED Act in case of delayed payments. Therefore, it is not surprising that buyers have even instructed their vendors not to sign up on the electronic platform to avoid the time bound commitment to pay.

Other related challenges with TReDS

While TReDS is a technology based solution to provide an institutional platform to boost MSME financing, its performance needs to be evaluated against the market's response. The market usually adopts a particular solution for two reasons - it can either be a business need or a legal obligation. Considering that the TReDS platform came about as a result of the practice of delayed payments by buyers, it is important that we examine the incentives for buyers to come on board.

1. Restriction on raising disputes: It must be noted that presently TReDS is an optional system. Assuming there is a corporate buyer X dealing with several vendors, under TReDS, X has to execute an agreement where it accepts the invoice of the seller (its vendors) and only after such acceptance, an invoice is made available for auction on the exchange. The TReDS Guidelines specifically require that X cannot dispute the goods or services received from the seller at a later stage. This is a major disincentive for buyers. Outside TReDS, X usually does not give acceptance to suppliers but merely records the event that it has received supplies - thereby keeping an option to dispute them in the event of any deficiency.
2. No recourse to seller: In the non-TReDS setup, if X defaults in paying the financier on the due date, the seller becomes a debtor vis-&agrave-vis the financier for recovery purpose. However, as discussed above, on the TReDS platform discounting is done without recourse to the seller. This means that if X fails to pay the invoice amount on the due date, the financier would have no recourse to the seller. Instead, the financier will have to pursue the buyer. While this mechanism may reduce the financier's risk, it may not attract buyers as they would now have to deal with an institutional lender who replaces the MSMEs.
3. Enhanced transparency: In case of default or any delay in payment by the buyer to the financier on TReDS, the delay/default gets duly recorded and can feed into the credit rating of the buyer. Outside TReDS, instances of such delay or default are not recorded, unless the MSME seller chooses to pursue action under MSMED Act at the cost of its future business relationship with the buyer. Therefore, the decision of a buyer to join TReDS would most likely depend on a cost-benefit analysis of aspects such as reduced flexibility in cash flow management, more transparency, etc.
4. Existing arrangements: Experts in the MSME financing area have pointed out during interactions that many big corporate houses have their own discounting business and their suppliers/vendors are required to avail discounting services from their group entities. For instance, Reliance Capital, Mahindra Finance, Tata Capital, Bajaj Finance, Aditya Birla Capital, etc are full fledged NBFCs and have dedicated invoice discounting divisions. Companies not having such an in-house discounting facility usually have pre-existing arrangements with banks or NBFCs. Moreover, these big houses usually consolidate their vendor payments into select groups not falling within the category of MSME who in turn buy products from MSMEs. This may be another reason for big houses to not come on board TReDS.
5. Cost of integration: Another barrier, especially from the buying corporates, is their reluctance to invest in the cost of integrating into a system like TReDS. Since the buyer bears the costs but the benefits accrue only to vendors, this
   may prove to be a disincentive for the buyers.
6. Poor awareness: Lastly, the level of awareness about any new solution determines its success. Based on inputs from several stakeholders such as discounting entities and banks, the overall level of awareness about TReDS does not appear to be encouraging. Further, in smaller towns and semi urban setups, banks are the predominant option available to suppliers for their financing needs. These sellers do not easily switch banks with whom they share an established relationship, unless the buyer takes the initiative to migrate their dealings onto TReDS.

Addressing the issues

In order to ensure that the TReDS platform achieves its objectives, broader issues related to invoice financing in India as well as TReDS specific concerns need to be addressed. Extending the timeline of 45 days for settlement of invoice, which presently could be the prime reason for buyers not coming onto the TReDS platform, may be considered. To begin with, the platform should be enabled to give an extension to buyers on a case by case basis. While balancing the conflicting interests of suppliers, buyers and vendors is a challenging task, a middle path can be arrived at by ensuring constant interactions between the regulator and the stakeholders, especially the buyers. Further, it is essential that RBI invests resources to increase the overall level of awareness about TReDS. As discussed previously, the focus of such an awareness programme should be smaller towns and semi-urban setups.

Alternatively, a light-touch approach to regulating the behavior of large buyers could involve doing away with the 45 day payment period for TReDS so as to incentivise big buyers to get onto the TReDS platform. Instead, buyers may be asked to disclose their payment practices. Such reporting is mandatory in the UK where firms are required to disclose payment practices as per the Small Business, Enterprise and Employment Act, 2015. Removing the time-line of 45 days and mandating disclosure of payment practices would require amendment of the MSMED Act, 2006. The disclosures, which can be made public on TReDS, should also form a part of the notes to accounts of financial statements of such firms so that they can be cross verified by statutory auditors. This would require amendment to Schedule III of the Companies Act, 2013.

Further, there could be a mix of other regulatory tools like:

• A code similar to the Prompt Payment Code in UK can be created and large buyers may be encouraged to sign on to this code. Such a voluntary code can in turn set a maximum payment term.
• A system for blacklisting companies which violate payment terms repeatedly may also be created based on the payment practices data.
• The role of MSME associations is important in this context to ensure that big buyers do not abuse market power. As is generally the case, a single MSME will be reluctant to file a complaint against a buyer for fear of losing business as well as the costs involved. Instead, MSME associations can give MSMEs the requisite support and can help MSMEs collectively protest against a buyer to enforce a change in behaviour.

Additional measures for boosting TReDS

RBI may take additional measures after taking stock of bottlenecks currently faced by the TReDS platform to ensure that the platform achieves its intended objectives. These include:

1. Presently, only banks and NBFCs are allowed to participate on TReDS. These entities lend as per the minimum credit lending rate. TReDS Guidelines do not allow any other entity to participate on this platform as a financier. Considering that the objective of TReDS is to boost MSME financing, RBI may consider lifting this restriction after doing a cost-benefit analysis. More participants such as urban cooperative banks, regional rural banks, high net worth individuals (HNIs), mutual funds, pension funds, etc. may be allowed to ensure the best rates for MSME suppliers. Such participation is allowed in other jurisdictions. For example, UK based MarketInvoice connects businesses with investors, including HNIs through its peer-to-peer invoice finance platform.
2. On the supplier side, the option of allowing non-MSME entities can also be explored. For example, a corporate buyer on board TReDS presently would have to maintain an additional payment mechanism for non-MSME segment. This leads to operational inefficiencies for the buyer. Allowing both segments on TReDS may ease their way of doing business.
3. Several MSMEs lack reliable information systems which can generate invoice suitable for discounting. To address this problem, in the Union Budget 2018-19, it was declared that TReDS would be linked to the Goods and Service Tax Network (GSTN). Further, as discussed previously in the post, financing in the TReDS environment is done on the credit worthiness of buyers on 'without recourse to seller' basis. This can potentially create disincentives for buyers to come onboard. If financiers are allowed to access the transactional data of MSME sellers available on GSTN, subject to certain safeguards like privacy of data, this can reduce their information asymmetry in terms of assessing the credit history of sellers. In other words, this measure can enable financiers to discount invoices based on the credit worthiness of sellers.

Technology solutions to address challenges

Some technological solutions are also being explored to address specific challenges with TReDS. The three licensed TReDS exchanges recently got together with MonetaGo, a US based startup, to implement a blockchain based solution for a specific problem - the problem of double invoicing and associated fraud. This permissioned blockchain solution, with each of the exchanges acting as a node, went live recently. This solution has enabled the three exchanges to work together to eliminate instances of double discounting while protecting confidential information of their clients. The system generates a hash which is used by the exchanges for validating whether an invoice has already been discounted or not.

In other parts of the world too, blockchain is being considered to develop end-to-end solutions for invoice financing. Several early implementations already exist - examples being Populous in the UK and the Hive Project in Slovenia. More specifically, blockchain is being used to:

• Ascertain the legitimacy of an invoice
• Find out whether the invoice has already been discounted
• Make available immutable contract information securely to all stakeholders, thus ensuring transparency
• Create incentives for quicker payments
• Reduce costs related to the invoice financing process

Need for a cautious approach

In view of the decision by the three exchanges to move the fraud-detection module of the TReDS platform onto a blockchain, going forward, authorities and other stakeholders must follow a cautious approach when considering a blockchain solution for other modules of the invoice discounting process of TReDS. A blockchain based solution is envisaged to reduce costs associated with invoice financing and also incentivise quicker payments by bringing in transparency of transactions through a distributed immutable ledger. However, certain considerations need to be made to come up with the most appropriate design approach in the Indian context:

1. Will a blockchain solution incentivise buyers? Considering the reluctance of buyers to come onboard TReDS due to
   the lack of a dispute resolution mechanism, it is critical for any future blockchain implementation to tackle this issue. Buyers may want a transparent mechanism on the blockchain which allows them to flag the quality of goods/services sold to them even after accepting the invoice.
2. Is a blockchain the best design choice?
   Various design choices, including centralised and distributed databases, must be considered and a cost benefit analysis must be done to choose the most efficient solution.
3. Will the solution help achieve RBI's objectives?
   Depending on RBI's objectives and factors such as trust among stakeholders, a permissioned or permissionless blockchain solution might be more suitable in case a blockchain solution is found to be the right choice.
4. Issues of security, scalability and governance: Blockchain solutions with public facing data and handling a large number of transactions have been known to struggle with issues of throughput capacity and security. Further, complex questions such as who controls the blockchain, who are the nodes in case of a permissioned blockchain with multiple stakeholders and what is the consensus mechanism need to be answered.
5. How will the solution respond to a complex and dynamic environment?: A blockchain based 'smart contract' solution for invoice financing should be able to quickly adapt to complex and fast-changing real world environments - for example, changes in the regulatory framework.

It is important that a blockchain solution is adopted only if it is addressing persistent challenges in the Indian context. Characteristics/features of the technology itself pose another set of questions when considering the solutions. Thus, a cost-benefit analysis is of paramount importance before deciding the design of the solution.

Conclusion

Several measures have been taken over the past few years to boost invoice financing in India. Although TReDS is a good initiative, we must carefully evaluate its effectivesness to address the lacunae in the system. To this end, we have examined the existing design and performance of TReDS after considering the market's response and expectations of stakeholders. Primarily, a lack of incetives for buyers is holding up widespread adoption of TReDS. This is due to structural issues in the invoice discounting space as well as challenges with the TReDS platform. This classification of challenges is necessary since merely fixing the technology platform may not address the underlying distortions. Thus, both types of challenges - structural ones such as the bargaining power of buyers and TReDS related challenges like the absence of a dispute resolution mechanism within TReDS - need to be addressed to ensure TReDS' success.

Further, a cautious approach needs to be adopted when considering novel technology solutions for such challenges. In sum, this multi-layered problem needs a concerted effort from the authorities to uncover issues at the ground level and come up with the appropriate policy and technical solutions.

References

Department of Economic Affairs, Industry and Infrastructure, Economic Survey 2017-18 Volume 2, 127-128.

Mohmad, K. M. Factoring Services in India: A Study, 2015.

Reserve Bank of India, Concept Paper - Trade Receivables and Credit Exchange for Financing of Micro, Small and Medium Enterprises, 2014.

Dylan Yaga et al, Blockchain technology overview, 2018.


The authors are researchers at the National Institute of Public Finance and Policy. The authors would like to thank Radhika Pandey and Anirudh Burman for useful discussions.

The editor for this article was Anjali Sharma.

Indian corporations have weak earnings growth

by Ajay Shah.

The broad set of Indian listed companies have a high trailing P/E ratio. This suggests that the market believes there will be high earnings growth in the future.

Some finance practitioners back out an earnings time series as Nifty market capitalisation divided by Nifty P/E. This `Implied Nifty Earnings' series shows strong growth over long time horizons.

In this article, we show that this quick-and-dirty method has an upward bias in the estimation of aggregate earnings growth. In truth, earnings growth by Indian firms has been stalled for a decade.

The trillion dollar question

Figure 1: The long time-series of the CMIE Cospi P/E ratio

The graph above shows the long time-series of the trailing P/E ratio of the CMIE Cospi index, which measures the broad market valuation. This shows that we are near some of the highest valuations in history.

These high P/E ratios would generally suggest that the stock market expects that a period of great earning growth is around the corner. It's important to look back at the recent history of earnings growth in order to evaluate this optimism.

Estimating aggregate earnings: a quick and dirty method

The P/E ratio is market capitalisation divided by earnings. Hence earnings is market capitalisation divided by the P/E ratio. It's easy to obtain a time-series of the Nifty P/E (from NSE), and the Nifty market capitalisation (obtained by summing up the market capitalisation of all Nifty member firms as seen in the CMIE database). This gives the time-series:

Figure 2: Time series of Nifty earnings (nominal rupees), quick and dirty method

As Nifty market capitalisation is measured in rupees, and the P/E ratio is dimensionless, the division yields an earnings value in rupees.

This shows pretty good growth in the earnings of the Nifty companies. In the latest few years, the growth is slow, but when compared with a decade ago, the earnings expansion is remarkable. Overall, it's a gain of 18$\times$ in 18 years, which is quite a performance. It is consistent with the common view that India is a high earnings-growth economy.

The quick and dirty method over-estimates earnings growth

The set of firms that make up Nifty changes through time. From 1996 to 2017, there were 118 firms which have been a member of Nifty atleast once.

The Nifty components at time $t_1$ are often different from those prevalent at time $t_2$. Some firms are added and some are removed. We tend to think that these are a few random fluctuations which would tend to cancel out. However, the changes in the set are non-random, and they do not cancel out.

The management of Nifty uses a rule set that roughly summarises to this: (a) A pool of eligible firms is formed where the firms have adequate stock market liquidity based on the Impact Cost measure, and (b) If an eligible firm is over 2$\times$ larger (by market capitalisation) than the smallest incumbent, then a set change is effected where the smallest incumbent is removed and the large new liquid firm is brought in. The earnings of the new entrant will generally be higher than the earnings of the smallest incumbent who is removed, as the market value of the new entrant is over 2$\times$ higher.

Here is one example, from the April-May-June 2016 quarter. In this quarter, three firms were removed (Vedanta, Cairn India, Punjab National Bank) and three firms were added (Aurobindo Pharma, Bharti Infratel and Eicher Motors) to Nifty. The remaining 47 firms were unchanged. Let's pull together the information about earnings across these changes.

Firm(s)	Earnings Q1 2016 (Rs. million)	Earnings Q2 2016 (Rs. million)	Change (Per cent)
The 47 common firms	735,021	635,358	-14
Vedanta	38,823
Cairn India	-2,459
Punjab National Bank	-53,671
Aurobindo Pharma		3,910
Bharti Infratel		14,769
Eicher Motors		3,371
The full 50 at a point in time	717,713	657,408	-8

Table 1: Example of how the quick and dirty method over-estimates earnings growth
The best estimator of earnings growth is that which is made using the identical set of firms observed at two points in time. In the above example, there are 47 firms in Nifty who were present at both points in time. Their aggregate earnings declined from Rs.735B to Rs.635B, a decline of 14%.

Three firms were present in Q1 2016 -- Vedanta, Cairn, PNB -- and when their earnings data is used, the aggregate earnings of the 50 firms in Nifty at that point in time works out to Rs.717B. These were replaced by Aurobindo Pharma, Bharti Infratel, Eicher Motors in Q2 2016, and when their earnings data is used, the aggregate earnings of the 50 firms in Nifty at that point in time works out to Rs.657B. The earnings growth obtained by comparing these two inconsistent sets was -8%, which is a more optimistic picture when compared with the decline of 14% for the consistent set.

There is a big discrepancy, of 6 percentage points across one quarter, and the direction of the bias in in favour of greater optimism.

The wrong method (merely comparing the profits across inconsistent sets across time) does not just introduce random noise, it is biased. It systematically overstates earnings growth of the Nifty set.

What actually happened to earnings growth of Indian firms?

How should we do this right? We exercise care with the following steps:

1. Oil companies have extreme earnings fluctuations based on fluctuations of global crude oil prices. Their profits do not describe what is going on in India. Finance companies have problems in earnings data, such as the concealment of bad assets by banks. Hence, we look at non-oil non-finance companies only. Aggregation of accounting data for this set of firms is an excellent source of insight into India's business cycle fluctuations.
   
2. At every two consecutive quarters, we construct a set of listed firms which are observed in both quarters. We sum up the earnings of this set at each of the two quarters. These two summed earnings are comparable across time, as they pertain to the identical set of firms.
   
3. This yields a nominal percentage growth of aggregate earnings from one quarter to the next.
   
4. We start an index at 100 and cumulate it up through time using each of these carefully constructed estimates of earnings growth.
   

This yields the following picture of the index of nominal aggregate earnings of the Indian corporate sector:

Figure 3: Index of earnings of all listed non-finance non-oil companies

This tells a story where the average earnings index grew from 126 in 2000 to 996 in 2010, but declined to 783.98 in the Oct-Dec 2016 quarter. Nominal earnings has stagnated in the last decade.

Ruling out an alternative explanation

There is one problem of non-comparability in the above analysis. The time-series of the P/E that was shown in Figure 1 pertains to the 2500 odd firms in the CMIE Cospi index. The time-series of earnings that's shown in Figure 3 pertains to the performance of all listed non-finance non-oil firms. These two groups are slightly different. Could this difference be an important issue? In order to examine this, we apply the careful method (that was used for Figure 3) to the full universe of all listed firms. The two series are superposed here:

Figure 4: Index of listed non-finance non-oil firms earnings and lll listed firm earnings (nominal)

This shows that there are some differences between the two groups, but this difference is small. From Oct-Dec 2006 to Oct-Dec 2016, i.e. in the latest decade, we have three estimates of the compound growth rate of earnings:

The quick and dirty method	8.19%
All listed firms, the careful method	-1.11%
Non-finance non-oil listed firms, the careful method	-0.41%

The compound average growth rate of earnings of all listed firms is similar to that of non-finance non-oil firms. Both estimates are roughly 8 percentage points per year below the quick and dirty method.

Conclusion

Fine points in handling firm data matter! It appears easy and expedient to use the Nifty market capitalisation time series, and the Nifty P/E ratio time series, to back out an estimate of Nifty earnings time series. The use of the phrase `Implied Nifty Earnings' sets off an analogy with the genius of implied volatility. However, this procedure is highly misleading. Let's superpose the wrong and the correct index time-series on one graph:

Figure 5: Superposing the quick and dirty earnings index and the careful index

The quick and dirty method suggests 18$\times$ earnings growth in 18 years. The correct method shows 8$\times$ earnings growth in the same period, and stagnation in the last decade.

The stock market believes that a great wave of earnings growth is around the corner, and India is generally considered a market with high earnings growth. However, earnings growth has been elusive for the last decade. More generally, in the past, the Indian stock market has done well on differentiating between firms -- in voting with a high P/E ratio for firms that will do well in the future -- but has fared poorly at macroeconomic thinking.

Reproducible research

This R program when run using data from CMIE Prowess DX (Mar-2017 vintage) replicates Figure 3 above.



I thank Nilesh Shah and Mahesh Vyas for valuable discussions. Pramod Sinha wrote the code and it was audited by Dhananjay Ghei and Shekhar Hari Kumar.

Monetary policy strategy for 2017

by Ila Patnaik and Ajay Shah.

India now has an inflation targeting central bank and a monetary policy committee. The first three monetary policy committee meetings have taken place. The first meeting cut the de jure policy rate, and the next two meetings chose to hold.

Winston Churchill once said If you put two economists in a room, you get two opinions, unless one of them is Lord Keynes, in which case you get three opinions. However, all the three meetings of the MPC featured six economists with one opinion.

In this article, we argue that conditions in the economy suggest that it is time to worry about forecasted inflation going closer to the low end of the target range.

Let's start at the measure of inflation that is used in defining RBI's objective, i.e. the year-on-year change of CPI:

Headline inflation, i.e. year-on-year CPI inflation

Y-o-y CPI inflation breached 5% in February 2006. After that, we had a long and painful bout of inflation. A recession began in India in 2012, and by mid-2013, inflation was on the decline. The latest value, for January 2017, shows 3.17%. This is benign when compared against the range from 2 to 6 per cent, which is coded into the RBI Act.

Each reading of year-on-year inflation is the average of twelve changes for the latest twelve months. To understand what is going on in the economy in recent days, it's useful to look at month-on-month changes. This requires seasonal adjustment. We have developed the models for seasonal adjustment at NIPFP, and will use this ahead.

Roughly half the CPI basket is food and food inflation is thus critical for the overall CPI. What is going on with food inflation? We use the WPI Food to look at this:

Month-on-month WPI Food inflation (SA, Annualised)

The values above are annualised month-on-month changes of seasonally adjusted WPI Food. This shows that from July onwards, we have had remarkably low food inflation. The CPI inflation that we have got stems from non-food inflation. Looking forward, the outlook for non-food inflation is limited because of softness in global prices of tradeables.

The poor man's statistical model of y-o-y CPI inflation is to forecast the m-o-m values using univariate time-series methods, and add up the latest 11 facts with 1 forecast to get a one-month ahead forecast. When we do this, the forecasts for February, March and April work out to 3.32%, 3.65% and 3.43%. These benign forecasts use no economic knowledge - they only reflect the time series structure of month-on-month inflation. These should be treated as the baseline on top of which we layer on economic thinking.

What about pressures on aggregate demand? There are four perspectives which suggest that the demand side will be weak in 2017 and 2018.

1. Exports growth is faring badly, partly owing to the difficulties of the global economy. The outlook for the global economy is poor, given the difficulties in China, Europe and the US.
2. From November 2016, we have been adversely affected by the demonetisation shock. We estimate that demonetisation induced a median -0.45 sigma shock to month-on-month seasonally adjusted changes in 27 macroeconomic series for November 2016, and a -0.15 sigma shock for 24 macroeconomic series in December 2016. For a comparison, when our surprise measurement methods are applied to 2008, we estimate there was a -0.25 sigma shock in September 2008 and a -0.44 sigma shock in October 2008. Demonetisation has adversely affected optimism of households. We expect that demonetisation will exert a sustained negative impact upon the economy through 2017.
3. Investment in India is faring poorly. The best measure of investment activity is the stock of projects classified as being `under implementation' in the CMIE Capex database. This stalled -- in nominal rupees! -- in 2012 and has not grown for five years. Things are likely to worsen on this front in the aftermath of demonetisation.
4. We are in the midst of a banking crisis. In December 2016, non-food credit grew by 5.32% nominal when compared with December 2015, which is 1.91% in real terms. The last time we saw lower values was at the time of the Lehman crisis in late 2008.

These four problems are, of course, inter-related. We overstate the gloom when we think of them as four orthogonal issues. Each of the four is a difficult problem which resists quick solutions. As an example, consider the time series of cash in circulation:

Cash in circulation (Trillion rupees)

If you extrapolate the straight line at the end, it will be many months before cash is back to pre-shock conditions. Similarly, consider the year-on-year changes of imports by the US from China:

Imports by the US from China

It is remarkable to see that the recent low value was as bad as that seen in the 2008 crisis. The sluggish values here bode ill for global demand for Indian exports.

These four difficulties suggest that output and inflation will evolve in a more negative way as compared with the baseline statistical forecasts described above. In this case, CPI inflation outcomes could be knocking on the lower end of the target range.

We feel that these issues will weigh on monetary policy in 2017 and 2018. Monetary policy acts with a long lag, so we have to look ahead when thinking about policy changes today. Further, monetary policy in India is relatively ineffectual, as the monetary policy transmission is weak. Mere 25 bps changes have little impact. When monetary policy in India has to move, large moves are required. We feel that substantial reductions of the short rate are required in 2017 in order to stay at the inflation target of 4%.


The authors are researchers at the National Institute for Public Finance and Policy.

Dating the Indian business cycle

by Radhika Pandey, Ila Patnaik, Ajay Shah.

Most macroeconomics is about business cycle fluctuations. The ultimate dream of macroeconomic policy is to use monetary policy and fiscal policy to reduce the amplitude of business cycle fluctuations, without contaminating the process of trend GDP growth. From an Indian policy perspective, this agenda is sketched in Shah and Patnaik (2010). The starting point of all these glamorous things, however, is measurement. The major barrier to doing Indian macroeconomics is the lack of the foundations of business cycle measurement.

The first milestone in this journey is sound procedures for seasonal adjustment of a large number of macroeconomic time series. At NIPFP, we have built this knowledge in the last decade, and insights from this work are presented in Bhattacharya et. al, 2016.

The next milestone is dates of turning points of the business cycle. As an example, in the US, the NBER produces a set of dates. These dates are extremely valuable in myriad applications. As an example, the standard operating procedure when drawing the chart of a macroeconomic time-series is to show a shaded background for the period which was a contraction. Here is one example: y-o-y CPI inflation in the US, with recessions shown as shaded bars. In the Indian setting, several papers have worked on the problem of identifying dates of turning points of the business cycle (Dua and Banerji, 2000, Chitre, 2001, Patnaik and Sharma, 2002, Mohanty et.al, 2003).

In a new paper (Pandey et. al., 2016) we bring three new perspectives to this question:

1. In the older period, India was an agricultural economy, and the ups and downs of GDP growth were largely monsoon shocks. It is only in the recent period that we have got structural transformation, and the market process of cyclical behaviour of corporate investment and inventory, which add up to a business cycle phenomenon that is recognisably related to the mainstream conception of business cycles (Shah, 2008). This motivates a focus on the post-1991 period.
2. We are able to shift from annual data to quarterly data by starting in the mid 1990s.
3. We have the laid the groundwork for this to be a system, with regular updation of the dates, rather than a one-off paper. 

Methods

One approach to business cycle measurement focuses on ``growth cycles'', and relies on detrending procedures to extract the cyclical component of output. The cycle is defined to be in the boom phase when actual output is above the estimated trend, and in recession when the actual output is below the estimated trend. This identifies expansion and contraction based on the level of output. In contrast, the ``growth rate cycle'' identifies turning points based on the growth rate of output. For the post-reform period in India, this is more appropriate.

At an intuitive level, the procedure works as follows. First, we remove the trend and focus on fluctuations away from the trend. Second, we remove the high frequency fluctuations (below two years) and the low frequency fluctuations (above eight years). What's left is in the range of frequencies which are considered `the business cycle'. Third, we identify turning points in this series.

In terms of tools and techniques, we use the filter by Christiano and Fitzgerald. The Christiano Fitzgerald filter belongs to the category of band-pass filters. This is used to extract the NBER-suggested frequencies from two to eight years. To this filtered cyclical component, we apply the dating algorithm developed by Bry and Boschan, 1971.

Our analysis is focused on seasonally adjusted quarterly GDP series (Base year 2004-05). This series is available from 1996 Q2 (Apr-Jun) to 2014 Q3 (Jul-Sep). The CSO revised the GDP series with a new base year of 2011-12. The revised series is available only from 2011 Q2. Hence we stick to the series with old base year for our analysis.

Results

De-trended, filtered, seasonally adjusted real GDP growth

As an example, look at the period of the Lehman crisis. It is well known that the economy was weakening well before the Lehman bankruptcy in September 2008. As an example, INR started depreciating sharply from January 2008 onwards. The evidence above shows that the economy peaked at Q2 2007, and started weakening thereafter.

Each turning point is a fascinating moment. In Q2 2007, i.e. Apr-May-Jun 2007,  growth was good but the business cycle was about to turn. It is interesting to go back into history to each of these turning points and think about what was going then, and what we were thinking then.

Dates of turning points in GDP:1996-2014

Phase	Start	End	Duration	Amplitude
Recession	1999Q4	2003Q1	13	3.3
Expansion	2003Q1	2007Q2	17	2.5
Recession	2007Q2	2009Q3	9	2.3
Expansion	2009Q3	2011Q2	7	1.3
Recession	2011Q2	2012Q4	6	0.9

Our findings on business cycle chronology are robust to the choice of filter and to the choice of the measure of business cycle indicator. We conduct this analysis using different measures of business cycle indicators such as IIP, GDP excluding agriculture and excluding government, and Firms' net sales, and find broadly similar turning points. Details about these explorations are in the paper.

A system, not just a paper

This is not a one off paper. We will review these dates regularly and update the files, while avoiding changes in URLs. When the methods run into trouble with future data, we will address these problems in the methods. This work would thus become a part of the public goods of the Indian statistical system.

All key materials have been released into the public domain. In addition to a paper web page, we have a system web page which gives a .csv file with dates at a fixed URL and can be used e.g. in your R programs.

An example of an application

An example of placing recession bars on a graph, of
growth in (non-finance, non-oil) firms net sales

The graph above shows the familiar series of seasonally adjusted annualised growth, of the net sales of non-financial non-oil firms, with shaded bars showing downturns. This series only starts after 2000 as quarterly disclosure by firms only started then. Placing this series (net sales of firms) into the context of the business cycle events gives us fresh insight into both: we learn something about the sales of firms respond to business cycle fluctuations, and we learn something about business cycle fluctuations.

Facts about the Indian business cycle

It is useful to know summary statistics about the Indian business cycle: the average duration and amplitude of expansion and recession and the coefficient of variation (CV) in duration and amplitude across expansions and recessions.

Summary statistics of GDP growth cycles

Exp/Rec	Average amplitude (in per cent)	Average duration (in quarters)	Measure of diversity in duration (CVD)	Measure of diversity in amplitude (CVA)
Expansion	2.5	12.0	0.34	0.38
Recession	2.2	9.3	0.31	0.45

The average amplitude of expansion is seen to be 2.5% while the average amplitude of recession is 2.2%. The average duration of expansion is seen to be 12 quarters while the average duration of recession is seen to be 9.3 quarters. These are fascinating new facts in India. There is more heterogeneity in the amplitude of a downturn when compared with expansions.

Changing nature of the Indian business cycle

In recent decades a number of emerging economies have undergone structural transformation and introduced reforms aimed at greater market orientation. There is an emerging strand of literature that studies the changes in business cycle stylised facts in response to these changes. Studies find that business cycle stylised facts have changed over time (Ghate et.al, Alp. et.al, 2012). In the paper, we explore some of these changes.

In the post-reform period, both expansions and recessions have become diverse in terms of duration and amplitude. Some episodes of recession are relatively more deeper and severe relative to others in the post-reform period. Similarly there is considerable variation in the duration of expansion and recession across specific cycles in the post-reform period. Some are short-lived while others are relatively more persistent.

References

Rudrani Bhattacharya, Radhika Pandey, Ila Patnaik and Ajay Shah. Seasonal adjustment of Indian macroeconomic time-series, NIPFP Working Paper 160, January 2016.

Radhika Pandey, Ila Patnaik and Ajay Shah. Dating business cycles in India. NIPFP Working Paper 175, September 2016.

Ajay Shah (2008). New issues in macroeconomic policy. In: Business Standard India. Ed. by T. N. Ninan. Business Standard Books. Chap. 2, pp.26--54.

Ajay Shah and Ila Patnaik (2010). Stabilising the Indian business cycle. In: India on the growth turnpike: Essays in honour of Vijay L. Kelkar. Ed. by Sameer Kochhar. Academic Foundation. Chap. 6, pp.137--154.

Recessions uncover what auditors do not

On 14 December 2008, I was nervously looking around at the world and wrote a blog post Goodbye great moderation, hello financial fraud?  Almost on cue, we got the Satyam scandal: 21 December, 24 December, and then 7 January. We also got a few other problems in India which (I think) surfaced owing to the Great Recession : NSEL, a rash of ponzi schemes, Sahara, Saradha.

In China, unprecedented times are bringing forth revelations on an unprecedented scale [link, link]. Some of the rackets that are described in China appear quite familiar to us in India, but the magnitudes seen there are astonishingly large. We had such problems in developed markets also -- Madoff and MF Global.

Institutional reform: Consumer protection

One part of addressing this problem is the familiar machinery of the Indian Financial Code (IFC) version 1.1. As an example, see this analysis of ponzi schemes. As an IFC quality law is not found in either China or India, we have a rash of such problems in both countries.

Institutional reform: Criminal justice system

An important subset of financial crime is about plain criminal law. While the main track of financial policy has been along the Indian Financial Code, we need to develop a work program on improvements of the criminal justice system also. Put together, these will create an enforcement machinery that will generate deterrence against big financial scandals.

Procyclicality of trust?

Watching China unfold in recent weeks, I wonder if there's a general proposition of the following nature. Recessions will uncover what auditors could not, but under conditions of low institutional quality, this will happen on a bigger scale. Conversely, when institutional quality is low, business and finance will be hampered at all times by low trust. But in good times, when it's easier for the crooks to keep things under wraps, fewer scandals will burst into the public consciousness, and trust will go up. Procyclicality of trust may be heightened in places with low institutional quality.

How bad is IIP growth after controlling for Diwali effects?

by Radhika Pandey and Pramod Sinha

Yesterday's data release showed a sharp contraction in industrial production in November. Some say that this contraction is an aberration as it was largely driven by seasonal fluctuations including the placement of Diwali and the number of working days in November.

A previous post on this blog How bad was industrial production in October? has highlighted the merits of using month-on-month seasonally adjusted numbers to provide timely and accurate information about the state of the economy. We apply our work on seasonal adjustment to analyse the true extent of contraction in the November IIP numbers. In the jargon of seasonal adjustment, Diwali is a `moving holiday': one that shows up in different months in different years.  A `Diwali effect' is found to be statistically significant for IIP and IIP (Manufacturing).

The figure above superposes the two time-series of seasonally adjusted annualised rates (SAARs). It has the SAAR IIP (without adjusting for Diwali) and SAAR IIP (with adjustment for Diwali). In most months, the two series are identical. But in some months, the interpretation of the IIP data strongly requires adjustment of Diwali as a moving holiday. The numbers for the month of November 2015 are worth noting. Without adjusting for Diwali effect, the month-on-month growth shows a sharp contraction of -71%, after adjusting for Diwali effect the month-on-month numbers modestly improve to -39%.

The figure above shows the same analysis for IIP (Manufacturing). If we look at the manufacturing sector, the seasonally adjusted month-on-month growth yields a negative growth of -44%. Adjusting for diwali effect results in modest improvement to -33%.

To conclude: After adjusting for the Diwali effect, IIP dropped by an annualised 39%, and IIP manufacturing dropped by an annualised 33%.


The authors are researchers at the National Institute for Public Finance and Policy.