The problem with re-basing GDP estimates

The Hindu, November 13, 2019 (with R Nagaraj)

In the next few months, the Central Statistics Office (CSO) proposes to replace the gross domestic product (GDP) series of 2011-12 base year with a new set of National Accounts using 2017-18 as the base-year. According to the chief statistician, this will be done as soon as the new consumer expenditure survey and the Annual Survey of Industries (ASI) results become available. Normally, rebasing is a routine administrative decision of any national statistics office.

Background to the dispute

But these are not normal times in India for users and producers of the national accounts. For the past four years there has been a raging controversy over the current GDP figures on account of questionable methodologies and databases used. According to official data, the annual economic growth rate has sharply decelerated to about 5% in the latest quarter, from over 8% a few years ago. The reality may, however, be far worse. Independent studies using multiple statistical methods to validate the official GDP estimates by the former Chief Economic Adviser, Arvind Subramanian, and Sebastian Morris of the Indian Institute of Management, Ahmedabad, have suggested that the annual GDP growth rates during the last few years may have been overestimated by 0.36 to 2.5 percentage points.

Why is there such distrust in the official GDP figures? To understand the origins of the dispute, one has to go back to early 2015 when the CSO released a new series of GDP with 2011-12 as base-year, replacing the earlier series with the base-year 2004-05. Periodic rebasing of GDP series every seven to 10 years is carried out to account for the changing economic structure and relative prices. Such re-basing usually led to a marginal rise in the absolute GDP size on account of better capturing of domestic production using improved methods and new databases. However, the underlying growth rates seldom change, meaning that the rebasing does not alter the underlying pace of economic expansion.

The 2011-12 base year revision was different, however. The absolute GDP size in the new base year 2011-12 contracted by 2.3% (compared to the old series), and the annual GDP growth rate went up sharply from 4.8% in the old series to 6.2% in 2013-14. Similarly, the manufacturing sector growth rate for 2013-14, swung from (-) 0.7% in the old series to (+) 5.3% in the 2011-12 series. Such large variations in growth rates for the same year may be justified if the material conditions of production warranted. But the higher growth estimates recorded by the new series did not square with related economic indicators such as bank credit growth, industrial capacity utilisation or fixed investment growth. Thus began the questioning of the new GDP series.

Demonetisation as shock

The suspicion of official output estimates became particularly intense after the demonetisation of high valued currency notes in November 2016. By most analyses, the economic shock severely hurt output and employment. For example, the Ministry of Finance’s Report on Income Tax Reforms for Building New India (September 2018; convenor: Arbind Modi), provided data on fixed investment in the private corporate sector based on actual corporate tax returns. It shows that the fixed investment to GDP ratio fell sharply from 7.5% in 2015-16 to 2.8% in 2016-17 (suspected to be on account of demonetisation). However, surprisingly, the ratio in the national accounts went up from 11.7% in 2015-16 to 12% in 2016-17.

Similarly, chief economist of the International Monetary Fund, Gita Gopinath’s academic research paper (co-authored) published by the highly regarded National Bureau of Economic Research in the U.S. in May 2019 showed an adverse effect of demonetisation on growth rate. Yet, the official GDP for the year 2016-17 grew at 8.2%, the highest in a decade.

The root of the problem

The source of the problem, according to many economists, is the underlying methodologies for calculating GDP (in the 2011-12 series) which they claim are deeply flawed, as well as the new dataset used in estimating the private corporate sector’s contribution. Some of the recent, prominent criticisms are as follows.

In a first, the CSO estimated value addition in the private corporate sector using the statutory filing of financial results with the Ministry of Corporate Affairs. The private corporate sector accounts for about a third of GDP, and spans all production sectors, and roughly about half of the private corporate sector output originates in manufacturing. The database of the Ministry of Corporate Affairs has been criticised by many as unreliable; hence it is possible that the private corporate sector output has been overestimated. For example, the Ministry’s database on “active” companies — that is companies claiming to have submitted audited financial results regularly for three years — seems to contain many companies that are actually inactive (not producing output on a regular basis). Last year when the National Sample Survey Office (the government’s premier, independent, data-gathering agency), used the Ministry of Corporate Affairs list of active companies to canvass a sample of companies in the services sector, it found that up to 42% of the sample companies were not traceable, had failed to provide the information for the survey, or had failed to provide audited accounts.

For estimating GDP of the private corporate sector, questionable methods are also used for blowing-up unverified “sample” data into estimates for the unknown and varying universe of “working” or active companies.

State domestic product (SDP) estimation uses many of the same databases and methodologies used in all-India GDP estimation. The methodological changes made in the 2011-12 base-year revision have adversely impacted the quality of SDP estimates on two counts. First, the Ministry of Corporate Affairs data does not have factory identifiers (that is, location of production units, but only has the location of the company head office); it has distorted distribution of the SDP estimates across States. Second, for estimating value-added in the informal or unorganised sector, State-specific labour productivity estimates are unavailable in the 2011-12 series. Hence the method used distorts output estimation.

The CSO has denied the claim that the underlying methodology is flawed and that there are serious problems with the new database being used. The official response throughout the debate has been that the 2011-12 GDP series follows global best practices (meaning, following the latest United Nations System of National Accounts guidelines) and applies better methods using much larger datasets; hence the official estimates are blemish-less. This ignores the fact that India has always followed UN guidelines, and that larger data sets are not necessarily better.

Need for a review

The proposed change over to a new base-year of 2017-18, is, in principle, a welcome decision. However, considering the methodological disputes and data related questions relating to the current national accounts series, as illustrated above, what would the rebasing potentially accomplish? Doubts will persist so long as the underlying methodological apparatus remains the same; feeding it with up-to-date data is unlikely to improve its quality.

In view of the problems with the current series, a chorus of academic and public voices has proposed setting up an independent commission of national and international experts to review the GDP methodology. The ideal time to do this would be now so that solutions could be found and incorporated into the new GDP series. Conversely, if a new rebased series is introduced without any changes it will only entrench the existing methodological problems, and ensuring that the debate will continue for the next half decade. And as the debate continues, so will the loss of credibility.

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Don’t Ignore The Credit Risk In NBFCs

Bloomberg Quint, February 25, 2019 (with Harsh Vardhan)

In the aftermath of the default of IL&FS in September 2018, the non banking finance companies (NBFCs) have been facing a significant amount of financial stress. Due to the increased cost of borrowing in the bond market, it has become difficult for the NBFCs to raise capital. In popular discourse, attention has been paid to the role played by the asset-liability mismatches in the balance sheets of the NBFCs in causing this crisis. In this article we highlight another problematic feature characterising the NBFC sector – potential underpricing of risk.

Background: A Decade Of Rapid Growth

The NBFCs have had a good run over the last few years. During the period 2009-2018, the aggregate loans extended by the NBFC sector grew at a CAGR of 17%. The sector registered the highest growth during the 2009-2014 period (24%). Several factors contributed to this good run. Given the burgeoning pile of non performing assets and capital shortfall to provide for the same, the public sector banks reduced their lending. Private sector banks' credit also slowed and remained concentrated in select segments. During 2009-2014, the CAGR of non-food credit extended by the banking sector was 12%. This created a credit supply shortage that presented the NBFCs with an expansion opportunity.

While banks reduced their exposure to the corporate sector, they continued to lend to the NBFCs. At the same time, increased investment of household financial savings in mutual funds meant that the NBFCs could raise funds from the debt mutual funds. The bond market emerged as the dominant source of financing for the NBFCs. During the period 2009-2018, the average share of bond market borrowing in the total borrowing of NBFCs was 51% whereas the share of bank borrowing was 26%.

Events in September 2018 effectively put a stop to the good run. Over the span of a few months, the growth of the NBFC sector rapidly declined and their margins came under pressure. This has resulted in some fundamental questions being raised about the NFBC business model. Here, we look at one crucial aspect of this business model.

Background: A Decade Of Rapid Growth

At the heart of any lending business is a rating arbitrage – the lender has to have, on average, better rating than the borrower so that its own borrowing cost is lower than what it charges the customer. The lender can also create a margin by running a maturity mismatch - borrowing short and lending long when the yield curve in upward sloping. Both these actions, credit arbitrage and maturity mismatch create risk for the NBFCs. In other words, the risk in the NBFC model has two components – market risk, conventionally called asset liability management (ALM) risk and credit risk that arises from lending to lower risk customers.

Following the IL&FS episode, there has been considerable discussion on the market risk embedded in NBFC balance sheets arising from the ALM mismatch problem. Regulatory actions have also focused on the market risk in terms of the reporting requirements for NBFCs and instructions to the rating agencies to consider market risk while rating the NBFCs. In our study, we take a closer look at the credit risk in the NBFC model.

NBFCs and Credit Risk

To understand the pricing of credit risk in an NBFC we have to look at two components – the pricing of credit risk when lenders such as banks and bond markets lend to an NBFC and the pricing of credit risk when the NBFC lends money to its customers. We call the former component ‘NBFC/ HFC premium’ while the latter ‘loan premium’. We define the NBFC premium as the difference between the average cost of borrowing for NBFCs and the 10-year government security yield. We define the loan premium as the difference between the average yield on loans given out by the NBFCs and the 10-year g-sec yield. The relative levels of these two premiums over time give a sense of the evolution of credit risk pricing in the overall NBFC sector.

For our analysis, we study the period 2009-2018. We focus on the post-global financial crisis period during which the NBFC sector grew steadily. We split our sample between NBFCs (non-deposit taking) and housing finance companies. We consider 11 largest NBFCs (excluding the government-owned ones) and 10 largest HFCs. We look at them separately because the nature of their businesses and hence the credit risk profiles are different. Together they represent about 80 percent of the entire NBFC sector.

We also look at the capital to loans ratio of the NBFCs/HFCs as a broad indicator of capital adequacy and the overall loan growth registered by the NBFCs/HFCs to get a sense of expansion of the sector. The chart below plot these variables for our sample of NBFCs/HFCs over the 10 year period.

Our observations from the charts can be summarised as follows:

1. For our sample of HFCs, the HFC premium, the HFC loan premium and the HFC credit to loan ratio remained fairly stable over the period under study. The spread between the credit premium and loan premium also remained stable. This suggests that for the HFCs, the credit risk in general has been stable.
2. For our sample of NBFCs, during the period 2009-2014, while the NBFC premium was stable, the loan premium steadily declined. The capital to loans ratio of the NBFCs remained stable throughout the period.
3. During the period from 2009 to 2014, NBFC loans grew rapidly at a CAGR of 27%. The period average of loan growth for 2009 -2018 was 23%. Hence, during the time when the loan premium charged by the NBFCs declined, their loan books expanded rapidly.

This implies that while the lenders to the NBFCs kept their credit premium constant, the NBFCs themselves started reducing the premium they were charging their own customers. We do not find this trend for the HFCs.

The lowering of loan premium by the NBFCs could happen for two reasons. It could be that the NBFCs started lending to more creditworthy, lower risk customers. There is however no evidence to suggest the same. On the contrary anecdotal evidence suggests that in search of growth opportunities, NBFCs started lending to riskier customers. The alternative explanation for reducing loan premium is that NBFCs started systematically underpricing credit risk at a time of rapid growth. There was no commensurate improvement in the capital levels to support this underpricing.

The underpricing of credit may have been a result of intensifying competition in the sector which would drive down the lending rates or a correction of previous overpricing of credit or based on a perception that the overall risk in the economy was declining. Notwithstanding the reasons of underpricing, it would have made the NBFC business model riskier. Arguably the underpricing of credit helped the expansion of the NBFC sector during their high growth period.

Another way of interpreting the data is that despite the NBFCs relatively under-pricing credit risk on their loans and their capital levels not going up, the lenders did not penalise them by increasing the risk premium they charge the NBFCs. This could be because the bond market became a dominating source of financing for the NBFCs presumably due to increased inflows into debt funds and the bond market was relatively less discriminating in pricing credit risk.

If it is indeed the case that the NBFCs have been systematically underpricing credit risk as is apparent from our analysis, this could be a plausible reason behind the heightened risk perception about the sector in recent times. This would imply that ALM mismatches in the balance sheets are not the only problem in the NBFC business model which has some fundamental challenges that need to be addressed. Systematic mis-pricing of risk results in misallocation of capital. The shadow banking system in general will become safer and stronger when resources are efficiently allocated.

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The Mismatch Between What MPC Says And What RBI Does

Bloomberg Quint, January 21, 2019

Under the new inflation targeting regime in India, the Monetary Policy Committee decides the repo rate and conveys the monetary policy stance. Both decisions are communicated through the monetary policy statements published on the Reserve Bank of India website six times a year after every meeting of the MPC. There are concerns, however, with the way the MPC has been conveying its monetary policy stance, especially recent times.

First, it helps if all inflation-targeting central banks, including the RBI, use the same language. This would facilitate a clear understanding on the part of stakeholders and also enable comparison across countries. The MPC has adopted some very unusual language to convey its policy approach. The ‘monetary policy stance’ typically refers to the current action of the central bank, whereas the MPC in India seems to be using it to signal future policy. Similarly, the world over, monetary policy stance is measured with respect to the neutral rate of interest. A ‘neutral’ stance means that the interest rate is at the neutral level, where the central bank is neither trying to push inflation down nor encouraging it to increase. The MPC however, seems to use ‘neutral’ to describe its attitude toward adjusting interest rates in the future.

Even there, the action taken does not seem to match the announcement. For instance, in the Aug. 1, 2018 meeting, the MPC retained its erstwhile ‘neutral’ monetary policy stance but raised the repo rate by 0.25 percent from 6.25 percent to 6.5 percent. In the next meeting on Oct. 5, 2018, the MPC changed its stance to ‘calibrated tightening’ but maintained the status quo on the repo rate. The adoption of a tightening stance may have hinted at a gradual increase in the repo rate over a period of time. Yet at the following meeting on Dec. 5, 2018, the MPC retained the ‘calibrated tightening’ stance. In addition, it left the repo rate unchanged and reduced the inflation forecast, hinting that rates might actually be cut in the future.

These instances show that the announced monetary policy stance of the MPC does not have anything to do with either the current or the future interest rate decision.

Finally, the monetary policy stance officially communicated by the MPC through the statements is often found to be in conflict with the actions taken by the RBI. The contradiction between MPC’s communication and RBI’s action points to a deeper problem about the conduct of monetary policy in India.

RBI’s Toolkit Working At Cross-Purposes?

There are three distinct instruments using which monetary policy can be conducted in any country. These are the short-term policy rate (in case of India, the repo rate) which is a price-based instrument, the cash reserve ratio and the open market operations by the central bank. The last two fall in the category of quantity-based instruments. In addition to CRR, in India, we have another quantity-based instrument which is the statutory liquidity ratio. SLR specifies the percentage of government securities that scheduled commercial banks are mandated to hold.

1. The effect of changes in the repo rate gets transmitted to the real economy through the bank interest rate channel.
2. The effect of changes in the quantity instruments get transmitted through the credit channel.
3. A reduction in the CRR or SLR or injection of liquidity through an open market purchase of government securities by the RBI are expected to result in an increase in the volume of loans disbursed through the banking sector, assuming that the channel of transmission works.

Ideally, the price-based and quantity-based instruments must operate in the same direction to reinforce the effect of monetary policy.That is not always the case in India. For instance, at the meeting of Aug. 2, 2017, the MPC announced a monetary policy expansion and the repo rate was lowered by 0.25 percent from 6.25 percent to 6 percent. Yet through the months of August and September 2017, the system was kept in a liquidity deficit mode. In fact, RBI conducted a cumulative open market sale of Rs 40,000 crore during these two months. An open market sale by RBI is akin to a monetary policy contraction.

At the last monetary policy meeting on Dec. 5, 2018, the MPC retained a ‘calibrated tightening’ stance. Around the same time, the RBI announced in its ‘Statement on Developmental and Regulatory Policies’ that it will reduce the SLR by 0.25 percent every quarter starting Jan. 2019, until the SLR reaches 18 percent of the net demand and time liabilities of the banks. Lowering of the SLR frees up liquidity which the banks can use to make loans.

This is tantamount to a monetary policy easing action which contradicts the ‘calibrated tightening’ stance communicated by the MPC.

During the period from June 1, 2018, to Oct. 5, 2018, when the MPC maintained a ‘neutral’ monetary policy stance, RBI injected Rs 40,310 crore through outright open market purchases. During the period from Oct. 5 2018, to Dec. 31, 2018, when the MPC changed its stance to ‘calibrated tightening’, RBI injected double the previous amount, Rs 1,36,002 crore into the system using OMOs. This shows that the monetary policy stance of the MPC contradicts the liquidity management operations of the RBI.

Poor Communication = Poor Transmission

The lack of symmetry between communication and action leads to confusion among financial market participants and distorts their inflation expectations. When the RBI injected liquidity using OMOs during the October-December 2018 period, the yields on government securities went down. One may argue that had the MPC’s communicated stance been in sync with RBI’s liquidity management action, the bond yields would have gone down further. G-sec yields are a crucial factor in determining the overall cost of capital in the economy. Lower yields could have benefited a stagnant private corporate sector.

The confusion caused by this inconsistency also weakens monetary policy transmission.

For quite some time the RBI has been concerned about the poor state of monetary transmission in India and has been experimenting with ways to link the interest rates charged by the banks to external benchmarks such as the repo rate. A relatively easier way to improve transmission would be to conduct the RBI’s liquidity management operations in sync with the MPC’s monetary policy stance.

The hallmark of a successful inflation-targeting regime is the anchoring of inflation expectations. Monetary policy communication can be used as an effective tool to influence the inflation expectations of private sector agents. The credibility of communication crucially depends upon the consistency between the message conveyed and subsequent action taken.

In absence of this consistency, the MPC’s communication loses its effectiveness.

This is particularly damaging for a new inflation-targeting regime such as in India where the MPC needs to build and establish its credibility. To achieve credibility and accomplish their goals, the RBI and the MPC need to start using standard language and eliminate the contradiction between the announced stance and the actual policy actions. In addition, the RBI needs to adopt a uniform strategy for conducting monetary policy, one that is symmetric across both price and quantity instruments.