Government managing the outcome of a financial market

Government and non-financial products

Suppose a government is unhappy at the price or quality of wrist watches that are being made in the private sector. Should the government indulge in muscular interventions into the free market's outcome?

The standard thinking of public economics runs in three steps: (1) What's the market failure? (2) What's the proposed intervention; does it attack the market failure at the lowest possible cost and (3) Do we have the State capacity to pull off this intervention?

Suppose you wanted to diverge from the orthodoxy. Parliament can write a law which uses coercive power to meddle in the wrist watch industry. This is called "industrial policy". It can decide that quartz movements are a better technology, and ban mechanical watches. It can put people in jail if watches are traded at a price that is different from the price specified with the government. What is wrong with undertaking such interventions?

The problems with such interventions run at three levels. First, the free market works pretty well, and even well meaning politicians and officials have little value to add (other than addressing market failures). Second, politicians and officials may not be well meaning uncles, they are regular guys and chase their own selfish objectives. Third, even if the right objectives are established, implementation is very hard; scarce resources (money and time of top management) will be used up in doing extraneous things which comes at the cost of those resources being applied to the core business of government, i.e. addressing market failures.

If a government insisted on running Hindustan Machine Tools, to make wrist watches, the outcomes are not horrible on a large scale. The outcomes go wrong at a narrow scale. When I look back at this episode, the original sin was barriers to foreign providers. If only HMT had been forced to compete with foreigners making wrist watches, it would have rapidly died, and the problem would have gone away. The twin evils of autarky + public sector production gave a shortage of good quality wrist watches in the country.

In the olden days, many countries tried to have State-run airlines or export-import banks, but such heterodox thinking hasn't stood the test of time. `Industrial policy' was once accepted as the job of government, but today it's generally seen as feeble thinking.

Government and the working of financial markets

All the above arguments apply in the field of finance.

The job of the government is to address market failures. Attempts at doing things that go beyond addressing market failures are a bad idea for the same four reasons as those described above. But diverging from the orthodoxy is  more damaging in finance than it is in non-financial areas.


The public administration problem. When a government tries to shape the outcome of a financial market, problems of malfeasance are amplified as politicians and officials will also do insider trading.  It is hard to create clean administrative structures for a public agency that's trading on markets or (in any way) trying to manage the market price.

As an example, the clean UK solution is that the Monetary Policy Committee votes on everything that the Bank of England does : on the short term money market, the Bank of England will do infinitely large trades to control the short term interest rate, and that target rate is voted on by the MPC, and when the zero interest rate lower bound was reached, the MPC votes on the magnitude of quantitative easing that is to be done in each month. EMs and developing countries have experienced all kinds of problems when trading rooms in the government have tried to do trading in financial markets without sound institutional arrangements. These problems never arose when the government tried to meddle in the market for wrist watches.


The Lucas critique. The next hurdle to cross is the Lucas critique. All participants in the market are rational and react to the actions of the government.

A government says that stock prices won't be allowed to go down? This changes the behaviour of investors who think it's a one way bet and pile on massively. When INR was a "managed appreciation" with small predictable appreciations week after week, this kicked off a surge of capital coming into the country. Risk taking is higher when there is "the Greenspan put".

A government says that exchange rate volatility will be kept down? This changes the behaviour of private participants who feel safe and don't hedge currency exposure. News breaks; INR should have depreciated; government props it up; capital flight by locals and non-residents benefits from a red carpet in the form of a more attractive exchange rate.

The essence of financial markets is risk taking. Everyone in the market should have a keen sense that there is no free lunch. Long term investors should be those who are comfortable dealing with ups and downs. Speculators should know that skill in forecasting will make giant profits, but there is no safety net when they make mistakes.

It takes years to create credibility in the eyes of market participants (a) That the government will keep out and (b) That the market will work properly. Each time these promises are violated, trust is lost and we start all over again.

E.g. Dr. Subbarao did a great job of getting people used to the idea that the rupee is a market and that the RBI does not have an exchange rate target. We didn't think of Uncle Subbarao when we thought about the exchange rate. RBI is now back in action doing currency policy. Now all kinds of private players are back to their old ways: trying to hack into the government processes through which exchange rate targets are determined on a day to day basis, trying to make profits from the daily market activities, leaving currency exposure unhedged, etc.

As the Indian government rediscovered in 2013, and as the Chinese government is rediscovering today, government management of financial market outcomes will always fail. These episodes generate a big mess for the economy. They give the government a bloodied nose.

In the case of the equity market, in the 1990s, when there were sharp declines in the stock price, government would try to get involved by encouraging LIC and UTI to buy shares. Market development took place, the market got more liquid, and these levers became increasingly feeble. To the credit of the government, the understanding took root that MOF or SEBI are not responsible for the level of stock prices or stock price volatility. MOF and SEBI are responsible for addressing market failures, which in this case means micro-prudential regulation of clearinghouses and their members, and enforcement against market manipulation. This new environment has been in place for a long time and the expectations of market participants have been largely reshaped. Nobody in India thinks that an Uncle Xi will determine the stock price.

In terms of scandals, we had the last big scandal in 2000, which is now 15 years ago. With 15 years of boring functioning, market participants are increasingly gaining trust that the equity market just works.

When end-users and financial firms are steeped in this environment, organisational capital starts developing in financial intermediaries, which is what gives depth of market efficiency and market liquidity. That process has been in place for 15 years in India.

In China's case, they would start from scratch in (say) 2016 after the present mess is sorted out.

In India's case, comparable intellectual quality was not found on the Bond-Currency-Derivatives Nexus and we are still in the dark ages.

What is the cost of one-rank-one-pension?

by Renuka Sane and Ajay Shah.

The question

If we switch one person from a simple nominal annuity to `one rank one pension', how much more expensive does the pension become?

Backdrop of India's pension reform

The traditional civil servants pension in India has proved to be very expensive. Bhardwaj and Dave (2006) estimated that the implicit pension debt on account of current civil servants alone, was already 64.5% of GDP. If one were to add new recruits to civil services, and military personnel, this would be even higher. Pension payments were growing sharply. In December 2002, the NDA government made a decision to move new recruits into an individual account defined contribution program, the National Pension System (NPS) [link, link].

The reform was never carried over into defence even though that was long expected to be done the moment NPS had stabilised. As a consequence, we now run two parallel worlds: uniformed defence personnel are on the traditional civil servants pension while others have shifted out to the NPS if they were recruited after 1/1/2004.

The present debate concerns one-rank-one-pension (OROP). In order to understand the fiscal implications of OROP, we must calculate what it costs to produce such a pension.

Calculations about one rank one pension

A pension, which is a stream of payments while the recipient is alive, is an "annuity". There are three kinds of annuities: nominal annuity, inflation indexed annuity i.e. where the annuity value is linked to inflation, and wage indexed annuity, where the annuity value is linked to wage growth. The third is the costliest and is also generally not produced by private insurance companies worldwide. In the extreme, OROP is tantamount to wage indexation i.e. the value of the pension is linked to the wage growth. Hence, in order to price a pension, we have to price the annuity embedded in it.

The full information base required to make these calculations correctly can only be accessed through the government. In the calculations shown here, we make suitable assumptions and proceed. At every step, we have complete transparency about assumptions and computer programs. The gentle reader is requested to actually run the code, and experiment with modified assumptions. The program is written in the free statistics software system, R.

How to price a pension?

Suppose we promise 100 people (all at age 60) that we will pay them Rs.1 per day for the rest of their lives. What is the cost of such a promise today? This depends on two things: the discount rate, and the number of people of this cohort who survive every year. Lets say that the last surviving member lives upto 100 years. This implies a horizon of calculation of 40 years. So in year 1, Rs.1 per day is paid to 100 people. In year two, if 2 people die, this is paid to 98 people and so on.

An approximate survivor function

The rate at which people die away is called the `survivor function'. Our first job is to obtain a survivor function for India and to look at its graph. We use the male mortality rate (as of 2015) from the 2010 the UN Population Projections for India and convert this into the survivor function:

# Convert conditional death probabilities into the survivor function.
calculate.survivorfn <- function(age){
    cooked <- rep(NA, 101)
    cooked[age+1] <- 100
    for(i in (age+2):100) {
        cooked[i] <- cooked[i-1] - a$qxm[i]*cooked[i-1]
    }
    return(cooked)
}

# Work out two survivor functions, starting at age 60 and starting at age 35 --
a <- read.csv("http://www.mayin.org/ajayshah/lfs/india_male_mortality2015.csv", sep=",")
a$cooked.60 <- calculate.survivorfn(60)
a$cooked.35 <- calculate.survivorfn(35)

# Draw a graph with the two survivor functions --
par(mai=c(.8,.8,.2,.2))
plot(35:100,a$cooked.35[36:101], type="l", lwd=2,col="blue", xlab="Age",ylab="Survivors")
lines(60:100,a$cooked.60[61:101], type="l", lwd=2,col="red", xlab="Age", ylab="Survivors")
legend(x="bottomleft",lwd=2,col=c("blue","red"), bty="n",
       cex=.75, legend=c("Starting at age 35","Starting at age 60"))

This shows that as the age of the cohort increases, the number of people surviving decreases. Of the 100 people who start out at age 60, by age 75, roughly half are still alive.

The data used here to calculate the survival function is the mortality rate of the general population. Survival is likely to be better for those with higher income and better access to health care, as is the case with employees of the government. Hence, our use of this survivor function makes annuities appear cheaper than they are in the context of government pensions.

Pricing the annuity using this survivor function

Once we have the survivor function, we work out the NPV of the annuity. Lets say we are paying Rs.1 per day or Rs.365 per year to this cohort, and that the discount rate is 7%. What is the cost of this promise?

# Make the NPV of an annuity p, where people die off based on the
# survivor function S, when the interest rate is r, l is the number of
# years the pension has to be paid.
value.of.pension <- function(p, S, r, l=0) {
  (1/r)^(1:l) %*% (p * S)
}

# As an example: Price an annuity of Rs.1 per day at age 60:
value.of.pension(rep(365,40), a$cooked.60[61:100]/100, 1.07,l=40)

Why has the interest rate of 7% been chosen, for the next 40 years? Here is a long answer. The short answer: Because India now has an inflation target of 4%, and assuming this works, the real return on government bonds may work out to roughly 3 per cent.

The code above yields an answer of Rs.3,163.22. This is a little lower than the price charged by LIC for this annuity, of Rs.3,800. That is to be expected, as our survivor function is of the general population, and not of the annuitant population. Also, our calculations do not take into account the administrative costs of providing the annuity.

This gives us the price of a nominal annuity. Now let's make things more difficult, by introducing inflation indexation and wage indexation.

Pricing inflation indexed and wage indexed annuities

In order to do this, we have to make assumptions about inflation and wage growth.

India now has an inflation target of 4%. This suggests three scenarios for inflation: 3%, 4% and 5%.

We also need to make a range of assumptions for wage growth. We propose three scenarios at 7%, 8% and 9% wage growth. At the baseline scenario of 4% inflation, these correspond to 3%, 4% and 5% real wage growth.

# Do scenarios based on inflation and wage growth --
inflation <- c(0.03, 0.04, 0.05)
wagegrowth <- c(0.07, 0.08,0.09)

# A function to make the stream of pension payments. l is the number
# of years these have to be paid, and index is either the inflation or
# wage index.
make.pension.mat <- function(l, index){
    p1 <- matrix(NA, l, 3)
    p1[1,] <- 365
    for(i in 2:l){
        p1[i,1] <- p1[i-1,1] + index[1]*p1[i-1,1]
        p1[i,2] <- p1[i-1,2] + index[2]*p1[i-1,2]
        p1[i,3] <- p1[i-1,3] + index[3]*p1[i-1,3]
    }
    return(p1)
}

# Now do lots of cases.
                                       # Price indexation
p1 <- make.pension.mat(40, inflation)
value.of.pension(p1, a$cooked.60[61:100]/100, 1.07,l=40)
                                       # Wage indexation
p1 <- make.pension.mat(40, wagegrowth)
value.of.pension(p1, a$cooked.60[61:100]/100, 1.07,l=40)

This gives us the following annuity prices (in Rs.):

• Inflation at 3% - 3940.37
  
• Inflation at 4% - 4269.79
  
• Inflation at 5% - 4644.56
  
• Wage growth at 7% - 5563.41
  
• Wage growth at 8% - 6128.46
  
• Wage growth at 9% - 6781.57
  

How does all this change when retirement is at 35?

The retirement age of the military is different from that of civil services. Approximately 80% of the military retires between the age of 35-40, 18-19% retires between the ages of 54 and 60. Only about 1% retire at the age of 60. This implies that expenditure on pensions will be incurred for a lot longer than if the workforce retired at 60. We estimate the cost of a pension for a person retiring at age 35. As before, we first estimate the survival function, and then the cost of the pension under a price and wage indexed annuity.

# Retirement at 35
value.of.pension(rep(365,65), a$cooked.35[36:100]/100, 1.07,l=65)

                                       # Price indexation
p1 <- make.pension.mat(65, inflation)
value.of.pension(p1, a$cooked.35[36:100]/100, 1.07,l=65)
                                       # Wage indexation
p1 <- make.pension.mat(65, wagegrowth)
value.of.pension(p1, a$cooked.35[36:100]/100, 1.07,l=65)

The code above yields an answer of Rs.4,518.73 for the simple nominal annuity at age 35. This rises to Rs.7,488.54 for an inflation indexed annuity (assuming 4% inflation), and Rs.14,998.25 for the wage indexed annuity (assuming wage growth at 8%.).

These calculations are conservative

All the steps of this calculation have made conservative assumptions:

• The survivor function is for the general population. Civil servants are likely to be healthier than the general population, and uniformed armed forces are likely to healthier than civil servants. When correct survivor functions are plugged into this calculation, annuity prices will go up.
  
• We have used the survivor function for males. Females live longer. Some employees are women. When this is taken into account, annuity prices will go up.
  
• We have used the mortality rate as of 2015. As life expectancy in India improves, this will go down, implying that more people will live till older ages. Annuity prices will go up.
  
• We have assumed that RBI will deliver on its inflation target of 4%.
  

While our assumptions are conservative, we have assumed an extreme form of wage indexation. It is possible that some variant of OROP is constructed without full wage indexation. The estimates of the implicit pension debt would be lower in that case. We have also assumed a constant discount rate of 7%. If the discount rate is higher, the expenditures will be lower than those described here.

Summary of calculations

We treat our computation for the nominal annuity for a 60 year old as the base line. For all other cases, the extent to which it is higher, in per cent, is also shown.

At age 60:
LIC nominal annuity	3800	+20%
Our computation for nominal annuity	3163	+0%
Inflation indexed at 4% inflation	4270	+35%
Wage indexed at 8% wage growth	6128	+94%
At age 35:
Our computation for nominal annuity	4519	+42%
Inflation indexed at 4% inflation	7489	+136%
Wage indexed at 8% wage growth	14998	+374%

We start at the old system: a nominal annuity at age 60. If we change this to an inflation indexed annuity, the implicit pension debt goes up by 35%. If we change this to one-rank-one-pension, the implicit pension debt goes up by 94%. If we do one-rank-one-pension at age 35, the implicit pension debt goes up by 374%.

Please experiment with alternative assumptions

Here's the R program.

Speculation

Civil servants are a tiny slice of the Indian economy. It was a real surprise when Bhardwaj and Dave, 2006, found that the implicit pension debt on account of the civil servants pension came up to 64% of GDP. This was an important impetus for the NPS reform.

Uniformed armed force personnel are also a tiny slice of the economy. Even if all they had was a nominal annuity, this could prove to be quite expensive, as the pension starts at a young age, and the health of this group is very good. On top of this, there is the problem of rapid turnaround. On a horizon of 60 years, we go through four cycles of taking in a person at age 20 who retires at age 35, who will live till 80. Therefore, for each person who is presently serving there will be four alive who are drawing pensions. We may speculate that the implicit pension debt on account of the armed forces pension may also be in the region of 50% of GDP. If so, policy changes which double or triple the value of the annuity map to 50 or 100 percent of GDP.

Policy process

When such questions are being analysed, policy makers should arm themselves with the full calculations, before making decisions.

The calculations that are needed are:

1. Replace the general population survivor function, which we have used, with the actual survivor function for armed folk. We suspect they are much healthier than the general population.
   
2. Use data for the stock of employees and pensioners, and rules about retirement, to work out the implicit pension debt associated with present or potentially modified pension arrangements.
   

Once such calculations are in hand, the political leadership can choose between alternative uses of the same money. E.g. 50% of GDP could pay for complete suburban metro systems for 50 cities, or for 50 aircraft carriers.

References

Towards Estimating India's Implicit Pension Debt by Gautam Bhardwaj and Surendra A. Dave, 2006. The Second International Workshop on The Balance Sheet of Social Security Pensions, Organised by PIE and COE/RES, Hitotsubashi University.

India's pension reforms: A case study in complex institutional change by Surendra Dave, page 149--170 in `Documenting reforms: Case studies from India', edited by S. Narayan, Macmillan India, 2006.

Indian pension reform: A sustainable and scalable approach by Ajay Shah, Chapter 7 in `Managing globalisation: Lessons from China and India', edited by David A. Kelly, Ramkishen S. Rajan and Gillian H. L. Goh, World Scientific, 2006.

Acknowledgments

We thank Ashish Aggarwal, Josh Felman, Shekhar Hari Kumar and Robert Palacios for valuable comments.

Concerns about the valuation of the broad equity market

The macroeconomic context

A group of articles here has explored present business cycle conditions. We started at the currency defence of 2013, to the question of present business cycle conditions, and went on to the balance sheet problems of banks and non-financial firms. These three components add up to a useful picture of the macroeconomy.

In this setting, what do we see about earnings growth and the valuation of the broad equity market? This fourth component of thinking about the macroeconomy is pursued ahead.

Equity market valuation

Equity market valuation is all about two things: projected earnings growth and the discount rate required by the market. In the past, Indian equities have obtained high valuations on the strength of the high growth rate of earnings. For a long time, there has been a nice set of thumb rules about India: nominal GDP growth of 10%, top line (i.e. revenue) growth for firms of 15% and bottom line (i.e. PAT) growth for firms of 20%. Once you get comfortable around the notion of sustained 20% earnings growth, a very big valuation can be sustained.

How have things been going of late?

Figure 1: Growth of nominal profit after tax (PAT) of non-financial firms

We start with an examination of the profit after tax of non-financial firms. The method for constructing an index of nominal net profit is the same as that used for nominal net sales that's described here. In the past, this has had very high growth rates. The index rose by ten-fold in less than a decade. However, there has not been much expansion of earnings from 2006 till today. In the latest recession, which began in Q1 2012, earnings have been on a slighly negative trend.

In the lower pane, the blue line is the long run median value. After Q1 2012, i.e. in the current recession, there have been only four quarters with a bigger value for growth when compared with the long run median.

Figure 2: Growth of operating profit (PBDIT) of non-financial firms

PAT is a very small number, and is sensitive to changes in depreciation, interest and tax. To understand the dynamism of the underlying business, it's useful to look at the operating profit (PBDIT). As the graph above shows, here the picture is better in that there has been some growth after 2006. However, in the latest period, the trend growth rate is 5.65% per year, which is anemic.

Here also, the blue line is the long run median growth. In the recession which began in Q1 2012, there have been only three quarters with growth that was above this long-run median.

Figure 3: Trailing P/E of the CMIE Cospi index

This takes us to the valuation of the equity market. We use the CMIE Cospi index as a measure of the broad market. The trailing P/E is computed as today's market capitalisation divided by the sum of four latest quarterly earnings. This went up from the region of 16 to 26, an increase of 62.5%. However, the prospects of earnings growth appear to be worse than the long run median value.

This can be rationalised in one of two ways. Either the market believes that the engine of earnings growth is about to get going again, or the market now has a reduced required rate of return. The dates of the 62.5% rally line up with expectations of a majority for the BJP in the elections of 2014, which emphasises an explanation based on optimism.

It is interesting to look at the first of the three recessions in this date range: from Q4 2000 to Q1 2002. Figure 1 shows that earnings immediately took off when the recession ended. But the stock market was mistrustful for a while. All the way till 2005, the trailing P/E had not gone up; the market had not understood that it was in the biggest ever earnings expansion of India's history. P/E ratios are good cross-sectional forecasters of future earnings growth but perhaps the market is not so good at understanding the business cycle. As an example, this thinking in 2007 was out of line with the optimism of the market.

Conclusion

The currency defence of 2013 was an important negative event for the economy; it damaged confidence and hurt the economy with a sudden 440 basis point hike in the interest rate. A business cycle downturn began in Q2 2012 which has not yet ended. This has gone along with slow earnings growth. In this period, there seem to be significant balance sheet difficulties with some banks and some non-financial firms. This debt overhang hampers the recovery of the economy and adversely affects the prospects of earnings growth. It is hard to reconcile this picture with the valuation of the equity market.

Balance sheet problems of the firms and the banks

While the official GDP numbers are showing an optimistic picture, trusted firm databases are suggesting that the recession which began in Q1 2012 has not ended.

An important dimension of this recession is the leverage of firms. On an international scale, there is increased interest in the debt `super cycle' which appears to play out distinctly from the business cycle. There are periods where households and firms are adding leverage, and these tend to be a good time for the economy. And there are periods where balance sheets are stretched, and this yields a drag on growth.

The credit boom from 9/2003 to 9/2008

This viewpoint emphasises the importance of credit booms. The great Indian credit boom took place in Y. V. Reddy's period as governor, where the pursuit of exchange rate policy gave lax monetary policy:

Figure 1: Nominal year-on-year growth of non-food credit of the banking system

The dashed lines focus on this period. Year-on-year credit growth peaked at near 40 per cent. This was the biggest-ever credit boom in India's history.

Such periods suffer from two problems. First, there is a surge in the quantity of loans being given out by banks. In such times, the level of scrutiny tends to go down. When the surge happens at the time of a business cycle expansion, it's particularly easy to be over-optimistic. Such a drastic credit surge was going to result in trouble.

These events are quite some distance away in time. This is a different time scale when compared with business cycle fluctuations. However, it appears that this credit surge matters to understanding what holds the Indian economy back today.

Credit distress of the firms

From 9/2003 to 9/2008, a lot of debt was taken on by firms. It is likely that some of this capital was misallocated by banks who gave loans to unworthy firms. Soon after that came the Lehman crisis, and soon after that the current recession began (in Q1 2012). Recessionary conditions have hampered profitability of many firms. For many firms, the combination of low profits and high debt has generated credit stress.

Figure 2: Interest cover ratio (ICR) of all non-financial firms observed in the CMIE database

The graph above shows the interest cover ratio (ICR), defined as PBDIT/interest. A firm at an interest cover ratio of 1 is in a lot of trouble: All it's operating profit is taken in paying interest.In the graph above, the blue line uses annual data, where the accounting data is of higher quality, and the black line uses quarterly data, where the data lag is lower.

At its peak, the average interest cover ratio was at 10, which means that the operating profit was 10 times the interest payment. There has been a dramatic decline in this ratio, with increased interest payments and reduced operating profit. Unlisted companies have consistently been under significant credit stress through this entire period; this low value of the interest cover ratio and its lack of time-series variation is a mystery that merits further exploration.

Figure 3: The fraction of total assets in firms where ICR < 1

Most people will agree that by the time the interest cover ratio has hit 1, the firm is in significant credit distress. The graph above shows the share of these distressed firms in the overall balance sheet size of India's large firms. This shows a period of acute distress in the late 1990s and early 2000s, where over a third of the corporate sector was in difficulty. Things got dramatically better by 2008; only 15% of the corporate sector was in credit distress. From that bottom, there's been a doubling. For the year ended 2013-14, 29.16% of the balance sheet of the corporate sector is in firms where the interest cover ratio is worse than 1. Roughly 20% of bank lending is stuck in these firms.

In a few months, we will know the situation for 2014-15. However, Figure 2 suggests that things have deteriorated when compared with 2013-14.

Credit distress and fixed investment

In an ideal world, capital should flow to firms with good projects, regardless of their present financial condition. This is not how the Indian financial system works. When a firm gets into credit difficulties, it appears to lose access to external capital. Low profits obviously hamper internal capital.

We pool all CMIE data from 1989 to 2015 and construct quartiles by the interest cover ratio, and within each quartile, report the sample mean of the growth in fixed assets (in real terms):

ICR quartile	Real GFA growth
Low (ICR < 0.8)	7%
Q2 (ICR from 0.7 to 1.7)	10.4%
Q3 (ICR from 1.7 to 3.17)	14.5%
High (ICR > 3.17)	16.6%

Gross fixed assets (GFA) growth has been 16.6% in real terms for firms where the ICR was above 3.17. For the firms where the ICR was below 0.8, it was 7%. This suggests that credit distress as measured by the ICR is correlated with reduced investment.

By this reasoning, when a third of the balance sheet size of Indian firms is at an ICR < 1, this would result in a drag in investment.

Where are your borrower's yachts?

Unhealthy borrowers give unhealthy banks. Banks in India have roughly 12x leverage. When loans get into trouble, the recovery rate (correctly calculated) is probably around 25%. This means that if a bank has NPAs of 10% of total assets, then there is substantial stress. It will lose 7.5% of total assets, which will be roughly as big as the equity capital.

Figure 4: The state of bank capital

The graph above focuses on the data being reported by banks. RBI and the banks are using various methods to hide bad news, including restructuring and CDR. At the bottom is official NPA data, which is below 2% of total assets. But when we add in CDR and `restructured' assets, this comes up to 6% of total assets. Equity capital is near 8% of total assets.

These are averages for the banking system as a whole. There are undoubtedly some banks who are better than the average, and there are some banks who are worse off.

The evidence above emphasises net NPAs. In this speech on 5 May, Mr. Mundra offers facts about gross NPAs which are of course larger.

The phenomena reported on in this blog post -- firms with credit distress, that are likely to have impaired investment activity, and are likely to face difficulties in repaying to banks -- are likely to be related to the phenomenon of `stalled projects' as seen in the CMIE Capex database.

The true extent of difficulties at banks may be larger than 6% of total assets, as RBI and the banks are collaborating in hiding bad news.

Here is one anecdote. Haldia Petrochemicals is a large firm. They owe banks money and have not repaid. However, this is not classified as a non-performing asset owing to an instruction from RBI. As the story by Pranav Nambiar in the Financial Express of 26 May 2015 says:

Ashutosh Bose, CFO & executive VP, HPL, confirmed to FE the central bank has provided the special dispensation enabling bankers to treat the HPL exposure as a standard account. “We will not be treated as an NPA and banks will not have to make any provisions on our account. The special dispensation by the RBI has been given due to the nature of our business which is impacted by extraneous factors like changes in naptha prices,”

We have a banking regulator who thinks that changes in naptha prices somehow change the fact that this is a non-performing asset. We have a banking regulator who has the power to instruct banks on a transaction-by-transaction basis, under complete opacity, to disregard the (subordinate) law. We have a banking regulator who is willing to use such powers.

This is just one anecdote, where the news spilled out into the open. There is no data, in the public domain, about what "special dispensations" have been given out by RBI.

Conclusion

The credit boom of 2003-2008 and its aftermath are an important element of understanding India's macroeconomic predicament. The balance sheet difficulties of banks and their borrowers are an important part of what happens from here on. Credit distress in India today is not as bad as it was in the late 1990s. But for perhaps a third of the corporate balance sheet, there is significant credit stress.

The key thing to watch is the growth of operating profits. If PBDIT grows, then the ICR will come back into shape. From Q1 2012 onwards, we have been in a recession, and operating profit growth has stalled. Many firms are in a debt spiral where interest payments go up, operating profit fares poorly, new loans are taken to keep the ship aloft, interest payments go further up, and so on.

There may be a bit of a feedback loop going. The economy is bad, so borrowers are faring badly, so the banks are facing difficulties (even with a sympathetic regulator), so the banks give less credit to healthy firms, which further hampers the recovery. This raises the possibility of `Japanisation' where bad news is hidden, firms and banks are absorbed in dealing with credit distress, many long years go by with sluggish growth.

We must learn more about Japan, and other unhappy episodes where the debt overhang hampered the revival of growth in a sustained way. We must also learn about how India bounced back from the last credit boom of the mid 1990s. As the graphs above show, the downturn after this boom in the late 1990s featured credit distress that was worse than what we see today. There was a lot of creative destruction. Large numbers of non-financial firms went under. Their exit, and the operational improvements of the survivors, set the stage for the revival of the economy.

Policy makers need to nudge things away from the Japanisation scenario. This requires working on four fronts:

• How to create new channels through which capital goes into healthy firms without the involvement of banks,
• How to be less like Japan or China, where bad news is hidden and incumbent banks and firms enjoy business as usual, 
• How to create capabilities at RBI for technically sound regulation and supervision,
• How to resolve failed firms and banks, so as to shift capital and labour to healthy organisations.

Every now and then, there is a call for a bailout of banks and their borrowers. We need to be careful in how we approach this. The scale of the problem is large, and the available fiscal space is limited. Do we want to throw good money after bad? If regulation and supervision has failed before, why will it not fail again?

New moves in regulating warehouses

by Anirudh Burman and Iravati Damle.

Warehousing is an important aspect of supply chains. Good warehousing has direct advantages in terms of storage of value, facilitating pledge financing, reduction in wastage, and reducing price shocks by allowing market participants to anticipate future demand and plan for the same. Warehousing has to be trustworthy for these benefits to accrue. Market participants have to be confident that the information provided in warehouse receipts (proof of deposit of a commodity) is accurate at all times. The warehousing sector in India does not reflect these attributes at present. Government regulation can correct some of these inefficiencies.

The warehousing sector in India is unorganised and fragmented (link). The organised segment is dominated by the public sector undertakings such as the Central Warehousing Corporation (CWC) and State Warehousing Corporations (SWC). Over the past decade, private firms have ventured into the business of warehouse services (warehouse service providers or "WSPs"). A large number of small and state level entities dominate the private warehousing segment (link). Under the current constitutional framework, state governments make legislation to regulate licensing of warehouses(For example, West Bengal Warehousing Act, Punjab Warehousing Act, Karnataka Warehousing Act). States across the country have different licensing norms; with different standards (See page 16 of a note on the proposed framework for registration of warehouses for comparison of State warehousing laws of Punjab, West Bengal and Karnataka)


This has resulted in the following problems in the warehousing sector:

• Lack of national standards and uneven levels of enforcement action on offences committed by Warehouse Service Providers (WSP).
• The absence of an integrated information system prevents high quality warehouse service providers from sharing information with potential customers. Therefore, they are unable to differentiate their services from the other players in the market and charge higher rents. As a result, the sector is currently in a low level equilibrium. This will ultimately hamper the growth of the warehousing market, with high-quality warehousing services withdrawing.

The market has started creating its own systems for dealing with inefficiencies. For instance: Banks undertake lending activities against warehouse receipts through Collateral Management Companies (CMC). These banks pay a premium to the collateral managers to insure themselves against the risks of bad warehousing. The collateral managers provide a range of services, including:

• Locating good warehousing facilities;
• Taking delivery of pledged commodities and/ or verifying their quantity and quality;
• Ensure the physical security and quality of the stock for the bank;

Establishment of WDRA

In 2006, the government passed the Warehousing Development Regulation Act, to make provisions for the development and regulation of warehouses, negotiability of warehouse receipts, establishment of a Warehousing Development and Regulatory Authority and for matters connected therewith or incidental thereto. The Act allows warehouses registered with the WDRA to issue Negotiable warehouse receipts (NWR), which are backed by a legal sanction. The WDR Act and accompanying rules and regulations stated registration requirements with the objective of keeping a check on the warehouse service provider. However, it has failed to receive the expected response from the market and bridge the information gap, with the use of NWRs being low.

How do we solve the problem?

WDRA was established to be a national regulator that would build trust and credibility in the instrument of warehouse receipts by ensuring warehouse operators are competent, follow the required processes correctly, and adhere to minimum standards set by WDRA. This is intended to add comfort to those trading in NWRs. As an agent of the State, WDRA has to do so by following the rule of law. Regulations drafted by it must follow a procedure where all stakeholders are informed of the rationale of the proposed regulatory system, the problems that the regulation proposes to solve, and the proposed method of solving these problems.

Lessons from FSLRC for WDRA

The recommendations of the Financial Sector Legislative Reforms Commission (FSLRC) are important in this regard (link).  The Financial Sector Legislative Reforms Commission drafted the Indian Financial Code (IFC) which makes substantial recommendations regarding the procedure that financial regulators must follow while making regulations (Report of FSLRC, Vol 2). By Section 52 of the IFC, if a financial regulator proposes to make any regulations:

• it must publish a draft of the proposed regulations
• this must be accompanied by a statement listing the objectives of the regulations, the problem that the regulations seek to address, the underlying principles, expected outcome, cost benefit analysis, process of making representations.
• it must list the reasons for preference of one principle over another.
• it must publish the representations received, take them into consideration and also state its response to the representations received.
• if the final regulations differ from the proposed regulations, it must list the details and reasons for the difference and also undertake a cost benefit analysis of the differing provisions.

NIPFP has supported WDRA in drafting two concept notes for making draft regulations pertaining to the registration and grading of warehouses. WDRA has solicited public comments on these notes:

• Cover Note: (link)
• Note 1, pertaining to the proposed regulatory framework for registering warehouses by WDRA: (link)
• Note 2, pertaining to the creation of a grading system for warehouses and the regulation of entities who may be allowed to grade warehouses: (link)

Feedback on the two notes may be sent latest by 20th July, 2015 to the Director (Tech.), WDRA at dirtech.wdra@nic.in or may also be sent to the Director (Tech.), WDRA, 4/1, Siri Institutional Area, August Kranti Marg, Hauz Khas, New Delhi 110016.

This style of rational, consultative and transparent regulation-making is, as yet, unusual in India.

Conclusion

The field of warehousing suffers from uneven regulation and the absence of national standards. Stakeholders argue that registration requirements with WDRA do not provide market relevant information about the credibility of the WSP/warehouses. The costs of registration outweigh the benefits. This is hampering the growth of the warehousing market in the country. The first note envisions a redesign of the registration process to establish minimum standards and provide quality assurance to market participants. The second note makes the case for WDRA to facilitate the process of generating credible information about registered entities through a system of grading warehouses and WSPs, after registration. While better registration norms will establish minimum standards, grading will ensure that the market gets information about the performance of the warehouse/WSP post registration. These interventions can enable WDRA to efficiently solve the market failures.

Lessons from the Indian currency defence of 2013

by Ajay Shah.

In 2013, the Indian government mounted a big defence of the rupee. The authorities appeared to throw everything that they could at the problem: enhanced capital controls on outflows, relaxed capital controls on inflows, exchange rate intervention, restrictive actions which damaged currency market liquidity to make it easier to manipulate the market by trading on the market, and monetary policy tightening. See this article for a narrative from that period, and a set of links to contemporaneous thinking.

It's important to look back at this period and ask: Did the currency defence deliver on its objectives? Can we identify costs and benefits?

As with all episodes of currency defence, we do not observe the counterfactual: What would have happened if the authorities had not mounted this currency defence? In this case, we have some interesting evidence ahead which suggests that the outcome was not influenced by all these actions.

Along the way, we suffered collateral damage of numerous kinds. Many components of a currency defence are effectively an interest rate defence. At a difficult time in the economy, defending the rupee by raising rates hurt the domestic economy further.

In what follows, on each of the graphs, we superpose the dates of various kinds of actions taken by the authorities. This helps us conduct an intuitive causality test: if an action mattered, it should have yielded some impact in its immediate after math. These graphs also help us see through the claims made by authorities. (Event studies have value in this broad area of research, but are problematic here as there is a lot of event clustering).

Level of the exchange rate

USD/INR exchange rate

The graph above shows the USD/INR exchange rate. The first action was undertaken when the USD/INR exchange rate was within range of Rs.60 to the dollar. After that a series of actions were taken, through June, July, August and September. At the bottom, the exchange rate was near Rs.70 to the dollar. After all the actions stopped, the exchange rate got back to the region of Rs.60 to the dollar.

USD/INR exchange rate, indexed to 100 at start

It is useful to see this same graph where the USD/INR exchange rate is indexed to 100 at the left edge. The currency defence began when there was a 5% depreciation and the bottom was a 25% depreciation. After the flow of measures taken by the authorities ended, the exchange rate recovered to a 10% depreciation compared with the starting point.

Did the currency defence work?

When we look at the graph above, we could tell two rival stories. One argument is that the actions had no impact. Another argument is that the cumulative impact of all the actions gave a reversal of the exchange rate.

We are able to resolve this debate using international evidence. Let's start at the J P Morgan Emerging Markets Currency Index:

USD/INR exchange rate superposed with the J P Morgan EM Currency Index

The graph above shows the J P Morgan Emerging Markets Currency Index on the left axis and the USD/INR on the right axis. The two series are remarkably alike!

All the actions taken by the Indian authorities can only have a small impact on the Emerging Markets  Currency Index, reflecting the weight of the INR in that index. That small weight simply cannot account for the extent to which the two graphs move together.

The two graphs are so alike! If the actions by the Indian authorities had scored some impact, then the Indian story would have unfolded differently from the overall average of all Emerging Markets. It did not. Therefore all the actions taken by the Indian authorities had no impact on the USD/INR.

In a similar vein, we look at the Citibank index of policy surprise in the US:

USD/INR versus the Citi US surprise index

The important dates in this series are the dates of policy surprise in the US. These policy surprises -- Bernanke's first speech and then his second speech -- are more likely to have shaped the USD/INR when compared with the actions of the Indian authorities.

All currency policy is monetary policy

 

The 91-day treasury bill rate

The currency defence was an interest rate defence, as all currency defences are. The short rate surged sharply.

RBI has numerous instruments through which monetary policy is implemented. All these reduce to one summary statistic in the form of the 91-day treasury bill rate. The graph above shows a huge increase in the rate, from roughly 7.5% up to 12%: an increase of 440 basis points.

Volatility

Squared returns on USD/INR exchange rate

These days, it is fashionable for the authorities to claim that they do not actually have a target exchange rate in mind, but they are only intervening to prevent episodes of high volatility. The graph above shows that squared daily returns on the USD/INR were slumbering when the rupee defence began. Volatility seems to have surged after the government got going. Even after October, volatility had not come back to the levels found in May. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

Realised volatility of USD/INR futures

Squared daily returns is a poor statistical estimate of volatility. Using IGIDR FRG data, we exploit  high frequency data to construct the daily time series of realised volatility of the USD/INR currency futures traded at NSE. This also shows a similar picture: volatility was slumbering when the currency defence began; it's hard to claim that the authorities were focused on `excessive volatility' and not concerned about the level of the rate. Volatility worsened through the period of the currency defence. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

Implied volatility of USD/INR

We use data from IGIDR FRG on USD/INR options trading at NSE to construct the implied volatility every day. This gives a forward looking indicator of future USD/INR volatility as seeen by the market. It shows that volatility was slumbering when the first actions began. Volatility generally worsened after the authorities acted. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

Collateral damage

Many people at the time noticed the impact on stock prices. Nifty dropped by 10% and Nifty Junior dropped by 15% before recovering late in the year. But many other elements of the collateral damage have not been widely noticed. Let's start at stock market liquidity.

Round-trip transactions costs for Nifty basket trades on the NSE spot market

The graph above shows the round-trip transactions costs (in basis points) faced when doing basket trades for the full Nifty portfolio on the NSE spot market. These are computed off the full limit order book that's observed at IGIDR FRG. The lowest curve is at a basket size Rs.2.5 million; the next one is at Rs.5 million and the highest one is at Rs.10 million. These `round trip transactions costs' are the sum of impact cost to buy and impact cost to sell.

Stock market liquidity held up reasonably well in the early part of the story. At event 6 (increase in interest rates), stock market liquidity worsened, particularly at larger transaction sizes. As an example, at a transaction size of Rs.10 million, the round-trip impact cost rose from the region of 8 basis points to the region of 12 basis points, a 50 per cent increase. This would, in turn, spill over into an array of downstream consequences such as enlarged no-arbitrage bands, increased costs of dynamic trading strategies, etc.

Market impact cost reflects a combination of implied volatility, funding constraints and asymmetric information. The outcome seen above reflects a deterioration on all three counts.

Implied volatility of Nifty

At first, the currency defence did not seem to make a big difference to the forward looking volatility of Nifty. But from August onwards, the outlook for Nifty became much more risky. (There are two things going on here: the impact of global developments and the impact of the actions taken by the authorities).

CMIE Bank Index

Another element of the collateral damage was banks. The increase in the short rates hurt banks who, on average, seem to be carrying an interest rate mismatch. There was a roughly 30% decline in the CMIE banking industry stock price index.

What can we learn from this episode?

1. USD/INR seems to have responded to global events and all the actions of the authorities seem to have had little impact.
2. The entire focus of economic policy in that period was on fighting the INR depreciation. Every day, new tools were being bandied about and implemented. There was a reverential approach to the power of central banks -- currency intervention, capital controls, choking off financial markets, arranging lines of credit, etc. But when we look back, we see that the central bank was ineffectual in delivering on the goal.
3. The cost of all the actions taken by the government was large, and the payoff obtained from the actions was elusive. Ex post, we see that the cost of mounting the defence was much larger than the costs envisaged at the outset by proponents of the currency defence.
4. The Ministry of Finance debated the views of many advisors, and chose to go with the folks who advocated a big muscular currency defence.
5. I feel that the academic literature on capital controls and currency policy does not adequately come to grips with the mess that we get in the real world when we try to do these things. It's easy to bandy about capital controls or currency trading. Such `heterodox thinking' has become fashionable. Capital controls are not irrelevant; they can deliver pricing distortions and reduce market efficiency. What's important to ask is: Do capital controls deliver on the goals of macroeconomic policy? The answer in India seems to be: No.
6. Some advisors at the time said that it was possible to `squeeze the shorts' and `hit the speculators' without contaminating monetary policy. But all currency policy is monetary policy, and all real monetary economists know this. Every currency defence becames an interest rate defence. The policy rate went up by 440 basis points, dealing a body blow to a weak economy. By August 2013, I felt the UPA was going to lose the next election.
7. When the authorities defend the rupee, this protects foreign currency borrowers. In addition, raising rates hurts local currency borrowers.  This is different from the conventional moral hazard argument, that currency policy creates moral hazard in favour of more unhedged foreign currency borrowing. The new thing I understood in 2013 was that a currency defence is not only good for unhedged foreign currency borrowers; it hurts local currency borrowers. These two factors come together to shape the incentives of firms on choosing between borrowing in local currency vs. borrowing abroad. If you borrow in USD, you will get support from the government in extreme events; if you borrow in INR, you will get hit by the government in extreme events.
8. Episodes like this interact with the bureaucratic politics of Indian finance. As an example, currency futures trading started in 2008, against the protests of RBI which feared the loss of turf (the role of SEBI on exchanges). RBI was also concerned about the possibility that exchanges might achieve liquidity and market efficiency, and thus undo decades of hard work in preventing a genuine currency market. The currency defence of 2013 was used as an opportunity to hamper the working of currency derivatives trading on exchanges. Even today -- June 2015 -- some of those restrictions imposed in the summer of 2013 have not been reversed.
9. Market liquidity got worse once the currency defence got going. This may have implications for the decision to stay exposed through a currency defence.
10. It takes a long time for the damage caused by a currency defence to heal. Some economists think you can switch off and switch on finance at a whim. But the working of the financial system is ultimately about the organisational capital in financial firms. When private firms want to build a business,  organisational capital can only be built slowly, and after such episodes, private firms may not feel like committing resources to build a business. Private firms lose respect and trust in the authorities when they see such episodes, and hold back from investing in building the business.

In the  1980s, an extensive literature worked on capital controls and found generally bad outcomes. In recent years, a new literature is utilising the improvements of econometrics of recent years and reopening the same questions. Forbes & Klein 2015 say:

large interest rate increases, major reserve sales, large currency depreciations, and new controls on capital outflows are ineffective at improving these three outcome variables and often imply substantial tradeoffs. More specifically, sharp increases in interest rates and new controls on capital outflows appear to significantly reduce GDP growth, have no consistent effect on unemployment, and are correlated with higher inflation. Large depreciations also appear to initially reduce GDP growth and be correlated with higher inflation, but there is some evidence that they can yield a lagged improvement in growth and reduce unemployment, especially during the 2000s.

We have a few papers in this rediscovery of the orthodoxy: Patnaik and Shah 2012, Patnaik et. al. 2012,  and Pandey et. al. 2015.

A political economy literature in the US suggests that the last one year prior to an election matters disproportionately (link, link, link, link, link, link). The currency defence of 2013 may have been unusually important in securing a clear majority for the BJP in May 2014. But this same currency defence also damaged the economy, and has helped worsen things in the first year of the BJP.

Concerns about RBI's `Strategic Debt Restructuring Scheme'

I have a column in the Indian Express today on this.