Women Are Bearing the Brunt of India’s Unemployment Problem

This piece is an extension of a piece on unemployment I wrote sometime back. Nevertheless, you don’t have to read that piece in order to make sense of this.

Honestly, this is probably the most disturbing data driven piece that I have ever written, despite the fact that I started writing on the issue of unemployment more than half a decade back, when it wasn’t very fashionable to do so.

The brunt of India’s unemployment problem is being borne by women. This is not to say that the men are having an easy time. They aren’t, given that many more men enter the labour force than women.

Nevertheless, the proportion of women who are employed and get paid was low to start with, and it has become even lower over the years. This, at a time, when more and more women are going to school and college.

As the All India Survey of Higher Education for 2018-19 points out: “Total enrolment in higher education has been estimated to be 3.74 crore with 1.92 crore male and 1.82 crore female. Females constitute 48.6% of the total enrolment.” But all this education isn’t helping them find paid employment.

Let’s start with the unemployment rate for men and women. The following chart plots this data since January 2016.

Source: Centre for Monitoring Indian Economy.

The above chart tells us several interesting things.

1) The unemployment rate for women is significantly higher than that of men. In February 2021, the unemployment for women stood at 12.39% whereas for men it stood at 6.23%.

2) The unemployment rate for women in February 2021 is much lower than it was in January 2016, when Centre for Monitoring Indian Economy (CMIE) published the unemployment data for the first time. Have things improved? Keep reading to know the answer.

3) The peak unemployment rate for women during covid was 29.22% as of April 2020. The rate has fallen since to 12.39%, as of February 2021. Again, have things improved?

In order to answer the questions raised above, we need to understand how unemployment is defined. (For those who have read the earlier piece I wrote on unemployment, the next few paragraphs may seem like a repetition, which they are. I have repeated these paragraphs, simply because it is important for every piece to stand on its own, so that first time readers can also read and understand it easily).

A person is categorised as unemployed “because of a lack of job and where such a person is actively looking for a job”. The word to mark here is actively. Hence, a person can be categorised as unemployed only if he doesn’t have a job and is searching for one.

As the Centre for Monitoring Indian Economy (CMIE) puts it, a person categorised as unemployed, “should be unemployed on the date of the survey, should be actively looking for a job in the 100 hundred days (approximately three months) preceding the date of the survey and should be willing to take up the job if a job is found.”

They further point out: “A person is considered to be actively looking for a job if such a person has contacted potential employers for jobs, contacted employment agencies, placement agencies, appeared for job interviews, responded to job advertisements, online employment sites, made applications, submitted resumes to potential employers or reached out to family members, friends, teachers to look for jobs from them.”

To put it in short, waiting for a job offer to come, is not considered as actively looking for a job.

Let’s move on and plot the next two charts, the labour participation rate for men and women.

Source: Centre for Monitoring Indian Economy.

Source: Centre for Monitoring Indian Economy.

Before we interpret these charts, we first need to define what labour participation rate is. Labour participation rate is the ratio of the labour force to the population greater than 15 years of age. And what is the labour force? As per CMIE, labour force consists of persons who are of 15 years of age or more, and are employed, or are unemployed and are actively looking for a job.

Now we are in a position to interpret the above two charts. Let’s do that pointwise.

1) The labour participation rate for women is miniscule on the whole. In February 2021, it stood at 9.42%. What does this mean? It means that a very small proportion of women over the age of 15, are employed and get paid for it or are unemployed and are actively looking for. a job. And the tragic part is that this rate is falling. It was at 17.7% in May 2016. Since then it nearly halved.

2) The labour participation rate of men is considerably higher. It was 67.82% in February 2021, even though it has been falling. Hence, two in three men over the age of 15, are employed and are getting paid for it, or are unemployed and actively looking for a job. For women, this ratio is less than one in ten. That’s the difference between the two sexes and it’s huge. 

3) Urban women are in a much worse position on this front. The labour participation rate for urban women stood at 6.56% in February 2021. The rate had peaked at 16.58% in August 2016 and has been falling ever since. What does this mean? It means that it is more difficult for a woman to be employed and get paid, if she is in urban India than in comparison to rural India.

Also, the dramatic fall in the rate since August 2016, tells us that once a woman loses a job or a source of income, it is very difficult for her to get it back. And finally, very few women in urban India are stepping out of their homes to go to work and get paid for it. This has only increased post the spread of covid. The labour participation rate for women was 9.92% in January 2020. It’s not at 6.56%.

4) Now comes the worst part. Between January 2016 and February 2021, the number of women greater than 15 years of age has gone up by 5.41 crore to 49.49 crore. Hence, the number of women who have entered the working age population has gone up by 12.26% (5.41 crore expressed as a percentage of 49.49 crore). On the other hand, the female labour force, has shrunk by 2.75 crore to 4.66 crore. In January 2016, it was at 7.41 crore. This is a collapse of 37% (2.75 crore expressed as a percentage of 7.41 crore).

Let me just repeat this again. While the working age population for women over the last five years has gone up by 12.26%, the female labour force has collapsed by 37.11%. This also explains the fall in unemployment rate for women, given that much fewer women are actively looking for a job. Many women who haven’t been able to find jobs, have stopped actively looking and simply dropped out of the labour force.

Economists have struggled to come up with an explanation for this phenomenon. One possible explanation lies in the fact that the number of jobs available haven’t grown at the pace that could accommodate the new individuals, both men and women, entering the workforce. Hence, in a patriarchal society, men in deciding positions, have offered jobs to other men, forcing women who have searched and not found jobs to stop actively looking for a job and drop out of the labour force altogether.

5) Let’s take a look at the overall population. The working age population, between January 2016 and February 2021, has gone up from 93.85 crore to 105.8 crore, this implies an increase of 11.95 crore. Nevertheless, the number of people employed or unemployed and looking for a job, that is the total labour force, has fallen from 44.76 crore to 42.85 crore, or by 1.91 crore.

Sothe working age population has increased by 11.95 crore between May 2016 and February 2021, but the total labour force as such has fallen by 1.91 crore. What does this really mean?

While, the total labour force has shrunk by 1.91 crore, 2.75 crore women have dropped out of it. This basically means that the number of men in the labour force has gone up.

Hence, the brunt of India’s unemployment problem is being borne by women. Women who lose their jobs find it difficult to find a new one and over a period of time simply drop out of the labour force. Many women who enter the labour force and actively look for jobs, are unable to find one and eventually stop searching and drop out of the labour force.

Given that chances of men finding a job are higher, they continue to look for a job and the situation is not as bad as it is for women. Between January 2016 and February 2021, number of men who crossed the age of 15 and entered the working age population, increased by 6.54 crore to 56.30 crore.

During the same time, the number of men entering the labour force (that is either they were employed or were unemployed and actively looking for a job) increased by 83.62 lakh to 38.19 crore. Hence, in this case of men, the labour force at least hasn’t shrunk.

6) Given that more and more women are dropping out of the labour force, it makes it easier for the men who don’t drop out of the labour force to find a job, from the opportunities that come up. (I am using the word easier here and not easy. Kindly appreciate the difference between the two).

7) Urban women are likely to be more educated, but their labour participation rate is very low. Hence, what that means is that they are unable to utilise their education to work and earn money in the process.

8) Now let’s take a look at how things have been post-covid.  In January 2020, before covid had struck, the working age population had  stood at 103.13 crore. By February 2021, this had jumped to 105.8 crore, a jump of 2.67 crore. Meanwhile, the labour force as of January 2020 stood at 44.24 crore. It has since shrunk to 42.85 crore, by 1.39 crore. So, post-covid, the working age population has gone up by 2.67 crore, but the labour force has shrunk by 1.39 crore.

How have the women done on this front? The working age population post covid for women has gone up by 92.23 lakh whereas the labour force has shrunk by 78.03 lakh. Again, more women have dropped out of the labour force than men, given that the labour force has shrunk overall by 1.39 crore. Also, do keep in mind that the fact that a lower number and proportion of women enter the labour force in the first place.

To conclude, the world celebrated the international women’s day a few days back (on March 8). On that day, the corporates and the government institutions talked about the importance of the women who worked for them. The social media influencers talked about women. Many women talked about what it means for them to be a woman.

But almost none of them talked about one of the most important issues at hand, the fact that Indian women are bearing the brunt of India’s unemployment problem.

If you are reading this(man or woman) please share it with your friends and family. The first step towards solving any problem is knowing and acknowledging that it exists.

RBI Gives a Covid Spin to Cash Touching Pre-Demonetisation Levels

We live in an era of narratives. Politicians create them. Corporates create them. Social activists create them. Commentators, public intellectuals, economists and analysts also create them. And there are days when we are even lying to ourselves in our heads and creating narratives for ourselves.

In all this, it is hardly surprising that the Reserve Bank of India (RBI), with Shaktikanta Das at its helm, has also padded up and gotten into the business of narratives and spin. Before I explain this in detail, let me give you some background to this piece.

In November 2016, the central government demonetised Rs 500 and Rs 1,000 notes. The citizens had to deposit these notes into their bank accounts. The result was that 86% of the currency by value suddenly went out of the financial system.

Paper money has different uses, but its main use is as a medium of exchange. It basically facilitates the process of buying and selling. Of course, if people want to, the process of exchange can be carried out through other means like issuing a cheque, making a demand draft, carrying out a money transfer or even paying money digitally.

But India back in 2016 was a country which believed in operating in cash. When the cash went out of the system, the economic transactions especially in the informal sector took a beating. The gravity of the situation never really came out fully, except perhaps anecdotally, given that the government data collection for the informal part of the economic system was and continues to remain abysmal. I guess, which is why it is called an informal sector in the first place.

Between November 2016 and now, I have closely tracked the total amount currency in circulation gradually increasing. Of course, as the economy expands, the currency in circulation is bound to go up. In order to take care of this, the data that needs to be tracked is the currency in circulation divided by the gross domestic product (GDP), expressed as a percentage. (I will refer to this as cash in the system). The GDP is the measure of the size of any economy. The cash in the system basically adjusts for the size of the economy.

My contention over the years has been that the cash in the system will eventually rise to touch the pre-demonetisation level. Earlier this year, in April 2020, writing in the Mint, I had said: “The cash in the system [as of March 2020] works out to 12% of GDP.” I had made this calculation on the basis of the currency in circulation as of March 27 and the GDP forecast for 2019-20 (up until then, the actual GDP numbers were yet to come in).

A formal confirmation of this came yesterday with the RBI  releasing its annual report. In the annual report, the RBI says: “The currency-GDP ratio increased to its pre-demonetisation level of 12.0 per cent in 2019- 20 from 11.3 per cent a year ago, indicating the rise in cash-intensity in the economy in response to the pandemic [emphasis added].” The currency in circulation constitutes of cash with banks and cash with the public.

Before analysing this statement, let’s look at the following figure, which plots the currency in circulation to the GDP ratio or the cash in the system, over the years.

Cash in the System

Source: Reserve Bank of India.

In March 2017, a few months after demonetisation was carried out and after the whole country had queued up to deposit the demonetised Rs 500 and Rs 1,000 notes into their bank accounts, the cash in the system fell to 8.7% of the GDP.

The reason for this was very straightforward; the government and the RBI couldn’t replace the cash in the system at the same pace as they had taken it out. There were all kinds of problems, including banks having to reset ATM trays in order to take care of the smaller size of the new notes.

As of March 2020, the cash in the system is back to 12% of the GDP, which is at an almost similar level of 12.1% of the GDP as of March 2016, before demonetisation was carried out. The RBI feels this has happened because there has been a dash for cash in light of the spread of the covid-19 pandemic. People hoarded on to more cash than they normally do and this led to a faster rise in the cash in the system than it normally would have.

The point to be remembered here is that we are talking cash in the system as of March 2020 and not August 2020. At that point of time, people had just started to take covid-19 seriously. Let’s take a look at the monthly increase/decrease in currency in circulation during the course of 2019-20.

Changes in currency in circulation

Source: Author calculations on data from Reserve Bank of India.

It is very obvious from the above chart that at Rs 99,040 crore, the maximum monthly increase in cash in the system during the year, happened in March 2020. Does this then imply that there was a dash for cash as the fear of the pandemic spread? In order to say this with surety we will have to look at weekly increase in cash levels in the system during the course of March 2020.

Dash for Cash?

Source: Author calculations on Reserve Bank of India data.

India went into a physical lockdown starting March 24, 2020. It is only around then that most of the country realised the gravity of the pandemic. This can be seen by increase in cash in the system in the week ending March 27. This implies a higher than normal increase in currency with public with a higher withdrawal of money from the banking system than would have been the case if all was well.

But the bulk of the increased withdrawals in March had happened before March 20. Close to 62% of the withdrawals in March (at Rs 61,354 crore) had happened before March 20. Interestingly, up until then the fear of the pandemic hadn’t really spread. This weakens the entire dash for cash argument.

Let’s say if things had gone on normally then it is safe to say that the increase cash in the system in March would have been around 80-85% of what it eventually got to. At this level of increase, the cash in the system as of March end would have been around 11.95% of the GDP, which is not significantly different from 12.03% of the GDP, it eventually came to. The RBI’s dash for cash argument hangs on a few basis points.

Even if assume, that increase in the cash in the system in March 2020 was at around 60% of the actual number, the cash in the system would have worked out to 11.84% of the GDP, which is slightly lower than 12.03% of the GDP. And even at 11.84% of the GDP, the cash in the system would have been higher than where it was as of March 2019, and would have continued to go up, as it has since November 2016. This is the more important point.

While India of November 2016 was a country which believed in operating in cash, so is the India of March 2020. Yes, digital transactions have gone up along the way and that’s a good thing. But that could have happened anyway without putting the country through the trouble that demonetisation did.

Also, it is time we realised that people don’t store their black money in cash. In fact, data from a White Paper on black money published in May 2012 showed that around 4.9% of the total undisclosed income admitted to during search and seizure operations between 2006 and 2012 was held in the form of cash. Cracking down on black money is much more complicated operation than just cracking down on cash in the system.

Further, societies with more cash aren’t necessarily more corrupt. If that was the case Japan with a cash in the system of around 20% of the GDP would be more corrupt than India. On the flip side, Nigeria which has a cash in the system comparable to that of Norway, wouldn’t be a country as corrupt as it is. The government needs to make peace with this fact.

To conclude, I think one reason the RBI might have resorted to this spin and is trying to create a narrative, lies in the fact that when demonetisation was carried out, the current RBI governor Shaktikanta Das was the finance secretary.

My guess is that a part of Das still wants to justify demonetisation as a good thing and show it by telling the nation that the cash in the system rose to the pre-demo level simply because of the Covid-19 pandemic, something that wouldn’t have happened otherwise.

But as I showed above that is a very weak argument. It is time the RBI sang a different tune on this front and moved a dash for cash to Das for cash.

Why HDFC Finds Homes to Be More Affordable, When They Clearly Aren’t

Summary: HDFC is getting better home loan customers that doesn’t mean homes have become more affordable. HDFC’s conclusion of homes becoming more affordable is an excellent example of survivorship bias.

Before I start writing this, I have a confession to make. I have written about this issue before, around five years back. But given that things haven’t really changed since then, it is a good time to write about it again. Hence, to all my regular readers who have been following me over the years and might have read this earlier, sincere apologies in advance.

Home loans in India are given by two kinds of institutions – banks and housing finance companies (HFCs). Among the HFCs, Housing Development Finance Corporation (HDFC) has been a pioneer in the area of home loans.

The company regularly publishes an investor presentation along with every quarterly result.

I am not sure for how long the company has been doing this, but its website has these presentations going as far back as March 2013, a little over seven years. Since then, the company has had a slide in its investor presentation which talks about the improved affordability of owning a home in India. Usually, it is the eight or the ninth slide in the presentation (sometimes, but very rarely tenth).

This is the slide in the latest presentation for the period April to June 2020.

Improved affordability of homes

Source: HDFC Investor Presentation, June 30, 2020.

Let’s look at the chart between 2000 and 2020, the last two decades. The home loan market in the country before that was too small and evolving and hence, prone to extreme results. So, it makes sense to ignore that data.

What does the chart tell us? It tells us that affordability of homes in the country has gone up over the years. The chart defines affordability as home price divided by the annual income of the individual buying the home.

In 2020, the average home price has stood at around Rs 50 lakh. Against this, the average annual income of the individual buying the home stands at around Rs 15 lakh. Given this, the affordability factor is at 3.3 (Rs 50 lakh divided by Rs 15 lakh).

Hence, the average individual in 2020 is buying a home which is priced at 3.3 times his annual income. (Please keep in mind that the property prices are represented on the left-axis and the annual income is represented on the right axis).

As can be seen from the chart, the affordability factor at 3.3 is the lowest in twenty years. Hence, affordability of homes has gone up. QED.

The trouble is, this goes totally against what we see, hear and feel all around us. Real estate companies have lakhs of unsold homes with absolutely no takers. They have thousands of crore of unpaid loans. The banks and non-banking finance companies (NBFCs) have restructured these loans over the years and not recognized them as bad loans in the process, with more than a little help from the Reserve Bank of India (RBI). Bad loans are loans which haven’t been repaid for a period of 90 days or more.

Further, investors who bought real estate over the years have been finding it difficult to sell it. Indeed, if homes had become more affordable, this wouldn’t have been the case. Real estate companies would have been able to sell homes and repay the loans they have taken from banks and NBFCs. And the RBI wouldn’t have to intervene.

So, what is it that HDFC can see that we can’t? Before I get around to answering this question, let me tell you a little story. During the Second World War, the British Royal Air Force (RAF) had a peculiar problem.

It wanted to attach heavy plating to its airplanes in order to protect them from gunfire from the German anti-aircraft guns as well as fighter planes. The trouble was that these plates were heavy and hence, had to be attached strategically at points where bullets fired by the German guns were most likely to hit. The British couldn’t plate the entire plane or even large parts of it.

The good part was that they had historical data regarding which parts of the plane did the German bullets actually hit. And this is where things got interesting. As Jordan Ellenberg writes in How Not to Be Wrong: The Hidden Maths of Everyday Life: “The damage [of the bullets] wasn’t uniformly distributed across the aircraft. There were more bullet holes in the fuselage, not so many in the engines.”

So, historical data was available and hence, the decision should have turned out to be a very easy one. The plates needed to be attached around the plane’s fuselage. But this logic was missing something very basic. The German bullets should have been hitting the engines of airplanes more regularly than the historical evidence suggested, simply because the engine “is a point of total vulnerability”.

A statistician named Abraham Wald realised where the problem was. As Ellenberg writes: “The armour, said Wald, doesn’t go where bullet holes are. It goes where bullet holes aren’t: on the engines. Wald’s insight was simply to ask: where are the missing holes? The ones that would have been all over the engine casing, if the damage had been spread equally all over the plane. The missing bullet holes were on the missing planes. The reason planes were coming back with fewer hits to the engine is that planes that got hit in the engine weren’t coming back.” They simply crashed.

This is what is called survivorship bias or the data that remains and then we make a decision based on it.

As Gary Smith writes in Standard Deviations: Flawed Assumptions Tortured Data and Other Ways to Lie With Statistics: “Wald…had the insight to recognize that these data suffered from survivor bias…Instead of reinforcing the locations with the most holes, they should reinforce the locations with no holes.”

Wald’s recommendations were implemented and ended up saving many planes which would have otherwise gone down. (On a different note, both the books from which I have quoted above, are excellent books on how not to use data, especially useful if you are in the business of torturing data to make it say what you want ).

If you are still scratching your head and wondering what does this Second World War story have to do with HDFC finding homes more affordable, allow me to explain. Like the British before Wald came in with his explanation, HDFC is also looking at the data it has and not the overall data.

Look at the left-hand of the corner of the chart, it says based on customer data. The analysis is based on HDFC’s own historical customer data. When HDFC talks about an average home price of Rs 50 lakh and an income of Rs 15 lakh, it is basically talking about the set of people who have approached the HFC for a loan and gotten one. Hence, HDFC’s conclusion of better affordability is drawn from the sample it has access to.

But does this really mean that affordability has improved? Or does it mean that the quality of HDFC’s customers has improved over the years? The customers that HDFC is giving a home loan to are ones who can afford to buy homes. The HFC clearly has no idea about people who want to buy homes but simply do not have the financial resources to do so.

They don’t show up as a part of any sample, hence, the evidence on them is at best anecdotal. These people are like planes whose engines were hit and hence, they did not make it back to their base, in the Second World War. And like there was no data on the planes which got hit and didn’t make it back, there is no data on these people as well. Basically, HDFC’s data and conclusion are victims of the survivorship bias

In fact, HDFC’s investor presentation has always carried another interesting slide on low penetration of home loans in India. The following chart is from the latest presentation.

Home loans as a percentage of GDP

Source: HDFC Investor Presentation, June 30, 2020.

Total home loans outstanding given by both banks and HFCs in 2020 stands at 10% of the GDP (On a slightly different note, the ratio of homes loans given by banks to home loans given by HFCs is 64:36). In March 2014, the total outstanding home loans in India had stood at 9% of the GDP. If homes indeed were affordable this ratio would have gone up faster.

To conclude, it’s time that HDFC remove this misleading slide from its investor presentation or at least say that the affordability has improved for its customers and not for the country as a whole.

The RISK of RISK of Investing in Stocks, which OPIUM Managers Don’t Talk About

Summary: Just because you have taken on a risk by investing in stocks, doesn’t mean high returns are going to materialise.

The only function of economic forecasting is to make astrology look respectable –
John Kenneth Galbraith.

It was sometime in October-November 2010. I had just joined a weekly personal finance newspaper, which for reasons I did not understand and for reasons above my paygrade, was to be run out of Delhi.

During the course of one editorial meeting, we had to decide what sort of return would systematic investment plans (SIPs) into equity mutual funds generate over the next decade. This was necessary as a part of a regular feature to be published in the newspaper, which would help a featured family come up with an investment-savings plan.

It was assumed that SIPs into equity mutual funds would generate 15% per year return. I protested against the assumption saying that 15% per year return was way too high but was overruled by the Delhi bosses.

At that point of time it had almost become fashionable to say that the stock market generates 15% return per year in the long term (In fact, there are people who still believe in this myth, which I shall write about in detail in the time to come).

Getting back to the point. We are now in 2020. 10 years have gone by. As I pointed out in a piece yesterday, the SIP returns on index funds have been rather subdued over the last decade. The average per year return over the last decade in case of the three Nifty index funds I checked was slightly over 9% (around 9.17% to be very precise). Index funds are funds which have a mandate to invest money in stocks that make up a stock market index, in the same proportion that they do.

The per year return of a little over 9% was nowhere near the assumed 15% per year return. Let’s say an individual had invested Rs 10,000 per month religiously through the SIP route for ten years. On this if he had earned a return of 15% per year, the value of his portfolio at the end of 10 years would be Rs 27.5 lakh.

If the return was 9.2% instead as it actually turned out to be, the value would be around Rs 19.6 lakh or around 29% lower. If the individual was saving towards a certain goal, he would end up way short. But that’s the rather obvious point here.

The question is how did the market narrative of stocks giving 15% return in the long-term come about? The first time I heard this 15% argument being made with a lot of confidence by marketmen was sometime in late 2006 or perhaps early 2007.

This, after the Indian economy had grown by greater than 9% in real terms for three consecutive years, 2004 to 2006. The zeitgeist or the spirit of the times that prevailed was that come what may India will now grow by at least 8% in real terms. Add an inflation of 5-6% on top of that and we will grow at 13-14% in nominal terms, year on year.

Assuming that the earnings of companies which are a part of India’s premier stock market indices would grow a tad faster than the nominal growth, we arrived at 15-16% year on year growth in earnings.

This would be reflected in stock prices growing by 15-16% per year as well. From here came the assumption, the stock market growing at 15% in the long-term. There is a lot more to this assumption including Sensex returns from 1979 on, but I will leave that for another day. For the time being knowing this much is fine.

In fact, over the years, I have seen this logic being offered by people who make their money in the stock market by managing other people’s money or OPM or even better OPIUM, with great conviction. These tend to include fund managers, analysts, traders, salespeople etc. (Oh, if you still didn’t get it, OPM and OPIUM sound the same. Rather childish, but good fun nonetheless). Those in the business of managing OPIUM really believe that stocks give 15% per year return over the long-term (I even wrote a piece on this titled Why Economic Growth Cannot Be Created on an Excel Sheet. You can read it here).

The trouble is that this assumption has turned out to be all wrong. The earnings growth has been nowhere near what the OPIUM managers have been projecting. This is reflected in the 10-year return on stocks, which as of August 20, 2020, stood at 8.7% per year (based on the Nifty 50 Total Return Index, which takes dividends paid by companies into account as well, unlike the normal index).

The funny thing is that the stock market has delivered a return of just 8.7% per year over the last decade, despite the valuations being at all time high levels. The price to earnings ratio of stocks that comprise the Nifty 50 index is around 32 these days. This basically means that for every rupee of earnings for these stocks, the investors are ready to pay thirty-two rupees as price. As I pointed out yesterday, such high valuation has never been seen before.

And despite such a high valuation the decadal per year return on stocks on an average is less than 9% per year. This is the irony of it all. It also makes me wonder why investors think that the stock market is doing well. Yes, it has done well in comparison to where it was in late March 2020, but clearly not otherwise.

Of course, when the OPIUM managers talk about 15-16% return per year from stocks over the long-term, they also highlight the fact that for higher return a higher risk needs to be taken on by the investor. The higher risk is the risk of investing in stocks for the long-term.

But what they don’t talk about is the fact that just because you are taking the risk of investing in stocks for the long-term, doesn’t mean that higher returns are going to materialise. I would like to call this, the risk of risk of investing in stocks, something which most OPIUM managers don’t seem to talk about.

The question is why does this happen? The answer lies in the fact that OPIUM managers are in the business of driving up assets under management for the firms that they work for. More the money that gets invested in a fund, the higher the fee earned by the firm to manage that money. And in this business of soliciting money, you need to sound confident.

The moment you start getting into nuance about high risk not guaranteeing high returns, you start losing the average prospective investor. Hence, the projection of confidence that the prospective investor is looking out for, leads to simplistic one-line market narratives like stocks will definitely give a 15% per year return, over a decade. Such narratives are easier to sell.

In a world full of complex uncertainties, the prospective investors are looking for certainty and those in the business of managing OPIUM can’t consistently project confidence to tackle the complex uncertainties, unless they believe in stocks giving 15% per year return in the long-term, themselves. This is the con of confidence which fools people on both sides.

The trouble is such narratives hurt. As  economists John Kay and Mervyn King write in Radical Uncertainty – Decision Making For an Unknowable Future: “Markets narratives are occasionally ‘dishonest and manipulative’, but normal people make honest use of narratives to understand their environment and guide decisions under radical uncertainty.” (King and Kay’s book is a terrific read though not a breezy one. Highly recommended).

This is not to say that one should not invest in stocks and invest all our money in bank fixed deposits. Not at all.

All I am trying to say is that just because you have taken on the risk of investing in stocks, doesn’t mean higher returns are going to materialise and which is why it’s called risk in the first place. So, you might end up short on the corpus you were trying to build (assuming you are trying to do this in a systematic way).This is something that needs to be kept in mind while investing in stocks either directly or indirectly through mutual funds. This is the risk of risk of investing in stocks. While all mutual fund ads have a disclaimer at the end saying that mutual fund investments are subject to market risk, nobody really explains to the investor what exactly this market risk is.

The economist Allison Schrager makes this point in the context of saving for retirement in her brilliant book An Economist Walks into a Brothel—And Other Unexpected Places to Understand Risk. The conventional wisdom is that when it comes to saving for retirement it makes immense sense to build up as large a retirement corpus as possible and then spend it at the rate of, say 4%, per year, after retirement.

The problem with this strategy is that 4% per year isn’t really a fixed amount. It depends on the retirement corpus one has been able to build up in the first place. And that in turn depends on how the stock market has been doing. As Schrager writes: “That’s where the strategy goes wrong.”

One way of getting around this problem is that in the years approaching retirement you take your money out of stocks and invest it in fixed income investments, everything from bonds to fixed deposits. This mitigates the risk to some extent but not totally.

What if the stock market is not doing well in the years before retirement? What do you do then? Do you continue staying invested in the stock market in the hope that it recovers, and you build a better corpus? What if it doesn’t?

That’s the risk of it all. At the cost of repeating just because you have invested in stocks and taken on a higher risk doesn’t mean higher returns are automatically going to materialise.

To conclude, it is important that as a stock market investor you realise this, irrespective of whether the OPIUM managers communicate this or not.

Stay safe and enjoy the weekend.

Will see you now on Monday (or perhaps Tuesday, depending on what my brain throws up over the weekend).

Disclaimer: This article is meant for educational purposes only.  

Is It Time to STOP Drinking Stocks SIP by SIP?

Summary: The idea at the heart of systematic investment plans (SIPs) is cost averaging and it works when the stock market goes both up and down. In the last seven years, the market has largely gone only one way and that’s up. Hence, SIPs have given fairly ordinary returns.  

In the last decade and a half, regular investing into equity mutual funds through the systematic investment plan (SIPs) route has become a regular habit for many middle-class Indian investors. And its been a good habit.

Given that money invested in equity mutual funds is used to buy stocks, SIP investors end up owning stocks indirectly.

Also, mutual funds need to invest in a certain minimum number of stocks to meet regulatory requirements, hence, the old investment adage of don’t put all your eggs into one basket, gets taken automatically care of.

Nothing Works Forever

As the old Hero Honda advertisement went, fill it, shut it, forget it. SIPs are a tad like that.

The trouble is nothing works forever. What made SIPs such an easy and a beautiful way to invest is the concept of cost averaging that comes into play.

What exactly is cost averaging? Let’s say you invest Rs 10,000 per month into a mutual fund through an SIP. On the day of investment, the net asset value (NAV) of a single unit of the mutual fund is Rs 40. You end up buying 250 units (Rs 10,000 divided by Rs 40). The NAV of a mutual fund is the price at which an investor can buy or sell a single unit of the fund.

Let’s say a year later, the stock market has fallen and the NAV of the mutual fund has fallen to Rs 20. This time when you invest you end up buying 500 units (Rs 10,000 divided by Rs 20).

Essentially, you end up buying more units when the stock market is doing badly, and you buy fewer units when the stock market is doing well. When the market recovers, it is the units that were bought when the NAV was low, which bring in the maximum return.

Also, since most retail investors don’ know exactly when the stock market is going to fall (this is not to say that the so-called experts and talking heads on TV, do), an SIP strategy needs the investor to keep investing for the long term. The question is how long is the long term? Of course, there is no definitive answer for this.

I still remember when mutual funds first started talking aggressively about SIPs a decade and a half back, they used to talk about an investment horizon of three years. A few years later this investment horizon became five years.

In my personal experience, having invested in mutual funds through the SIP route for nearly 15 years, I think the real fun starts only after ten years. This is when the stock market has gone through various cycles and the investor has ended up buying enough mutual fund units during periods when the stock market was doing badly.

And as mentioned earlier, these are the units help the investor earn a good return when the stock market starts to do well again.

Of course, this is not the best possible way to invest but a pretty optimum one, especially for individuals who are busy running the rat race of corporate life and trying to balance it with their very demanding family and social lives as well. All this doesn’t really leave much time for them to research and invest in stocks directly. Hence, investing in stocks through the SIP route turns out to be a reasonably good bet.

The corollary to all this is that for SIPs to work the stock market needs to come down time to time as well.  Only then does the SIP investor end up buying units at lower NAVs, which benefit him later (I know I can’t seem to hammer this point enough).

But over the last few years, the stock market has only gone in one direction and that is up. Take a look at Figure 1, which basically plots the price to earnings ratio of the Nifty index.  It might look a tad complicated to everyone who switches off when they look at any chart but believe me this is very simple.

Figure 1: Price to earnings ratio of the Nifty 50 Index.

Source: www.nseindia.com

Let’s divide the chart into two parts, pre 2013 and post 2013. Pre 2013, the price to earnings ratio has gone up and down and up and down and so on. Post 2013, it has largely gone only one way and that is up (except the one big fall earlier this year).

What does that mean? It basically means that the prices of stocks that make up for the Nifty 50 index have gone up much faster than the earnings of the companies these stocks represent.  And this has gone on for seven years now.

Stock prices ultimately should be a reflection of expected future earnings of any company. But when the price to earnings ratio keeps rising for seven years at a stretch what it means in simple English is that the price of the stocks has gone up much faster than their earnings and the expected future earnings of the companies have never really materialized.

As of August 18, the price to earnings ratio of the Nifty 50 index stood at 32.03, the all-time highest level (in the data that is available since 1999). The average price to earnings ratio since 1999 has been around 20. This tells us clearly how high the current stock price to earnings ratio is.

The question is how did we reach here? Take a look at Figure 2, which basically plots the inflows or the money being invested into SIPs every month, since April 2016.

Figure 2: SIP inflows (in Rs crore).

Source: AMFI India.

Before we interpret Figure 2, let’s take a look at Figure 3. Figure 3 plots the money invested by foreign institutional investors (FIIs) into Indian stocks over the years.

Figure 3: FII investment into Indian stocks (in Rs crore). 

Source: NSDL.

What Figure 3 tells us is that between 2015-16 and 2019-20, foreign investors did not invest much in Indian stocks, except in 2016-17, when they invested Rs 55,703 crore. Hence, during that period it was money coming through the SIP route which was invested into equity mutual funds and then into stocks, that kept the stock prices buoyant despite the company earnings not seeing the expected growth.

As ironic as it might sound, it was money coming in through the SIP route which essentially ensured that stock prices did not fall, and in the process ensured that cost averaging went out of play.

Before SIPs became a popular of investing, between 2012 and 2014, the foreign investors invested a lot of money into Indian stocks. Money invested by the foreign investors and SIP investors over the last seven years has ensured that the Indian stocks have been at levels their earnings do not justify. Nonetheless, the hope still persists that these stocks will someday give earnings they are expected to. But hope cannot be an investment strategy.

Hence, the part of cost averaging where stock prices fall and which leads to SIP investors ending up buying more mutual fund units, hasn’t really played out in the last seven years.

Take a look at Table 1 which basically lists the SIP returns of three index funds, as of August 18, 2020. Index funds are mutual funds which invest in stocks that make up for a particular index.

Table 1: SIP returns of index funds.

Source: Value Research and National Stock Exchange.

What does this table tell us?

1) Over the last few years, the stock market has just gone one way and that is up. This has led to fairly limited SIP returns. Even the 10-year SIP returns of index funds are not in double digits. And if 10-years is not long enough, I don’t know what is.

2) What we also come to realise is that the SIP returns are on the lower side, despite the stock market valuation being at an all-time high level. Hence, all the money brought in by the SIP investors and the foreign investors has led to just about mediocre returns even over a 10-year period.

Lest we get accused of looking at the returns only on a certain date, let’s take a look at 10-year returns of these index funds in previous years.

Table 2: 10-year SIP returns of index funds.

Source: Value Research.

Table 2 makes for a very interesting reading. The 10-year SIP returns in years before 2020, are higher. In fact, if we leave out 2019, largely they are in double digits or very close to double digits. The reason for this lies in the fact that the 10 year-SIPs before 2019, ran through a longer-periods of the stock market going down (go back and look at Figure 1 again). This allowed cost averaging to come into play properly, something which hasn’t happened in the past few years.

Using this logic, a few months back I completely stopped all my SIPs. Honestly, there hasn’t been a more SIP man than me, having relentlessly been at it for close to 15 years. Whatever little I have saved in life is thanks to SIPs.

Of course, this is the past.

What about the future?

As long as the direction of the market stays one way and it doesn’t fall for an extended period of time, SIPs as a way of investing will have a fundamental flaw, as explained earlier. Hence, the 10-year returns one saw around between 2014 and 2018, are unlikely to be repeated in the years to come.

For a period of 18 months between November 2018 and May 2020, more than Rs 8,000 crore was invested into mutual funds every month through the SIP route. In the last two months, the investment has fallen below Rs 8,000 crore, nevertheless, it still remains strong. A bulk of this money has gone into equity mutual funds.

The foreign investors ignored Indian stocks for the last few years. But in 2020-21, the current financial year, they have come back with a bang. The reason for this lies in the fact that there a lot of money printing happening across the Western world.

Between February 26 and August 12, the Federal Reserve of the United States has printed close to $2.8 trillion in a bid to drive down interest rates in the United States and help the post-covid economy.

As has been the case in the past, some of this money has been invested in stock markets all across the world including India. The easy money policy of the Western central banks is likely to continue in the months to come, at least for the next one year, until the world starts coming out of the economic contraction that is happening thanks to covid.

In this scenario, the chances of the stock market and the price to earnings ratios falling, are rather low. Another reason which will ensure that stock prices may not fall is the fact that post tax bank fixed deposit return is now lower than 5% in most cases and the inflation is close to 7%. Hence, a segment of savers will try to drive up the investment return by buying stocks.

Whether the stock market will go up from here, the situation is too convoluted to say anything definitively. For that to happen, much more money needs to be invested into the stocks.

If as an investor you feel that stock prices will only go up from here despite the lack of company earnings, then you are better buying stocks directly and making irregular one-time investments into equity mutual funds than going through the SIP route. By going through the SIP route, you will keep escalating the cost of purchase of mutual fund units and in the process drive down returns.

That’s the way I see it at least. And I say it, the way I see it. The time to fill it, shut it, forget it, when it comes to SIPs, is over.

Disclaimer: The article is meant for educational purposes only.