Population

China’s Population Control Model is an Outdated and a Bad Idea for India

Hum do hamare ho do,
paas aane se mat roko.
— Indeevar, Rajesh Roshan, Amit Kumar, Sadhana Sargam and Rajesh Roshan, in Jurm (1990).

 Here’s a scene from a middle class Indian drawing room of the late 1980s and early 1990s. Four men are sitting and chatting.

“You know what India’s biggest problem is?” asks the first.

“Our population,” replies the second.

“The government should do something to control it,” says the third.

“Indeed,” affirms the fourth.

Three decades and more later, whether similar conversations continue to happen in the middle class Indian drawing rooms, I have no idea, simply because I haven’t been in one for many years. But some Indians still think in a similar fashion, that is, India has a population problem and that the government should do something to control it, like the way China did. (Okay, we might want to boycott Chinese goods but we don’t have such inhibitions when it comes to their population control policy).

In fact, one such individual, even filed a public interest litigation with the Supreme Court and as reported in the Sunday edition (December 13, 2020) of The Times of India, pleaded that “to have good health; social, economic and political justice; liberty of thoughts, expression and belief, faith and worship; and equality of status and opportunity, a population control law, based on the model of China, is urgently required.” (Ironically, the above paragraph mixes the Preamble of the Indian Constitution with the Chinese population control law). 

This is precisely the kind of lazy thinking that prevails when one forms an opinion on something and continues holding on to it, without looking at the latest data. Let’s look at this issue pointwise, in order to understand that such thinking is totally wrong.

1) There is no denying that India has a large population and that creates its own set of problems, everything from lack of employment opportunities to lack of public infrastructure. But is population control the answer to that? No. Look at the following chart, which plots the total fertility rate of India.


Source: https://data.worldbank.org/indicator/SP.DYN.TFRT.IN?locations=IN

The total fertility rate in 2018 stood at an all-time low of 2.222. This meant that on an average 1,000 Indian women have 2,222 babies during their child-bearing years. The chart has a downward slope, which means that the fertility rate has been falling over the years. This means on an average  Indian women have been bearing fewer children over the decades.

The replacement rate or the total fertility rate of women at which the population automatically replaces itself, from one generation to another, typically tends to be at 2.1. India’s fertility rate is almost at the replacement level.

As per the Sample Registration System Statistical (SRSS) Report for 2018, the total fertility rate in urban India was 1.7 and in rural India was at 2.4. Hence, urban India is already below the replacement rate.

2) The point being that the Indian population is increasing at a much slower pace than it was in the earlier decades. How has that happened?

As Hans Rosling, Ola Rosling and Anna Rosling Rönnlund write in Factfulness—Ten Reasons We’re Wrong About the World – And Why Things Are Better Than You Think:

“Parents in extreme poverty need many children… for child labour but also to have extra children in case some children die… Once parents see children survive, once the children are no longer needed for child labour, and once the women are educated and have information about and access to contraceptives, across cultures and religions both the men and the women instead start dreaming of having fewer, well-educated children.”

Hence, as the infant mortality rate falls due to a variety of reasons, from more women getting educated to a higher economic growth to urbanisation, the fertility rate comes down as well. Take a look at the following chart, which basically plots the infant mortality rate of India over a period of time. The infant mortality rate is defined as the number of children who die before turning one, per 1,000 live births.

Source: https://data.worldbank.org/indicator/SP.DYN.IMRT.IN

The infant mortality rate has fallen from 161 in 1960 to 28.3 in 2019. As more children born have survived and grown into healthy adults, parents have had fewer children. That is one clear conclusion we can draw here.

As the Roslings write: “Every generation kept in extreme poverty will produce an even larger next generation. The only proven method for curbing population growth is to eradicate extreme poverty and give people better lives, including education and contraceptives.”

India’s adult female literacy rate (% of females aged 15 and above) had stood at 25.68% in 1981. It has since gradually improved and in 2018 had stood at 65.79%. As more women have learned to read and write, the infant mortality rate and the fertility rate have both come down.

As the SRSS Report points out:

“On an average, ‘Illiterate’ women have higher levels of age-specific fertility rates than the ‘Literate’. Within the ‘Literate’ group there is a general decline in the fertility rates with the increase in the educational status both in the rural and urban areas, barring a few exceptions.”

Also, faster economic growth post 1991 has helped in bringing down poverty levels and in turn led to a lower fertility rate as well.

In 1960, the total fertility rate was at 5.906. It fell to 4.045 by 1990. By 2018, it had fallen to 2.22. Clearly, the rate of fall has been faster post 1990.

3) Now let’s talk about the China model of population control, which led to one Ashwini Upadhyay petitioning the Supreme Court, pleading that India adopt such a law as well. But before we do that let’s look at the following chart which basically plots the total fertility rate in China over the years.

Source: https://data.worldbank.org/indicator/SP.DYN.TFRT.IN?locations=IN-CN

China’s coercive one-child population control policy was launched in 1979. At that point of time, the Chinese fertility rate was 2.745. The interesting thing is that it had been falling rapidly from 1965 onwards when it had peaked at 6.385.

As Mauro F. Guillén writes in 2030: How Today’s Biggest Trends Will Collide and Reshape the Future of Everything:

“Back in 1965, the fertility rate in urban China was about 6 children per woman. By 1979, when the one-child policy came into effect, it had already declined all the way down to about 1.3 children per woman, well below the replacement level of at least 2 children per woman. Meanwhile, in rural China, fertility hovered around 7 children per woman in the mid-1960s, a number that decreased to about 3 by 1979.”

The point being that in 1979 when Chinese leaders pushed through the one-child policy the fertility rate in urban China was already at 1.3, much lower than the replacement rate. In rural China it was at 3, greater than the replacement rate of 2.1, but it was falling at a very fast rate. Hence, the decision to push through the one-child policy was not a data backed decision but basically politics.

As Guillén writes:

“The policymakers were unaware of the reality that fertility in China had been dropping precipitously since the 1960s, with most of the decrease driven by the same factors as in other parts of the world: urbanization, women’s education and labour force participation, and the growing preference for giving children greater opportunities in life as opposed to having a large number of them.”

Clearly, Upadhyay like the Chinese  before him, did not look at the Indian data before filing the public interest litigation in the Supreme Court and thus wasting the time of the Court as well as that of the government.

4) One of the impacts of the coercive one child policy in China was that parents preferred to have boys than girls. As Guillén writes: “While it was the law, the one-child policy created a gender imbalance of about 20 percent more young men than women, driven by the cultural preference for boys.”

The male-female ratio went totally out of whack. In 1982 there were 108.5 male births per 100 female births. This went up to 118.6 per female births in 2005. It has since fallen to 111.9. This has led to an intensified competition in the marriage market, with many Chinese men being unable to find brides.

As per the Sample Registration System Statistical Report for 2018, India’s sex ratio at birth was 1,000 males to 899 females. This works out to around 111 males for 100 females. Of course, like the Chinese even Indian parents have a cultural preference for a male child, who they believe will take care of them in their old age and also ensure that their family continues.

Imagine the havoc any coercive population control policy could have caused or can still cause, to the sex ratio in India.

In lieu of this fact, it was nice to see that the Modi government responded in an absolutely correct way in the Supreme Court. The health and family welfare ministry told the Court: “India is unequivocally against coercion in family planning… In fact, international experience shows that any coercion to have a certain number of children is counter-productive and leads to demographic distortion.”

Clearly, the government doesn’t want to become a victim of the law of unintended consequence where it wants to do one thing and ends up creating other problems. Kudos to that.

5) The Health and Welfare Statistics of 2019-20 project that India’s total fertility rate will be 1.93 in 2021, which will be lower than the replacement rate of 2.1. It is expected to fall further to 1.80 by 2026-2030.

Of course, a fertility rate of close to the replacement rate doesn’t mean that all states have low fertility rates. Recently, the data for  the first phase of the fifth National Family Health Survey (NFHS-5) was released. This had data for 17 states and five union territories. Among the large states, Bihar was the only state which had a total fertility rate greater than the replacement rate. The total fertility rate of the state stood at 3. (The data for other laggard states like Uttar Pradesh, Rajasthan, Madhya Pradesh etc., wasn’t released in this phase).

A look at the data from Health and Welfare Statistics of 2019-20 tells us that the poorer states which have higher infant mortality rates also have higher fertility rates, most of the times. This evidence is in line with theory.

6) States with a lower fertility rate will not see an immediate fall in population. This is primarily because of the past high fertility rate because of which more people will enter or be a part of the reproductive age group of 15-49. This is referred to as the population momentum effect.

As C Rangarajan and J K Satia wrote in a column in The Indian Express in October: “For instance, the replacement fertility level was reached in Kerala around 1990, but its annual population growth rate was 0.7 per cent in 2018, nearly 30 years later.” Nevertheless, population growth has slowed down and will continue to slow down further.

The larger point here being a growing population is a very important part of economic growth (of course, this is a necessary condition for economic growth but not a sufficient one).

As Ruchir Sharma writes in The 10 Rules of Successful Nations: “Throughout, increases in population have accounted for roughly half of economic growth… The impact of population growth on the economy is very straightforward, and very large. If more workers are entering the labour force, they boost the economy’s potential to grow, while fewer will diminish that potential.”

Many Indian states with a fertility rate lower than 2.1 will start facing the situation where fewer people will enter their workforce, in the next couple of decades. This includes Southern and the Western states. It also includes states like West Bengal, Punjab, Himachal Pradesh and Jammu and Kashmir.

Clearly, these states will need workers from other states to keep filling the gap in their working age population (something which is already happening). Also, as workers from high fertility states move to work in low fertility states, they will see an increase in their incomes. This will have an impact on their own fertility rates, which will fall.

In this scenario, states trying to reserve jobs for locals, is a bad idea in the medium to long-term, though it might work in the short-term by being politically popular. Also, states with lower fertility rates on the whole have higher per-capita incomes. Given that, locals do not always want to take on the low-end jobs. And for that, people from other states need to come in and take on those jobs.

People who move from less developed states to more developed states in India are those who are low-skilled or semi-skilled, largely. Alternatively, they have very high-level skills.

One indirect effect of a rise in migrants in any given state is that migrants spend a part of the money they earn and this leads to the overall increase in demand for goods and services within that state. It also leads to the government earning more indirect taxes.

This works well for the overall economy and the population as a whole though it may not be perceived in that way by the local population. As Abhijit Banerjee and Esther Duflo write in Good Economics for Hard Times: “ Migrants complement, rather than compete with, native labour as they are willing to perform tasks that natives are unwilling to carry out.”

To conclude, India has largely done whatever it had to stabilise its population growth, without resorting to any coercive policies (except for a short-time during the emergency). So, population growth has been slowing down for a while now and will continue to slowdown in the decades to come. In this environment, it is important to learn the right lesson from this entire issue, which is that societal level changes take time but they do happen at the end of the day, if the government keeps working towards it.

Also, going forward, it is important that young workers are allowed to move freely from one part of the country to another in search of an occupation; from the poorer parts to the better off parts.

As Rutger Bregman writes in Utopia for Realists: The Case for a Universal Basic Income: “Opening up our borders, even just a crack, is by far the most powerful weapon we have in the global fight against poverty.”

Of course, Bregman is talking in the context of international migration, with people moving from poorer countries to richer ones. But there is no reason why the same logic can’t apply to moving within the country as well.

Postscript: I just hope the Supreme Court judges are looking at the right data while listening to the PIL.

Why Monsoon Still Matters So Much

monsoon

The stock market wallahs have been excited since April 12, 2016. On that day, the India Meteorological Department(IMD) came up with the forecast for the monsoon season rainfall for 2016. The forecast this time is that the monsoon rainfall during the period July to September 2016 will be 106% of the long period average (LPA), averaged over the country, with a model error of ± 5%. At 106%, this will be an above average monsoon.

The long period average over the country as a whole for the period 1951-2000 is 89 cm. So the question is how good have the IMD’s forecasts been over the last few years? The short answer is—not good.

In 2015, the IMD had forecast a rainfall of 93% of the long period average. The actual rainfall was 86% of the long period average. The actual result was outside the ± 5% model error that IMD works with. When the IMD forecast a rainfall of 93% of long period average, it was essentially forecasting a rainfall of anywhere between 88% and 98% of the long period average.

In 2014, the IMD had forecast a rainfall of 95% of the long term average. The actual rainfall was 88% of the long period average. This was also outside the model error of ± 5%. In 2013, the actual rain was 106% of the long term average, in comparison to a prediction of 98%. This was also outside the model error of ± 5%.

In 2012, the actual rain and the predicted rain were at 92% and 99% of the long period average. This prediction was also outside the model error of ± 5%.

In 2011, the actual rain and the predicted train were at 101% and 98%. This was within the model error of ± 5%. Hence, in the last five years, the IMD has got only one prediction right. This makes one wonder if the stock market wallahs have taken this bad record of IMD at predicting monsoons into account or not. Between April 11, 2016 and April 18, 2016, the BSE Sensex has gone up by around 3.2% in four trading sessions.

The tragic thing is that nearly 70 years after independence from the British in 1947, the country is still so highly dependent on the monsoon season. As Raghuram Rajan, the governor of the Reserve Bank of India, told the Wall Street Journal in a recent interview: “We’re looking for signs of a good monsoon. Unfortunately, India is still somewhat sensitive to monsoons.”

So why is India so dependent on the monsoon season? Data from World Bank suggests that in 2012, only 36.3% of India’s total agricultural land had access to irrigation. This number would have improved since then. Nevertheless, nearly 60% of India’s agricultural land still does not have access to irrigation.

Hence, for water, Indian agriculture is majorly dependent on the monsoon rains. In this scenario, it is important that monsoon rains arrive on time, are well spread over the season and across different parts of the country, which do not have access to irrigation systems.

Once there are adequate rains, the farmers will be able to grow a good crop and then sell it at a good price. (Of course, a good crop does not mean a good price, there are other issues at play as well. But we will leave that for some other day).

The money that they thus earn will be spent on consumer goods, two-wheelers and so on. This will benefit the companies that manufacture these things, along with those who supply the inputs to these companies and so the multiplier effect will work. For example, more two-wheeler sales mean more sales for tyre companies. More tyre sales mean more demand for rubber and so on. The same logic applies to other inputs that go into making a two-wheeler.

At least this is how the stock market wallahs are thinking. But there are a few caveats that need to be made here. First and foremost, as I said earlier in this column, IMD forecasts more often than not have turned out to be wrong in the last few years. But given that they have made a forecast of 106%, unless they go majorly wrong, the rains this year will be better than the last two years. Second, a good crop need not necessarily mean a good price for the farmer. The agricultural markets around the country still don’t function like they should and benefit the trader community more than the farmers.

Third, the agricultural crop that will benefit from the monsoon rains (i.e. the kharif crop) will start hitting the market only in October 2016. Hence, the fillip to consumption (if any) will start happening only around then i.e. in the second half of this financial year.

Over and above this, there is another important point that needs to be made here. Data from the World Bank tells us that in 2014, India’s population was 129.5 crore. The population growth rate in 2014 was at 1.2%. Assuming that the population in 2015 grew at the same rate, the population for 2015 comes in at around 131 crore.

Data from World Bank shows that 50% of India’s population depends on agriculture. Hence around 65.5 crore out of the 131 crore are still dependent on agriculture. Data from the Central Statistics Office(CSO) shows that in 2015-2016, the total contribution of agriculture, forest and fishing to the gross domestic product (at current prices) was Rs 2,082,692 crore.

Hence, the per capita income of every individual dependent on agriculture, forest and fishing, works out to around Rs 31,797 (Rs 2,082,692 crore divided by 65.5 crore).

Now how do things look for the other half which is not dependent on agriculture? Their total contribution to GDP was Rs 101,69,614 crore. Hence, their per capita income works out to Rs 1,55,261 (Rs 101,69,614 crore divided by 65.5 crore).

What these calculations tell us is that in 2015-2016, those not working in agriculture earned nearly 4.9 times those working in agriculture. If we were to use GDP at constant prices (at 2011-2012 prices), the ratio comes to 5.5. Constant prices essentially adjust for inflation.

And this huge differential in per capita income between those who work in agriculture and those who don’t, is India’s single biggest problem. (Of course since I am using averages here, a lot of other issues are getting side-lined, but the broader point remains valid nonetheless).

This also shows the tremendous amount of inequality present in the country between the haves and the have-nots.

Agriculture no longer yields enough to feed the number of people dependent on it. The only solution to this is to improve crop yields (i.e. more production per hectare), ensure that the farmers are able to sell this increased production through a proper market which works and finally, people need to be gradually moved out of agriculture into doing other things.

This is going to be a slow process because people dependent on agriculture simply do not have the required skillset to be moved to do other things, in most cases. Until then we will simply be dependent on monsoon rains.

The column originally appeared in Vivek Kaul’s Diary on April 19, 2016

Why believing that real estate prices will never fall is a stupid idea

India-Real-Estate-MarketVivek Kaul

In a piece I wrote yesterday I said that banks in India play an important role in ensuring that real estate prices do not fall. The main point was that loans given by banks to commercial real estate, between November 2012 and November 2013, has grown at a much faster rate than their overall lending.
This has happened in an environment where real estate companies have a lot of unsold homes(or what is referred to as inventory in technical terms). The number of new projects being launched by real estate companies has also fallen significantly.
Hence, fresh loans given by banks has helped real estate companies pay off their old loans. And this has ensured that they haven’t had to cut prices in order to sell their unsold inventory. If bank loans to commercial real estate hadn’t grown as fast as it has, then the real estate companies would have had to sell off their existing inventory to repay their bank loans. And in order to do that they would have to cut prices.
In response to this piece several readers said that real estate prices never fall. Still others agreed that there is a real estate bubble in India but that bubble would never burst (whatever that meant). And this is not the first time I have received such responses.
So what is it that leads people to believe that real estate prices never fall? People have seen real estate prices only go up over the last 10 years. A home that was bought for Rs 25 lakh is now worth Rs 2 crore. Hence, there is a firm belief that real estate prices can only keep going up.
In fact such confidence was observed even during the American real estate bubble that ran from the late 1990s to late 2006.
As Alan S. Blinder writes in After the Music Stopped “A survey of San Francisco homebuyers… found that the average price increase expected over the next decade was 14 percent per annum…The Economist reported a survey of Los Angeles homebuyers who expected gains of 22 percent per annum over the same time span.”
At an average price increase of 14% per year, a home that cost $500,000 in 2005 would have cost $1.85 million by 2015. At 22% it would have cost $3.65 million.
If we apply this in an Indian context we get some fairly interesting numbers. A three bedroom apartment near the Sector 12 metro station in Dwarka, a sub-city of Delhi, went for around Rs 25 lakh nearly 10 years back.
Now it costs around Rs 2 crore. If prices rise at 14% per year it will cost Rs 7.4 crore in 10 year’s time. At 22% it will cost Rs 14.6 crore. If prices rise at the same rate as they have in the last ten years, then the home would cost around Rs 16 crore. And these are huge numbers that we are talking about here. This small calculation tells us how ridiculous it is to assume that real estate prices will continue to go up at the same rate as they have in the past.
We all know what happened in the United States. The real estate bubble peaked in 2006. Prices started to fall after the last. For the last 16 months real estate prices as measured by the 20 City S&P/ Case- Shiller Home Price Index, have been rising. But they are still 20.7% below their 2006 peak.
A similar thing is playing out in the Indian context as well, wherein people are extrapolating the price rise of the last 10 years over the future. They are “anchored” into the price rise that real estate has seen over the last 10 years and this has led them to believe that prices will continue to rise forever.
What they forget is that real estate prices fell dramatically between 1997 and 2003. As Manish Bhandari of Vallum Capital writes in a report titled The End game of speculation in Indian Real Estate has begun “The previous deleveraging cycle in year 1997-2003 witnessed price correction by more than 50% in Mumbai Metro Region (MMR) property.” Yes, you read it write, prices fell by 50% in Mumbai, the last place you expect prices to fall, given that the city is surrounded by the sea on three sides and can grow only in one direction.
Other than the price rise, another reason behind the belief that real estate prices will continue to go up is the fact that there is only so much land going around. In fact, this reason has been offered for more than 100 years.
As George A. Akerlof and Robert J. Shiller point out in Animal Spirits “In a computer search of old newspapers, we found a newspaper articles from 1887—published during the real estate boom in some U.S. cities including New York—which used the idea to justify the boom amid a rising chorus of skeptics: “With the increase in population, the demand for land increases. As land cannot be stretched within a given area, only two ways remain to meet demands. One way is to build high in the air; the other is to raise price of land…Because it it perfectly plain to everyone that land must always be valuable, this form of investment has become permanently strong and popular.”
The point I am trying to make here is that the ‘limited land’ argument to justify high real estate prices is as old as land being bought and sold. Nevertheless, in most cases there is enough land going around. This is reflected in the American context in the fact that real estate prices have barely risen over the last 100 years, once they are adjusted for inflation.
As Akerlof and Shiller write “Moreover, real home prices in the United States rose only by 24% from 1900 to 2000, or 0.2% per year. Apparently land hasn’t been the constraint on home construction. So home prices have had negligible real appreciation from the source.”
What about India? While land maybe an issue in a city like Mumbai, it clearly is not much of an issue anywhere else. There is enough land going around.
Economist Ajay Shah did some number crunching in a May 2013 column in The Economic Times. He showed that there is enough land to house India’s huge population. As he wrote “A little arithmetic shows this is not the case. If you place 1.2 billion people in four-person homes of 1000 square feet each, and two workers of the family into office/factory space of 400 square feet, this requires roughly 1% of India’s land area assuming an FSI(floor space index) of 1. There is absolutely no shortage of land to house the great Indian population.”
Also, it is worth pointing out here that real estate prices have fallen dramatically even in countries like Japan where land unlike the United States is scarce. “Urban land prices have recently fallen in Japan (where land is every bit as scarce as it is in other countries). In fact they fell 68% in real terms in major Japanese cities from 1991 to 2006,” write Akerlof and Shiller. And the property prices in Japan are still lower than they were in the 1980s.
The moral of the story is that just because something has continued to happen till now, does not mean that it will continue to happen in the future as well. There are many fundamental reasons behind why the Indian real estate bubble is unsustainable (I made some of them in yesterday’s piece).
Let me make a few more here. Indian real estate has now become totally unaffordable. As Bhandari writes “The current real estate price represents affordability of very few, while average users have to sell their twenty years of future earnings to afford a house.”
The employment situation remains extremely grim. In a report titled Hire and Lower – Slowdown compounds India’s job-creation challenge,Crisil estimates that “employment outside agriculture will increase by only 38 million between 2011-12 and 2018-19 compared with 52 million between 2004-05 and 2011-12.” This in an environment where “India’s working age population would have swelled by over 85 million. Of these, 51 million would be seeking employment.”
With fewer non agriculture jobs being created a direct implication would be that incomes will not continue to grow at the same pace as they have in the past. And that in turn will mean a lower amount of money waiting to get into real estate. There are other economic indicators also which clearly show that the Indian economy has slowed down considerably than in comparison to the past. And the real estate sector will have to adjust to this reality.
Bhandari believes that the scenario that played out during the period 1997 ad 2003 will play out again, very soon. As he points out “One of the most important proponents of fall in the property prices is likely to start from the deleveraging cycle, by the Indian banking sector, which is running a multi decade investment to deposit ratio (108%). The reversal of easy business cycle, scarcity of capital, tight monetary cycle in domestic and international market will force scheduled commercial banks to deleverage their balance sheet over the next three to four years. One can observe the same scenario, witnessed in 1997-2003, when deleveraging by the Indian Banking Sector was accompanied by deleveraging corporates that had accumulated huge debts on their books during good times. This augurs a difficult time for the Real Estate Industry.”
Even with all these reasons it is difficult to predict when the Indian real estate bubble will start running out of steam. But that does not mean that real estate prices will never fall in India. It may happen this year. Or in 2015. Or the year after that.
But in the end, all bubbles burst. It is just a matter of time. As Blinder aptly puts it “Anyway, one thing we do know about speculative bubbles—whether in houses, stocks, or anything else—is that they eventually burst.” And what that tells us is that days of earning huge returns from Indian real estate are more or less over.
The article originally appeared on www.firstpost.com on January 8, 2014
(Vivek Kaul is a writer. He tweets @kaul_vivek)