China’s Population Control Model is an Outdated and a Bad Idea for India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


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.


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.


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.