NCERT Solutions Exploring Society: India and Beyond Chapter 13 –190End of chapter — Questions and activities

Because a country cannot plan for people it has not counted or described. Demography turns a crowd into information a government and an economy can act on.

For the government, the chapter lists what the information is used for: planning infrastructure such as schools, hospitals, roads and housing, and municipal services like water, energy and waste management. The connection is direct and specific:

• “if a place has many young children, more schools are needed”;
• “if the proportion of the elderly in a population is higher, more facilities for healthcare are required”;
• “a higher proportion of young working-age population implies a greater need for gainful employment generation”.

For businesses, the same data tells them who their customers are — the chapter says they “assess their potential customers based on characteristics like age, gender, and income to create products that match people’s needs.”

The instrument for collecting all this in India is the census, conducted generally every 10 years, which records age, gender, marital status, education, languages spoken, religion, family size, occupation, employment status and income levels. Without it, every decision above would be a guess.

A low fertility rate does not affect the economy today — it affects it on a delay, and the delay is what makes it dangerous. A baby born this year enters the workforce in about 20 years and retires in about 60. So a fall in births works its way up the age ladder like a gap moving through a queue:

The chapter states the consequences of that final stage precisely: “As more people grow older and retire, there are fewer younger workers available to work in farms, factories, and offices. This could potentially lead to a slow down in the economic growth of the country.” Alongside that, the government faces “challenges paying pensions and providing services because of fewer young taxpayers”, and healthcare systems come under pressure because older adults need more care.

The evidence is China. Its fertility rate fell from 7.5 in 1963 to 1 in 2025, and the results are now visible:

How countries respond, and how well it works. Japan has raised the retirement age and uses technology and automation to compensate for labour shortages, with large-scale community centres and robots and AI assistants to help care for older adults. China ended the One-Child Policy in 2015 and now allows up to three children. Yet the chapter’s verdict is sober: China’s “fertility rate still remains below replacement level, showing the policy’s lasting impact on society and the economy,” and “policies in many nations have attempted to revive the fertility rate but have not been very successful so far.”

It is worth adding the other side honestly. In the short run, fewer children per family means fewer dependants for each worker to support, so more income can be saved, invested and spent on educating each child well. A low fertility rate is a problem for the workforce only when the small cohorts finally reach working age — but by then it is far too late to change it.

No. A high birth rate is neither good nor bad in itself — what matters is whether the country can educate, keep healthy and employ the people it produces. Both sides of the argument are real, so weigh them.

When a high birth rate helps. Babies born today are workers in twenty years. A country that had a high birth rate a generation ago finds itself, now, with a large working-age population — which is exactly India’s situation. Nearly 65 per cent of Indians are in the 15–59 group and the median age is 28. That is the demographic dividend: more people working means a larger volume of economic activity, and workers earn and pay taxes that build schools, hospitals, roads and parks. This is what the chapter calls the youth bulge of Stage 2 of the demographic transition.

When it does not help. A birth rate is a count of dependants today and of workers only much later. Children need food, housing, schools and healthcare for fifteen to twenty years before they produce anything. If the economy cannot supply those, a large young population becomes a large number of poorly-educated, unemployed young adults — a burden rather than a dividend. The chapter’s own example is China in the 1970s, where the government “was worried that its fast-growing population would cause shortages of food, jobs, and housing.” Its migration section adds the crowding problem: concentrating a very large population in a limited area “can put pressure on resources like water, air, and land, and strain services like transportation and housing.”

And the reverse is not automatically good either. China and Japan show that a very low birth rate creates its own set of problems — a shrinking workforce, unpayable pensions, and a whole generation growing up without siblings or kinship networks. There is no single correct birth rate. There is only the rate that a country’s schools, hospitals and job market can keep up with.

Neither is better by itself. The size of a population matters far less than its age structure and whether the country invests in its people. The clearest way to see this is to set the two situations side by side.

My view, and the reason for it: the better question is not “more or fewer?” but “can the country keep up?” A population growing faster than a country can build schools, clinics and jobs creates poverty; a population shrinking faster than a country can automate and reorganise creates stagnation. The chapter’s own answer to both is the same — invest in human capital, that is, in education, employment and healthcare — which is why it says the demographic dividend “can boost the country’s growth through the development of human capital.”

Because what a person needs, and what a person can give, both depend almost entirely on their age. An age structure is therefore a shopping list for services and, at the same time, a supply list of workers.

Examples that show the same principle in different places:

• India today needs colleges and jobs, not more primary schools. In the table in Question 11 the widest bar is 20–24 (9.5 per cent of males, 9.1 per cent of females), while 0–4 is smaller (8.6 and 8.2). The largest single group is at the age of finishing education and looking for work — which is exactly why the chapter stresses “gainful employment generation” and investment in “good education, skilling and healthcare”.
• Different Indian states need different things at the same moment. Fig. 6.19 shows the elderly share ranging from about 6.7 to 12.6 per cent, with Kerala the highest. Kerala and Tamil Nadu, which are ageing first, already need more geriatric care and pension support than Bihar and Uttar Pradesh, which still need schools and jobs for a young population.
• Japan, with a median age of 50, needs a different economy altogether. It has raised the retirement age, uses automation to make up for missing workers, and has built large-scale community centres and deployed robots and AI assistants to help care for older adults.

Part 1 — why families are getting smaller. India’s fertility rate has fallen from 5.7 in 1950 to 1.9 in 2025. The chapter gives three reasons and a fourth follows from its own data:

• Girls’ education. “One important factor is improvements in access to quality education, especially among girls.” A girl who stays in school marries later, so her child-bearing years start later and are fewer.
• Women in formal jobs. Women “are working in formal jobs” and so “are more likely to marry at a later stage and have fewer children.”
• Urbanisation and cost. “With rapid urbanisation, the cost of living is high particularly in cities, so people prefer to have smaller families.”
• Children now survive. In 1950, 271 of every 1,000 Indian children died before the age of five — more than one in four. By 2023 the figure was 28 per 1,000, about one in 36. When most children died young, having many was the only way to be sure some grew up. Now that almost all survive, parents can reach the family size they want with far fewer births.

Part 2 — does having fewer children ensure better education and health? No. It makes both much more likely, but it does not guarantee them.

What is true: a household’s income, food, time and attention are divided among fewer people, so each child’s share is larger. School fees, books and medical care for two children are affordable where for six they were not.

What “ensure” gets wrong: a child’s education and health also depend on things a family does not control — whether there is a good school and a health centre within reach, whether the household income is enough in the first place, and whether daughters are given the same food, medicine and schooling as sons. A small family in a village with no functioning school does not get a better education than a large one. The chapter’s own evidence supports this: Fig. 6.11 shows Bihar with the lowest female literacy and the highest fertility, so low fertility and good schooling tend to arrive together, as parts of the same development — one does not simply produce the other.

Part 3 — how it affects families and society.

a) Yes — dramatically, and the rise is still continuing. Reading the graph:

That is a gain of roughly 22 to 28 years in the 60 years from 1950 to 2010 — about five months of extra life expectancy for every year that passed. The chapter’s own text gives the same story more simply: life expectancy at birth in India “has risen from just 30 years at independence to 70 years in 2024.”

The reasons are the ones the chapter gives for the world after 1950, and they apply directly to India:

• Medical advances — antibiotics, vaccines, and oral rehydration solution.
• Better water and sanitation reaching more people.
• A sharp fall in child deaths from malaria and diarrhoea. India’s child mortality fell from 271 per 1,000 in 1950 to 28 in 2023.
• Better nutrition and rising incomes, as “the benefits of economic prosperity were reaching a larger population”.

b) In 2010, life expectancy at birth was about 65 years for males and about 68–69 years for females — women could expect to live roughly 3 to 4 years longer than men. (Read the two markers above the 2010 label: the blue one sits a little below the 65 gridline mark and the red one just under 70.)

The demographic dividend is “the economic growth potential of a nation resulting from a younger and productive age structure of a country”. It is not money and not a payment — it is a possibility created by the shape of a population.

Why a young age structure creates that possibility. Three separate effects add up:

1. More producers. “Young people can work in factories, offices, farms, or start businesses. The more people work, the larger the volume of economic activities, which helps the country grow.”
2. More taxpayers. The working-age population “earns money and pays taxes. The government uses that money to build schools, hospitals, roads, and parks.”
3. Fewer dependants for each worker. With roughly 65 per cent of Indians aged 15–59 and only about 27 per cent under 15 and 10 per cent over 60, each working person supports fewer non-working people than in an older or a much younger society — so more of what is produced can be saved and invested instead of simply consumed.

India’s position on this is unusual. Compare the median ages in Fig. 6.17:

Why the window closes. The chapter is precise about this. The number of children under five peaked in 2007; the number of Indians under 15 peaked in 2011 and is now declining. In Fig. 6.18 the 25–64 curve climbs to a maximum of about 0.95 billion around 2050 and falls after that, while the 65+ curve keeps rising and crosses the 0–14 curve at roughly the same date. So India’s working-age population stops growing around 2050, and the country then begins to move towards an older population.

What India needs to do before then — the chapter’s own list, with the reason each item is necessary:

How to do this activity: pick one country from early in the demographic transition and one from late in it — the contrast is what makes the exercise worth doing. Collect the four indicators for both from a reliable source (this chapter’s own figures, the Census of India, or United Nations population data in your school library), record the year each figure refers to, and put them in a single table. Then look at the two population pyramids side by side, because the pyramid explains the table.

Sample answer — India and Japan, using only figures printed in this chapter:

How they differ, in one sentence: India has many more people and they are much younger; Japan has far fewer and they are much older.

Why — the two countries are at different points along the same three-stage path in Fig. 6.12. Japan reached Stage 3 decades ago and has stayed well below replacement level ever since, so each of its birth cohorts has been smaller than the one before, for two generations. India’s fertility rate “began declining” only after the mid-1980s and is only now below replacement, so India still has enormous cohorts passing through their working years. Because the pyramid is a stack of past birth cohorts, a difference of thirty or forty years in when fertility fell shows up as a completely different shape.

How ageing countries are managing this (from the chapter):

• Japan has raised the retirement age, employed technology and automation to compensate for labour shortages, and built large-scale community centres and deployed robots and AI assistants to help care for older adults.
• China ended its One-Child Policy in 2015 and now allows up to three children — yet its fertility rate remains below replacement level.
• The chapter’s general finding: “Policies in many nations have attempted to revive the fertility rate but have not been very successful so far.” Ageing societies have had more success adapting to a smaller workforce than in reversing the decline that created it.

How to run the survey. Take permission from an adult before knocking on doors, and go in a pair. Aim for 20–25 people of different ages and occupations so your sample is not all of one kind. Tell each person what the survey is for and ask if they are willing to answer. Record exactly what they say, not what you expected them to say. When you write up the results, use totals and percentages rather than naming individuals.

The questionnaire. Section A identifies the respondent, Section B measures whether and when they migrated, and Section C asks why.

Section A — About you

1. Name (this will not be used in the report): ______________
2. Age: _____ years    Age group: ☐ under 18 ☐ 18–30 ☐ 31–45 ☐ 46–60 ☐ over 60
3. Occupation: ☐ student ☐ farming ☐ daily wage / construction ☐ shop or own business ☐ factory ☐ office or service ☐ homemaker ☐ retired ☐ other: ______

Section B — Where you live and for how long

4. Present place of residence (locality, town/village, district): ______________
5. Were you born in this place? ☐ Yes ☐ No
6. If no, where did you live before? (village/town, district, state): ______________
7. How many years have you lived in this place? _____ years
8. In which year did you move here? ______
9. Did you move alone or with your family? ☐ alone ☐ with family ☐ family joined later
10. Is the move ☐ permanent ☐ temporary ☐ seasonal (a few months each year)?
11. Do you send money back to your earlier home? ☐ yes ☐ no

Section C — Why you moved

12. Which of these applied to the place you left? (tick all that apply)
    ☐ limited job opportunities ☐ low wages ☐ lack of facilities such as schools or hospitals ☐ poor infrastructure — roads, electricity, water ☐ small or no landholding ☐ natural disaster — flood, drought, cyclone ☐ political instability or unrest ☐ other: ______
13. Which of these attracted you to this place? (tick all that apply)
    ☐ better employment ☐ higher wages ☐ education for self or children ☐ healthcare ☐ family already living here ☐ marriage ☐ other: ______
14. Which single factor mattered most? ______________
15. Would you go back if that factor changed at home? ☐ yes ☐ no ☐ not sure. Why? ______________

How to record and analyse it. Make a tally table with one row per respondent and one column per question, then work out simple figures such as:

• What share were born elsewhere? (e.g. if 12 out of 25 were, that is 12 ÷ 25 = 48 per cent.)
• Which age group migrated most? (You will probably find the 18–30 band largest — which is exactly what the THINK ABOUT IT box on page 177 is about.)
• Which factor was ticked most often in Question 12, and which was named most often in Question 14?
• How many arrived in each five-year period? Plotting this shows whether migration into your locality is rising or slowing.

How to draw it. Rule a vertical line down the middle of the graph paper — this is the age axis. Mark the seventeen age groups up it, 0–4 at the bottom and 80+ at the top, giving each an equal band. Along the horizontal axis measure the percentage, running outwards from the centre line in both directions: males to the left, females to the right. Use the same scale on both sides, or the pyramid will be misleading. Since the largest value is 9.5, a scale of 1 cm = 1 per cent fits comfortably. Draw each bar as a horizontal rectangle from the centre line, label the axes and give the figure a title.

Drawn accurately, the pyramid looks like this:

Check your work by adding up. Each column should come to 100, since these are percentages of that sex’s population:

The small difference from 100 is only rounding — every figure is given to one decimal place.

What the finished pyramid tells you. Group the bars and the chapter’s claims fall out of the arithmetic:

The working-age figure of about 64 per cent is the chapter’s statement that “nearly 65 per cent is in the working-age group (15–59 years)” — you have now verified it from the raw data yourself.

Three things to notice in the shape:

• The widest bar is 20–24, not 0–4. Males: 9.5 at 20–24 against 8.6 at 0–4. So the base has already begun to narrow — this is the “bell shape” the chapter describes, no longer the plain triangle of 1950 in Fig. 6.16. It is the fertility rate falling to 1.9 that has done this.
• Above age 35 the female bar is longer than the male bar in every single band (7.4 against 7.1, 6.7 against 6.3, and so on up to 1.2 against 0.9), while below 35 the male bar is longer in every band. That is the life-expectancy graph of Question 7 showing up in a different form: more boys are born, but women live longer, so the female share rises steadily with age.
• The wide middle is the demographic dividend, and the narrow base is its deadline. The large bars at 15–29 are today’s and tomorrow’s workers; the smaller bars below them are the workers of the 2040s. When those small cohorts reach working age, the wide middle will move upwards and become a wide top.

The first observation is that it is not a pyramid at all. The narrowest part of the graph is the bottom, and the widest parts are in the middle and upper-middle. Its shape is an urn — almost exactly the opposite of India’s 1950 triangle in Fig. 6.16.

Observations, reading values off the graph:

1. The base is very narrow. The youngest single-year cohorts measure about 0.4 million on each side, while the largest bars reach about 1.0 to 1.1 million. So each new cohort of Japanese children is only
   0.4 ÷ 1.05 = about 38 per cent the size of the biggest cohorts
2. There are two bulges, not one. The upper bulge is at about ages 74–78, that is people born around 1945–1949, just after the Second World War. The second is at about ages 49–54, people born around 1969–1974 — the children of the first bulge.
3. There is no third bulge. The 1970s generation did not produce a bulge of its own. Fig. 6.10 explains why: Japan’s fertility rate has stayed between roughly 1.2 and 1.45 throughout 2000–2023, far below the replacement level of 2.1.
4. The top is wide, and it is more female than male. Above about age 75 the right-hand (female) bars are visibly longer than the left-hand (male) bars — Japanese women outlive Japanese men.
5. It matches the median age of 50 given for Japan in Fig. 6.17: half of Japan is older than 50, which is why the graph’s centre of gravity sits so high.

What can be inferred from all this:

• Japan’s population must be shrinking. Each cohort arriving at the bottom is far smaller than the large cohorts now reaching the end of life at the top, so deaths outnumber births. This is what the chapter means when it lists Japan among the countries “already seeing shrinking populations”.
• Japan’s workforce is about to shrink sharply. The 1969–74 bulge is now around 50. During the 2030s it will pass 65. At that moment Japan loses its largest working cohort and gains its largest retired one simultaneously — the labour shortage the chapter describes is about to get much worse.
• This is Stage 3 of the demographic transition, carried far past replacement level. Deaths are low and births have fallen below them.
• It explains Japan’s policies. Raising the retirement age, using technology and automation to compensate for labour shortages, and building community centres with robots and AI assistants to care for older adults are all responses to this exact shape.
• It is a preview for India. The chapter says India’s pyramid “may start to age slowly, like what’s already happening in countries such as Japan or China”, and Fig. 6.18 puts a date on it: India’s 65+ curve overtakes its 0–14 curve at around 2050. Japan’s graph is what India’s could look like several decades after that.