Global population trends

The dependency ratio is a measure comparing the number of people of non-working age (those aged under 15 and those aged 65 and over) with the number of people of working age (15–64).

It is expressed as the number of dependents per 100 people of working age; a higher ratio means the working population must support more dependents.

1. Rising pension and healthcare costs. A larger elderly population increases spending on state pensions and on health and social care, straining government finances.

2. A shrinking workforce and labour shortages. With fewer people of working age relative to the retired, there are fewer workers and taxpayers, which can slow economic growth and raise aged dependency.

3. Pressure to change policy. Countries may have to raise the retirement age, encourage immigration of workers, or introduce pro-natalist policies to offset the decline in the working population.

A pro-natalist policy aims to increase the birth rate, usually where a population is ageing or declining. Governments offer financial incentives — baby bonuses, generous maternity and paternity leave, subsidised childcare and tax breaks — to encourage larger families. France is a leading example, using family allowances (allocations familiales) and state-funded childcare to keep its fertility rate (around 1.8) higher than most of Europe.

An anti-natalist policy aims to reduce the birth rate, usually where a population is youthful and growing rapidly. It uses family planning, contraception, education and sometimes legal limits or incentives to have fewer children. China's former one-child policy (1979–2015) is the strongest example, sharply cutting the fertility rate.

The key difference is direction: pro-natalist policies raise births (to fight ageing), while anti-natalist policies lower births (to slow rapid growth).

Describe — Country X. The pyramid has a very wide base, showing a high proportion of children (0–14) and therefore a high birth rate. Its steeply concave sides show each older age group is much smaller than the one below, and the very small top shows few people aged 65 and over.

Describe — Country Y. The pyramid has a narrow base (few children, a low birth rate), near-vertical sides (each age group is a similar size) and a broad top, showing a large elderly population and, at the oldest ages, more females than males.

Explain — Country X. The wide base and rapidly narrowing sides reflect a high birth rate and a relatively high death rate with a low life expectancy — typical of a less developed country in Stage 2 of the DTM (e.g. Niger). Limited healthcare, high infant mortality and high fertility produce this youthful structure with heavy youthful dependency.

Explain — Country Y. The narrow base and broad top reflect a low birth rate (below replacement) and a low death rate with a high life expectancy — an ageing population in Stage 4/5 of the DTM (e.g. Japan). Good healthcare and a low fertility rate mean most people survive to old age, and because women live longer than men, the very top of the pyramid is female-dominated, producing high aged dependency.

The two pyramids therefore show opposite ends of the demographic transition: youthful and rapidly growing (X) versus ageing and possibly declining (Y).

Formula. The dependency ratio = the non-working-age population (under 15 + 65 and over) divided by the working-age population (15–64), multiplied by 100:

$\text{Dependency ratio} = \frac{12 + 29}{59} \times 100$

Working. The dependents total $12 + 29 = 41$ and the working-age share is $59$, so:

$\frac{41}{59} \times 100 = 69.5 \approx 70$

Japan's dependency ratio is therefore about 70 — for every 100 people of working age there are roughly 70 dependents.

Comment. The figure is high, but the important point is its composition: the aged share (29%) is more than double the youthful share (12%), so this is overwhelmingly aged dependency. The aged dependency alone is about $\frac{29}{59} \times 100 \approx 49$, close to half. This confirms Japan is an ageing population in Stage 5 of the DTM, with a shrinking workforce supporting a large elderly population — placing heavy pressure on pensions and healthcare.

An ageing population is one with a rising proportion of people aged 65 and over, caused by high life expectancy and a low birth rate. Japan is the clearest example: about 29% of its population is aged 65 and over, its total fertility rate is roughly 1.3 (well below the 2.1 replacement level) and life expectancy is around 84 years, placing it in Stage 5 of the DTM with natural decrease.

Economic consequences. The most serious effect is a rising aged dependency ratio. As the workforce shrinks and the retired population grows, there are fewer workers and taxpayers supporting more pensioners. This raises the cost of state pensions and, because the elderly need more medical and social care, of healthcare — a growing burden on government finances and on the shrinking working-age population. Japan also faces labour shortages in sectors such as care and construction, which can slow economic growth.

Social consequences. A large elderly population increases demand for care homes, medical services and age-friendly infrastructure. In Japan, rising numbers of elderly people live alone, and the strain on families and carers is significant. There can also be a shortage of younger workers to staff essential services.

But there are opportunities too. The elderly are not only a cost: the 'grey pound' (spending by wealthy retirees on leisure, travel and healthcare) creates jobs, and older people provide an experienced workforce and unpaid childcare for families. Analysing the balance shows the consequences are mainly negative for public finances and the labour supply, but not wholly so.

Overall, Japan's ageing population raises dependency, pension and healthcare costs and shrinks its workforce, which is why it has responded with automation, a higher retirement age and encouragement of female and older workers.

Population structure creates very different challenges at the two ends of the Demographic Transition Model: ageing populations (Stage 5, e.g. Japan, Germany) and youthful populations (Stage 2, e.g. Niger). Whether ageing is the greater challenge depends on the type of pressure created and on a country's wealth and capacity to respond. This essay examines both sides before reaching a judgement.

The case that ageing is the greater challenge. Ageing populations create severe, long-term economic pressures that are hard to reverse. In Japan, around 29% are aged 65 and over and the fertility rate (~1.3) is far below replacement, so the workforce is shrinking while the number of pensioners grows. This raises aged dependency, pushing up pension and healthcare costs at the same time as the tax base contracts — a structural problem, because you cannot quickly create more workers. Ageing also brings labour shortages and, in a declining population, weaker economic growth. Crucially, the causes (long life expectancy and low fertility) are difficult to change, so the challenge is enduring.

The case that youthful populations are the greater challenge. However, youthful populations arguably create more urgent and larger-scale problems, especially in poorer countries. In Niger, about 50% of the population is under 15 and the TFR is around 6.7, giving an extremely high youthful dependency ratio (over 100). This means overcrowded schools, high demand for maternal and child healthcare, and — critically — the need to create millions of new jobs as the young reach working age. Because such countries are low-income with limited capacity to cope, rapid growth can lock them into poverty, food and water insecurity and unemployment. Demographic momentum means the population keeps growing for decades.

The role of wealth and capacity to cope. The decisive factor is often development level. Ageing mainly affects wealthy countries (Japan, Germany) that have the resources, technology and institutions to adapt — through automation, raising the retirement age, immigration and pro-natalist policies (as in France, TFR ~1.8). Youthful populations mainly affect poorer countries with far less capacity to manage them. So the same demographic pressure is far harder to cope with where resources are scarce, suggesting youthful populations can be the greater challenge for the countries that face them.

Conclusion. On balance, neither structure is universally the greater challenge — it depends on context. Ageing is a serious, structural and long-lasting problem, but it occurs in rich countries with the capacity to cope. Youthful populations create more immediate, larger-scale pressures on services, jobs and resources, and they occur in poorer countries least able to manage them. The most accurate view is therefore that ageing is the harder problem to reverse, but a youthful population is usually the greater challenge in practice, because it strikes the countries with the lowest capacity to cope.

Governments use population policies to try to change their population structure — anti-natalist policies to slow the rapid growth of a youthful population, and pro-natalist policies (or immigration and retirement reform) to offset an ageing population. This essay assesses how far such policies actually work, using China, France and Japan/Germany as examples.

Anti-natalist policies — China's one-child policy. Facing a rapidly growing youthful population, China introduced the one-child policy (1979–2015), using incentives, penalties and access to contraception. In its own terms it succeeded: China's fertility rate fell sharply and hundreds of millions of births were prevented, easing pressure on food, resources and services and helping economic growth. However, the policy had severe unintended consequences: a gender imbalance (a cultural preference for sons led to sex-selective abortion, leaving tens of millions more men than women), a rapidly ageing population, and a shrinking future workforce. These problems were so serious that China relaxed the policy to two children (2016) and then three (2021) — yet the birth rate has not recovered, showing that policies can be very hard to reverse. So the policy managed one problem but created others.

Pro-natalist policies — France. To counter low fertility, France offers generous, long-term incentives: family allowances (allocations familiales), extended maternity and paternity leave, subsidised state childcare and tax breaks for larger families. This has been relatively successful, keeping France's fertility rate (around 1.8) among the highest in Europe, above countries with weaker support. This suggests pro-natalist policies can work — but they are expensive, act slowly (a child born today only joins the workforce ~20 years later) and rarely raise fertility back to the replacement level of 2.1.

Alternatives to natalist policies. Where changing the birth rate is slow or difficult, countries manage structure in other ways. Germany and Japan have used immigration to fill labour shortages and boost the working-age population, though this can be politically controversial. Many ageing countries also raise the retirement age, invest in automation and robotics (Japan), and encourage female and older-worker participation. These can be more immediately effective than trying to change fertility.

The limits of population policy. Overall, policies face real limits: they are slow to take effect, can be costly and politically sensitive, may produce unintended consequences (China's gender imbalance and ageing), and cannot easily reverse deep social changes such as women's education and the high cost of children. Structural change is driven as much by development and culture as by government action.

Conclusion. Population policies can manage the problems of a population structure, but only partially and with important limits. Anti-natalist policies clearly can slow rapid growth (China), and pro-natalist policies can support fertility (France) — but the first created new problems and the second cannot fully reverse ageing. The most accurate assessment is that policies are most effective when they are long-term, well-funded and combined with other measures (immigration, retirement reform, automation), and least effective when they try to force rapid demographic change, because a population's structure has powerful momentum that governments can influence but not simply control.

The relationship between population and resources is one of geography's oldest debates. Malthus (1798) argued that population grows geometrically (1, 2, 4, 8…) while food supply grows only arithmetically (1, 2, 3, 4…), so population must eventually outstrip resources and be cut back by checks — famine, disease and war. Boserup (1965) took the opposite view: population pressure drives innovation ('necessity is the mother of invention'). This essay evaluates how far Malthus was right.

The case that Malthus was right (the pessimist view). There is real evidence for Malthusian limits. Localised famines — for example in parts of the Sahel, including Niger, where a rapidly growing youthful population (TFR ~6.7) presses on fragile farmland and water — appear to show population outstripping food, exactly as Malthus predicted. Neo-Malthusians extend this to all resources: the Club of Rome's Limits to Growth (1972) and Paul Ehrlich's The Population Bomb (1968) warned that finite resources, climate change, water stress and biodiversity loss would eventually check human numbers. Rising food prices and environmental degradation suggest that resource limits are real, supporting the statement.

The case that Malthus was wrong (the optimist/Boserup view). However, the historical record has largely contradicted Malthus's central prediction. Since 1798 the global population has grown from ~1 billion to over 8 billion, yet food production has grown even faster, and famines have become rarer and more localised. This fits Boserup: population pressure stimulated the Green Revolution — high-yield crops, irrigation, fertilisers and mechanisation dramatically raised output in countries such as India, which went from food shortages to self-sufficiency. Technology and trade allow food to move from surplus to deficit areas, and falling fertility in developed and emerging countries (China's TFR is now well below replacement) is slowing growth without the catastrophe Malthus feared.

A more nuanced position — it depends on management and distribution. The evidence suggests famines are usually caused less by absolute shortage than by poverty, conflict and poor distribution than by a global lack of food. The world produces enough food for everyone, but access is unequal. This undermines the inevitability in Malthus's argument while accepting that local Malthusian crises can occur where innovation and governance fail — a partial synthesis of both thinkers.

Conclusion. On balance, I largely disagree with the statement. Malthus was right that resources place limits on population and that unmanaged growth can cause local crises (Niger, the Sahel), and neo-Malthusian concerns about the environment and climate are serious. But his central prediction has been proved wrong on the global scale: as Boserup argued, population pressure has repeatedly driven innovation (the Green Revolution, India) so that food supply has outpaced population for over two centuries. The most accurate view is that population growth need not outstrip resources where innovation, good governance and fair distribution exist — so 'always' in the statement is too strong.