IB ESS HLTopic 8 — Human PopulationsPaper 1 & 2HL only~10 min read
Dependency Ratios and Population Momentum
Two ideas here, and they are the reason age structure matters more than population size. The dependency ratio asks how many people each worker has to support. Population momentum explains why a country can keep growing for decades after families have already stopped having large numbers of children.
📚 What you need to know
Populations split into three groups: young dependants (0–14), economically active (15–64) and elderly dependants (65+).
The dependency ratio compares dependants with workers, expressed per 100 workers.
A low dependency ratio means more workers per dependant, which supports economic growth.
The same ratio can come from very different structures, so always say which dependants dominate.
Population momentum is continued growth after fertility has fallen, caused by a large young generation reaching child-bearing age.
Negative momentum is continued decline even after fertility rises, because there are too few women of reproductive age.
The three age groups
The boundaries are set by convention, not biology. They are meant to divide people who mostly earn from people who mostly depend on those earnings.
0–14, young dependants. In school or too young for it, supported by parents and by state spending on education and child health. High in countries with high fertility, such as Nigeria.
15–64, economically active. The working population. They earn, pay tax and support both other groups.
65+, elderly dependants. Mostly retired, drawing pensions, healthcare and social care. High in ageing countries such as Japan.
These are assumptions, not facts. Plenty of 16-year-olds work, many people over 65 work, and a great deal of unpaid care work is done by people counted as “dependants”. The ratio is a rough tool, and saying so is worth an evaluation mark.
Calculating the dependency ratio
Dependency ratio
DR = ((population under 15 + population over 65) ÷ population aged 15–64) × 100
The answer is the number of dependants for every 100 workers. A ratio of 60 means 100 workers support 60 dependants. Anything above 100 means the dependants outnumber the workers.
The middle block pays for the two outer ones through tax. That is the whole idea behind the dependency ratio.
WORKED EXAMPLE
A country of 4.8 million has 1.44 million people under 15, 3.00 million aged 15 to 64, and 0.36 million aged 65 or over. Calculate the dependency ratio.
Step 1: add the dependants1.44 + 0.36 = 1.80 millionStep 2: divide by the working population1.80 ÷ 3.00 = 0.60Step 3: multiply by 1000.60 × 100 = 60Dependency ratio = 60Check your total adds up: 1.44 + 3.00 + 0.36 = 4.80 million. If it does not, you have misread the data.
Splitting the ratio in two
A single dependency ratio hides the most important information: which dependants. So it is often split.
Youth and old-age dependency
youth DR = (under 15 ÷ 15–64) × 100
old-age DR = (65+ ÷ 15–64) × 100
The two add up to the total. For the country above, youth DR is 48 and old-age DR is 12, which tells you immediately that this is a young country with a growing school-age population, not an ageing one.
WORKED EXAMPLE
Country B has 60 million people: 7.8 million under 15, 36.0 million aged 15 to 64, and 16.2 million aged 65 or over. Calculate its dependency ratio and compare it with the country above.
Step 1: total dependants7.8 + 16.2 = 24.0 millionStep 2: divide and scale(24.0 ÷ 36.0) × 100 = 66.7Dependency ratio = 66.7Step 3: split ityouth DR = (7.8 ÷ 36.0) × 100 = 21.7old-age DR = (16.2 ÷ 36.0) × 100 = 45.0Step 4: compare
Both countries are near 60, but the first is carrying children and the second is carrying pensioners.
Similar ratios, opposite problemsYoung dependants become taxpayers in fifteen years. Elderly dependants do not. That is why the split matters.
There is a window in between, when the big young cohort has grown up but has not yet retired and is having fewer children of its own. The dependency ratio dips, and the country has an unusually large workforce. Economists call this the demographic dividend. It only lasts a few decades, and only pays off if there are jobs and schools ready for it.
Population momentum
Population momentum is the tendency for a population to keep growing even after fertility has dropped to or below replacement level.
The reason is age structure. If a country has spent decades with high fertility, it now has a huge number of children. Those children grow up. Even if each of them has only one or two children of their own, there are so many of them that the total number of births stays high. Growth continues until that large generation has passed through its child-bearing years.
Fertility can fall to replacement level at the moment shown on the left, and the population will still grow for another forty years or more.
India is the standard example: fertility has now fallen to roughly replacement level, but the population is still growing because such a large share of the country is young. China shows the same effect after its one-child policy — fertility dropped sharply in the 1980s, yet the population kept rising for decades before flattening.
Think of a heavy train. You cut the power, but the train keeps moving because of everything already in motion. Population momentum works the same way, and it is the reason a policy passed today changes almost nothing about the population thirty years from now.
Negative momentum
The effect works in reverse too. If fertility has been very low for a long time, the generation now reaching adulthood is small. Even if fertility rises again, there are simply not enough potential parents, so births stay low and the population keeps falling.
Japan is in this position. Its fertility rate could rise tomorrow and the population would still shrink for years, because the number of women of reproductive age has already fallen.
The sentence that scores. Population growth depends on two things, not one: the number of children per woman, and the number of women of reproductive age. Momentum is what happens when those two point in opposite directions.
WORKED EXAMPLE
A country’s TFR fell from 5.8 to 2.1 over thirty years, yet its population is still growing by 1.4% per year. Explain why.
Step 1: what a TFR of 2.1 means
2.1 is replacement level, so each generation is only replacing itself.
Step 2: look at the age structure instead
Thirty years of high fertility produced a very large young generation, which is only now reaching child-bearing age.
Step 3: put it together
Fewer children per woman, but far more women having them, so total births stay high while deaths remain low.
Population momentumExpect growth to continue for roughly 40 to 60 years, until that generation has passed through its reproductive years.
💡 Exam tip
Show every stage of the dependency ratio calculation. Adding, dividing and multiplying by 100 are three separate opportunities for method marks.
Check the three age groups add up to the total before you start.
Never leave a dependency ratio uninterpreted. Say whether the burden is young or old, and what it means for spending.
Use the phrase “population momentum” explicitly. Describing the effect without naming it usually loses a mark.
Remember momentum works both ways. Negative momentum is the harder half and is worth mentioning.
Watch the wording: a ratio of 60 means 60 dependants per 100 workers, not 60 percent of the population.
⚠ Common mix-ups
Dividing by the total population instead of the working-age group. The denominator is 15–64 only.
Forgetting to multiply by 100. An answer of 0.6 is not a dependency ratio.
Assuming a high dependency ratio is always bad. A high youth ratio can become a demographic dividend; a high old-age ratio cannot.
Thinking momentum means the growth rate is rising. The growth rate is falling; the population is still going up.
Saying momentum lasts forever. It fades once the large generation passes reproductive age, typically over 40 to 60 years.
Treating everyone aged 15 to 64 as employed. Students, the unemployed and unpaid carers are all counted as active.
Up next: Global Population Patterns — putting these tools to work on real countries, with two case studies you can use in almost any long-answer question.
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