The Demographic Transition Model Explains Population Change

The Demographic Transition Model (DTM) is a framework geographers use to explain how a country's birth rate and death rate change as it develops economically and socially — and what those shifts mean for population size and structure. Before diving in, one key term: the difference between birth rate and death rate is called the natural increase rate — positive means the population grows from within, negative means it shrinks. Stage 1 is pre-industrial society: both birth rates and death rates are very high, so population stays roughly stable and low. Think of medieval Europe or isolated pre-contact communities — high birth rates compensate for frequent deaths from famine, disease, and conflict. Stage 2 begins when death rates fall sharply, usually because of better sanitation, clean water, vaccines, and improved food supply. But birth rates stay high — cultural norms, agricultural labor needs, and limited family planning keep them elevated. The gap between falling deaths and still-high births creates a strongly positive natural increase rate, causing rapid population growth. Much of sub-Saharan Africa moved through this stage in the 20th century. Stage 3 sees birth rates begin to fall. As countries urbanize, children become less economically necessary, women gain education and workforce access, and contraception becomes available. The natural increase rate shrinks but stays positive — population still grows, but more slowly. Stage 4 is the post-industrial stage: both birth and death rates are low, natural increase approaches zero, and societies age — more older people, fewer children. Germany, Japan, and South Korea illustrate this today. Some geographers argue there is a Stage 5 where birth rates fall below death rates, making natural increase negative and causing population decline. Key limitation: the DTM was built from Western European history. Some countries — like those that received foreign aid before industrializing — skipped patterns the model predicts. Use it as a useful lens, not a law.