The Demographic Transition Model is a powerful tool for describing population growth, birth rate, and death rate trends. However, the model was developed based on European countries over 100 years ago and does not always accurately predict current trends.
In 2022, we explored some limitations of the DTM. We’ll pick up that exploration with new data and other case studies that highlight areas where the DTM isn’t as useful as a predictive model.
Variable Trends in Birth Rates
Our world is a complex place with many factors beyond industrialization impacting birth and death rates. We know that the number of children born depends on a wide variety of things: like the cultural and political value of women in society, the level of education that women reach, access to contraceptives, the cost of housing and childcare, among others. And while these other factors are certainly related to industrialization, they can also impact birth rates directly and distort expected trends in the DTM. Here we’ll look at three countries whose birth rates don’t fit cleanly into the DTM.
Want to better understand the population trends behind the Demographic Transition Model? Read Understanding Basic Demography: What Are Birth Rate and Death Rate? for a closer look at how these key measures shape population change.
Example 1: Israel
Israel is an advanced and industrialized economy and shares many characteristics with stage 5 countries. It is a high-income nation with a low death rate, low infant mortality rate, stable birth rate, and long life expectancy. But Israel is categorized as a late stage 3 country in the DTM. Israel’s birth rate is one of the highest of all developed and industrialized nations due to political, cultural, and religious factors. As a result, Israel’s place in the DTM does not accurately predict the country’s real level of industrialization.
Example 2: Botswana
Botswana is a stage 3 country. Typically, a country in stage 3 of the DTM would see a sharp decrease in the birth rate and slowing population growth. This was true in the 1980s. But starting about 20 years ago, Botswana’s birth rate stalled. As a result, Botswana’s population continues to steadily climb. Whether or not the stalled birth rate reflects Botswana stalling in its transition through the DTM to an industrialized, advanced economy remains to be seen.
Example 3: Romania
Romania passed an abortion ban in 1966 to increase population growth. At the time, women had very little access to contraception and relied on abortions to determine their family size. The birth rate almost immediately doubled as a result of the ban, a spike completely unrelated to industrialization or urbanization trends. It did not take long for women in Romania to find other ways to manage fertility, and the birth rate began to fall. Abortions became legal again in 1989. Within months, the birth rate returned to 1966 levels and (for the most part) continued to trend downward until present day.
Want to explore the Demographic Transition Model from the beginning? Read our guide starting with Stage 1 of the Demographic Transition Model to learn what population patterns look like before demographic transition begins.
The Importance of Time and Context: Croatia
It may seem simple to categorize a country based on its birth rate, death rate, and how the total population is changing. But it is risky to only take a snapshot of these three figures and assign the country to a specific stage. For example, consider Croatia.
Since 2021, Croatia’s death rate has fallen dramatically, its birth rate has fallen slightly, and its population has risen. Based on those factors, Croatia might be categorized as a late stage 3 country. But a snapshot of the last four years does not take into account the longer trends. And when zoomed out, it’s clear that Croatia is a stage 5 country.
In 2021, COVID-19 ravaged Croatia. The death rate spiked to the highest levels since the 1950s. The fact that the death rate fell over the last four years only means that the death rate returned to pre-pandemic levels. A longer look shows a low, steady and slightly climbing death rate (except for COVID), an even lower and declining birth rate, and a steady declining population, all signs of a stage 5 country.
The Importance of Scale of Analysis: Ethiopia
Examining birth rates, death rates, and population totals for entire countries can distort important trends happening within nations. Take Ethiopia, for example, which is a stage 2 country. The death rate dropped significantly, the birth rate is high but falling, and population is increasing overall. On average, the typical Ethiopian woman has close to 4 children.
But change the scale of analysis from the national level to the city level, and this won’t necessarily be the case. In the capital city of Addis Ababa, the birth rate dropped dramatically and the city’s fertility rate is currently below replacement level at 1.5 children per woman. Women in the capital have more access to contraceptives and general healthcare, stay in school longer than their rural neighbors, and have more employment opportunities, leading to smaller families. If using the DTM to make assumptions about Ethiopia’s population, changing the scale of analysis reveals important trends critical for good governance.
Conclusion
The Demographic Transition Model provides a framework to understand connections between birth rates, death rates, population totals, and economic industrialization. But the DTM misses real-life details and trends that have implications in our changing world. Understanding gaps in the DTM, and places where real-life deviates from the expected trends will be critical for policy-making and planning for a more sustainable future.
Frequently Asked Questions About the Demographic Transition Model
What does the Demographic Transition Model show?
The Demographic Transition Model (DTM) shows how birth rates, death rates, and population growth tend to change as societies develop economically and industrialize. The model divides this transition into stages, beginning with high birth and death rates and generally progressing toward lower birth and death rates and slower or declining population growth.
The DTM is useful for identifying broad demographic patterns, but individual countries do not always follow the stages exactly.
What are the limitations of the Demographic Transition Model?
One major limitation of the Demographic Transition Model is that it simplifies complex demographic changes into a series of stages. Birth rates and death rates can be influenced by factors that the model does not fully capture, including government policy, cultural and religious beliefs, access to contraception and healthcare, economic conditions, education, conflict, and major events such as pandemics.
The model was also developed largely from the historical experience of European countries, so the same pattern may not describe demographic change equally well in every country or region.
What are the strengths of the Demographic Transition Model?
The DTM provides a simple framework for understanding the relationship between birth rates, death rates, population growth, and economic development. It allows researchers and students to compare broad demographic patterns between countries and observe how populations can change over time.
Its strength is therefore not that it predicts every country’s future perfectly, but that it provides a starting point for identifying and discussing population trends.
How does the DTM explain population growth and decline?
Population grows when births substantially exceed deaths. In the earlier transitional stages of the DTM, death rates often decline while birth rates remain high, resulting in rapid population growth.
As birth rates later decline, the rate of population growth slows. In the later stages, birth rates and death rates may both be low, and if births remain below deaths for an extended period, the total population may begin to decline.
Why don’t all countries fit neatly into the Demographic Transition Model?
Countries can experience demographic changes for reasons that are not directly related to industrialization. Government policies, cultural practices, healthcare access, economic conditions, migration, and major historical events can all influence birth and death rates.
Israel, Botswana, Romania, Croatia, and Ethiopia illustrate different ways that real demographic patterns can diverge from what the DTM might lead us to expect. These examples show why the model is most useful when it is considered alongside the historical and social context of individual countries.
Can different parts of the same country be in different demographic transitions?
National averages can hide major demographic differences within a country. Urban and rural areas, for example, may have very different fertility rates because of differences in education, employment, healthcare, and access to contraception.
Ethiopia provides a useful example. While national fertility remains comparatively high, fertility in Addis Ababa is much lower. Looking only at the national DTM classification can therefore overlook important regional differences in how populations are changing.