Connecting the dots of population growth in Germany

Luc Guillemot

Hi, I’m Luc from the visualization team. This week, I am looking at population growth in Germany using a chart type notoriously difficult to read: a connected scatterplot!

World population is set to peak in 2086 at around 10 billion people. But some countries may already have passed their peak: China, for example, is projected to reach 2100 with only half as many people as it has today. In China’s case, declining population is due to its low and falling fertility rate, which currently stands at 1.17 children per woman (well below the replacement rate of 2.1).

The United Nations is also projecting a population decline for Germany:

But this drop isn't nearly as dramatic as China's. In fact, fertility rates in Germany have been below replacement since 1972, but the country has still experienced two periods of population growth since then. The explanation, of course, is immigration. Let’s have a look at the relationship between migration and population growth in Germany over the past 72 years with the help of a connected scatterplot:

This is not a line chart! The migration rate on the vertical axis reflects the ratio between the number of immigrants (people coming into Germany) and the number of emigrants (people leaving the country). A migration rate above zero means there are more immigrants than emigrants. The horizontal axis represents the country's total population. Follow the path of the dots to see how those two measures have changed over time.

When first looking at this chart, I was immediately compelled by these two loops! There's no simple relationship between migration and population growth. With my limited knowledge of German history (and the help of Wikipedia), I tried to explain the dynamics in the chart following the periods of population growth and decline that I also highlighted in the population chart above.

1. Immediately after the Second World War, the population was still declining on net — but immigration increased dramatically, starting with the ethnic Germans who came to the country from all over Europe.
2. After 1952 and until the 1970s was a period of post-war prosperity known as the Wirtschaftswunder (economic miracle). Large scale immigration continued with guest workers from Turkey and Italy, among other countries, to compensate for the lack of labor force. Germany's fertility rate at that time was also still above replacement, which together explains the rapid increase in population.
3. The 1970s was a period of economic crisis everywhere in the world. Not only did fertility rates go down, but immigration declined to a point where there was more emigration than immigration, putting Germany back into population decline. In the 1970s, the country passed family reunification laws, which helped to boost the immigration rate.
4. In the years leading up to German reunification in 1991, the increase in immigration brought a return to population growth. Many of these immigrants were Spätaussiedler (later ethnic German "returnees") and refugees from the Yugoslav wars. But these sources of immigration slowed down and the population started to decrease again.
5. In the 2000s the expansion of the European Union triggered a new wave of large scale immigration from European countries, especially from Poland and Romania.

The economy of Germany probably wouldn’t have been so prosperous in the second part of the 20th century without the labor force coming from abroad. With an aging population and immigration being such a hotly debated question in Germany (as in other countries), whether this prosperity will continue depends a lot on the political path that the country chooses in the coming years.

How I created the connected scatterplot

To create a connected scatterplot with Datawrapper, you can start with a regular scatterplot and use the experimental “Custom lines and areas” feature in the Annotate tab. I used code to generate the coordinates of the line segments, which can be found in this Observable Notebook. All data are from the United Nations 2022 report World Population Prospects.

Despite connected scatterplots being difficult to read and full of shortcomings, I like how visually dramatic a looping line can be. I hope I can convince you that connected scatterplots have a place in the beautiful world of data visualization. For chart types and for people, I believe that diversity can only lead to a better society!

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Luc Guillemot

(he/they, @Luc) is a developer on Datawrapper’s visualization team. Before that, he created visualizations as a data journalist and social scientist. When not making sure your labels have the correct position, you can spot him running in the forests around Berlin or listening to hyperpop.

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