44:["$","title",null,{"children":"Total Fertility Rate | Plain Vanilla Mary | Observable"}] 4c:T942,html `

Total Fertility Rate

Galneder & Mugrauer · FH-Hagenberg · Human-Centered Computing · May & June 2021

The total fertility rate, also known as the fertility rate, is a measure used in demography that indicates how many children a woman would have on average over the course of her lifetime if the age-specific fertility rates determined at a uniform point in time were valid for the entire period of her fertile life phase. It is determined by summing the age-specific fertility rates and dividing by 1000.

The total fertility rate is not to be confused with birth rate, which is the total number of live births per 1000 population divided by the length of the period (in years). This rate is used together with the mortality and migration rates to calculate the population growth.

The first collected data we have found dates back to 1541 in the UK, but it is only since around 1950 that fertility rates have been regularly calculated.

Hypothesis: The total fertility rate has declined worldwide over time.

To validate this hypothesis, we have visualised a timeline for each of the countries covered. It is clear that the fertility rate has declined since the beginning of the record.

`4d:Tc01,html `

With the help of this visualization, we can approve the first hypothesis, that the fertility rate declined over the years worldwide. The brighter the bars are, the lower the fertility rate was. This helps us to identify clear differences. You may have observed that the big downturn in many countries can be seen from the 1960s onwards, which is due to the introduction of the birth control pill in America in August 1960. This started a new era of contraception and thus the next hypothesis can also be validated:

Hypothesis: Political decisions as well as medical development and the associated development of various contraceptives, have an influence on the total fertility rate.

If we look at China, our hypothesis has also turned out to be true. The policy measures to control population growth and the related introduction of the one-child-policy in 1980 caused the total fertility rate to drop enormously. Since 2015, there have been relaxations of this policy, since which one can see a minimal growth in the fertility rate.

After my grandmother told us that after the soldiers returned from the Second World War, just about every man in the village fathered at least one child, we wanted to investigate this too. Unfortunately, too little data was collected at that time to give a generalised answer.

The Vietnam War lasted from 1955 to 1975 and it was precisely during this period that the highest fertility rate was measured in Vietnam, which was very interesting but also astonishing for us.

Another hypothesis we made is:

Hypothesis: In more developed countries with relatively high GDP, the total fertility rate is lower than in less developed countries.

To validate this hypothesis, we created a world map to get a better overview how the total fertility rate is in more and less developed countries. There's the option to change the year, so the change of the fertility rate is visible very well.

`4e:T501,mapChart = { const svg = d3.create("svg") .style("display", "block") .attr("viewBox", [0, 0, width, mapHeight]); const defs = svg.append("defs"); defs.append("path") .attr("id", "outline") .attr("d", path(outline)); defs.append("clipPath") .attr("id", "clip") .append("use") .attr("xlink:href", new URL("#outline", location)); const g = svg.append("g") .attr("clip-path", `url(${new URL("#clip", location)})`); g.append("use") .attr("xlink:href", new URL("#outline", location)) .attr("fill", "white"); g.append("g") .selectAll("path") .data(countriesFeatures.features) .join("path") .attr("fill", d => color(mapData.get(d.properties.name))) .attr("d", path) .append("title") .text(d => `${d.properties.name} ${mapData.has(d.properties.name) ? mapData.get(d.properties.name) : "N/A"}`); g.append("path") .datum(topojson.mesh(world, world.objects.countries, (a, b) => a !== b)) .attr("fill", "none") .attr("stroke", "white") .attr("stroke-linejoin", "round") .attr("d", path); svg.append("use") .attr("xlink:href", new URL("#outline", location)) .attr("fill", "none") .attr("stroke", "black"); return svg.node(); }4f:T653,html `

The visualised world map, combined with the scatter plots below, shows very clearly that our hypothesis that the fertility rate is higher in less developed countries is true. Most countries in Africa have a low GDP compared to the average European country. In contrast, the fertility rate is high. The scatter plot at the center top, GDP is compared to the total fertility rate and it highlights the relation between GDP and the total fertility rate very well.

Argentina is the country with the second highest GDP in South America. The exciting thing is that Argentina was politically strongly influenced by immigration from Europe until the middle of the 20th century, which is also evident in the total fertility rate.

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\n

Fertility rate over time for each country

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\n ${TimeAxis({ interval, start, stop, width })}\n

\n ${childs_per_woman_per_year.map((d) =>{\n return timeChart(d.data, { title: d.country })\n })}\n

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World's fertility rate for a specific year

Fertility rate compared with GDP and sex ratio

Code

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