WorldData.info

“Numbers don't lie”

Let's take land area as a simple example. One would think that such mundane data would be an easy task in this age of satellites and GPS-based land surveying. Far from it. There are so many different calculation methods that even die-hard statisticians get confused. At what width is a river deducted from the land mass? The most common source for such figures is Google. But that is precisely where there is no reference to the source or explanation of the calculation method. It is assumed that Google uses its own map service for this. Whether or when 2- or 3-dimensional models are used here is not apparent. With a 3D model, the surface area would also be measured correctly on mountain slopes.
Google's data often corresponds with that of the World Bank, which usually takes several years to obtain the data from the United Nations. This is probably the most official source, but it only reproduces information from the FAO, which in turn obtains all its data from external sources. The term "imputed by a receiving agency" usually refers to the country itself. As in many other areas of statistics, questionnaires are sent to each country annually. Those who do not fill them out are simply estimated. External agencies may also be responsible for these estimates. These figures are then included in the statistics under the heading "figure from external organization." If there are territorial disputes, such as in the Indian-Pakistani border regions, large areas are often claimed twice.

The situation is even worse when it comes to unemployment figures, which are calculated differently in virtually every country. In many western industrialized countries, people who are undergoing further training precisely because they are unemployed are no longer considered unemployed. Such tricks are used in many countries. The ILOEST system attempts to bring all this down to a common, cross-border denominator. The figures differ significantly from those reported by the countries themselves. But this system also has weaknesses because it requires countless additional data that can often only be vaguely estimated. In addition, poorer countries in particular offer no incentives to register as unemployed. If there is no state pension, no health insurance, and no unemployment benefits, people do not register as unemployed. Why should they?

The financial sector is probably the best documented topic. But what turnover can a state measure (or tax) when someone exchanges a sack of rice for a few pounds of meat, which they then use to pay for vegetables at the nearest corner shop? These are all cash-equivalent transactions in an informal economic cycle, but they bypass any statistical system. There are countless regions around the world where trade works in exactly this way. The International Labor Organization estimates that around 2 billion people worldwide lived like this in 2019. That is about 60% of the global workforce. This phenomenon is particularly prevalent in poorer countries, meaning that statistics can only be based on rough estimates.

The sensible use of tax money is debatable. But comparing tax rates is even more so. We have a comparison of income taxes and levies per country. But what does this figure say in comparison to other countries? Statutory health insurance, pension systems, social assistance, unemployment benefits, and even the provision of kindergarten places are regulated differently in each country and are virtually impossible to compare. However, they are all financed by tax money.

Comparisons of height or intelligence can lead to open hostility and even death threats. These are mostly summaries of studies conducted by doctors, educational or health organizations. When hundreds or thousands of these studies are summarized, a comparable picture emerges across countries. There can be no claim to completeness or universal truth here.

It is often helpful to look behind the numbers. This not only shows what was collected and how, but also provides a connection to real life.