Intro

In examining the Height differentials between different militaries and their cadets, we are interested in seeing the variations by which different militaries accept candidates for their programs and forces as well as to see the different characteristics of each military and compare these with the intent of looking at how their specific heights factor into a debate on the welfare of the country itself and whether it is respective of a population at large.

In terms of a research question, what we are interested in looking at is ‘Where are height differentials most divergent and how are these differentials expressed in military and civilian populations across the world?’. Hopefully by examining the data, we can see diversity in the similarities and differences of populations across the world in the time period we have picked also. In terms of relevance, the period of the 19th century is an important part of rising welfare levels across the world and the hope is that we can see this made manifest in the data. As we will subsequently refer to, height and welfare levels are positively correlated, so examining these elements will let us draw a conclusion based on where and how these peoples heights were increasing.

The sources themselves are varied and diverse in examining these differential populations. To spare the reader from a cacophony of noisy data and to separate a signal from this noise, we have elected to look at institutionalised data especially, rather than census or voluntary data. The reasoning being that the datasets for these institutional populations will be far more evident and clear than a mess of data in the 19th century with questionable relevance. Prison populations, conscripts, and military applicants have been examined.

North America and Europe

The variance in height of various military applicants and personnel across Europe and in the United States of America has been studied by scholars extensively. (Komlos, ;Baten, 1999;Baten & Fertig; Stolz, Baten & Reis, 2013) Most of these studies look at one country and a single dataset. In this data story, we aim to combine several datasets which reside within the microHeights-dataset. Interesting to note here is the difference between the continents when looking at height. In the graph below, information for the US, Germany and Portugal is combined to look at the average height of their military personnel during the nineteenth century. US applicants to the West Point Academy are the tallest. This has likely to do with height requirements for applicants there. Conscripts in Portugal are at least 6 centimetres, on average, smaller than these US soldiers. Germany hangs in between, being at least 2 centimetres smaller than their US counterparts but 4 centimetres higher than the Portuguese. The Portuguese are known in Europe as one of the smallest people, Stolz, Baten and Reis examined this extensively and come to the conclusion that this had to do with a delay in human-capital formation.

Intra-American Height Differentials

Looking at and examining the differentials in height between the Cuban, Argentine and US census data we see quite a large differential between the populations of the US, Cuba, and Argentina. Twrdek’s work on the height of the Cuban and Argentinian recruits as well as their investigations into the usage of Height data as a measurement of welfare is an interesting method, but of course comes at the cost of being interpreted as such. I will also look at the data of the Peruvian Prison population to create a more inclusive comparison of other American countries especially considering that the similarity (according to the authors) Looking at the time period that data is available for is also interesting to note, as the Cuban and US data end at the 1940s but the Argentine data ends at the 1910s making any generalizations of past 1920 hard to ascertain fully (Twrdek, 2010). Twrdek’s usage of this data is not unfounded and corresponds with their outlook in examining the different levels of welfare in Central and Southern America, especially in their reconstruction of datasets pertaining to the heights of Peru and Argentina. The comparative between the Peruvian and Argentina is necessary to compare even specific cases Southern America context also. As the graph shows, Argentina and Cuba were both of the same height echelon to a point until Cuba diverges in the early 20th century. Source wise, however, this database does leave a little to be desired as it shows that the need for a more modern database construction for Peru and Argentina. Noticeably absent also is Brazil, which at this period would be the second-largest country in the Americas population wise. That being said, this analysis is still relevant as this is a large turning point for the Americas, looking at their faltering dependence on metropoles, the large retaliation against these metropoles, and the increasing presence of the United States in the economic and political life of these countries.

Comparing Neighbours

The last graph compares the development in two neighbouring regions over more than a century. One is the historical kingdom of Bavaria, then containing some territories in Southwestern Germany that are not part of the modern state of Bavaria. The other one is “Austria” in the form of the old Habsburg Empire. The Austrian data does however not cover the whole empire, but only areas which today lie in Austria, Czechia, Hungary, Ukraine, and Poland. Taking into consideration which areas are really represented in these datasets, the graph seems to provide us with a good overview of height changes in Central and Eastern Europe in the 18th and 19th century. Height seems to have peaked in the mid-18th century and then decreased steadily until the 1840s when the data ends. However, this is deceptive.

The big break in the Bavarian data makes it clear that not all data here comes from the same dataset. Indeed, it is a combination of 4 different datasets, 3 of which provide the Bavarian data. The Austrian data on the other hand comes from a single dataset. While the Austrian data is completely derived from soldiers, Bavarian data from 1820 onwards includes some male prisoners. But more important than the small number of prisoners included is the fact that the newer Bavarian data consists of (almost) all Bavarian men, who were measured when they reached a certain age, no matter whether they were actually conscripted or not. The data for the 18th century on the other hand consists of actual soldiers, most of whom were volunteers and not conscripts, who were measured at different ages. As there was a minimum height for soldiers those bellow it could not be included in this set but would be measured nonetheless in the 19th century.

Even though the Austrian data comes from a single dataset it is not uniform. Like in the Bavarian case most of the soldiers measured in the 18th century were volunteers with conscripts only dominating in the 19th century. This means that the Austrian data is as inconsistent as the mix of Bavarian datasets. This calls into question what the graph really shows us. Instead of showing a clear decline in male height for both countries, all that might be reflected in the numbers might be changes in the composition of the two armies and in the way they measured (or did not measure) their potential recruits. This should be seen as a warning to really think about how comparable datasets are and how they were constructed before they are used for comparisons with each other.

Conclusion.

From the queries below and the graphs they create, the conceptual framework for examining the heights and the registration within the European, and Intra-American situations are divergent. By digging deeper into the dataset we see that in the turn of the 20th century there was a clear divergent upward trend in the 'Western countries' such as the US, Germany, Portugal etc. but in the global south (South America especially). Why is this? As the literature aforementioned states there are many reasons to fundamentally consider, but welfare levels rising in certain parts of the world do correlate with some of the measurements taken. That is outside the scope of this investigation however, and as such should be treated as a separate research question, but a no less interesting one at that.