The dataset I’ve chosen to work with is the Albany Muster Rolls 8th Militia dataset. It is a census of recruits for the Revolutionary war in the early 1760s, and is a textual dataset. There are a total of 944 men listed in this dataset, with 13 categories filled out. These categories include last name, first name, enlist date, age, where the recruit was born, what their previous occupation was, whose command they were under, their physical attributes, and what volume and page their information could be found on in the physical text.
Despite this array of information, some details are still life ambiguous. For example, though the dataset tells us where each recruit was born, it does not say whether or not the recruits were citizens of Albany or if they were simply stationed there. Additionally, it is difficult to see familial relationships, if any exist, between the recruits, meaning that analysts won’t necessarily be able to use this dataset to see if relatives enlisted together. The dataset does give us a nice range of information, however, in the form of enlistment date and age. From the dataset we can see that the youngest recruit was 16 years old, and the oldest was in his late 50s. The enlistment dates all fall between 1760 and 1762.
The physical description of each person is given in the form of complexion, eye color, hair color, and height. With some variation that might be due to typos or to old terminology, the complexion category is filled with descriptions ranging from black, brown, dark, fair, freckled, “Indian,” “Mulatto,” “Negro,” pale, “pockpitted,” ruddy, sandy, and swarthy. There is no indication of whether “black,” “dark,” and “Negro” are the same thing and it might be detrimental to assume that they are.
That being said, trying to compare “complexion” to the “born” column also gives us little insight into what sort of ethnicity or race a recruit was. Two men listed as having “black” complexions are noted as being born in Philadelphia and Germany. A quick online search reveals that Germany did not practice slavery like the United States or the UK did, and that Pennsylvania’s social politics were influenced by this. Knowing this, one could make the assumption that they were free black men fighting in the revolutionary army, but just by looking at the dataset it is unclear whether the black recruits were free men, or slaves, or servants, etc. To be fair, knowing what we know of racial definitions pre-21st century, the fact that the men are listed as being “black” could mean that the census takers simply thought the men were very tan with very curly hair.
I think the biggest difficulty I might have in using this dataset is confusing correlation with causation. For instance, many recruits who have a height of 5’4” tall also happen to be listed as “labourers”. This correlation is almost certainly meaningless, since there are many other “labourers” who are not 5’4”, and it would be very difficult for me to argue that, for example, all men who are 5’4” are just too short for any other occupation. I might have difficulty coming to a convincing conclusion when I look at the relationship between ethnicity/complexion with the recruits’ officers. Other categories seem to have a more straightforward connection, however. Recruits from Connecticut seemed to overwhelmingly have joined in the Spring of 1960. There is no telling why this happened just by looking at the dataset, but one could see this correlation and, reasonably I think, use it as a basis for research in what was happening in Connecticut in 1760.
Other correlations seem similarly straightforward. The dataset implies that most of the younger recruits had more general occupations, such as “farmer” or, overwhelmingly, “labourer.” The older the recruits, the more varied the occupation list. This could probably be explained with common sense, since younger men seem like they’d obviously have less skilled jobs than most middle aged men. Still, even though this correlation seems very obvious, I think it would be wrong to rely on “common sense” too much. After all, the presence of the correlation says nothing about the relationship between location and occupation, or socioeconomic class and occupation, or family size and occupation. Similarly, I need to be careful when I look at the relationship between ethnicity/complexion and officers not to rely on quick, “common sense” analyses.
Two quick caveats: though it’s not clear from the dataset (and therefore you had no way of knowing this), this militia is all drawn from men living in the Saratoga/Albany area–so assume that at the date of their enlistment, they were living in Albany, while their birthplace location indicates if they moved to Albany sometime before their enlistment date.
You’re already onto something with the observation re: birthplace and “black” as a racial category–remember that racial categories evolve over time, and 18th century categories weren’t the same as ours. If you go through the birthplaces of men noted as “black,” I think you’ll find lots of European birthplaces–the 18th century used black and dark as physical descriptors without tieing them to the racial category of African descent like we do now–someone with no African ancestry might have been noted as black because they were a relatively dark phenotype-d white person. So just be careful that you don’t make assumptions about what your terms mean without good reason.
Trade may be another one worth breaking apart based on birthplace–you have a very good observation with the correlation with age, but does birthplace make a difference as well? This may give you some additional insight into your timeline question as well.