1883 Pensioners Final Project

Data Description

For this project I chose to look at the 1883 Pensioners. I wanted to explore a data set that involved some aspect of war but with a different twist. When we look at war data it is usually casualty figures that are the main topic of discussion. People forget that for those that are wounded and the families of those killed in action, some sort compensation, a pension, is given as a way of saying thank you for your service and to help alleviate the difficulties that may result from their time in the service. The reasons for the pension can be anything from visible wounds, to psychological trauma, to the loss of the head of household—the breadwinner.

When looking at any given data set, you are almost certainly going to be given numeric data, textual data, and/or geographic data in some quantity. In regards to the 1883 Pensioners data set we are given all three. But, each is represented in a different amount. The textual data is on a very large-scale, as is the numeric figures. There are dozens and dozens of different categories for wounds. From gunshot wounds, to diseases and illnesses, to amputations, to being widowed, the list is extensive. We are also given the names of each person and the month and year of the first pension claim.

For each category that is listed, a number of sub-categories of numeric data is also given. The most interesting of which comes in the form of the monthly rate of payment received by the individual. With this number you can then twist it around to see the average monthly rate, the median monthly rate or the monthly sum. What this allows someone reading this data to do is to compare and contrast the various pension claims to see how they stack up in severity, in terms of monetary compensation. Another aspect of numeric data is seen in the number of records of each pension claim. This too allows us to see what the largest pension claim was.

Geographical data is present in this data set. However, an address is not given. Instead, it is referred to as “Post Office.” This is the city or town that the individual is currently living in at the time of pension submission. This does not provide much in terms of useful information as this list includes individuals strictly in the Capital region. If an address had been provided, we could then create a map of the city of Albany along with the various outskirts and examine just where these veterans or their families are living. Are they generally in poorer neighborhoods as is the general trend with war enlistees? Is it an area that is higher concentration African-American or Caucasian? Unfortunately, when this list was compiled they were not expecting someone to look at it decades later and attempt to create patterns and trends from it. No, it was instead meant to organize those that were receiving government pensions for their role in the Civil War.

Data Visualization 1

For my first visualization, I wanted to take a look at the number of records for claiming a pension. In this visual we will be looking at the “Number of Records” tab.

At first glance it does not look like much. I wanted to create a story that correlated with the figures that show that most of the deaths associated with the Civil War were in fact, not combat related. When I say combat related I am talking about gunshot wounds, hand-to-hand combat and artillery barrages. Instead, the number one killer of soldiers, on both sides, was dysentery (www.civilwar.org). Most of the soldiers that fought during the Civil War were from the rural countryside. They lived their lives on small mom and pop farms and interacted with only a handful of other people outside their community. When you do not have much contact with significantly larger groups of people, your immune system becomes more susceptible to contracting diseases and illnesses that others, from a city for example, would not come down with. For this reason, a substantial number of the deaths during the war were from disease as these farmers interacted with hundreds and thousands of other men for the first time. Other illnesses include smallpox, malaria and chicken pox. Because the medical field had not advanced to the point of having proper medicine to treat this diseases and viruses, large outbreaks were not uncommon with both the Union and Confederates.

While it was my initial goal to show this pattern in my data set, the opposite, in fact, developed. Now before going any further I need to emphasize something about this data set. This data is not an accurate representation of the makeup of soldiers in the Civil War, on either side. It is a rather small sample size of just under 1,000 claims. A claim does not necessary entail that they played a direct role in the Civil War, rather it could be the family applying for a pension for a deceased family member. In looking at the data in the “Number of Records” tab, we see that the largest number of claims fall under the category of Mother/Father/Minor, followed by General Wounds, Gunshot Wounds, Loss of Limb due to Combat, Disease, Injuries, Other, and lastly Amputations. According to this graph, there were a total of 918 records. 408 of these fall under the category of Mother/Father/Minor and only 51 falling under Disease. This data set is telling a different story than what the national story is. Instead of having most of the cases be relegated to be disease related, they are instead a second-party claiming the pension, for example a widow. The General Wounds are second with 215 respective cases, these being injuries as a result of chopping wood or breaking a bone. While there is a significantly large difference between claims as a result of disease or illness and being widowed, it can be safely assumed that a portion of the widowed claims are a result of their loved one dying from some sort of disease.

Process Documentation 1:

All of the groups that are represented in all my visualizations have numerous sub-categories within. For example, if you look at the Gunshot wounds bar, what you have is different types of gunshot wounds making up the 119 total claims. There are gunshot wounds to the head, face, jaw, left leg, right foot, etc. This visual is fairly new. When I first set out on this journey, this particular example was not composed of a neatly grouped list. Instead, I had every single different type of gunshot wound, disease, illness; you think of it and I probably had it listed. Now you would think that the process to create categories to house all the various wounds would be fairly simple. Well, you would be dead wrong. You see, when each reason for filing a pension claim was originally listed, there was no universal language or code to organize things. It was all dependent on the person writing at that moment. Each person had their own unique abbreviations and wordings for various items. This meant that some reasons could be grouped under more than one category. After I was able to discern what should be placed where, I chose to use the default colors that were given to me each time I dragged the “Grouped Wounds” Dimension into the table. These colored groups represented my columns. Next, for my rows, I decided to utilize the “Number of records” measure to show the amount of claims filed in each bar. It was not complete but to further show the differences between each column, I chose to show bars with higher amounts of pension claims as larger in width than those with much smaller amounts. While this makes sense, in practice it can create a problem in viewing such thin bars.

Argument 1:

As I mentioned earlier, the main argument for this particular visualization is that disease and illness were not a particularly large contributor to pension claims, in terms of this data set on the Capital region. Poor hygiene along with interacting with large groups of other soldiers resulted in numerous cases of malaria, dysentery, etc. Outbreaks within regiments were not uncommon and men would often be forced to leave the service to due being sick. This is backed up the need to apply for a pension. Because the people listed in this census are from a highly populated region in the northeast, for the most part at least, their immune systems have been able to build up some sort of tolerance to the various illnesses out there. I would argue that this is the main reason for the low numbers of disease and illness claims filed by these men and their families. This assumption is supported by looking at other pension records that are available (Google Books: 291-303). For example, if we were to look at the city of Utica in Oneida county, we would see a rather large list of wounds that pertain to injuries sustained in battle or by other means. While there are cases of diseases and illnesses, they are trumped by the various wounds. We can also look at the town in which I grew up in, Clinton, also in Oneida County. By today’s standards, Clinton is a very small village. At last check, the population was just under 2,000 so we can only imagine how much smaller it was over 150 years ago. Out of the list of pensions for Clinton, there is only one entry that claims an illness: dysentery which was the number one killer during the Civil War. But the remaining entries are either combat related or because a widow or family member is claiming the pension because they lost a family member in the war. The north was much more populated than the south during the war, same goes today as well, so it makes sense that most of the pensions were in response to wounds other than disease and illness. If you look at South Carolina, (Google Books: 184-189) a startling trend emerges. Here we see the opposite of the north. Instead of mostly gunshot wounds, we see an overwhelming number of widows and illnesses. This can be explained by the sheer number of southern males that were lost during the war as a majority of the fighting took place in the south. The south is also nearly exclusively rural, hence the hundreds of plantations, so the increase in illness cases can be explained by the weaker immune systems of the Confederate Army.

Data Visualization 2:

For my second visualization I chose to examine the role of women during the Civil War. While women have always played key roles in every conflict the United States has fought in, the exact extent to how much they can be involved has always been controversial. Only in the past few years has combat roles been opened up for women to be involved in though they are required to pass the same physical requirements as their male counterparts. But during the Civil War women played a much more behind-the-scenes type of role. Most often referred to as Camp Followers, these women did exactly as their title suggests: they followed the train of soldiers. These were women who, generally, did not have much at home after their husband, brother, father, etc., went off to war. If they had no means to sustain themselves and make money, they often followed their loved ones as they went off to war. They acted as cooks, nurses, cleaners, and prostitutes. Some even went as far as dressing up as a man (Sam Smith) and fighting on the front lines, some even died (www.civilwar.org). While they no where were close in numbers as the men, it is believed that around 500 women secretly fought in the war. Probably the most famous woman fighter is that of Jennie Hodgers, better known as “Albert Cashier” (Civil War Trust). She enlisted in the 95th Illinois Infantry on August 6, 1862 and would go to fight in over 40 different engagements. It is also believed that at one point she was captured by the Confederates but she broke out of prison and returned to duty. She served three years before her unit was discharged for heavy losses due to combat and illnesses. But her story does not end here. She went on to continue living under the guise of a man, collecting a pension, and would only be discovered in 1910 when she was hit by a car, though the hospital kept her secret quiet. However, 3 years later, as dementia set on she was discovered and forced to live the remainder of her life as a female. She would die 2 years later and be buried in her uniform with full military honor.

Process Documentation 2:

The creation process for this visual is nearly identical to the first. The main differences here are the different dimensions and measures that were used. I chose to have a horizontal graph for this one in order to break the data up into male vs. female. In terms of data, I included type of injury sustained, the average monthly payout and the number of cases for each category. As with the first visual, I created larger bars for the categories that included larger number of records and smaller bars for the categories that included a small number of records.

Argumentation 2:

As I have mentioned earlier in the first visual, this data set is a rather small sample size. To accurately create a significant argument, we would need to also be graphing other 1883 pensions from nearby counties. Luckily, Albany county seems to have had its fair share of females taking part in the fighting during the Civil War. Whether they were injured while fighting or were simply in the wrong place at the wrong time as a camp follower is impossible to know for they may have never been discovered. In any case, what we have in our data set is 2 records of females sustaining gunshot wounds and then gaining a pension as a result. In scouring the nearly 1,000 entries, I was unable to find their names which may have led to further research and possibly finding out if they had in fact disguised themselves. Nevertheless, these two women received an average pension of $4.00 a month compared to her male counterpart whom received an average of $5.93. The sample size is way too small and distorted in favor of the men with 117 records of gunshot wounds so it is difficult to say whether they received such a low amount because they were women or because their wounds were not as severe as some of the men. In any case, they did in fact receive a pension for gunshot wounds. Even if we take out the gunshot wounds from the data, historians know for a fact that women played a key role in the Civil War. Clara Barton herself, founder of the Red Cross, was a nurse during the war. Women took on every aspect of the war that the men did including fighting and performing dirty tasks such as amputating limbs. While historians acknowledge their contributions, I do not believe classes teach how much women played a direct role in the war. Up until the Fall of senior year in college, I was unaware of the camp followers. To show that they played this large role in the war, we should be teaching more about them.

Further research Questions:

  1. Were pension claims during the Civil War and claims 20 years after the end of the war greatly differ in terms of monthly payments for similar injuries?
    1. To figure this out I would rearrange the data set in chronological order. I attempted to do this but my efforts were futile as I could not figure out how to correctly get Tableau to do it. With a lot of time on my hands I suppose I could create an Excel spreadsheet that is in chronological order and could even uncover more patterns in the data that we are unable to see otherwise.
  2. Did the pensions differ based on whether you served as a Union soldier or a Confederate?
    1. In a preliminary scour of the Arkansas, Missouri, and South Carolina pension rolls, this would seem to not be the case. No matter the wound, you received nearly the same amount whether or not you were a northerner or a southerner.
  3. Were African-American veterans afforded the same treatment by the Pension Bureau?
    1. When I first chose this data set, I was under the belief that this was a list of African-American soldiers and their families that were receiving pensions. I quickly learned the opposite, that it was probably whites that represented the bulk of the data. That is not to say that African-Americans are not present on the list, however I would imagine if they were a significant contributor to the war effort (which they were), wouldn’t the Pension Bureau have a separate column for race? Every 1883 Pension roll uses the same layout with the name, record number, cause for pension, etc. None of them include the race of the individual. So this begs the question of if they just did not deem that important enough to distinguish in the records or did they just not offer any pensions to African-American veterans. This would, of course, violate the 14th Amendment. I would bet that with some more digging, one could uncover the records of African-Americans receiving pensions, I just do not know where to look for that.



“620,000 Soldiers Died during the Civil War, Two-thirds Died of Disease, Not Wounds: WHY?” Civil War Trust. Accessed May 11, 2016. http://www.civilwar.org/education/pdfs/civil-was-curriculum-medicine.pdf.

Council on Foreign Relations. Accessed May 12, 2016. http://www.civilwar.org/education/history/biographies/jennie-hodgers.html.

United States Pension Bureau. “List of Pensioners on the Roll January 1, 1883.” Google Books. January 1, 1883. Accessed May 11, 2016. https://books.google.com/books?id=aLkqAAAAMAAJ.

United States Pension Bureau. “List of Pensioners on the Roll January 1, 1883.” Google Books. January 1, 1883. Accessed May 12, 2016. https://books.google.com/books?id=t7oqAAAAMAAJ.

Smith, Sam. Council on Foreign Relations. Accessed May 12, 2016. http://www.civilwar.org/education/history/untold-stories/female-soldiers-in-the-civil.html.

Final Project Story

In this visualization I decided to research the differences in the payment of pensions for people that had the same injuries but started receiving pensions at different time. From this visualization I organized the data into years and injuries and then went from there. My original question for this visualization was to find the differences in amounts of pay for each individual. As you would guess many of the payments are equal, however I had also thought about how there would be differences in pay, and I was right about that as well. When you look at this data the person who created it did not include how severe certain injuries were, like there are labels for various gunshot wounds but the severity is not specified. This was the basis of my question. Why was there discrepancies? If there wasn’t a reason for there to be a difference in classification why is there different pay? The amounts also fluctuated by time of issuance. It wasn’t like there was a correlation going up or down the numbers were just random, again, a reason to include the severity of the injury that each person had received. This could’ve all been cleared up by a couple extra words on the sheet.

There are some very different stories that come from this chart as well. Like the many people who are affected by gunshot wounds, presumably from the Civil War. This shows the variety of injury that come from having such a bloody war. There appears to be a a gunshot would for every appendage possible. I suppose that there is the possibility that the injury happen during some incident outside of the war but I think it’s reasonable to say that the most of injuries occurred as a result of the war. When you look at some of the other ailments You see some injuries like an axe to a particular appendage and see that they only see that they receive like $4 a month. That number is very surprising to me,even in those days where four dollars was a nice amount to have for some income but I feel like that wouldn’t be enough to support someone who was severely hobbled by an injury. There were some cases like a dependent mother who might have multiple kids and use that as their only income and the pension was eight dollars Again that seems incredibly low. The pension for women, especially in those times would most likely be the only opportunity for women to make money so this seems low in the sense that these women could have multiple kids. Conversely, there were a couple people who were blind who received $70, this was interesting to me because that seems like it would be a reasonable amount for people who probably don’t have a lot of expenses.

Overall I feel like that given these circumstances, in my opinion people could’ve been paid more, but I don’t know what a good salary per month was for the time, so this could’ve been fair.

Final Story Rough Draft

The nineteen forty Albany census provides us with resident’s basic personal information, but it contains a much deeper story in between the lines. When simplifying the information and the numbers, many judgments can be made about the circumstances of this time period. The unemployment and employment rate of each gender, income of specific households and citizenship based on birthplace are just a few of the pieces of data that can be discovered within the census. My data visualization displays the average age of employed persons based on gender. On the female side, the average age of unemployed women was over forty years old, just about forty-two, and the average age of employed women was just over thirty-five years old. This tells me that the average women in the nineteen forty’s worked for pay for a shorter span of their lives than males did. With that being said, Men in the nineteen forty’s saw an average age of unemployment of just under forty years old, only a few years younger than their female counterparts. The average age of males who were employed for pay is greater than the average of those who are unemployed, being just over forty. This represents that males may have been struggling to find jobs when they were younger, but as they aged and gained experience then they were able to find a job and work there until they retire. Persons whose gender is nullified on the census demonstrates a higher average age of unemployed persons than employed persons. This relationship is similar to that of females so this leads me to believe that most of the persons whose gender is nullified on the census are women. For those whose employment status was nullified in the census they saw a very low average age for both females and males, about 15 and 18 respectively. These persons were most likely nullified when it comes to employment status because they are so young and may not have been expected to work just yet because of academic reasons.

Yo this is a story all about how Albany’s 1915 census got fliiped-turned upside down

I think there’s a few interesting points to take away from the graphic and data behind occupations in Albany in 1915. There’s a few jobs that no loner exist or are called different names now, plus a surprising difference in the number of women and men who worked (or at the very least, were listed as working) at the time. I purposely chose “gendered” colors for the bar graph, but I think the story behind that has to go in a separate part of the final post. But there are a few interesting stories behind the data itself.
I’m assuming no one here really knows what a “teamster” is (or maybe you do, being history buffs and all). Today we’d use the plebeian term “truck driver.” But it’s hard to imagine an 18-wheeler peeling down the dirt roads of Albany a hundred years ago; a little more research shows that these were the men who delivered goods to stores, usually on an animal-driven vehicle. A whopping 12 men in the city of Albany were blessed with sitting behind the back-end of an ox or horse for a living, twice the number of the next most popular listed job in the city. But I’m sure they weren’t only delivering groceries to the whopping 2 grocers in Albany at the time; out of 64 employed males at the time, about 5 of them weren’t directly related to selling or handling delivered goods (and that’s taking a good guess at what some of these other jobs are). So I’m sure these 12 men were nice and busy staring at these animals’ backsides all day as they delivered books, ice, and masonry tools.
Of course, when you first look at the graph the first thing you probably notice is the big pink bar shooting up highest. With only a dozen less listed workers than men, more than half of the women listed as employed are employed in “Housework.” What sort of housework? Not homeschooling, as school teachers are listed in another category, nor seamstressing, as dress and shirtmakers are categories unto themselves. Perhaps, even though it was work that was expected of them as women, house work within their own homes was considered an occupation? No, I think that since it was listed as an occupation, it was something that they were paid for, and not something they did in their own homes. Or rather, it wasn’t something they were acknowledged for doing in their own homes. They just had to come home from cleaning someone else’s home all day, and then clean their own.
There are some jobs which are surprisingly gendered (as there is not one single listed occupation that has both men and women working in it) occupations, ones that might expect from the other sex. The only listed gardener in the census is a male, while the only person in charge of manufacturing boxes in all of Albany is a woman (at least, according to the census). Weren’t they afraid that her wandering uterus would make her unable to deal with such a heavy workload?
All in all, I think the most important and interesting piece of information to take away from the graphic is that there were an almost-equal amount of men and women listed as being employed in Albany a hundred years ago, when we usually imagine that the men went out to make the money for the family while the women sat at home and either tended to their children or made sure the house was comfortable for their husbands, fathers, and brothers to come home to.

Final Story Rough Draft

When looking at all of the data from the Albany census of 1860 a few stories begin to emerge. I think the most immediate story that jumps out is the racial diversity of Albany in 1860 according to this census. The race column uses a one-letter label and when scrolling through the hundreds of names you see the letter W, for white, almost exclusively. When you take a magnifying glass to the census and search for more you are able to find some small diversity. The letter B, as in black, and M, as in mulatto, are also listed on this census, but only for a total of eight records between the two races. There are three recorded black members of the community and eight-recorded mulatto members of the community. Due to the time period we are looking at this may not sound too surprising, but when you consider that all slaves living in New York were freed in 1827 it becomes slightly more surprising. When removing the scope of Albany, New York from our data you can see that the number of white persons in the United States still greatly outweighs the number of free persons of color. Slaves make up thirteen percent of the population while freed persons of color make up only one percent of the population. So while it is not surprising that only a few persons of color lived in Albany in the grand scheme of America in 1860 it is slightly interesting due to New York’s dense population of almost four million and the abolition of slavery years earlier. When comparing the totals of the United States to New York’s totals in population, New York has about one percent of freed persons making up their population similarly to the entire country. Out of all of the free persons in the United States about ten percent were living in New York during 1860. That is a lot considering many states still were under slavery during this time. Out of all of these free people living in New York at this time only 0.01 percent of them were living in Albany. A majority that were had presumably been here for a long time because the average age of a black person living in Albany in 1860 was seventy years old. Presuming that not many people would travel to a new location after being freed at seventy years old and I would also take into account that both black males living in Albany during this time both were working jobs that were both also worked by white males during this time, one as a preacher and one as a waiter. When you consider that Albany is the capital of New York I might have guessed that more freed persons would be living there, but you have to take New York City into account. Out of the forty-nine thousand free persons of color in New York during this time period twelve thousand people, or twenty four percent, were living in New York City during this time. These numbers had been on the decline though and continued through the 1863 Draft Riots in New York. From 1870 on the numbers showed a steady increase.

1940- Visual 1

Education and the type of occupation one holds are often correlated with one another. From the time a child is put in school until the time they finish, it is drilled in the head of the individual that a good education is needed in order to obtain a well-paying job. It is taught that hard work breeds success and young men especially are taught to be providers for their families which comes into play with the types of jobs they seek and level of education they want to achieve. This proved true for those living in Albany in the 1940s; the 1940 census helps to show that those that received a decent education held jobs that not only benefitted them but their families as well.
The census provides a wide range of information including the types of jobs that were held, the gender of those that held these jobs and their level of education. It seems as though many women did not work and often stayed at home to care for their children and other household duties. Although many of the women did not work, some did and they held quite prestigious jobs. The women that worked seems to not begin work until they are well in their late teens if that but most are in their very late twenties, early thirties and older. The census does show that a greater amount of men worked in comparison to women and they began to work quite early. As previously stated, although mostly men worked, some of the women that did had higher education levels then that way which made it possible for them to work higher paying jobs. The census shows that a few of the women received college degrees or higher and began lawyers and doctors whereas that was not quite the case for the men.
There are numerous jobs held by the people in the 1940 census; some of the jobs include accounting, barber, bartender, book keeper, lawyer, carpenter, cook and much more. The different jobs shows the level of education that the people that held these jobs had and how many people the criteria applied to. For example, one of the jobs held is a file clerk. Some of the people that were file clerks had different backgrounds in education. The census shows that twenty-four people completed high school and eleven completed elementary school and this shows that being a file clerk is not a job that may qualify as being of high standing or one that a person needed to have much experience in. In comparison to those that are lawyers, three people completed college and nine people went beyond a college degree and that shows that being a lawyer was a great accomplishment, one that not many could achieve for one reason or another. This pattern could be seen throughout the entire census; there are jobs that people hold which require little to no education and then there are the few jobs that require a higher education level. This comparison between the education level and occupation is an interesting one because it is evident that a good education gets you a better job but it is clearer on paper.