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The first two graphs that I made for my dataset show the difference in occupation between genders. Many occupations at this time were exclusive, or nearly exclusive, to a single gender. Males usually had what would be considered skilled jobs, such as laborers or blacksmiths. Some of these jobs would still have a woman or two though and this surprised me when making these graphs. Women had other jobs that were exclusive to them. These would be jobs like sewing or a servant. The only three listed occupations that were about even were attending school and being a tailor or tailoress. Attending school makes sense because it consists of mainly children who were attending school during the time of the census, but tailor and tailoress and school teacher were more of a surprise. I do not know which gender I would have assumed as taking the gender role for a tailor or school teacher, but I was surprised to see that it was evenly distributed. When looking through the jobs filtered by gender I was not surprised to see men had what were known as skilled jobs during this period and women had more traditional female jobs in the sense of serving and sewing. When you look into the history of women you really begin to see them emerge in jobs outside of the norm during wartime when they are necessary to the workforce. During the 1920’s you also begin to see more independent women start to join the workforce in job roles that were not totally normal at the time. My understanding was that this was when women began to come into their own in the workforce and even during this time it was still tough for women to get jobs. This is why I was surprised to see that some women in Albany in 1860 were already assuming some skilled position jobs. When looking through the graph you will see that, while dominated by men, women have a small population in occupations like laborer, carpenter and boilermaker. In a paper by Shannon Banaszak titled “Women in the Workforce: Before 1900” it is stated that although economic historians agree that there is a steady influx of women into the workforce between 1800 and 1900 that there is a drop from twenty percent to fifteen percent between 1860 and 1870. Due to my prior knowledge of wartime and the roaring twenties being the time for women to shine in the workforce paired with this data from Banaszak I was very surprised to find women appearing in skilled jobs during this time period in Albany history. Banazsak does go on to state that women actually were beginning to become a big part of the workforce, really beginning to take off in 1840, which was surprising to me. She does state the jobs they had were normally sewing or domestic service. (Banaszak) This information correlates with my graph and census. In the grand scheme of the workforce women played a larger role than I would have expected, but apparently it was becoming a normal occurrence during this time for women to enter the workforce.
The next graph that I would like to point attention to is birthplace by race. There is little records outside of the white race in the sample of the 1860 Albany Census that I was looking at, but I made this graph originally to see if there was a difference between the five other recorded races and the white race. A huge percentage of the white race was coming from New York and I was curious if this was the case with the black and mulatto sections of the census. Besides one black man coming from Pennsylvania, all other persons of color in the census come from New York, which keeps with the trend of the rest of the census. It made sense that New York would be the number one place that people were coming from considering that it is an Albany census, but what came as a surprise to me was the amount of people that were from Ireland in the census. It is second to New York by a lot, but it is the next highest percentage by far. Out of the one thousand people in the sample of the census I was looking at six hundred and twenty four people came from New York and two hundred and eleven came from Ireland. The next highest number is twenty-four from England. This made me wonder if this was unique to Albany or if Irish immigrants were this popular throughout America in 1860. Upon further research I found that during the 1800s more than half of the Irish population came over to America. The article called Irish and German Immigration states that this was true in Ireland and Germany due to many hardships and unemployment. Immigration to America would total in over seven and a half million coming to the United States between 1820 and 1870. About a third of that was from Ireland. This rush of immigrants from Ireland and Germany had major effects on every city in America. (Irish and German Immigration) After reading about the influx of Irish immigrants into America during the time period that this census was taken it made a lot more sense why the Irish population in Albany was higher than any other by far. After doing this research and finding out that a major reason that they immigrated was trouble finding any work in their native land I decided to make a graph to look at the Irish population of Albany’s place in the workforce. I had read in the same article I referenced earlier that Irish immigrants would do a lot of jobs that labor-intensive all over the United States. This was true in Albany as well. The most popular job among Irish immigrants in Albany was a general laborer. I read a letter while doing research about the Irish immigration that was written from an immigrant to an Irish national stating that he was happy in America and although he loved Ireland he recommended everyone move during these tough times and come over to America for a better chance at life. He spoke of the famine. I did some other research and found that the main source of income for Irish nationals was too farm potatoes. Even when this business was doing well it was low income. When there was a five-year famine in the late 1840’s it caused starvation and killed many, which played the largest factor in driving many to immigrate to America. (Famine Wiki) Even when the famine was over in the 1850’s immigrants would write their family to join them for a better life. This is what led to the huge immigration numbers to major cities all across America. Due to the huge influx during this time period there are currently more Irish Americans in the United States than there are Irish Nationals in Ireland. Cities all over America served as refuge for Irish natives and Albany was a spot where Irish could come to get a job and live out their life.
Slavery plagued America like so many other countries in the past. Today slavery still isn’t completely eradicated from the world however, we are still examining the troubles that occurred on our own soil. With the help of the data set of slave sales used you can get a clearer idea of what happened during the times of 1742 to 1865. Arguments can be made based on the way one chooses to construct the given numbers, dates and text shown. Although it hardly paints a full picture some conclusions can be discovered. The way I choose to arrange the data set was split between male and female and their value based on skills and defects.
The data shown in the two tree graphs labels each enslaved person with a particular skill on one and a defect in the other. From there the data is then broken down into male and female showing each skill men have and on the other side women this is also shown in the defects chart as well. Next the tree graphs shows the appraised value a male or female enslaved person would receive upon having a particular skill or defect. Although values for enslaved people rises during the years there is a connection between having a particular skill or defect that can also effect the value. Skills are shown to commonly be separated by gender; this might make sense in a southern society that believes in gender roles even when it comes to enslaved people.
Males tend to have more of the common masculine skills, from mechanic, blacksmith, shipbuilder and cartman or someone who drives a horse carriage. Based on these skills their values varied accordingly, the average appraised value for a mechanic was around $1,200 which is the highest appraised value for a male enslaved person with a skill. The lowest appraised value for a male enslaved person with a skill was $250 as a rope maker. Both the highest and lowest appraised valued skill tends to me a more male oriented occupation.
The females had skills that varied in more of the famine roles and occupations. There were skills in hair dressing and seamstress. As well as cooking, baking and other household skills including raising children and laundry. The highest appraised value for a female with a skill was $1,000 as a hair dresser, the lowest appraised value was a spinner at $200.
Already there is a pattern where the men enslaved persons are getting a higher appraised value then the women enslaved persons the argument can be made that the skills were different from male to female but that isn’t always the case. There are particular jobs that both men and female enslaved people share that aren’t distinct between male and female gender roles as well as certain skills that are shared among the two genders that do cross gender roles. Jobs that have no gender role for an enslaved person in the south would be that of a labor or field worker. When thinking of slavery in the south the image that appears in the mind is that of both men and women doing hard labor on a plantation. Labor work has no gender role and the male enslaved are valued at $630, while women enslaved have an average appraised value of $550. There were also skills that crossed across gender roles. Enslaved females also worked as mechanics. There was one recorded record of an enslaved female during the data set timed period in the small number of states listed that had the mechanic skill. Her appraised value was $600 in the state of Louisiana where there were thirty five male mechanics all valued at twice her rate.
Slave Sales 1775-1865.
The data set for the Slaves sales from 1775 to 1865 holds the information of individual slaves, their gender, and information considered important for potential slave buyers. The data can be considered numerical, textual, and geographically information. The numeric information for the data set is columned by the age of slave in years and month, the date of the entry of the slave, and the slaves appraised value. The two columns that describe the slaves age in years, the youngest being 0 years old, to the oldest being ninety nine. The column for age in months is empty throughout the entire data set. I can argue that the months column is completely empty because infants were rarely bought, and sold with in the slave trade itself. For the date of entry column the beginning date is 1775 and continue through 1865, although the data set itself is all over the place with which particular year a slave was sold. The column regarding the appraised value of a specific slave has variation based on the age, skill sets and defect of the slave. I can also argue from studying the data set that gender also played a major role in the appraised value for a specif slave, as well as the geographical location that a particular slave was sold in. The textual information for the data has text data with in the columns that include any defects that each particular slave may or may not have, and any skill set that some of the particular slaves may or may not acquire. The defects column of the data set included terms such as “runs away” or “deaf”, and the columns for skill sets include terms such as “house servant” and Laundry”. The textual information tends to be historical accurate in proving how real racism was, not just between the years of 1775 and 1865, but through out American history. The geographical information only has two columns; State and county. The states include Georgia, Louisiana, North Carolina, South Carolina, Tennessee, Virginia, Maryland, and Mississippi. The columns for county include all the counties with in the specific state. All the areas associated with this data set are southern states, some of which would succeed at the beginning of the Civil War to protect their institution of slavery, that they strongly believed was a given right.
The first of my data visualization is meant to show the correlation between the age, and gender of a slave, and their average appraisal price. This visualization makes me question not only the institution of slavery, but also sexism with in slavery. The varying prices between age can be, although horrifying, expected, but the variation between the genders of a particular slave seem unjustified.
The average peaking value for a female slave from the years of 1775 to 1865 was at the prime age of twenty two, with the average price of $566.9. The value of a female slave drastically declines after the age of twenty nine, and again at the age of thirty nine, although there are a few unexplained outliers. The lowest average value for a female slave from the years of 1775 to 1865 was at the old age of ninety, with the average price of $9.4, females at the age of one were valued more then elderly females at the average price of $71, although one elderly female slave was appraised at the average value of $800.0 at the age of 79. I can argue from reviewing the data set of the Slave sales that females slaves were ideally in their prime at the age of twenty two until thirty nine for work purposes, and more likely breeding purposes. Women were not value as high as men though, for obvious reasons such as labor, and strength.
The average peaking value for a male slave from the years of 1775 to 1865 was at the prime age of twenty nine at the average price of $795.1. The value of a male slave begins to drastically decline after the age of forty three with the average appraised value at 609.6, although like the female data, there are a few outliers within the data sets. For example, eighty four year old man was appraised at the average value of $227.5. The lowest average appraised value for a male slave was a ninety nine year old whose appraisal value was $19.6.
The data set of the Slave Sales from 1775 to 1865 shows the range of values for a set of slaves sold in the states of Georgia, Louisiana, Maryland, Mississippi, North Carolina, South Carolina, Virginia, and Tennessee. A closer study of the Slave Sale data set from 1775 to 1865 revealed a pattern between the value of a slave, their gender, and their age, only after I excluded the unknown ages of specific male, and female slaves within the data set. I found the average value for specific slaves varying on their gender, male or female, and their age ranging from age one to age ninety nine.
For my first visualization I created I decided to use a dot graph to show the variations between the price of a gender, and the age when slaves were being bought and sold in the years of 1775-1865. I chose the dot graph because it clearly shows the decline in average appraisal price as a slave in the slave trade aged. I chose the color blue to represent the male slaves, and the color pink to represent the female slave because these specific colors are always associated with the genders. I thought the colors would intensify my argumentation of how racism, and sexism went hand and hand in the years prior, and after 1775 to 1865.
For my second visualization is meant to show the “defects”, the gender of the slave with the “defect”, and the average appraisal value for this specific “defect.” I am using quotations around the word defect in this sense because certain “defects” in this visualization are not considered defects by today definition. The range of “defects” in this data set include thing such as: very tall, short, crippled, one handed, missing fingers, broken back, and etc. Other “defects” include things such as: lame, idiot, dirt eater, dumb, deaf, drunk, nursing a child, and etc. The highest appraisal value for a female in this data set is $800.00 with the “defect” of being deaf. The highest appraisal value for a male in this data set is $866.70 with the “defect” of having a broken back. The lowest appraised value for a female in this data set is $30.00 with the “defect” of being sick with cancer. The lowest appraised value for a male is $5.00 with the “defect” of being deaf. I can only argue that the female with the defect of being deaf is appraised at a higher value because she is either younger then the male, or is still able to communicate while being deaf because typically throughout the data set male slaves are always value higher then females. I chose to show the “defects” with in the range of genders to reiterate my first visualization of how sexism, and racism went hand and hand prior, during, and after the years of 1775 to 1865.
For my second visualization I created a bar graph that is brightly colored. The bar graph is again separated into the genders of male, and female to shows the difference in appraised value between “defects” and genders. The brightly colored bars within the graph are meant to show the wide range of varying “defects” that the slaves being sold were labeled with. The bars also show the appraised value of each slave with the “defect” and how each “defect” was compared prices wise to another.
The argument for my first visualization, as well as my second visualization is centered around how sexism, and racism went hand and hand in the years of 1775 to 1865. For both male, and female slaves slavery was an absolutely devastating experience, but the circumstance of enslavement were different for both the male, and female slaves. Although most planters in colonial North America favored robust young men as slaves, the bulk of these were shipped to the West Indies, so early on, slave buyers turned to purchasing female field hands, who were not only more readily available, but also cheaper. In fact, because skilled labor, such as carpentry and blacksmiths, was assigned only to male slaves, who were also more expensive because of the skill set, so the pool of black men available for agricultural work was further reduced. During the time period of 1775-1865 Women slaves were considerably cheaper, than a man that was their exact same age for what I believe to be attributed to strength, and after further research different types of work. One thing the data set does not tell me is what specifically each slave was being brought for whether it be plantation work, a house maid, or a stable hand. Appraisal value could have most likely varied between the job each slave would be doing, and the geographical location of that job. No matter the circumstances sexism was embedded into the context of slavery, and racism. Whether a female was considered less appealing for a job because of strength reasons, or job details in my opinion in that era even if a woman was equal to a male, the male would still have been sold for a more considerable profit simply based on his gender.
The argument for my second visualization again revolves around sexism, and more so for this data set the historical racism that it presents to use. The “defects” column of the data set ranges from what would be considered disabilities in today’s world such as: deaf, blind, broken back, one hand, cancer, or crippled. The “defects” with in the data set that show the historical racism that was involved in the slave trade include: dirt eater, dumb, idiot, complaining, bad character, runs away, steals, and insane. These “defects” show how lowly the slave trades thought of their slaves, but shows me that during an excruciating time some still had the urge to fight for their independence. In today’s time the idea that slave had a “defect” because they run away, steal, complain, or have a bad character means that specific slave had the will to stand up against their owner. In the years between 1775 and 1865 the slaver traders did not see things so positively though, these “defects” seriously declined the price of a slave. Along with the first data set, females with “defects” were considerably less costly then a male with the same or more extensive “defect.” Again showing how in the years prior, during, and after 1775-1865 that sexism, and racism were two institutions that coexisted together.
The first question prompted by my data visualization is what time of work each slave was going into. I would like to further research this so I can properly compare the appraised price of each slave, and further learn why two of the same aged, and skilled slaves could be appraised for different values. In my opinion this would help my complete understanding of the slave trade, and how in the years of 1775 to 1865 it worked. The second question prompted by my data visualization is if a “defect” such as a dirt eater was racially driven, or there is a historical explanation behind why a slave would be actually be eating dirt. I would like to know if it was a religious exercise, or something culturally driven. The third question prompted by my data set is why specifically some elderly slaves were valued higher than some slaves considered to be in their prime of life. Is it because a slave at the age of eighty has been a slave for a long time and was setting an example for the younger slaves, or simply for educational purposes for the other slaves? The final question prompted by my data set would be why in specific geographical locations did the appraised value of slaves vary so much? Were slaves valued higher in different states in the United States because of different work loads, or during this time period was one state booming more then another?
When doing research for my data I was intrigued to find many different connections for all aspects of the data. The New York State religion census is categorized in a variety of number classification such as the location, the county of the church within New York State and what denominations the church is affiliated with. The information that is given to the viewers allows them to piece together the lives of the citizens who lived during this time and to get a better sense of what religion theses citizens practiced. In the data shown, 1850-1890, the viewer can see the steady growth of most all of the various religious denominations that were established in New York State. Additionally, using my first bar chart, featuring the different dimensions over time, it can be concluded that the most dominant religion sect establish itself throughout New York State were the Methodist, Baptist and Congregational. Presbyterians however, shown by the data, did not see dramatic increases its church presence throughout New York State. Throw the 1850-70 the number of churches stood about seven hundred every time the data was collected. The data also concluded that the Quaker churches in New York was the only demonion that was a decrease in numbers. By 1870 the data that was collect suggests that there was no Quaker churches with the New York county. Final by 1890 they re-appeared but with only ninety-two churches.
I believe the influxs of churches correlates what was going on in American history, the first and second wave of immigrants and the urbanization of America. The first waves of immigration to the United States happen in 1840-1860. The immigrants that were coming to the land of opportunity were mostly Irish and German. As the second wave approached in 1880-1940, this where the data set stopped in 1890. The immigrants that were arriving to the United States were mostly eastern and southern europeans. During the first wave on average about 2.4 million came to the land of opportunity. For the second wave on average about 5.2 million came to America. This example the sudden increase Judaism at the end during the 1890. Because Judaism is a promett religion is the eastern and southern parts of europe. With America’s rise of immigration, this also lead to Urbanization. A process by which towns and cities are formed and become larger due to the increase people living and working in that areas. Once the immigrants got off the boat many of them had never little money to travel out of the city where the ship ported. In correlation with the immigration increase, leads to Industrial growth. In my map data set of New York over times the number of churches and the variety of religion found in the county of Queens, Kings, New York, Richmond and even Suffolk doubles in size. During this times America developes into working class. Wage labor when prior we were a nation of farms. Going from someone that control your wages and the amount of food you can provide for family. Hugh shift from farming and being independent, to someone (Boss) controlling your wages.
When looking at any historical information, you may see a pattern regarding disparities based on societal factors. The 1940s census provides information on a select number of people living on neighboring streets, their marital status, income, gender, education level, occupation, age, and more. Demographic data is a good way to track populations, but requires good observation to find correlations within the data. The patterns we see today are presented in the census once we create visualizations. There is a distinct trend in the societal differences based on class, gender, and race. Three main points within this argument are that men make more money than women, the higher education obtained, the better the occupation is, and the presence of white privilege/lack of representation for people of color in any data.
The first trend within the argument is education levels influence occupations. The census data lists the number of people who have either attended elementary school to college. Although several names have no information regarding the highest level of education received, a pattern is still present. There is a subset of data that shows people who have only completed elementary school with an occupation as an unpaid family worker, wage or salary worker, janitor, or laborer. Those with only high school education had similar jobs, or some more advanced such as beautician, attendant, and typist. College education residents in Albany were lawyers, civil engineers, stenographers, and accountants. Those job titles/careers advance as the education level increases, showing the value society has always places on receiving a higher education. The 1940s census does however, lack a large amount of occupations for residents. People in Albany overall did not attend college. You also see that many people that have an elementary school education hold the same positions of those who attended college. Today, that same issue applies, where people attend college and do not always immediately find work in their desired career. Also ten years prior to 1940, the United States went through economic turmoil. This can have an effect on people who worked in state, local, and financial offices that may have lost their jobs during that time.
The second correlation found within the census is the difference in income based on gender. It is already a known fact that men make more than women today. History has shown us that society has trained women to be comfortable in the household. They are created to be wives, mothers, and attend to duties in the home. All of this work fulfilled and even when doing so, that work is still not measurable to work for pay. When visualized in a bar chart, you can see men made more than women. The census does lack information on occupations for women. The marital status also plays a role, as you can see who is married versus who is the head of the household. Typically, the relation to head of household for women is wife, mother, or daughter. The men would be the head of the household or the son. Both the head and the son still would have occupations listed in the census more frequently than women during this time.
The last pattern within the argument is racial disparities that continue to exist. With a simple glance at the census, it is clear that over ninety percent of the population in Albany was white. It is uncertain if people of color were undocumented purposely, or they simply did not reside in Albany. Three other races were present in the census: Chinese, Filipino, and Black. Based on their demographic information, they all lived on Fleetwood Avenue or Vanschoick Avenue. Their education levels, occupations, and income varied. The data alone does not show if one minority was more established financially than others. It is however a small sample of information to make any major conclusions. By assessing the information, you can question whether the area in which people of color lived was worse financially, safe or unsafe, encouraged or lacked opportunity for growth in comparison to the neighborhoods where white people resided. Those factors are a part of the present struggle of equal opportunities for all races today.
There are other correlations that can be made with the census data. You can compare martial status to head of household, immigrant status and occupation, home ownership and income, gender and employed for pay/non-pay. The census is simply surface material, yet can unveil many trends about populations in specific areas. These patterns are not new findings to how society is run today. They give additional information for how these patterns came to life.
My first visualization looks at the relationship between the Albany 8th County Militia, their place of birth, their age and the “complexion” that each man is marked as. The use of complexion in this graph is not to examine any person in the militia based off of looks or bias, but rather to understand better the racial identity of the men of the militia. The graph itself is a stacked bar graph that a mix of textual and visual data. In order to fit all these categories into one graph, it not only had to use both of these types of data, but also required the graph to be broken down into sections. Initially the graph separates the men from each place of origin. Origins other than the United States are specifically labelled by their country. In comparison births in the United States are broken down into states in which they were born. This is further broken down if the birth was in New York, due to the majority of births in the United states being in New York. The New York births are divided into county or area of of birth, since the counties of Western and Northern New York have changed considerably since the American Revolution. From within the category of the location of birth, the graph is further broken down by complexion. These categories divide by the broad skin based or racial complexions. Indians, blacks and mulattos are generally labelled by race while the rest merely state generally skin tone, or in the case of dark and brown, hair color. Its from this category that bars on the graph are representative of, visually showing the number of each complexion. These bars break down into the last category of age. The bars are broken into color coded sections, each representative of an age range. These ranges are each roughly ten years, except for teens, as one had to be 16 to join a militia. A filter to the right edge of the graph allows the viewer of the graph to see specifically whatever specific category they want in terms of age and complexion.
The story or point of this graph was to show the general story of the men of the 8th Albany Militia. Specifically to answer who were the these people who decided to answer the patriot call to arms for their ideas of freedom and rule. From what Ive read on the Revolution I know it was not the grand, united, nationalist, patriotic fight against tyranny that we are generally lead to believe. Generally it was about normal people, many with little to no opinion on the matter, caught in the ideas and fights of who could run this land better. This visualization I believe goes a long way in showing a story of the men who fought in the Revolution. In the country of origin section I think we see the most interesting piece of the story. That being that a majority of the men fighting were not born in America, but rather Ireland, Germany and England. Three countries, that in one way or another, the patriots fought in the revolution. The Germans fighting on the British side were mostly from Hanover and the Irish were more utilized by the British rather than willingly fought us. Still its fascinating to see that the men fighting for the patriots were from the same places that they fought against. These men were generally not blue blooded American born patriots but recent immigrants. I cant vouch for there motives in fighting but I cant say its for freedom from tyranny. The complexion section tells a different but equally interesting story. While many men were from these North-Western European countries, those men were generally same in the complexion. Essentially they were mostly fair and pale, brown and dark(in this case meaning hair color, not skin tone). This is fairly standard for Western European countries. What I find interesting is that when we look at American locations of birth, we now see the diversity. Indians, blacks and mulattos pepper the categories. While not high numbers, the presence of minorities shows two things. One the willingness of minorities to fight and die for the patriot cause, and two the beginnings of the boiling pot culture America is known for. Even so early in America’s history, the country shows its blending of peoples together for a single vision. Finally the age category shows a trend that while not too shocking is interesting. A majority of the men who are in the militia are young, with twenties being the highest age range. Also surprising is that teenagers are the third highest age range, surpassing forties and fifties. These two points of information may say something about the willingness of young people to fight for a cause they believe, but probably just points to the greater numbers of young people that are able to actually fight.
I chose these three major categories of age, complexion and place of birth because as I said earlier I wanted to show a broad picture of the men who were fighting for the patriot cause. While one cities data cant speak for a whole nation, I think it can show at least some interesting views of Albany’s Revolutionary history. I felt that based on the information given on the muster role, that these three categories not only best showed that picture of Albany’s fighting men, but could also all work together on one graph. My original plan was to use a map to show visually the locations of the company’s births, but ran into some problems. Mainly that the geodimensions made you pick either county/region, state/province or country. With my data including all three of those and feeling that none of them could or should be compromised for the sake of a map, I scraped that idea I tested the other visualizations. None of the other visualizations I felt could fit all this information I wanted to put in very clearly, so I chose the stacked bar graph. For tall three categories I had to group together certain similar divisions to make the graph actually readable. For location of birth I had to group specific cities together, for complexion I had to group similar tones or hair colors and for age I decided to divide it into ten year intervals. From there I decided that since the locations were the most numerous category that it should be the first to be divided. This allowed the less dividing complexion and age to both share the bars on the bar graph. With age having the fewest possibilities I decided to divide each bar by age to make it easily readable. Choosing the colors for the age divisions I decided to make each division one color interval different. That way it would be easy to know what age range your looking at. The older you were going, the closer to green (and then yellow) you were going.
An unfortunate aspect of war is the sheer amount of casualties that are suffered on both sides of the conflict. Throughout the history of the United States war has been an ever-present facet of out society. Most of us have difficulty remembering a time when America was not involved in some sort of armed conflict. During the 19th century, the United States faced a series of conflicts within the confines of her borders that resulted in some of the largest and bloodiest fighting seen to date.
The 1883 Pensioners lists hundreds of casualties stemming from the early 19th century, through the War of 1812 and through the American Civil War. Within this list lies the names of the pension recipient, the cause for which the pension is being offered, and the date in which the first payment was submitted. At first glance this census, of sorts, provides few details to create much of an argument out of other than it can be assumed these pensions were direct results of the various conflicts America fought during the century. However, upon looking through many of the reasons for the pension, we as historians can uncover some rather interesting little tid bits. First off, gunshot wounds, while the major cause for a pension, was not the sole injury sustained. In many cases diseases and illnesses could result in a person obtaining a monthly check. Epilepsy receives an average payout of $8.00 whereas chronic diarrhea saw a person receiving half of that. While you will have difficulty arguing that chronic diarrhea should be classified as something the government should include as reasoning for a pension, it is clear that it was a rather rampant problem that plagued many people during this period.
An argument that I would like to bring up concerning a rather heartbreaking part of this data is the amount of dependent mothers and widows represented within the data. Females makeup roughly fifty-percent of the population, give-or-take, but when we, as students of history, think about war we directly assume the victims are male. We forget that there are women back home caring for the family and painfully trying to make ends meet. Today strides have been made to recognize this forgotten section of society as women are increasingly making up larger and larger sections of the armed forces. But in the 19th century, women were decades from achieving the right to vote, let alone go off to war. So the subsection of pensions for women that are represented in this data has to do with them becoming widowed and needing to care for a family, presumably. The average payout here is the same as epilepsy at $8.00 a month. This would have the same buying power as around $250.00 today. $250.00 is not a lot of money one bit considering the various bills now accrued by the widow from her husband and also raising her children. There are numerous different injuries including in this data set but to pull one out to compare: an injury to the right foot received $25.00, over three times the amount that a widowed mother is now receiving.
There is no doubt that serious wounds such as amputations and other handicaps sustained as a result of battle require a large degree of money. But by giving women a measly $8.00, I would argue, society is valuing their life and their contributions to society in a much smaller degree. This can be seen throughout the history of the United States and around the world. Women have tended to be treated as inferior to men. They had been refused the right to vote up until 1919, lacked equal rights within a marriage, have their bodies regulated by church and state alike, are refused entry into certain military branches, the list goes on and on. During a time when women had difficulty even obtaining a job, they are now without the love of their life and their breadwinner. How, as a society, could this be allowed to happen?
Slavery –the practice or system of owning slaves (Random House Inc., 2016). Such a system served as a pillar of the U.S. economy and social structure. By 1850, slaves in the U.S. were worth 1.3 billion dollars. Or in other words, American slaves were worth one fifth of the entire nation’s wealth (Goyette, 2014). Such information makes sense of the data that is displayed in the slave sales data set. It’s easy for people to think about how rich America’s history is, but how often do these people think about the hands that made it great? From the Caribbean to the mainland slaves hands were goldmines. Cotton wouldn’t have boomed without people to grow, harvest, and pick it. Tobacco would be a delicacy if lives weren’t stolen and then bought in order to harvest it. These statements ring true for many of the goods produced by slaves. This may be contrary to popular belief, but the American Economy must have depended on slavery for the better part of its history before the start of the 20th century. As a result, slaves were in high demand. But the question is –which slaves were in high demand and why?
According to measuring worth, a slave’s value was truly the value of the how much they’re expected to produce (Williamson and Cain, 2011). In other words the value of a slave was not really the slave’s value per say, but the value of the service that they could provide. For example, an elderly woman wouldn’t be expected to produce much, especially if she has any outstanding physical condition (or “defects”) such a missing finger or cataracts. As we see in this data visualization, males were clearly expected to produce more because generally, more money was spent on males. In Louisiana, males that were between the ages of 15 and 44 had the highest values and men within the 24-35 age-range held the peak values. As for females, those in the approximate age range of 14-33 years old held the highest value. This is no surprise, since these are typically a female’s peak child-bearing years (Williamson and Cain, 2011). Slaves weren’t only valued for what they could produce in the fields, but for their skills as well. Premiums were paid for slaves that had artisan skills such as cooking, carpentry, and blacksmithing, among other domestic skills. On the other hand, a slave’s value was depleted if they had characteristics or deformities that would inhibit their production such as drinking, being crippled, or being a frequent runaway (Williamson and Cain, 2011).
The spreadsheet itself uses appraised values that are generally under one thousand dollars. However, if we were to convert these prices to what they’d be today, the average range for which a slave would be sold would be 12 thousand to 176 thousand dollars. In other words, a slave was worth anywhere between the price of buying a used car and a mortgage. For example, a slave that would be sold for $400 in 1850 would be worth about $82,000 today. (Williamson and Cain, 2011). For slave owners, perhaps foregoing purchasing a home or another luxury item was worth investing in a few decades worth of slave services that would have a major return in the long run.
Though all states in the slave sales data set purchased slaves to some degree, the massive amount of capital spent on both female and male slaves by Louisiana is strikingly higher than the other states. Louisiana was most likely subject to the other factors such as the cotton boom that justified the desire across the country for slaves in their prime. If this is the case, why was Louisiana so much more passionate (according the data visualization) in the buying of slaves? At the top of the 18th century, Louisiana was the resting ground for only ten people of color. However, the French imported about six thousand slaves in Louisiana (Whitney Plantation). After the Seven Years War that concluded in 1763, Louisiana was occupied partly by Britain and partly by Spain. Subsequently the territory was reopened to large scale imports of slaves. By 1795, about thirty years later, the amount of slaves ballooned to almost 20,000. A few years later in 1807, the Atlantic slave trade was prohibited. However, this didn’t stop those that were persistent about sustaining slavery. Thousands of slaves were smuggled into the territory from Africa and the Caribbean illegally in addition to the domestic slave trade in the upper southern part of the U.S. If we fast-forward towards the end of the data visualization in 1860, there were over three hundred thousand slaves in Louisiana and nearly 20,000 free people of color.
In the time period that the slave sales data set spanned, Louisiana had avid reasoning for demanding so much slave labor. While the territory was under French rule, the services that slaves provided varied and the territory was highly dependent on slave labor. Such tasks included cooking, hulling rice with mortars and pestles, carpentry, and raising cattle (oxen, sheep, cows, and poultry among other animals). Female slaves also took care of their master’s personal task of caring for their children. Though aiding in raising their children mad a masters life easier, the mass importation of slaves gave masters a new lease on life. Wealth was easily in a master’s reach with the slave trade (Whitney Plantation).
Coupled with indigo production, the mass importation of slaves gave masters a more prestigious standard of living. Another reason for Louisiana’s higher dispensed capital for slaves is indigo production under Spanish rule. Females were a main part in raising indigo crops and males extracted them –which makes sense of why the territory spend large amounts of capital on both females and males (Whitney Plantation).
Slaves were a part of American culture for centuries, and part of that time is covered in the slave sales data set. The U.S. depended on slaves for their free services in order to make capital. So much so, that they were willing to shell out what would now be thousands upon thousands of dollars on slave labor because of its returns. Where would the U.S. be on a global scale without slave labor? –A question that can answer itself.
Slavery. Dictionary.com. Dictionary.com Unabridged. Random House, Inc. http://www.dictionary.com/browse/slavery (accessed: April 25, 2016).
Goyette, Braden. “5 Things About Slavery You Probably Didn’t Learn In Social Studies: A Short Guide To ‘The Half Has Never Been Told'” The Huffington Post. October 23, 2014. Accessed April 26, 2016. http://www.huffingtonpost.com/2014/10/23/the-half-has-never-been-told_n_6036840.html.
Whitney Plantation. “Slavery In Louisiana.” Slavery In Louisiana. Accessed April 26, 2016. http://www.whitneyplantation.com/slavery-in-louisiana.html.
Williamson, Samuel H., and Louis P. Cain. “Measuring Worth – Measuring the Value of a Slave.” Measuring Worth – Measuring the Value of a Slave. 2011. Accessed April 26, 2016. https://www.measuringworth.com/slavery.php.
When doing research for my data I was intrigued to find many different connections for all aspects of the data. When looking I found that many of my question were answered to a certain extent. Within minutes of searching for how pensions were paid out I found a few fast facts that really applied across the board for this data set. The main idea of the first article that I came across was specifically how Civil War pensions were paid. In this data set many of the pensioners were likely receiving such pensions from injuries suffered from the Civil War so I thought this would be a good place to start. The main question that I had from the first visualization was how there were so many differences in pensions paid and this article was very helpful. Almost instantly my question was answered, it turns out that those who applied for pensions earlier were likely to receive lower pensions. Using today’s logic that seems like it would be really unfair and it was, and it only got worse. Due to pension laws evolving over time a person would receive more for the same injuries on different types of technicalities. So, if someone who was certain about their ailments significantly effecting their lives to receive a pension would in fact be paid lower than someone who would try to work or survive without a pension. And the pay wouldn’t change over time for those earlier applicants either, someone would just get paid more based on whichever technicality was passed before they applied. I found this to be particularly disturbing because as I said before, people who were certain that they would need a pension to keep going who benefit less by applying than someone who thought that they could at least make ends meet for a while without a pension. Similarly, in regards to different pieces of pension qualifications being passed your ability to be pensioned changed as well. So if you weren’t eligible to be pensioned one day the next day you could be eligible. So overall, one day you would not be eligible to receive a pension and then the next day, you could not only receive a pension but have a higher monthly payout that someone who has been receiving one for years. From these two statements alone my data started to make so much sense. Perhaps it was that people knew that they could receive more if they waited, but for those who really needed the pensions, who couldn’t wait, were not to benefit. This is kind of disappointing, how most of these people, veterans, or widows from the war, had a portion of there income governed by dates on a page possibly missing a potential meal by a day or two.
I’ve also learned that the whole pensions system was kind of weird. Just for the sake of taking a look I Googled the 1886 census and looked at some data from a county in Illinois, and shockingly the data was almost exactly the same. Many of the pensions were in the same area and some were exactly the same for a certain field. In the mother category most people received $8, this was the same in this county in Illinois as we’ll as Albany. Also many of the abbreviations for the certain injuries. So somewhere along the way they made some sort of rigid system that was adopted by every census taker in the country. If this kind of communication was out there why couldn’t as system be to create some sort of uniformity for the payments? It was like they made all these new rules, let new people in, adjusted a little for inflation but forgot about the people who filed for pension earlier on. The weirdest part of all of this was that there was no federal government action at this time. Civil War veterans did not start receiving pensions from the federal government until 1930. After all this I look at the relative similarities as nothing short of a miracle. Especially in those times where there was no form of really efficient communication, this would’ve been ridiculously hard. So since there was no federal structure the whole payment of pensions was based on state or county rules. It would’ve been up to the counties that the people lived in to keep track and maintain the records for the pensioners, another tall task, so I can see where the temptation to cut corners comes in. One thing I thought that was interesting about how states and counties paid pensions was that some counties would have a competitive rate compared to another county that could have a greater ability to pay a higher pension. I would think a county like Albany who at that time was fairly prosperous would presumably have higher pensions then say a smaller county in a lesser state or especially the South. The fact that almost across the board pension rates are similar is simply astounding.
Overall I find that the this dataset’s true story is lost in history somewhere. It’s not one of the more glorious parts of history but it’s in there. For this set the main factor that drove the pay scale was when you applied to be pensioned. This is something that you wouldn’t guess from first glance. And that is something that I experienced first hand. At first I looked at this data and thought why would there be a difference at all? Even though injuries ranged from challenging to life altering it was basically all left up to a date. That is something in today’s times could never happen. To think of something that could change the ability to feed your family left up to a chance date, especially set by the government would not fly. By today’s standard that is borderline unfathomable.Though most logic would go against the actual truth of how the pension was paid it’s still interesting to find it.