Data Description
Anytime we hear the word “slavery”, we tend to think back to a time of great controversy within America. Although slavery has long been abolished, it is still important to look back at information about the slave trade to better understand such a complex system. The Slave Sale (1775-1865) data-set is an example of how we can use historical facts to create stories and arguments about how the slave system operated. The data presented in the spreadsheet are about the basic information used when selling a slave. Within the spreadsheet there are nine columns: state, county, date entry, gender, age, appraisal value, skills and the defects. Unfortunately, the data is only specific to seven southern states: Georgia, Louisiana, Virginia, North and South Carolina, Mississippi, and Maryland. However, the data-set still provides a variety of information, such as numerical, textual and geographic data. For instance, the age, entry date and the appraisal value of a slave would represent the numerical information. While gender, skills and defect would be textual. Finally, the states and counties would represent the geographical information.
Despite the data set providing a variety of information, within the data there are some incomplete aspects. For example, there were many slaves whose age were unknown. However, from the information given we can infer that the oldest age was about 80 and the youngest was about 3. Another example of incomplete information was the appraised value section. Each master expected to receive the recorded amount for his slave(s) whether he bought a male or a female slave. Based on the data, an appraisal value ranged from the hundreds to the thousands range. Many factors went into determining how a slave would be valued. As a matter of fact, the idea of viewing slaves as property and recording them by their value and not by their names was just a way to further de-humanize slaves. The last two columns skills and defects provides insight on what factors might have played a role in appraising. For instance, the men had skills such as cabinet makers or gardeners and women would be cooks or midwives. As for defects, being old or too young fell under this category. Also, deformities and disabilities were considered a defect.
The main question behind the slave sale was How can a slave benefit its master? The Slave Sale data-set does not tell viewers how each column may connect with one another, it only gives the basic recordings. However, once you begin to find these connections then you can formulate stories and create arguments. For this data set there were many columns that correlated. For example, gender vs. appraisal, gender vs. skills,and age vs. appraisal. Sometimes more than two columns can correlate to create a more complex story. It is also helpful to use the data as a platform to ask questions that the information my not directly answer. Such as, Were these slave masters looking for women who could bear children or looking for men who were physically well? Questions are a way to generate answers, hopefully so you can have a better understanding of the slave trade.
Data Visualization
In order to create a story based on my first visualization, I had to find a connection between each group in the data set. My first visualization focuses on the appraisal value between gender and the states. Before I could start my story I needed to accurately identify if there was a relationship between the value of a slave and gender. The data set determined that male slaves valued at a much a higher price point than female slaves. The average male slave would be sold between the average price of $172 and $777, depending on the state. Women average price were between $113 and $639. However, because the data set shows correlation but not causation, it does not give the reason why the gender difference affected value. However what stood out the most from my visualization were the three dominant state: Mississippi, Louisiana, and Georgia. It also important to consider that these three dominant states were also the closest to down south. According to the visual, it did not matter the gender of a slave, if they were sold to one of these three states it was guaranteed that these slaves’ average appraisal was higher. For instance, if we take a look at the visualization Mississippi stood out the most for appraising women at an average of $639 and men at $777. This is also important to notice because we are observing a commonality between the state and genders.
Another aspect of the story was the correlation between age and appraisal value. The visualization showed that is was more common for a male or female slave to have a higher price value if the were about 18 to 27 years of age. This gap between ages was a slave’s “primal years”, where their bodies were capable of withstanding more labor than someone who was just a child or middle to elderly years. The last aspect is how the different states play a role in my visual story. Incorporating the state gives a different perspective of how the selling of slaves differed in distinct territories. For example, state state such as North/South Carolina, Tennessee, Virginia, and Maryland showed their average age of slaves being between the ages of 4 to 12, and not 18 to 27 like Georgia, Louisiana, and Mississippi. This is not to say that only children were sold to those specific areas or adults to the other. In some ways a master will always consider his slave(s) a worthy “property”.
For my second visualization I continued my earlier story topic, focusing on gender and appraisal differences. However, by incorporating skills as a new variable I was able to create a different story about the slave trade. Skills played an important role in the value of a slave. This goes back to the idea of How can a slave benefit its master? The types of skills slaves possessed varied, sometimes by the gender of the slave. Focusing on women vs. skills, the most dominant recorded skills women had were hair dresser, driver, leather dresses, mechanic, fieldwork and house servant. The less dominant skills were gardeners, plowman, cigar makers, dairy, laundry and cook. The average appraisal value of a skill determines what was dominant (common) and what was not. For example, the visual showed that a woman’s hair dresser and mechanic valued an average of $1,000, and a leather dress-maker valued 800. Comparing these three skills, to being a gardener or plowman who valued 450 or a cigar maker who valued 350, you can figure out which skills would be considered more common.
The next aspect of the story was male vs. skills. Based on the visualization, the most common skill for a male slave to have were either being a mechanic, brass molder, constructor, seamstress, sugar refining, leather maker, blacksmith, and silk trawer. Out of these eight skills, there were four skills that were the most common based on the appraisal value given. For instance, being a male mechanic was the best skill because it valued an average of $1,283. Following a mechanic, was a brass molder who valued $1,013. Finally, being a constructor and a seamstress averaged a price of $1,000 each. The less common skills were tool-maker, brick mason, shoemaker, cattle minder and butcher. The average price for these skills were about between $500 to $300. It’s also interesting to notice that even though there were records of slave with skills, there were also those with no talents that still had substantial value.
Often times when we compare men vs women, we revert to stereotypes to classify the genders. For instance, women are often times viewed as being delicate and fragile. Therefore, we can infer that women slaves’ skills would be more domesticated. While a male slave who was considered more physically fit would work in the fields or have hands on skills. Looking at the data-set from a stereotype aspect, the visualization does show the idea that each gender had their own type of skill. The woman had domesticated skills as well as care giving skills. For example, women were nurses, midwives, sewed and tailored clothes, did laundry, and cooked. While men had more labor work such as, military, butcher, shipbuilder, butcher, and cart-man. Even when a skill overlapped between the genders if it was a more “domesticated” skill then a female slave would be appraised higher. This is clear is the laundry skill, men average value was $408 while women were $461. Even a woman tailor was worth more than a male tailor . The skill that stood out the most was a hairdresser, a female hair dresser surpassed a male $1,000 to $275. The same concept applies to overlapping skills that were more hands-on. For example, fieldwork had a value of $634 for men, but $551 for women. Another example was male mechanics whose average price was $1,238 compared to the $600 value of female mechanics. The last aspect of my story that I also found interesting was the breaks in stereotypes. Having a driving skill is considered gender neutral. However, we would expect that male drivers would be priced higher, but on the contrary, it is women whose price was $1,000 compared to $657 for men. Next, was seamstress which would be more of a woman’s skill to have. However males average value were $1,000 while women were $522.
Process Documentation
When creating my visualizations, it was important that the information I displayed also connected with my stories and arguments. Before I created my visual I had to find which variables connected with one another. For me the variable that I focused on was appraisal value. Within the spreadsheet I noticed values increasing and decrease, and concluded that there were other factors that could possibly be affecting the appraisal value. From there I created my first visualization, the bar graph. I chose a bar graph as a visualization because it organized my information well, and made it easier to compare each category with one another. The information displayed on the visualization also connects with my arguments. For example, I argued why there were gender differences in appraisal value, why age was an important factor, and why states differed in prices. My bar graph separates into two sections, the top based on women and the bottom based on men. This way viewers can easily compare how gender affects appraisal value. Next, each bar is identified by a state, and there are seven in total. The purpose of the states was to show how appraisal value can change depending on the location. The last aspect is age vs. appraisal value. In order to show the age variable I decided to incorporate a color into my visualization. The intensity of the color determines the age of the slave. For example, if the age was closer to 4 it would display as a light green, but if the age was closer to the 20’s range it would display as a darker green. The information displayed on the bar graph also connected with my arguments.
For my second visualization I decided to use a tree chart to display my information. However, I left the same green theme that was in my previous visualization. The reason for doing this was because I continued with the idea of gender differences. However, I replaced the age and state variables from my bar graph with skills, in order to create a different story and argument for my tree chart. The chart separates between male and female, but this time the genders are displayed side by side. As a result, it makes comparing gender and skills much easier. The tree chart also identifies each box with a specific skill, making it easier to find similar skills within the gender. Overall, the chart was a good visualization choice because it connects with my argument that skills were valued by gender. In some parts of the chart, you see a darker green meaning the skill was valued the highest. On the other hand, there might be some skills with a lighter green. Color plays an important role, especially when skills overlap. Sometimes a skill found in both genders displays itself in different shades depending on the gender, proving that skills were sometimes valued by gender.
Argumentation
Before we can truly understand the way the slave trade works, we must first understand the slave trade process. The information given in the data set tells the story of how different information can correlate, but does not imply causation. According to Historian Herbert Gutman, “once every 3.5 minutes, 10 hours a day, 300 days a year, for 40 years, a human being was bought and sold in the antebellum South”[1]. Most slaves were primarily sold to work and maintain their white master’s plantation. Other times, when a master experienced a decrease in profits they would sell their best slaves to help with their economic struggles. It also did not help that as America began moving westward the opportunity to own land increased. As a result, a higher demand was placed on slaves, which encouraged the slave sale.
Based on the bar graph, there is a distinguished difference between male and female appraisal values. Whether a slave was sold into a big or a small plantation, a master’s goal was to acquire a slave who was able to work quickly, withstand gruesome hours, and carry heavy loads all for the sake of producing the most products. Most times male slaves were more appealing because of their physical ability to work on the fields which gave them an advantage over women. Even with a difference in gender value, both men and women shared a commonality when it came to average age. For example, if there were three men whose ages were 25, 15 and 35 and each had the same set of skills, the 25-year-old slave would be priced at a higher value. This meant that if a male or female slave were in their prime age (18-27) they would be priced at a higher value. However, some states show an average age of (4-12) which does not necessarily mean only children were sold, but a result from insufficient records. The masters as well as the slaves did not keep good records of their ages, some slaves’ names were barely acknowledged. This was a way for masters to keep their slaves oppressed, it was all about considering slaves as less than human beings and more about considering them as property. The better the master’s “property” the better the chances that he will make a greater profit.
The last aspect of the graph is the different states that divide each column. Out of the seven states, Georgia, Louisiana, and Mississippi are the three dominate states. From a geographical aspect these three states were located further south, where slavery was more prominent. The cotton gin invention could also explain why many plantations increased in size. For example, in Georgia the slave population by 1800 doubled to 59,699, and by 1810 the number of slaves had grown to 105,218 meaning that more slaves were being sold into the state [2]. In Louisiana, by 1840 – 1860 Louisiana’s annual cotton crop rose from about 375,000 bales to about 800,000 bales [3]. By 1860 Louisiana produced about one-sixth of all cotton grown in the United States, creating a higher demand for slaves to work the fields in this area [3]. As for Mississippi, it was the state with the highest appraised value for both male and female slaves. This is due to the fact that by the first half of the 19th century, Mississippi was one of the top producers of cotton in the United States. As the white settlers’ population increased so did the slave population and by 1859 Mississippi made a name for itself, producing over a million pounds of cotton [4].
Based on the information displayed in the tree chart, men and women were given different skills as well as different values for each skill. Sometimes if a slave had a demanding skill or performed his task well they might be preferred by their master because they were beneficial. To understand why there was a gender difference in skills, we must look at the social order on a plantation. First there is the white male master then, the few women slave owner and finally, the slave. However, even within the slaves a male slave was superior to a female slave. This is why we see higher values on the male side of the tree chart than on the female. Despite, how women were portrayed they still held some privileges such as having children and raising them. Pregnancy could be a reason why women had more domesticated skills. If a female slave became pregnant, her body would not be able to withstand the gruesome fields like a physically strong man. Therefore, this was an advantage to have skills like nurse, laundry or cook. However, women still had many responsibilities. Aside from taking care of their own children, women slaves might be in charge of taking care of the master’s wife children. Also, they could be responsible for fieldwork, but still have tasks to do in the master’s home.
Despite living in a life of oppression and dehumanization, a slave with a skill was a slave who had value. In a way, giving a slave the opportunity to learn and do a task was allowing them to have some control. Earlier in my tree chart story I observed that there were certain skills in both genders had the highest appraised value out of all the skills. For example, a driver which at first I thought meant an actual driver, but after research I found another definition for driver. A slave who was a driver was crucial to the flow of a plantation. He or she had to help other workers and also know the crops. A driver should know what was the necessary task to do to produce a successful crop [5]. As a result, anyone who could do this task would have a high value. Other highly priced skills were mechanics, construction, brass smolder, and blacksmith. All these talents made a slave an artisan, someone skilled at making things by hand. It also made these skills essential to having on a plantation so that it could run smoothly. For many masters having a slave with these demanding skills came with little cost because a slave was expected to do these tasks without expecting a pay. Even though skills might have allowed slaves to do other work than just field labor, they still continued to face exploitation.
[1]. Berry, Daina Ramey. 2007. “”in Pressing Need of Cash”: Gender, Skill, and Family Persistence in the Domestic Slave Trade”. The Journal of African American History 92 (1). Association for the Study of African American Life and History, Inc.: 22–36. Accessed May 8, 2016. http://www.jstor.org/stable/20064152.
[2]. Young, Jeffrey R. “Slavery in Antebellum Georgia.” New Georgia Encyclopedia. September 28, 2015. Accessed May 8, 2016. http://www.georgiaencyclopedia.org/articles/history-archaeology/slavery-antebellum-georgia.
[3]. “Antebellum Louisiana: Agrarian Life.” Antebellum Louisiana: Agrarian Life. Accessed May 11, 2016. http://www.crt.state.la.us/louisiana-state-museum/online-exhibits/the-cabildo/antebellum-louisiana-agrarian-life/.
[4.] Dattel, Eugene R. “Cotton in a Global Economy: Mississippi (1800-1860).” Mississippi History Now. Accessed May 8, 2016. http://mshistorynow.mdah.state.ms.us/articles/161/cotton-in-a-global-economy-mississippi-1800-1860.
[5.] Littlefield, Daniel C. “The Varieties of Slave Labor, Freedom’s Story, TeacherServe®, National Humanities Center.” The Varieties of Slave Labor, Freedom’s Story, TeacherServe®, National Humanities Center. Accessed May 8, 2016. http://nationalhumanitiescenter.org/tserve/freedom/1609-1865/essays/slavelabor.htm.
Further Research Question
We may truly never gain a full understanding how the slave trade operated, due to insufficient or lost information. Unfortunately, the Slave Sale dataset is an example. The information presented can only allow an understanding of slave trade in seven states. Something that I wish the spreadsheet recorded were names. Although I am sure there might be other records with names of slaves, it would have been beneficial for this recording. Introducing names also give a different aspect to analyze. For example, slave sales often times broke apart families, with more names recorded we can see if any family member were sold to different states. Were there children being separated from their mothers?
Once variables from the dataset began to connect with one another, I was able to better understand the process behind how slaves were being valued. For instance, the records show how slaves were being appraised and what factors might have contributed to their increased or decreased value. However, it does not explain the processes behind how a master sold his slave. Did masters make announcements on posters about slaves being sold? Were slaves placed on display and auctioned off to the highest bidder? Or was each slave inspected and given a non negotiable price?
Another interesting aspect where skills. Slaves were always oppressed from learning knowledge. Laws were made to prevent slaves from reading and writing. However, giving a slave a skill or task meant that you were giving the slave some type of control and responsibility other than just field work. Based on my visualization some of the skills that slaves possessed were construction or shipbuilding. For the artisan skills who were teaching the slave how to perform those hands on task? Or was this something the slave knew how to do already? Did these skills allow slaves to truly have more freedom?
There are many questions that this spreadsheet nor my visualizations can answer. In order to answer some of the questions it would require more outside research. For example, I would try to find other records of slave sale from different states to compare whether my observations are true for other southern plantations. As well as comparing northern and southern plantation. Based on the geographic location of the plantations there were some differences between more northern states like Maryland and Virginia and southern states like Mississippi and Louisiana. Therefore I would like to further explore whether there were different values, different skills or different types of defect depending on the plantation’s location. I also want to find other general recordings about African-American slave trade. Slave stories and non-fictional novels are also important factors to look at because they are first hand accounts. The stories can come from a slave who was sold away from his or her family or a slave who had multiply skills on a plantation or even a slave with a defect like old age or a bodily defect and how having a defect affected their life on a plantation.
