Data Visualization Chunk 2 Blog Post

            Two weeks ago, my class began an exploration of the wild frontier of raw datasets with the goal of ultimately finding ways to visually represent said data. ^[1] I wrote about my experiences (read: battles) here and here. The second week gave us a specific tool to use, DataWrapper which was introduced to us by Dr. Tom Liam Lynch, as well as an added goal of processing the data in some way prior to said visualization. What this means is that my professor wanted us to (virtually) get our hands dirty and manipulate the raw data into new formats that helped give the data meaning. To further clarify, I don’t mean that we altered the data in any unethical way, just that any comparisons or functions completed using the data (e.g. percent changes, comparing data from one chart with data in another chart) would be done by us and not the original collector/creator of the dataset. Unbeknownst to me, I had already done this when I re-organized/re-created the Current Population Survey’s (CPS) tables on poverty. My job, then, was to dive more deeply into the data and work with a narrower section of the data.

            To recap, the questions on I had about my dataset were primarily concerned with comparing the enrollment of public-school aged folks across race and sex. What I ended up actually visualizing, after that first week, was the comparison of all age groups of White responders and Black responders for both sexes, for only females, and for only males. If that was a little confusing you can see what I did here. I started my second week by looking into the data for the questions I did not answer, the ones regarding public-school aged folks. I soon realized, to my slight embarrassment, that I forgot to carefully read the CPS’ definition of enrollment which states that they only asked people above the age of sixteen if they were enrolled in high school, college, or university. Thus, my questions from last week had to be thrown out and I went back to the drawing board.

For what felt like ages and was probably only a day or two, I struggled to come up with a way to either narrow the data in an intriguing way or look at the data in more nuanced and/or different ways than I had the first week. While I’m not convinced that I successfully did that, I ended up looking at the data for all the racial categories ^[2] that the CPS collected information on and comparing not only across racial lines for all ages but also for specific age ranges that the CPS had data on such as 16- to 17-year-olds. I also delineated information for both sexes, information for only females, and information for only males. In this way I felt that I was able to get a new perspective on the data and see the story that they told. ^[3]

Before moving on to the charts I made and some of the trends I was able to  discover, I think I should explain why I was interested in this data in the first place as well as make explicit some of the theories I had going in. As is most likely obvious, I have a passion for social justice and equity in education and society writ large. While I believe that education has the potential to reinforce the status quo just as much as has the potential to be liberating, I also know that to economically succeed in the U.S. you need an increasingly higher level of educational attainment. When I researched the school-to-prison pipeline I looked into the educational attainment of folks in prison and then the correlation between educational attainment and living in poverty. ^[4] I had wanted to provide evidence both that people in prison are not, and often cannot, get an education and that that dearth leads to lasting consequences. This impacted how I went about this project because I had originally wanted to look at the rate of suspension, detention, and expulsion for folks who were living below the poverty line and/or were houseless, ^[5] but I could not easily find reliable and up to date information on that topic.

This then led me to think about something I had discovered when doing research into social class in the U.S.—there is a federal definition of poverty but not one of any other designation (e.g. middle-class, upper-class, etc). While this can be explained away as necessary in order to know if you can apply for certain federal programs, it also easily lends itself to the toxic ideas of an us v. them binary, the culture of poverty, and the undeserving poor. ^[6] Moreover, it means that only people living in poverty can be researched because they are the only ones that we can categorize in a scientifically meaningful way. From that research I knew that the Census Bureau had tables that I could use. I had initially thought the enrollment tables were perfect because they were getting at the problem I originally wanted to investigate, the intersection of the institutions of education with people living in poverty/who are houseless, from a different angle. Learning that I had to change my focus from the K-12 system to the high school and college system ended up not really impacting the heart of my topic since high school and college are both institutions of education, just for a slightly different age group. All of this prior knowledge led me to believe that most of the responders living below the poverty line would at least have a high school diploma, that they would mostly be people of color, ^[7] and that there was likely to be a disparity between the sexes although I wasn’t sure in what way. ^[8]

Moving on to the visualizations, I have embedded the charts I made into this post. If they do not work, please let me know in the comments. 

 

Even though I had those theories going in, I was still surprised by some of my findings. I was surprised at how big of a percentage of people across racial lines, ages, and sexes, were not enrolled and did not have a high school diploma. Unfortunately, it was often also the category with the largest percentage of people in it. I had been expecting most responders to have a high school diploma even if they were not currently enrolled in any form of education so to see that that was not the case was striking. Similarly, the fact that this category continued to have the largest percentage even in age categories that included sixteen-year-olds, who are the traditional age for high school, was sad. When I see that data, I can’t help but think that the educational system failed them.

While I was right that most of the responders were not White, I think I accidentally fell into the trap that assumes White people are rarely or never poor, so the amount of White responders was a bit surprising to me. Looking at the data for all ages, more of the responders were female than male ^[9] and for all racial categories there were more females who were not enrolled and did not have a high school diploma. Looking back, I now realize that I should have made charts that compared females to males so it would be easier to make the comparison I just did. Taking all of that together, I learned that educational institutions and people who are living in poverty often run parallel, not intersecting, lives; I also now have more evidence supporting my belief ^[10] that one’s lack of educational attainment has a negative impact on your economic status and well-being.

According to the project description, I should use this next section to write about the questions I still have as well as where I might go with this research if I were to continue it. However, I am quickly running out of steam, and I already think I made this too long. ^[11] The right thing to do would be to leave writing the rest of this post for tomorrow but I really want to publish this because I have (what feels like) a million other assignments to get done. If someone ^[12] asks me, I will definitely add any necessary finishing touches, but until then: thank you for reading!

 

 ^[1] I originally put wild jungles, but in our cultural lexicon, jungles are generally associated with either the inner-portion of the African continent (e.g. Heart of Darkness and Tarzan) or South America (I can’t think of any specific media but I’m sure there are plenty I’m blanking on). Given the historical use of “wild and untamed” locations, and thereby people, to oppress, colonize, and exploit, I figured it was better to use a different metaphor. After all, in these types of situations, especially given my knowledge of the history, my intentions matter less than the impact of my actions. I’m not even sure I should use frontier, to be honest, because the idea of the Wild West (and implicitly the frontier which it represents) predicates itself on the assumption that the land was empty when Americans got there and that vacuum is what led to the stories of lawlessness. In reality, the west was not only inhabited by indigenous tribes and peoples from what we now call Mexico, it was also where we forced all of the indigenous peoples, those who originally lived in the east, to live.  

 ^[2] The CPS and the Census Bureau writ large have allowed for people to identify as multi-racial, however, the data I use are from the responders who put down only one race. For instance the CPS had tables labeled "White Alone" and "White Alone or in Combination," I chose to only work with the former. Furthermore, being "hispanic" is an ethnicity, and more technically, all it refers to is that you belong to and/or are from a Spanish-speaking culture, country, and/or home. In this way, you can have someone who is hispanic and White or hispanic and Black. You can also be Latinx but not hispanic (e.g. if you're from Brazil where the language is Portuguese).  

 ^[3] Thanks again to my awesome professor who gave me the suggestion to think of the data and my visualizations as a narrative!  

 ^[4] Here is a chart and the article I found it in by UC Davis’ Center for Poverty and Inequality Research that shows the educational attainment of people living in poverty and how much of the total U.S. population they make up:

 Admittedly the data is a bit old 2014, but I’m not going to take the time to look for an updated version.

 ^[5] I specifically write houseless instead of homeless because activists have advocated for the change in terminology. Their perspective is that no one is without a home in the metaphorical sense and to say that they are takes away something that should never be taken away. They explain it more eloquently than me but that’s the gist.

 ^[6] The binary of us v. them is a general concept that can be applied to most situations of inequity, but in this context means specifically those who are not poor (us) v. those who are poor (them); the culture of poverty has historically been used as an explanation of generational poverty that places the blame on the individuals and/or the individuals’ culture; the undeserved poor is the idea that certain groups of people who are poor do not “deserve” outside/societal help. Similar to the concept of the culture of poverty, this idea is often based in the assumption that it is the individual’s fault that they are poor. For example, people who lose their valuables and house to a natural disaster “deserve” help, while those who are poor because of other reasons (drugs, abuse, veterans, mental disabilities, etc) do not “deserve” help.  

 ^[7] Due to the vicious and historical cycle of race and economic disenfranchisement those who are living in poverty are also often people of color. One example is the inability for Black G.I.’s to benefit from the G.I. Bill which in turn led to an inability to gain intergenerational wealth from houses and then when they could buy houses there would often be a “White flight” where all the White occupants left and future White families refused to move in which inadvertently led to a devaluing of those neighborhoods. That’s a simplified explanation but gets across the ways that people of color, and especially Black Americans, were barred from gaining wealth.

 ^[8] It could have been females who had less educational attainment because, assuming that they are cisgender, women of color are more likely to get hired than men of color because the men have been sucked into the prison system and cannot get easily hired when they get out and/or because women in general are more likely to have gaps in their careers/education due to pregnancy and motherhood. On the other hand, males might have less because, again assuming that they are cisgender, men of color are more likely to get pulled out of education as a form of punishment (as in suspension and/or expulsion) and they either never went back or got incarcerated, the latter of which would also interrupt and potentially stall their educational attainment.

 ^[9] Although we discussed in class last night that the percentages did not make sense as a whole and this could mean that the data itself is skewed.

 ^[10] Technically it’s not really a belief of mine because I have seen previous evidence suggesting that, but I’m not sure if theory or idea is more accurate because I’m not the first/only person to have this thought and see this relationship.

 ^[11] Yes, I do recognize that this is at least partially due to my footnotes. Although, if I'm honest, I think I only care about the length because I fear that my writing is becoming as boring as my brain is becoming a useless mush (haha) and I don't want anyone to feel like reading the post is becoming a chore.

 ^[12] Yeah this is totally an oblique reference to my professor, I am apparently not above being fake sneaky in this blog/post.