What are the men and women of America studying in college and how do their outcomes differ based on their major? Through the lens of gender and ethnicity, this visualization of US bachelor's degrees examines achievement and outcome in terms of unemployment and median income.
This visualization underscores an unavoidable complexity in discussing educational background and post-college outcomes. Even in the broad scope of the US Census, the data, like truth, resist simplicity (Green).
Let's take some examples... While this visualization highlights similarity between genders in overall educational attainment, some degrees like "Engineering" have a great deal more men than women. However, at the same time, some fields like "Psychology" have a disproportionate number of females compared to males. Meanwhile, looking at unemployment, men are jobless at higher rates in "Communications" than their female counterparts but women suffer from higher unemployment than men in "Computers, maths, and stats". Of course, this extends far beyond gender and unemployment. For example, "White" Americans hold the lion's share of bachelor's degrees but, adjusting by the size of the overall national population per ethnic group, it turns out that "Asians" earn degrees at higher rates. Additionally, regardless of gender, "African Americans" have higher unemployment rates than all other ethic groups, even after they achieve the same degree. Yet, still, the numbers become fairly close in some disciplines like "Education". That said, men certainly earn more income than their female counterparts in all fields but, even in that generalization, the disparity varies greatly depending on the discipline at hand. Given this nuance, this project cannot tell its readers what to think but it can invite them to dive deeply into the data, asking them to mouse over the visualization and to configure the radio buttons that choose the metrics displayed.
Note that the US Census provides a description of each category (pg 21).
Anyway, despite some room for improvement, I hope this work takes a logical step in those long lines of thought within visualization science. All that said, these design considerations and the overall visual structure are very unlikely to suit general purpose visualization and the their tools. Some trade-offs were made in regards to the dynamics of this very specific dataset.
You can find more info about me and my past work at my personal website. This was a self-directed personal (two) weekend project. This is not an official Google product. It is open source and hosted at github under the Apache 2 license. Please see github for the license and copyright information. The views and opinions expressed here are those of the author and do not necessarily reflect the views of Google, its affiliates, or its employees.