A couple of weeks ago, fellow interaction designer and data visualization enthusiast, Sera Koo and I worked together on a challenge put out by Visualizing.org. The challenge, to visualize the economic effects of increased high school graduation in the US, specifically in terms of additional spending, tax revenue, home and vehicle sales, jobs, and GDP. The winning poster would then be displayed in front of policymakers and to the general Public.
By emphasizing on scale and contextualization of the data provided, we can tell an effective story that informs the public and government on why they should invest in education and how this would boost the overall economy.
The ties between education and government are pretty complex. As designers not fully involved in this specific sector, we were limited in our understanding of how the different actors within the system work together. This has both its advantages (not getting drowned in minute details, being able to see ‘out-of-the-box’ to some extent) and disadvantages (possibly missing key points, making the wrong assumptions). With that said, to better understand the system, we mapped the education/government relationship to something we’re familiar with… second-order feedback loops.
What the hell is a second-order feedback loop, you ask?
The second-order feedback loop does two things: 1) It’s basically a system with built-in feedback that informs how close the system is to achieving its goal; 2) and if it’s not meeting its goal, it attempts to change it (stick with us here). In our case, we wanted to map out the economic ripple effects of investing in education. Then we wanted to see if there was a step or goal that needed to be changed to make this system more effective. All of this, we hoped, would be conveyed in our final infographic.
Based on a real-world example, the model shows the nested relationships and influences on many levels. Even without any quantitative details, the model is instructive in showing the complexity and interdependencies of the nested system. (Pangaro & Dubberly Design Office)
To map out the second-order feedback loop, we had to first establish the actors within the system.
After a few rounds of Post-It exercises, we realized that in order to tell a strong story, the actors had to be quantifiable. It would require the “quantitative detail” that the second-order feedback system lacked (shown above in the Yellowstone Ecosystem).
Fortunately, the data set (Education and Economy) provided by the Alliance for Excellent Education had already quantified each of the 8 economic indicators, which were used to project the economic effects of higher graduation rates.
These indicators eventually ended up being the actors within our system. After establishing an overview of the relationship between education and government, we then tied it to the provided data.
Now that we had a better understanding of the overall system, we were ready to dive into the dataset. We spent some time in Processing generating the data into standard bar graphs, then further analyzing those through line graphs. Our initial goal was to explore how the GDP growth breaks down in two ways, by state and by race.
States vs. Population
States with the biggest returns also happened to have among the highest state populations, which leads us to our next point.
Population vs. GDP
Looking at the data in groupings only revealed the obvious (i.e. the bigger the population of a state, the greater the GDP – in most cases). From our perspective, this didn’t really argue for the need for more investment in education in a compelling way.
Race vs. GDP
Ethnic groupings was misleading in terms of scale. From our data output, it looked as though the White, Non-Hispanic and Hispanic groups would yield the most GDP growth. But this is misleading. If the racial groups are broken down to the individual level, it actually shows that Hispanics have higher GDP return on an individual basis, followed by White, Non-Hispanic and with Asian/Pacific Islanders very close behind (a difference of approximately $1000). Our initial graph showed otherwise, positioning Asian/Pacific Islanders near the bottom.
Basically, when visualizing the economic benefits via groups, our initial research revealed very little insight. Patterns were pretty much consistent and similar across all indicators. If anything, the visualizations were misleading. We were looking for something else… an anomaly or some other revelation that would compellingly argue the case for investing in education.
Given that the scale was deceptive, we decided to visualize the data on the level of an individual. We also hoped that doing so would humanize the story, as its more compelling to relate to the potential of one person than to the potential of the masses.
Bringing It Together
After gathering all the necessary data points, the next and final step was to tell a story. Starting off with a system map that shows the overall economic effects of education, then indicating the current state of education (including how many students are dropping out and who these students are), and finally, what investing in education could yield economically for the US as well as the individual student.
This visualization explores the systematic impact of investing in education, including the economic results when no child is left behind. It’s clear that economic benefits are evident for both students and their communities at large.
Data Sets Used