Investigating Economic Data with Visualization

For this week’s assignment I chose to look at the Economic Dataset from here. The data types consist of the Post-WW2 Election Year and the Unemployment Rate, Inflation Rate, Presidential Approval, and Consumer Confidence for that given year. Original data comes from the Federal Reserve Economic Data (FRED), a Gallup Poll, and a University of Michigan study on consumer confidence. I thought providing a visualization for this set would allow us to get a better understanding of economic trends, how they relate to one another, and provide us with a better direction for further research.

I chose to work with Google Fusion Tables to create a linear graph (“continuous variable chart”) using year on the X-axis and the numerical rating on the Y-axis for both the unemployment and inflation rate. I used this type of visualization so we could better understand how these rates have changed over time as well as how they may relate to one another. The visualization is below and can be accessed here as well.

Unemployment & Inflation Rate by Year

In Data Points, Nathan Yau discusses several visual cues and principles that we are built to recognize and make sense of—I kept these all in mind to create a visualization that would be well-received by the viewer. First, I used the visual cue of position by choosing a continuous variable chart because the viewer will first look to each point and where it is relative to others to understand it. I chose the “continuous” graph instead of a scatter plot so there would be a line created, thus allowing the longer segments in the graph to communicate a significant change. Lastly, the angle and direction of the continuous shape show sharp increases and decreases in the data to allow the viewer to quickly determine differences in the graph so further investigation can be taken.

By using these principles to guide the creation of the data visualization for the Economic Dataset, it’s much more visible and clear that the inflation and unemployment rate tend to change with one another; meaning, unemployment tends to rise as the inflation rate rises. This does not show causation but rather these two economic indicators are most likely affected by the same variables. However, we also notice a breach in this assumption in the years 1948 and 1980. In 1980, inflation shot to a record 14.2, while unemployment was comfortably at 6.3 and descending. This occurrence is able to be clearly seen through my data visualization due to the spatial gap and positioning that communicates an obvious breach/gap from the normal trend. Although this information is present in the original spreadsheet, it was nowhere near as apparent because the spatial gap and change in direction are not visible in the spreadsheet.

After being able to analyze the trends from the data visualization, it would allow me to take a more specific direction if I were to continue economic research. For example, I would obviously look to the economic and fiscal policy that governed the late 1970’s-early 1980’s to try and analyze why/how the inflation rate increased to such a high rate so quickly while the unemployment rate was descending and at a comfortable rate. I could also use this to create a humanities research question, using literature, movies, or songs that discuss the consequences of the high inflation rate as an indicator for the social sentiment of the time period. Whichever research direction I decided to take, whether it be policy or humanities driven, the decision can be attributed to the findings from my data visualization that showed me variables that are worth further investigation. I also acknowledge that much more advanced data visualizations could be constructed from this data but as a beginner I thought this was a simple visualization that had great impact!