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Different Shades of the Truth
Let’s expand our analysis of income inequality beyond the borders of one nation. Here’s a new claim that introduces comparative evidence and its source. Unlike the prior US examples that showed historical data for three income tiers, this global example focuses on the most current year of data available for the top 1 percent in each nation. Also, instead of measuring income in US dollars, this international comparison measures the percentage share of the national income held by the top 1 percent. In other words, how large a slice of the pie is eaten by the richest 1 percent in each nation?
Claim 3. Income inequality is more severe in the United States, where the richest 1 percent of the population currently receives 20 percent of the national income. By contrast, in most European nations the richest 1 percent receives a smaller share, ranging between 6 to 15 percent of the national income.7
Following the same train of thought above, let’s supplement this claim with a visualization to evaluate its persuasiveness. While we could create a table or a chart, those would not be the most effective ways to quickly display information for over 120 nations in our dataset. Since this is spatial data, let’s transform it into an interactive map to help us identify any geographic patterns and to encourage readers to explore income levels around the globe, as shown in Figure 0.3.
Is Figure 0.3 more persuasive than Claim 3? While the map and the text present the same data about income inequality in the US versus Europe, there should be no difference. But the map pulls you into a powerful story that vividly illustrates gaps between the rich and poor, similar to the chart example above. Colors in the map signal a crisis. Income inequality in the US (along with Russia and Brazil) stands out in dark red at the highest level of the legend, where the top 1 percent holds 19% or more of the national income. By contrast, as your eye floats across the Atlantic, nearly all of the European nations appear in lighter beige and orange colors, indicating no urgent crisis as their top-tier holds a smaller share of the national income.
Now let’s introduce the alternative map in Figure 0.4, which contains the same data as shown in Figure 0.3, but is displayed in a different format. Which map should you believe?
Why does the second map in Figure 0.4 look different than the first map in Figure 0.3? Instead of dark red, the US is now colored medium-blue, closer on the spectrum to Canada and most European nations. Did the inequality crisis simply fade away from the US, and move to dark-blue Brazil? Which map tells the truth?
This time, neither map is misleading. Both make truthful interpretations of the data with reasonable design choices, even though they create very different impressions in our eyes. To understand why, look closely at the map legends. The first map sorts nations in three categories (less than 13%, 13-19%, 19% and above), while the second map displays the entire range in a green-blue color gradient. Since the US share is 20.5 percent, in the first map it falls into the top bucket with the darkest red color, but in the second map it falls somewhere closer to the middle as medium-blue color. Yet both maps are equally valid, because neither violates a definitive rule in map design nor intentionally disguises data. People can mislead with maps, but it’s also possible to make more than one portrait of the truth.
The interpretive nature of data visualization poses a serious challenge. As the authors of this book, our goal is to guide you in creating truthful and meaningful charts and maps. We’ll point you toward principles of good design, encourage thoughtful habits of mind, and try to show by example. Occasionally we’ll even tell you what not to do. But data visualization is a slippery subject to teach, sometimes more art than science. We know that charts and maps can be manipulated—just like words—to mislead your audience, and we’ll demonstrate common deception techniques to help you spot them in other people’s work, and consciously avoid them in your own. But newcomers may be frustrated by the somewhat fuzzy rules of data visualization. Often there is no single correct answer to a problem, but rather several plausible solutions, each with their own strengths and weaknesses. As a learner, your job is to continually search for better answers without necessarily expecting to find the one right answer, especially as visualization methods and tools continue to evolve, and people invent new ways to show the truth.
World Inequality Database, “Top 1% National Income Share,” 2020, https://wid.world/world/#sptinc_p99p100_z/US;FR;DE;CN;ZA;GB;WO/last/eu/k/p/yearly/s/false/5.070499999999999/30/curve/false/country.↩︎