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Teaching Income Inequality with Data-Driven Visualization

Sang T. Truong and Humberto Barreto (hbarreto@depauw.edu)

The American Economist, 2023, vol. 68, issue 1, 140-155

Abstract: The distribution of household income is a central concern in economics due to its strong influence on society’s well-being and social cohesion. Yet, non-expert audiences face serious obstacles in understanding conventional measures of inequality. To effectively communicate the extent of income inequality in the United States, we have developed a novel technique for visualizing income distribution and its dispersion over time by using U.S. household income microdata from the Current Population Survey. The result is a striking dynamic animation of income distribution over time, drawing public attention, and encouraging further investigation of income inequality. Detailed implementation is available at github.com/sangttruong/incomevis . An interactive demonstration of our project is available at research.depauw.edu/econ/incomevis . JEL codes: A2, C1, D6, E6, I3

Keywords: 3D; data visualization; data-driven education; Gini; survey; microdata; bootstrapping (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:amerec:v:68:y:2023:i:1:p:140-155

DOI: 10.1177/05694345221129022

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