- Online Media
Inequality and DataCounts Learning Modules
A recent Data in the News post spotlighted a daily chart from The Economist that compared inequality in a number of OECD countries. The United States did not fare well, finishing third from the bottom in relative poverty (“measured by the share of the population earning less than half the median income”), only ahead of Chile and Mexico (Brazil is also on the chart, but it is not an OECD country). The Gini coefficient, another measure of inequality for which a score of zero indicates perfect equality and one perfect inequality, also suggested that the United States is trailing comparable countries in the fight against inequality. The U.S. finished fourth from the bottom, beating out South Korea, Mexico and Chile.
Considering these figures, it might be prudent to ask, “What does inequality in the United States look like?” Who is rich in America and who is poor, and what do we mean when we say ‘rich’ and ‘poor’? In which income brackets do most people fall? What can social demographic data tell us about people in different economic brackets? What are their incomes; their races; their genders; and their ages? Can this information help improve policy?
We think these are important questions, and we believe that everyone should have the tools to answer them—and other questions whose answers require data. Our DataCounts project teaches students of demographic data how to access, analyze, and draw conclusions and policy suggestions from data. One of our modules, “Income Inequality in the United States,” by Tim Thorton, teaches students how to figure out what inequality in the United States looks like. Here is a PDF version of the module, with instructions on how to access data, and questions (and assignments) that push students to think about and fully understand what the data is telling them.