Chin, G. & Culotta, E. (2014). What the numbers tell us. Science, 344(6), 818-821.


“In 2011, the wrath of the 99% kindled Occupy movements around the world. The protests petered out, but in their wake an international conversation about inequality has arisen, with tens of thousands of speeches, articles, and blogs engaging everyone from President Barack Obama on down. Ideology and emotion drive much of the debate. But increasingly, the discussion is sustained by a tide of new data on the gulf between rich and poor.

This special issue uses these fresh waves of data to explore the origins, impact, and future of inequality around the world. Archaeological and ethnographic data are revealing how inequality got its start in our ancestors (see pp. 822 and 824). New surveys of emerging economies offer more reliable estimates of people’s incomes and how they change as countries develop (see p. 832). And in the past decade in developed capitalist nations, intensive effort and interdisciplinary collaborations have produced large data sets, including the compilation of a century of income data and two centuries of wealth data into the World Top Incomes Database (WTID) (see p. 826 and Piketty and Saez, p. 838).

It is only a slight exaggeration to liken the potential usefulness of this and other big data sets to the enormous benefits of the Human Genome Project. Researchers now have larger sample sizes and more parameters to work with, and they are also better able to detect patterns in the flood of data. Collecting data, organizing it, developing methods of analysis, extracting causal inferences, formulating hypotheses —all of this is the stuff of science and is more possible with economic data than ever before. Even physicists have jumped into the game, arguing that physical laws may help explain why inequality seems so intractable (see p. 828).

In the United States, the new information suggests a wide rift between top and bottom. Tax data from the WTID suggest that today the top 1% control nearly 20% of U.S. income, up from about 8% in the 1970s. But inequality is increasing within the 99%, too, as a consequence of a growing premium on college and postgraduate education: The fates of the tech-savvy worker at Google and the blue-collar employee at General Motors have been decoupled (see Autor, p. 843). According to surveys by the Census Bureau, in 2012 the richest 20% of Americans enjoyed more than 50% of the nation’s total income, up from 43% in 1967. The middle 20%—the actual middle class—received only about 14% of all income, and the poorest got a mere 3% (see graphic).

Flip to a world map, and America’s inequality, despite reaching levels last seen extreme. Many nations, especially emerging economies, have even larger chasms between the super-rich and the poor. One widely used metric, the Gini coefficient, estimates inequality as an index between 0—at which point everyone has exactly equal incomes—to 1, in which a single person takes all the income and the rest get nothing. The U.S. Gini, at 0.40 in 2010, seems relatively high compared with, for example, Japan at 0.32. But South Africa is a sky-high 0.7.”