In 1994, a book was published suggesting that intelligence is the most important factor in determining descent into - or emergence out of - poverty. Authored by Richard Herrnstein and Charles Murray, The Bell Curve sparked a great deal of controversy in the social science research community that has yet to put the notion fully to rest. Some have tried; a team of UC Berkeley sociologists used the same data to refute The Bell Curve’s findings - but UCI Chancellor’s Professor of sociology Charles Ragin says the issue still rages due to a reliance on a faulty premise. 

“All of these researchers take data from the National Longitudinal Survey of Youth and attempt to isolate the effects of one variable from the others,” he says.

In other words, they use the same methodology and differ only in their ideas about which variable – whether it’s test scores, parental income or something else – is the most important, with the other variables held constant.

“The problem is that this misses a crucial point: Poverty is not primarily determined by one factor but by a broad range of factors that jointly create conditions that foster or suppress it,” he says. “Those with the most money tend to have more highly educated parents, live in nicer neighborhoods and send their children to better schools—advantages coincide.”

The same is true at the other end of the economic scale, where disadvantages co-occur. The notion that we can zoom in on the unique impact of one of these factors while keeping all the others held constant is very problematic, he adds.

“Ultimately, these varying influences combine rather than compete with one another to shape life outcomes.”

He goes into this topic in detail in his new book, Intersectional Inequality: Race, Class, Test Scores & Poverty, co-authored with Peer Fiss, USC Marshall Professor of Management and Organization. Their research leverages a methodological toolkit, developed by Ragin starting in the 1980s, called Qualitative Comparative Analysis (QCA), which they maintain yields more valid results than earlier works’ reliance on 'net-effects' thinking. Their goal was to identify the different ‘causal recipes’ associated with life chances—to determine what combination of circumstances could protect someone from experiencing poverty – or push them more deeply into it.

Using fuzzy sets and Boolean algebra to identify patterns in the data, Ragin and Fiss find that blacks are 'doubly disadvantaged.' For example, the co-authors show that white males who consistently avoid poverty combine being educated with any one of the following advantages: a favorable family background, a favorable domestic situation, or good test scores. A very high proportion of white males have these combinations of advantages. For black males to achieve comparable levels of poverty avoidance, they must combine three advantages—being educated, good test scores and either a favorable family background or a favorable domestic situation. Only a very small proportion of black males combine these multiple advantages.

“Not only must they cope with having fewer advantages, but they also must accumulate more advantages than whites in order to achieve comparable outcomes,” says Ragin.

Ragin and Fiss point out that the debate over intelligence and its relationship with poverty has always been fueled by competing political perspectives. The result, they say, is that the agenda manipulates the data instead of being driven by it. Intersectional Inequality reframes the conversation, paving the way for an informed policy discussion that puts partisanship aside in favor of comprehensive solutions.

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Intersectional Inequality is available online from The University of Chicago Press.

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