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Intersectional Inequality

Race, Class, Test Scores, and Poverty

For over twenty-five years, Charles C. Ragin has developed Qualitative Comparative Analysis and related set-analytic techniques as a means of bridging qualitative and quantitative methods of research. Now, with Peer C. Fiss, Ragin uses these impressive new tools to unravel the varied conditions affecting life chances.

Ragin and Fiss begin by taking up the controversy regarding the relative importance of test scores versus socioeconomic background on life chances, a debate that has raged since the 1994 publication of Richard Herrnstein and Charles Murray’s TheBell Curve. In contrast to prior work, Ragin and Fiss bring an intersectional approach to the evidence, analyzing the different ways that advantages and disadvantages combine in their impact on life chances. Moving beyond controversy and fixed policy positions, the authors propose sophisticated new methods of analysis to underscore the importance of attending to configurations of race, gender, family background, educational achievement, and related conditions when addressing social inequality in America today.

192 pages | 9 line drawings, 46 tables | 6 x 9 | © 2016

Political Science: Political and Social Theory

Sociology: General Sociology, Methodology, Statistics, and Mathematical Sociology, Race, Ethnic, and Minority Relations

Reviews

Intersectional Inequality makes an original and substantive contribution to the Bell Curve debate, offering a methodological guide to those who wish to apply set theoretic methods to survey data. This is one of those very rare books that offers genuine innovation. Its combination of substantive and methodological material and argument is increasingly rare—and I welcome it as the sort of book that will educate students about what social science at its best can offer and also provide a model of the configurational approach for other researchers to follow.”

Barry Cooper, University of Durham

“This is a breakthrough book. Ragin’s substantial corpus of research has demonstrated how QCA and related methods can be used with small and moderate size data sets. In this new research with Fiss, he shows how these methods cannot only be applied to large data sets, but to a central problem of sociology—the prediction of poverty. In doing so, they demonstrate that their methods can provide new insights that are wholly missed by regression and related methods.”

Christopher Winship, Harvard University

"Investigates which combinations of causally relevant conditions are linked to specific outcomes, using a diversity-oriented, intersectional understanding of such connections and focusing on the predictors of poverty status."

Journal of Economic Literature

Table of Contents

Acknowledgments

Introduction


One / When Inequalities Coincide

Two / Policy Context: Test Scores and Life Chances

Three / Explaining Poverty: The Key Causal Conditions 

Four / From Variables to Fuzzy Sets

Five / Test Scores, Parental Income, and Poverty

Six / Coinciding Advantages versus Coinciding Disadvantages

Seven / Intersectional Analysis of Causal Conditions Linked to Avoiding Poverty 

Eight / Conclusion: The Black-White Gap and the Path Forward for Policy Research

Bibliography

Index

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