Sean Reardon

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This article investigates how the growth in income inequality from 1970 to 2000 affected patterns of income segregation along three dimensions: the spatial segregation of poverty and affluence, race-specific patterns of income segregation, and the geographic scale of income segregation. The evidence reveals a robust relationship between income inequality(More)
Purpose: To develop measures of segregation that are appropriate when either the groups or organizational units are defined by ordered categories. These methods allow the measurement of segregation among groups defined by ordered educational attainment categories or among ordered occupational categories, for example. Approach: I define a set of desirable(More)
In the absence of a randomized control trial, regression discontinuity (RD) designs can produce plausible estimates of the treatment effect on an outcome for individuals near a cutoff score. In the standard RD design, individuals with rating scores higher than some exogenously determined cutoff score are assigned to one treatment condition; those with(More)
OBJECTIVES We examined whether retail tobacco outlet density was related to youth cigarette smoking after control for a diverse range of neighborhood characteristics. METHODS Data were gathered from 2116 respondents (aged 11 to 23 years) residing in 178 census tracts in Chicago, Ill. Propensity score stratification methods for continuous exposures were(More)
The census tract-based residential segregation literature rests on problematic assumptions about geographic scale and proximity. We pursue a new tract-free approach that combines explicitly spatial concepts and methods to examine racial segregation across egocentric local environments of varying size. Using 2000 census data for the 100 largest U.S.(More)
In this paper we examine aggregate patterns and trends in segregation among white (non-Hispanic), black, Hispanic, and Asian public school students in 217 metropolitan areas during the period 1989-1995. We first describe a set of methodological tools that enable us both to measure the mutual segregation among multiple racial groups and to partition total(More)
Assumptions of Value‐Added Models for Estimating School Effects Sean F. Reardon Stanford University Stephen W. Raudenbush University of Chicago Prepared for the National Conference on Value‐Added Modeling April 22‐24, 2008 University of Wisconsin at Madison The work reported here was supported by funds from the William T. Grant Foundation (Reardon)(More)
Spencer foundations, the Exelon corporation, and the Chicago Community Trust, and visiting scholar awards to Jens Ludwig from the Russell Sage Foundation and LIEPP at Sciences Po. We are grateful to the staff of Youth Guidance and World Sport Chicago (the two non-profit organizations that implemented the intervention we study here), to Wendy Fine of Youth(More)
A large literature finds substantial variation in teachers’ effects on student achievement. Moreover, this research finds that little of this variation in effectiveness can be explained by traditional measures of quality, such as years of teaching experience. There remains, however, a gap in our understanding of how the choice of test measure—and teachers’(More)
The increasing availability of data from multi‐site randomized trials provides a potential opportunity to use instrumental variables methods to study the impacts of multiple hypothesized mediators of the effect of a treatment. We describe nine assumptions needed to identify the impacts of multiple mediators when using site‐by‐ treatment interactions to(More)