A note on the measurement of racial integration of schools by means of informational concepts†

Theil, Henri; Finizza, Anthony J.

doi:10.1080/0022250x.1971.9989795

Cited by 217 publications

(158 citation statements)

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“…For any given value of p, we can dichotomize the income distribution at p and compute the pairwise segregation between those with income ranks less than p and those with income ranks greater than or equal to p. Let H p ð Þ denote the value of the traditional information theory index of segregation computed between the two groups so defined (Theil, 1972;Theil & Finezza, 1971;Zoloth, 1976). Likewise, let E p ð Þ denote the entropy of the population when divided into these two groups (Theil, 1972;Theil & Finezza, 1971). That is,…”

Section: Measuring Segregation With the Rank-order Information Theorymentioning

confidence: 99%

Income Segregation Between Schools and School Districts

Owens

Reardon

Jencks

2016

American Educational Research Journal

283

186

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Although trends in the racial segregation of schools are well documented, less is known about trends in income segregation. We use multiple data sources to document trends in income segregation between schools and school districts. Between-district income segregation of families with children enrolled in public school increased by over 15% from 1990 to 2010. Within large districts, between-school segregation of students who are eligible and ineligible for free lunch increased by over 40% from 1991 to 2012. Consistent with research on neighborhood segregation, we find that rising income inequality contributed to the rise in income segregation between schools and districts during this period. The rise in income segregation between both schools and districts may have implications for inequality in students' access to resources that bear on academic achievement.ANN OWENS is an assistant professor of sociology and (by courtesy) spatial sciences at the University of Southern California, 851 Downey Way, Los Angeles, CA 90089-1059; e-mail: annowens@usc.edu. Her research interests include sociology of education, urban sociology, social policy, and social stratification. Current research focuses on the causes, trends, and consequences of income and racial segregation between neighborhoods and schools. SEAN F. REARDON is the endowed Professor of Poverty and Inequality in Education and professor (by courtesy) of sociology at Stanford University. His research focuses on the causes, patterns, trends, and consequences of social and educational inequality; the effects of educational policy on educational and social inequality; and applied statistical methods for educational research. CHRISTOPHER JENCKS is the Malcolm Wiener Professor of Social Policy at the Harvard Kennedy School. His recent research deals with changes in family structure over the past generation, the costs and benefits of economic inequality, the extent to which economic advantages are inherited, and the effects of welfare reform. His books include The Academic Revolution (with David Riesman);

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Section: Measuring Segregation With the Rank-order Information Theorymentioning

confidence: 99%

Income Segregation Between Schools and School Districts

Owens

Reardon

Jencks

2016

American Educational Research Journal

283

186

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“…indicator that measures economic structure using the distribution of the overall employment among all the sectors in a region [45]. The basic idea of this index was first developed by Theil and Finizza [46] and has been widely used in measuring the diversity level of overall economic structure (for example,it is used by Attran [23],Malizia andKe [20] andTrendle [22]). The Entropy index is calculated by:…”

Section: Comparing the Msi To Commonly-used Diversity And Specializatmentioning

confidence: 99%

The Influence of Multiple Specializations on Economic Performance in U.S. Metropolitan Areas

Hong

Xiao

2016

Sustainability

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Scholars have recently been debating how economic structure affects regional economic performance. Regional economic structure typically indicates how the industries in regions are organized. The attribute of industrial organization in regions is typically measured by how the employment in a region is distributed among various sectors. If the employment in a region is highly concentrated on a limited number of sectors, that region is industrially specialized. On the contrary, when the employment is more evenly distributed among various industries, that region is highly diversified in its industrial organization. In this context, some researchers recognized that diversity and specialization are not opposite concepts. Instead, they can coexist, for example in the form of diversified specializations. In this study, this body of literature was extended by formulating an indicator to measure the extent of multiple specializations in regional economies and by examining the effect of multiple specializations on regional economic performance. Empirical analysis showed that specializing in multiple industrial pursuits helped regions to achieve both faster and more stable economic growth.

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“…An alternative to the fractionalization indices is the Theil Entropy (or Information) Index (see Theil (1972) and Theil and Finezza (1971)). The entropy index for the metropolitan area is…”

Section: Spatial Entropy Indicesmentioning

confidence: 99%

Poisson indices of segregation

Mele

2013

Regional Science and Urban Economics

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Existing indices of residential segregation are based on an arbitrary partition of the city in neighborhoods: given a spatial distribution of racial groups, the index provides different levels of segregation for different partitions.This paper proposes a method in which individual locations are mapped to aggregate levels of segregation, avoiding arbitrary partitions. Assuming a simple spatial process driving the locations of different racial groups, I define a location-specific segregation index and measure the city-level segregation as average of the individual index. After deriving several distributional results for this family of indices, I apply the idea to US Census data, using nonparametric estimation techniques. This approach provides different levels and rankings of cities' segregation than traditional indices. I show that high aggregate levels of spatial separation are the result of very few locations with extremely high local segregation.I replicate the study of Cutler and Glaeser (1997) showing that their results change when segregation is measured using my approach. These findings potentially challenge the robustness of previous studies about the impact of segregation on socioeconomic outcomes.JEL Classification: C14, C21, J15

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A note on the measurement of racial integration of schools by means of informational concepts†

Cited by 217 publications

References 1 publication

Income Segregation Between Schools and School Districts

Income Segregation Between Schools and School Districts

The Influence of Multiple Specializations on Economic Performance in U.S. Metropolitan Areas

Poisson indices of segregation

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