More reliable inference for the dissimilarity index of segregation

Rebecca Allen (), Simon Burgess (), Russell Davidson and Frank Windmeijer

Econometrics Journal, 2015, vol. 18, issue 1, 40-66

Abstract: The most widely used measure of segregation is the so‐called dissimilarity index. It is now well understood that this measure also reflects randomness in the allocation of individuals to units (i.e. it measures deviations from evenness, not deviations from randomness). This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if there is no underlying systematic segregation. Our response to this is to produce adjustments to the index, based on an underlying statistical model. We specify the assignment problem in a very general way, with differences in conditional assignment probabilities underlying the resulting segregation. From this, we derive a likelihood ratio test for the presence of any systematic segregation, and bias adjustments to the dissimilarity index. We further develop the asymptotic distribution theory for testing hypotheses concerning the magnitude of the segregation index and show that the use of bootstrap methods can improve the size and power properties of test procedures considerably. We illustrate these methods by comparing dissimilarity indices across school districts in England to measure social segregation.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://hdl.handle.net/10.1111/ectj.12039

Related works:
Working Paper: More reliable inference for the dissimilarity index of segregation (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:emjrnl:v:18:y:2015:i:1:p:40-66

Ordering information: This journal article can be ordered from
http://onlinelibrary ... 1111/(ISSN)1368-423X

Access Statistics for this article

Econometrics Journal is currently edited by Jaap Abbring, Victor Chernozhukov, Michael Jansson and Dennis Kristensen

More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().