Skip to main content

Statistical inference for richness measures

Buy Article:

$51.63 plus tax (Refund Policy)

Abstract:

Richness indices are distributional statistics used to measure the incomes, earnings or wealth of the rich. This article uses a linearization method to derive the sampling variances for recently introduced distributionally sensitive richness measures when estimated from survey data. The results are derived for two cases: (1) when the richness line is known and (2) when it has to be estimated from the sample. The proposed approach enables easy consideration of the effects of a complex sampling design. Monte Carlo results suggest that the proposed approach allows for reliable inference in case of ‘concave’ richness indices, but that it is not satisfactory in case of ‘convex’ richness measures. The standard bootstrap methods give similar results for ‘concave’ measures, but they are also unreliable for ‘convex’ indices. The performance of the bootstrap inference can be improved in some cases using a semi-parametric approach. The variance formulae are illustrated with a comparison of wealth richness in Canada, Sweden, the United Kingdom and the United States.

Keywords: C12; C46; D31; affluence; distributional indices; richness; statistical inference; variance estimation

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/00036846.2014.880106

Affiliations: Faculty of Economic Sciences, University of Warsaw, 00–241, Warsaw, Poland

Publication date: May 13, 2014

More about this publication?
routledg/raef/2014/00000046/00000014/art00004
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more