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st: Getting Started with ineqdeco

From   <>
To   <>
Subject   st: Getting Started with ineqdeco
Date   Fri, 13 Jul 2012 09:52:43 +0100

Date: Thu, 12 Jul 2012 17:06:36 +0000 (UTC)
Subject: st: Getting Started with ineqdeco


I'm a newbie and statistics is not my long suit to say the least. I have
approximately 250 communities and I want to give each one a gini
coefficient that indicates the degree of income inequality prevailing
there. For each community I have median income for each quintile. It
appears that I have to use ineqdeco, which I have installed on my
computer. After looking at the material on line, I'm not even sure where
to start. I'm guessing that I have to create a single variable for each
community that includes this data, but I'm not sure. Can anybody provide
a "ineqdeco for dummies" description of exactly what I need to do? 


For each community, you have "grouped" data, not unit-record data
(observations on each and every unit within a community), and appear to
have the median for each quintile group*, not the mean.

Grouped data should not be used with -ineqdeco- (or other inequality
measure programs on SSC) because they all assume that you have
unit-record data. Put differently, if you do apply them to grouped data,
as is, you will end up under-estimating inequality -- because you are in
effect assuming equality within each quintile group.

There are methods for estimating inequality indices from grouped data. A
good reference is FA Cowell and F Mehta, (1982). "The estimation and
interpolation of inequality measures", Review of Economic Studies,
49(2), 273-290, and references therein. See also FA Cowell's book,
Measuring Inequality (several editions).  

Note that these methods typically assume that you have the _mean_ income
within each group, not the _median_.

In sum, I think your issues are not to do with -ineqdeco- or related
programs. Rather, the fundamental issues concern the (lack of)
information in the data at your disposal. You as researcher have to
decide how to address those. One crude way might be to assume that the
median within each quintile group is the same as the group mean, and
apply one of the grouped data methods. Whatever, with only 5 pieces of
information per community, summarising inequality accurately within each
community is a difficult task


* [Pedant's corner]  The term "quintile" refers to an income value;
there are 4 quintiles, not 5 (and 99 deciles, not 100, etc.). Talk about
"quintile groups", not "quintiles", or -- more simply -- refer to the
"poorest fifth", ..., "richest fifth".

Professor Stephen P. Jenkins <>
Department of Social Policy and STICERD
London School of Economics and Political Science
Houghton Street, London WC2A 2AE, UK
Tel: +44(0)20 7955 6527
Changing Fortunes: Income Mobility and Poverty Dynamics in Britain, OUP
Survival Analysis Using Stata:
Downloadable papers and software:

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