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Copyright ¤ UNU-WIDER 2004
* UNU-WIDER, Helsinki.
This is a revised version of a paper presented at the UNU-WIDER Conference Inequality, Poverty and
Human Well-being, May 2003 in Helsinki. It has been prepared within the UNU-WIDER project on
Spatial Disparities in Human Development, directed by Ravi Kanbur and Tony Venables, with Guanghua
Wan.
UNU-WIDER gratefully acknowledges the financial contributions to the 2002-2003 research programme
by the governments of Denmark (Royal Ministry of Foreign Affairs), Finland (Ministry for Foreign
Affairs), Norway (Royal Ministry of Foreign Affairs), Sweden (Swedish International Development
Cooperation Agency—Sida) and the United Kingdom (Department for International Development).
Discussion Paper No. 2004/01
Spatial Decomposition of Inequality
Anthony Shorrocks and Guanghua Wan*
January 2004
Abstract
This paper reviews the theory and application of decomposition techniques in the
context of spatial inequality. It establishes some new theoretical results with potentially
wide applicability, and examines empirical evidence drawn from a large number of
countries.
Keywords: inequality, index, decomposition
JEL classification: C43, D31, D63, R12
The World Institute for Development Economics Research (WIDER) was
established by the United Nations University (UNU) as its first research and
training centre and started work in Helsinki, Finland in 1985. The Institute
undertakes applied research and policy analysis on structural changes
affecting the developing and transitional economies, provides a forum for the
advocacy of policies leading to robust, equitable and environmentally
sustainable growth, and promotes capacity strengthening and training in the
field of economic and social policy-making. Work is carried out by staff
researchers and visiting scholars in Helsinki and through networks of
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www.wider.unu.edu publications@wider.unu.edu
UNU World Institute for Development Economics Research (UNU-WIDER)
Katajanokanlaituri 6 B, 00160 Helsinki, Finland
The views expressed in this publication are those of the author(s). Publication does not imply
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any of the views expressed.
ISSN 1609-5774
ISBN 92-9190-568-2 (printed publication)
ISBN 92-9190-569-0 (internet publication)
1
1. Introduction
Spatial disparities in living standards have been the subject of a great deal of attention in
recent years. At the global level there has been concern at the prospect of rising inequality in the
world distribution of income and the extent to which this is fuelled by factors linked to
globalization: see, for example, Milanovic (2002); Sala-i-Martin (2002); Bourguignon and
Morrison (2002); Fischer (2003); Kremer and Maskin (2003). Similar concerns surface within
individual countries, especiallythose countries where income inequality has been rising over time
and where average incomes varyconsiderably across regions or provinces. In China, for example,
unease with the growing disparity between the living standards in the coastal areas and the inland
regions has prompted the Chinese government to launch a campaign to develop the western
regions (Kanbur and Zhang 2003). The problem becomes a more intense political issue when
spatial inequality is perceived to be related to discrimination against particular groups of citizens
such as rural farmers (compared to urban residents), ethnic minorities concentrated in remote
areas, migrants in certain districts, or religious groups in particular regions (eg. Muslims in
Xinjiang Region in China).
The region of residence is not, of course, the only factor which accounts for differences in
living standards: there are typically wide disparities in incomes within, as well as between,
regions. Therefore, in order to appreciate the significance of geographical location it is necessary
to have a method of separating out the contribution of the spatial factors. Many empirical studies
of living standards make use of regional dummy variables, but the implications for the aggregate
level or trend in inequality are rarely explored — at least in terms of the inequality measures
commonly employed elsewhere.
The principal alternative procedure begins with an inequality value for the whole population
which is broken down or ‘decomposed’ into contributions associated with different spatial
dimensions. Typicallythe aggregate sample data are partitioned into a set of geographical regions
or districts, and the data analysed in terms of the inequality observed within each of the regions,
the inequality due to variations in average incomes across regions, and, in some cases, the
inequality attributable to ‘interactions’ or ‘overlaps’ between the regional income distributions.
Similar decomposition procedures are routinely applied to partitions into population subgroups
defined according to a wide variety of other criteria, including gender, age, education level, and
(y
1
, ... , y
n
)
These are all standard properties of measures of relative inequality. For more details see Silber (1999).
1
2
so on.
The aim of this paper is to review the current state of knowledge regarding inequality
decomposition in a spatial context although, for the reasons explained above, the broad thrust of
the comments also apply to many other types of decomposition by population subgroups. We
begin in section 2 with a discussion of the foundations of the methodology for inequality
decomposition. Section 3 outlines a number of general results on spatial decomposition which
should be helpful in interpreting and assessing empirical evidence on the subject. Empirical
evidence drawn from a number of country studies is then reviewed in the light of the theoretical
insights. Section 5 concludes the paper.
2. Theoretical Foundations of Spatial Decomposition of Inequality
The analysis of spatial inequality typically begins with a measure of living standards or
resources defined for a population of individuals or households. We follow common practice in
referring to the measure of living standards as ‘income’, although it should be stressed at the
outset that the income concept must be interpreted broadly to encompass not only home
production and non-pecuniary income, but also all the advantages and disadvantages
systematicallyassociated with geographical location, including climate, regional price variations,
local public good provision and environmental quality. In essence, the analysis assumes that
individuals with the same income at different locations are equally well-off.
The formalities below are framed in terms of a (homogeneous, equally weighted) population
of individuals represented by N = {1, 2, ... , n}, with incomes given by the vector y =
and mean income denoted by µ. Income inequality is captured by an inequality index I(y)which
is assumed throughout to satisfy the following five basic properties:
1
f
1
, f
2
,...,f
n
y
1
, y
2
,...,y
n
.
E
c
(y)
1
c(c 1)n
M
i N
i
y
i
µ
p
c
1
"
E
1
(y)
1
n
M
i N
y
i
µ
ln
y
i
µ
E
0
(y)
1
n
M
i N
ln
µ
y
i
E
c
(y)
1
n
M
i N
φ
c
(y
i
/µ)
φ
c
(t) (t
c
1)/[c(c 1)] φ
1
(t) t ln t φ
0
(t) ln t
Note that (A2) extends to situations in which frequencies are attached to the income levels
2
Successive application of the principle of transfers implies that inequality will be reduced by any
equalisation of two income levels which preserves the overall (weighted) mean income.
3
(A1) symmetry (or anonymity);
(A2) the Pigou-Dalton principle of transfers (or strict Schur convexity): a mean-preserving
progressive transfer reduces inequality;
2
(A3) scale invariance (or homogeneity of degree zero);
(A4) replication invariance; and
(A5) zero normalisation: the minimum value of I is zero (achieved when all incomes are
identical).
The decomposition of inequality according to a partition of the aggregate population into
geographical regions (or, more generally, into any set of mutually exclusive and exhaustive
subgroups) is most often undertaken with one of the entropy indices popularised by Theil (1967,
1972) and later explored in more detail by Bourguignon (1979), Shorrocks (1980, 1984, 1988),
Cowell and Kuga (1981), and Foster and Shneyerov (2000), amongst others. The single
parameter entropy family may be written:
(1a) , c 0, 1,
(1b) ,
(1c)
,
or in the more condensed form:
(2) ,
where , c 0, 1; ; and . Special cases
include the Theil coefficient (corresponding to c = 1), the mean logarithmic deviation (c =0),and
one half of the square of the coefficient of variation (c =2).
The decomposition properties of this class of measures are best illustrated by considering the
E
0
y
k
E
0
(y) E
0
(y
1
, y
2
,...,y
m
)
1
n
M
m
k 1
M
i N
k
ln
µ
y
i
M
m
k 1
n
k
n
1
n
k
M
i N
k
ln
µ
k
y
i
1
n
M
m
k 1
M
i N
k
ln
µ
µ
k
M
m
k 1
ν
k
E
0
(y
k
)
M
m
k 1
ν
k
ln
µ
µ
k
M
m
k 1
ν
k
E
0
(y
k
)
M
m
k 1
ν
k
ln
µ
µ
k
E
0
(y
1
, y
2
,...,y
m
)
E
0
I(y
1
, y
2
,...,y
m
) I(w
1
b
1
, w
2
b
2
,...,w
m
b
m
)
ˆ
I(w
1
, w
2
,...,w
m
, b)
(b
1
, b
2
,...,b
m
)
4
index and by supposing that the set of individuals, N, is partitioned into m proper subgroups
N (k = 1, 2, ..., m), with respective income vectors y , mean incomes µ , population sizes n ,and
k kk
k
population shares ν = n / n. It will also be convenient to let denote the distribution obtained
kk
by replacing each income in the vector y with the subgroup mean µ . Then
k
k
(3) = =
=
==W+B
where
(4a) W =
is a weighted average of subgroup inequality values, traditionally referred to as the ‘within-
group’ component of inequality; and
(4b) B = =
is the ‘between-group’ contribution to inequality, representing the level of inequality obtained
by replacing the income of each person with the mean income of their respective subgroup. Thus
— for the index at least — the overall level of inequality for a country can be expressed in an
intuitively appealing fashion as an exact sum of the average inequality within regions and the
inequality due purely to differences in average incomes between regions.
To appreciate the special attraction of the decomposition indicated by (3), it may be noted that
for anyinequalityindex I(.) which satisfies properties (A1)-(A5), the aggregate level of inequality
may be written
(5) ,
where w = y /µ indicates the vector of relative incomes within region k,andb =
kk
k
, b = µ /µ, denotes the vector of relative mean incomes across regions.
kk
Equation (5) makes it clear that aggregate inequality in a country is completely accounted for by
E
c
(y) E
c
(y
1
, y
2
,...,y
m
)
M
m
k 1
ν
k
b
c
k
E
c
(y
k
)
M
m
k 1
ν
k
φ
c
(b
k
)
M
m
k 1
ν
k
b
c
k
E
c
(y
k
)
M
m
k 1
ν
k
φ
c
(b
k
) E
c
(y
1
, y
2
,...,y
m
)
5
differences in relative income within regions (as captured in the vectors w ) and differences in
k
relative mean income between regions (as captured by b). In this context, it is natural to regard
the inequality contribution of w as the amount by which aggregate inequality falls if relative
k
incomes in region k are equalised, ceteris paribus; and the contribution of b as the amount by
which aggregate inequality falls if regional mean income differences are eliminated, holding
constant relative incomes within regions (ie by proportionately scaling incomes within each
region until the regional mean matches the population value). For the index E , the within and
0
components (4a) and (4b) conform to these interpretations. Furthermore, the values of the
contributions are invariant to the order in which within- and between-group differences are
eliminated.
Other inequality indices drawn from the entropy family (1) satisfy a similar decomposition
equation given by
(6) = = = W + B,
which again leads to a natural interpretation in terms of the within- and between-group
contributions to inequality:
(7a) W =;
(7b) B == .
However, the decomposition provided by (6) is less satisfactory than that given by (3) for two
reasons. First, while the within-region term remains a weighted sum of regional inequalityvalues,
the weights typically do not sum to one, unless c = 0 or 1. (The latter corresponds to the Theil
coefficient, where the weights are given by the regional income shares).Soitisusuallywrong
to interpret W as the average level of inequality within regions. Secondly, the within-group
component now depends both on within-group differences and (via the weights) on between-
group differences. So any attempt to eliminate between-group variation along the lines suggested
following equation (5) now has an indirect effect on the value of the within-group term. As a
consequence the quantitative impact of eliminating within- and between-group variations is now
y (y
1
, y
2
, ... , y
m
)
G(y)
2
n
2
µ
M
i N
r
i
(y
i
µ)
This special property of E corresponds to the ‘path independence’ property discussed by Foster and Shneyerov
3
0
(2000). They observe that there are two ways of deriving W and B. As discussed earlier, the first obtains the
between-group B contribution as the level of inequality which results after within-group inequality is eliminated
by redistributing incomes equally within each region. W is then taken as the residual. The second defines W to be
the level of inequality which results when inequalities between groups are eliminated by proportionally scaling each
subgroup distribution until it has the same mean as the overall distribution, with the residual now taken to be B.The
decomposition is said to be path-independent if these two methods produce identical results.
See Shorrocks (1988) for a more detailed discussion.
4
6
sensitive to the order in which the factors are considered.
3
The main appeal of the decomposition provided by (6) rests on the fact that the inequality
indices are subgroup consistent in the following sense: holding regional mean incomes and
population sizes fixed, an increase in inequality within each region must lead to an increase (or,
at least, not a decrease) in inequality in the country as a whole. This property is evidently true for
the entropy measures, since the ceteris paribus clause implies that the between-group term B is
constant in (6), and that the rise in regional inequality translates into a rise in the weighted sum
of regional inequality values captured by the within-group component W.
Subgroup consistency is an intuitively appealing and relatively weak property satisfied by the
Atkinson class of inequality measures as well as the Entropy family. However the Gini
4
coefficient is not subgroup consistent, and therefore not amenable in general to a decomposition
along the lines of (6). This has not discouraged many researchers from attempting to decompose
the Gini index in specific contexts or using different principles. The method which most closely
resembles (6) can be formulated by numbering the regions in order of increasing mean incomes,
and by supposing that person i occurs in the ith position when the income distribution is written
, and in position r when all incomes are arranged in increasing order. The
i
value of the Gini coefficient is then given by
(8)
and yields the decomposition equation (see, for example, Lambert and Aronson 1993)
G G(y
1
, y
2
... , y
m
)
2
n
2
µ
M
m
k 1
M
i N
k
r
i
(y
i
µ)
2
n
2
µ
M
m
k 1
M
i N
k
i(y
i
µ
k
)
M
i N
k
i(µ
k
µ)
M
i N
k
(r
i
i)y
i
#
W B R,
W
2
n
2
µ
M
m
k 1
M
i N
k
i(y
i
µ
k
)
M
m
k 1
ν
2
k
b
k
G(y
k
)
B
2
n
2
µ
M
m
k 1
M
i N
k
i(µ
k
µ)
M
m
k 1
b
k
ν
k
w
M
k
j 1
ν
j
M
m
j k
ν
j
~
G(y
1
, y
2
,...,y
m
)
r
i
i
ν
k
b
c
k
ν
2
k
b
k
For early contributions, see Soltow (1960), Bhattacharya and Mahalanobis (1967), Rao (1969), Mangahas (1975)
5
and Pyatt (1976). More recent developments include Silber (1989), Yitzhaki and Lerman (1991), Yitzhaki (1994)
and Sastry and Kelkar (1994).
7
(9)
where
(10a)
is a weighted sum of the within-group inequality values, and
(10b) =
is the ‘between-group component’, representing the value of the Gini coefficient when the
income of each individual is replaced by the mean income of the subgroup to which they belong.
The final term, R, in equation (9) is a residual or ‘interaction’ effect which vanishes when the
regional income ranges do not overlap (so that , for all i), and is otherwise strictly positive.
When the regional income ranges are non-overlapping there is a very clear correspondence
between the Gini decomposition (9) and the Entropy formulation (6); the only substantive
difference is that the regional inequality weights are given by in (6) and by in (9). In
this case also, it is natural to regard the ratio B/G as a measure of the proportional contribution
of regional income variation to total inequality, mimicking the analogous expression B/E used
c
in the context of Entropy indices. The situation becomes more problematic when the regional
income ranges overlap, because the interaction term introduces a third, poorly specified, element
into the decomposition equation (9). It is also important to note that (9) is not the only form of
Gini decomposition on offer. Many other specifications have been suggested. The more recent
proposals typically retain the division into within-, between-, and interaction terms, but differ in
the formulae used for each of the components. In the absence of an obviously superior
5
alternative, we proceed on the assumption that the between-group term B defined in (10b)
I(y
1
, y
2
,...,y
m
)
Note however that eliminating between-group inequality by scaling incomes within each subgroup until they
6
match the population average will in general not only affect the within group term (as with most Entropy indices)
but also the interaction component.
8
expressed as a proportion of the overall Gini value captures what we mean by the importance of
the contribution of average income variation by region to total inequality as measured by the Gini
coefficient.
6
Another departure from the traditional decomposition framework explores the implications
of generalising the notion of average regional income to measures other than the mean. The idea
dates back to Blackorby et al. (1981) but has recently been explored in greater detail by Foster
and Schneyerov (2000). In this framework the between-group term is constructed by replacing
the income of each individual with a suitably defined representative income level for the region.
Employing representative income levels other than the mean expands the set of inequality indices
that have simple and attractive decomposition properties. This opens up some interesting lines
for future research, but since the empirical applications have not yet seen the light of day they are
not pursued further in this paper.
3. Some General Theoretical Results
Despite the widespread use of decomposition techniques, little attention has been given in the
past to establishing general decomposition results. The range of indices considered in the last
section, combined with the possibility of alternative decomposition specifications, may have
made it seem difficult to draw general conclusions about the way in which spatial factors impact
on inequality. This turns out to be unduly pessimistic. The results below apply to any inequality
index I() which satisfies (A1)-(A5), and document the properties of a ‘between-group term’ B
defined by
(11) B =,
in accordance with (4b), (7) and (10b) above.
Consider first the range of values for B and how the value compares with the overall inequality
I(y
1
, y
2
,...,y
m
)
(y
1
, y
2
,...,y
m
)(y
1
, y
2
,...,y
m
)
Note that successive application of Proposition 2 allows Proposition 1(a) to be derived from Proposition 1(b) and
7
1(c).
We are indebted to Ravi Kanbur for suggesting that this result may hold.
8
9
value I = . Intuitively, when only one group is identified (i.e. m = 1) then
average incomes do not vary across regions and B must be zero. At the other end of the scale, if
the number of regions is the same as the size of the population (i.e. m = n), then each region
contains a single observation and B must equal I. It is also reasonable to expect that these two
cases represent the minimum and maximum values that B cantake,sothat:
Proposition 1:(a)0 B I;(b)B =0ifm =1; (c)B = I if m = n.
To establish Proposition 1, note that (b) and (c) are both immediate consequences of the
definition of B given by (11), because B is the inequality value obtained by replacing the income
of each person by their corresponding regional mean. In addition, we have B 0 because the
index I(.) is always non-negative (by A5); and B I because the ‘regionally equalised’
distribution is obtained from the original distribution by
applying an equalizing (and hence inequality reducing, by A2) procedure to each region in turn.
The argument in support of Proposition 1 also suggests a non-decreasing relationship between
the number of regions and the magnitude of the between-group term. An increase in the number
of regions will increase the opportunities for differentiating between the regional mean values
used in the calculation of B, thereby causing the value of B to rise. This is most easily seen by
reversing the question and asking about the consequences of reducing the number of regions via
a merger between two regions. The impact on the value of B is equivalent to that of a mean-
preserving equalization of the two subgroup income levels which, by the principle of transfers
(A2), cannot increase the value of B. Hence:
Proposition 2: The value of B does not increase if any two regions are combined.
7
The ‘finer partition’ characterisation in Proposition 2 is one way of capturing the idea that B
increases monotonically with m. Another possible interpretation is that the between-group term
is larger on average when more regions are identified, in other words:
Proposition 3: The expected value of B increases with m.
8
π
r1
(m)
(nm)!
r!(nmr)!
(
1
m
)
r
(
m1
m
)
nmr
, r 0,...,nm
π
r2
(m)/π
r1
(m)
nmr
r1
1
m1
nr1
(r1)(m1)
1
r1
π
r2
(m1)/π
r1
(m1)
M
s
r 0
π
r
(m)
M
s
r 0
π
r
(m1) , for all m and all s
10
This proposal is still not well formulated, since it is not clear how the expectation is to be taken
over the space of partitions and over the allocation of individuals to subgroups. For example,
each of the partitions into m regions may be treated as equally likely, or they may be assigned a
probability corresponding to the likelihood that this partition is observed when n individuals are
randomly distributed across m categories.
While a formal proof of Proposition 3 is beyond the scope of this paper, intuition suggests that
the result must hold under a variety of interpretations for the following reason. For a fixed-size
population, an increase in the number of regions causes the average size to fall, so the
distribution shifts towards smaller sized regions. But, as the mean value of smaller samples
exhibits greater variability, the net effect is an increase in the expected inequality value captured
in the between-group term (11).
The shift towards smaller sized classes can be formalised when n individuals are randomly
allocated across m regions, each containing at least one person. The probability that a region
contains r + 1 members (r 0) is then given by the multinomial value
(12) ,
from which it follows that
(13) .
In other words, the frequency of larger regions falls off faster as the number of regions increases.
According to Proposition B.1 of Marshall and Olkin (1979, p.129), condition (13) ensures that
π(m) is majorised by (ie Lorenz dominates) π(m+1) for all m,sothat
(14) .
This is the formal sense in which the distribution of regions shifts towards smaller sizes as the
number of regions increases. A similar condition is likely to hold when alternative methods are
used to allocate a given population of individuals to a given number of groups.
(y
1
, ... , y
n
)
11
Let us now fix the partition level (m), the sizes of regions {n , ... , n }, and the overall income
1 m
distribution y = , and consider what can be said about the way in which B depends
on the allocation of individuals (and hence incomes) across the regions. The following two
observations follow immediately from the definition (11) of the between-group term.
Proposition 4: (a) if the distribution of income is the same in each region then B =0;
(b) if the regional mean incomes are all equal then B =0.
Note that the prerequisite in part (b) of Proposition 4 is significantly weaker than the
corresponding requirement in part (a).
Proposition 4(a) refers to the situation in which the subgroup distributions overlap to the
greatest possible extent. It seems plausible to suppose that a reduction in the degree of overlap
will translate into a smaller between-group term, but the precise relationship is difficult to
formalise given that a reduction in overlap between two subgroups may not necessarily cause the
subgroup means to move apart. At the other end of the scale, however, it is possible to establish
that if the regional income ranges (strictly) overlap then the between-group term is not a
maximum, and hence:
Proposition 5: B is a maximum only if the regional income ranges do not overlap.
The argument is as follows. Suppose that regions k and have strictly overlapping
distributions and that µ µ Choose i N and j N such that y > y. Then swapping the
k # k # ij
incomes y and y between the two regions raises the mean income in the ‘more affluent’ region ,
ij
so the switch corresponds to a regressive Pigon-Dalton transfer between the two subgroup
income levels which, by appeal to (A2), must increase the inequality value represented by B.
Note that disjoint income intervals is a necessary condition for B to be a maximum, but it is
not sufficient unless all subgroups have equal size. In other cases, it is necessary to consider how
the different sized groups should be positioned in the income range. Drawing on the lessons of
the similar exercise in Davies and Shorrocks (1989), it seems likely that the larger groups will
be positioned at the centre of the income distribution, and that the subgroup sizes will decline
Interestingly, Davies and Shorrocks (1989) show that the between-group component in the Gini decomposition
9
can closely approximate total inequality with a relatively small number of subgroups, as long as the subgroup
income ranges are non-overlapping and the group sizes are chosen optimally.
12
monotonically towards each tail.
9
4. Empirical Evidence
There is now a large empirical literature on inequalitydecomposition bypopulation subgroups
defined in terms of spatial location. The number of studies which report inequality decomp-
ositions using non-spatial elements (education, age, gender, etc.) is even greater. Given the focus
of this paper, attention is confined here to spatial applications. But, as in the previous sections,
the conclusions may well apply also in the non-spatial context.
The type of questions we will attempt to address are as follows: Do any general patterns or
conclusions emerge from the empirical literature? Is any empirical regularity observed in the
relationship between the number of groups and the magnitude of the share of the between-group
component? To what extent do the decomposition results depend on the measure of inequality?
Are the results sensitive to the ‘income’ variable used in the analysis?
Most empirical spatial decomposition studies use either the mean logarithmic deviation
index E or the Theil Index E . Tables 1A and 1B summarise the results obtained from applying
01
the decomposition of E to many countries and points of time. Given the differences in sample
0
size, choice of income variable, selection of regions, etc. reliable general conclusions are hard
to draw. For this reason, it is safer to use the term ‘observation’ rather than finding or conclusion.
Tables 1A and 1B here
Observation 1: The magnitude of the between-group component.
As is typical of most subgroup decompositions, the between-group component is small
relative to the within-group component except in the case of urban-rural divide (see
Observation 2). This is particularly true when earnings data are used (see Observation 3).
13
Excluding these two sets of circumstances, the share of the within-group component averages
12% with a minimum of 0% and a maximum of 51%.
Some have concluded from this type of evidence that space or location is a relatively
unimportant explanation of inequality (see, for example, Cowell and Jenkins (1995) ). However,
before this conclusion is drawn, it should be noted that, as a determinant of inequality, space is
poorly defined. Spatial location is often not of interest itself, but rather because of its association
with many other important influences such as natural resources, weather conditions, cultural
traditions, and even institutional arrangements. While some of these factors may contribute
positively to the between-group component, uniform institutional arrangements such as nation-
wide policies are likely to make a negative contribution. Current procedures assign all of these
factors to location without trying to disentangle the associated influences. The estimated
between-group component cannot therefore be taken as a measure of the spatial contribution
unless and until the definition of space is clarified. Furthermore, caution needs to be exercised
when drawing policy implications from the empirical evidence. As noted by Kanbur (2002), if
space is related to race or ethnicity, a small between-group component may not accurately reflect
the significance of space as a determinant of inequality.
Observation 2: The rural-urban divide.
The rural-urban division seems always to produce a much larger between-group component.
It ranges from 9% for Greece to as much as 78% for China. This latter result was obtained using
regional level rather than household level data for China, and is therefore not strictly comparable.
However, household-level data for China still yields a between-group component share of almost
38% (Lee 2000). Overall, the studies applying a rural-urban split to household data yield an
average between-group component of 19.6%, almost 8% higher than the average reported in
Observation 1 above.
The between-group component depends on both the number of subgroups and differences in
group means (or representative group values). Empirical evidence suggests that differences in
means are the more important of these two factors, because the rural-urban distinction employs
the minimum number of subgroups. Other spatially defined decompositions often involve a much
larger number of groups but produces a smaller within-group component, as is evident from the
Table 1A data for China, Indonesia and the Philippines.
14
What is the reason for the relatively large between-group component for the rural-urban
divide? As mentioned earlier, the cause may well lie in the inability of current decomposition
techniques to control for other variables. Lower prices and/or availability of home produced food
in rural areas may not be fully reflected in the data on living standards. Furthermore, the rural-
urban divide in developing countries is often associated with other differences linked to the
provision of infrastructure, the development of capital markets, education, health care, and so on.
Controlling for these effects is likely to lead to smaller between-group components for the pure
spatial effect in the context of the rural-urban divide.
In China, the rural-urban separation has been largely institutionalized. This separation results
in dramatic differences in employment opportunities, education, infrastructure, health care, and
access to capital and technology. Further the separation also causes different returns to these
factors, because the markets are not integrated. Added together, it is not difficult to understand
the reasons for the very large share of the between-group component in China.
Observation 3: Alternative income concepts
The data in Table 1A refer to income or consumption. Table 1B reports similar data for
earnings in one country (the UK). The percentage share of the between-group component turns
out to be much lower, ranging from 1% to 12%, with an average of 4.8%. Interestingly, total
inequality in earnings is not smaller than inequality of income or consumption, which suggests
that there is considerable wage differentials across occupations or sectors, but relatively little
variation in occupational wages across locations. While it is dangerous to extrapolate from data
from a single country, the same result may apply to other market economies where there are no
constraints to migration, and where returns to labour and human capital are more or less
equalized across space. Collective bargaining, the strength of labour unions and national wage
setting policies may also be influential.
Equal factor returns are not sufficient, of course, to produce a negligible between-group
component in decompositions of earnings inequality: the employee structure of the workforce
must also be similar across space. It would therefore be useful to decompose the within-group
component further into a ‘returns’ effect and a ‘workforce structure’ effect, the former reflecting
market development and migration, and the latter reflecting industrial structure.
15
Although the distribution of consumption is known to be more equal than income, this does
not appear to carry over to the proportional contribution of the between-group component of
income inequality. For example the between-group component is relatively small for the income
observations from Finland and Switzerland.
Observation 4: Alternative measures of inequality
A number of empirical studies report decomposition results based on different inequality
indices, enabling us to compare the percentage share of the within-group component across
indices. The correlation coefficients are presented in Table 2. The correlation amongst the
various Entropy measures tends to be quite high; the correlation with the Gini values are
somewhat lower. Overall, Table 2 suggests that the results obtained using one index should
broadly carry over to other indices.
Table 2 here
Observation 5: Country coverage.
Although spatial decompositions exist for the UK, USA, and some other developed countries,
results on regional inequalityare dominated bydeveloping country evidence. The limited number
of studies for developed countries does not imply that spatial inequality is not of interest in the
developed world. However, the greater attention in developing or transition economies may
reflect the fact that weak market forces, or restrictions on factor mobility, prevent returns to
income generating factors from converging. In the search for explanations for the existence of
spatial inequality, it may be useful compare the values of the between-group component obtained
for developed and developing countries.
Observation 6: Spatial price variations.
The majority of empirical studies reported in Table 1 do not adjust for spatial price
differences, although such differences exist and may substantially change the results for both
developing countries such as China (see Wan 2001) and free market economies such as the US
(see Ram 1992). Price levels are often correlated with living standards, so adjusting for spatial
price differences will tend to lower the between-group term in the spatial decomposition while
To assist the visual presentation, observations with more than 27 subgroups are excluded: these will be discussed
10
later. Also excluded are the results for the rural-urban divide in China, as they are treated as outliers.
These observations were excluded from Figures 1-4.
11
16
not altering inequality within regions (although the within-group component may be affected in
an unpredictable fashion due to a change in the weights). Thus, despite its relatively small value
(see Observation 1 above), the reported share of the between-group component is likely to be
exaggerated, particularly in countries with a big land mass and underdeveloped markets.
Observation 7: The number of subgroups and the size of the between-group component.
As discussed in Section 3, the between-group component is expected to rise as the number of
groups increases. To examine the empirical evidence, Figures 1-4 present scatter plots of the
share of the between-group component against the number of subgroups. The graphs do not
10
show any obvious positive relationship; if anything, the reverse appears to be the case. This
apparent conflict with the theoretical predictions is not completely surprising, however, because
other factors are not held constant. In particular, comparability is compromised if different
criteria are used to group the sample observations. This is easily detected in the results for China,
Indonesia and the Philippines. In the relevant studies, the samples were divided into urban-rural
areas as well as into regions. In doing so, the number of subgroups increases from 2 to 26 or 27
for Indonesia (2 to 13 for the Philippines, 2 to 3 or 26 for China), but the between-group-
component falls in most cases. This clearly indicates the dominant impact of differences in living
standards between rural and urban residents, which more than offsets the contribution of the
number of subgroups.
Figures 1 - 4 here
To examine properly the positive relationship between the number of subgroups and the size
of the between-group component requires progressive aggregation of subgroups. This has been
done by Elbers et al. (2002) for Ecuador, Madagascar and Mozambique and by Cheng (1996)
11
for China. Results from Elbers et al. indicate small increases in the between-group component,
even if the number of groups increases dramatically. Using consumption data, Cheng (1996)
reports a rise in the between-group component from 28% to 37% when the number of groups
17
increases from 3 to 26. Using data on the gross value of industrial and agricultural outputs
(GVIAO ), the corresponding change is from 39% to 51%.
To explore further the relationship between the number of subgroups and the share of the
between-group component, we employed the regression model:
S = f(m, D),
where S is the share of the between-group component, m is the number of subgroups, and D
refers to a set of dummy variables to control for different income concepts, different inequality
measures, and the rural-urban division versus other spatial partitions. To allow for possible non-
linearities, the model specification takes the Box-Cox form. The standard linear model always
produced an insignificant parameter for the core variable m, but a simple χ test suggested
2
preference for the Box-Cox model.
Estimation results with the Box-Cox model are reported In Table 3, with E as the reference
0
index. The results indicate that (a) the size of the between-group component is positively related
to the number of subgroups at any conventional level of statistical significance; (b) increasing
the number of groups by one leads on average to an increase of 0.07 in the percentage share of
the between-group component; (c) earnings data tends to yield a smaller between-group
component, (d) the urban-rural partition gives a larger between-group component; and (e) the
Gini coefficient produces larger shares for the between-group component compared to other
indices.
Table 3 here
In summary, this section has attempted to present empirical results that may help future
research, both empirical and theoretical. Many questions have been raised which require attention
from theorists and empirical researchers. Of particular importance are the appropriate measure
of spatial proximity; the relationship between the number of groups and the between-group
component; the use of subgroup means or alternative measures of representative incomes; and
the choice of inequality index.
18
5. Concluding remarks
This paper has ranged over a number of theoretical and empirical issues linked to
decomposition analysis in a spatial context. Various other issues have yet to be explored. For
example, with the exception of Kanbur and Zhang (1999), there is little in the way of empirical
literature on the time profile of the within or between-group component. Availability of data is
an obstacle here. Nevertheless, a time profile would enrich the empirical literature by adding a
dynamic dimension to the studies of spatial inequality decomposition.
More could be done also to link inequality decomposition to the recent extensive literature on
growth. Inparticular, examination of the pattern of the between-group component may be a better
way of studying convergence than the commonly-used sigma convergence approach.
Another set of issues requiring attention concern the underlying factors which ultimately
contribute to spatial inequality, factors like economic geography (climate, natural resources),
policy regimes, market orientation, and related socio-economic variables. Whether or not spatial
differences persist or whither away over time is perhaps influenced most by the freedom to
migrate, both within countries and internationally. The extent to which labour migration can help
reduce regional disparities is an important question with obvious policy significance.
19
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23
Table 1A: Spatial decomposition of E
0
: income or expenditure
Country No. of
groups
Year Total
inequality
Between
%
Within
%
Category Source Remarks
Canada 9 1991 0.264 1.9 98.1 province
9 1992 0.272 1.5 98.5
9 1993 0.273 1.8 98.2
Gray et al.
(2003)
survey data on
total household
income
9 1994 0.272 1.5 98.5
9 1995 0.272 1.8 98.2
9 1996 0.281 1.8 98.2
9 1997 0.312 1.6 98.4
China 2 1983 0.079 6.5 93.6 coast/inland
2 1984 0.076 6.6 93.5
2 1985 0.075 6.0 94.0
Kanbur and
Zhang (1999)
2 1986 0.080 6.3 93.7
regional data on
per capita
consumption
expenditure
2 1987 0.083 6.7 93.4
2 1988 0.089 8.0 92.0
2 1989 0.088 7.2 92.8
2 1990 0.091 7.5 92.5
2 1991 0.098 9.1 90.9
2 1992 0.108 11.6 88.4
2 1993 0.112 12.9 87.1
2 1994 0.117 14.7 85.3
2 1995 0.120 17.3 82.7
China 2 1983 0.079 78.1 21.9 urban/rural
2 1984 0.076 75.8 24.2
2 1985 0.075 77.0 23.1
2 1986 0.080 74.5 25.5
2 1987 0.083 74.8 25.2
Kanbur and
Zhang (1999)
regional data on
per capita
consumption
expenditure
2 1988 0.089 74.7 25.3
2 1989 0.088 73.3 26.7
2 1990 0.091 74.9 25.1
2 1991 0.098 75.5 24.5
2 1992 0.108 73.5 26.5
2 1993 0.112 75.1 24.9
2 1994 0.117 73.3 26.7
2 1995 0.120 70.7 29.3
China 2 1994 0.330 37.7 62.3 urban/rural Lee (2000)
3 1994 0.330 28.0 72.0 zone
26 1994 0.330 36.8 63.2 region
1994 county/city
data on per capita
consumption
China 2 1994 0.390 25.8 74.2 urban/rural Lee (2000)
3 1994 0.390 39.0 61.0 zone
26 1994 0.390 51.5 48.5 region
1994 county/city
data on per capita
GVIAO
county within:
China 4 1994 0.141 24.0 76.0 Jilin
4 1994 0.070 20.0 80.0 Shandong
Cheng Y.
(1996)
4 1994 0.075 9.0 91.0 Sichuan
4 1994 0.232 9.0 91.0 Guangdong
1994 household
survey data on per
capita income
4 1994 0.139 4.0 96.0 Jiangxi
China 5 1994 0.222 39.0 61.0 province
24
Ecuador 3 1994 na 0.0 100.0 rural region
21 1994 na 1.3 98.7 rural province
Elbers et al.
(2002)
195 1994 na 5.9 94.1 rural Canton
915 1994 na 14.1 85.9 rural parroquia
1994 estimated
household data on
per capita
expenditure
Ecuador 5 1994 na 6.6 93.4 urban region
19 1994 na 7.3 92.7 urban province
Elbers et al.
(2002)
87 1994 na 8.6 91.4 urban Canton
664 1994 na 23.3 76.7 urban zonas
1994 estimated
household data on
per capita
expenditure
Finland 4 1971 0.127 12.5 87.5 region
4 1981 0.076 6.3 93.7
Loikkanen et
al. (2002)
household data on
per capita income
4 1990 0.069 7.6 92.4
4 1993 0.075 7.3 92.7
4 1998 0.104 4.4 95.6
Ghana 3 1996 0.269 29.6 70.4 residence area
6 1996 0.269 29.1 70.9 residence area
Jacqueline
(2002)
household data on
per capita
consumption
Greece 2 1974 0.196 10.1 89.9 urban/rural
2 1982 0.160 9.3 90.7
Tsakloglou
(1993)
9 1974 0.196 12.4 87.6 region
household data on
per capita
consumption
9 1982 0.159 8.7 91.3
India 17 1977 0.277 5.0 95.0 region
17 1983 0.182 5.3 94.7
17 1977 0.214 10.9 89.1 rural region
Mishra and
Parikh (1992)
household data on
per capita
consumption
17 1983 0.164 8.2 91.8
17 1977 0.219 1.8 98.2 urban region
17 1983 0.180 3.4 96.6
Indonesia 2 1987 0.228 22.3 77.7 urban/rural
2 1990 0.223 22.0 78.0
Akita et al.
(1999)
2 1993 0.239 25.2 74.8
household data on
per capita
expenditure
Indonesia 27 1987 0.232 15.1 84.9 province
27 1990 0.227 15.0 85.0
Akita et al.
(1999)
27 1993 0.243 17.3 82.7
household data on
per capita
expenditure
Indonesia 26 1990 0.223 13.0 87.0 province
26 1993 0.239 15.0 85.0
Tadjoeddin
(2003)
26 1996 0.216 21.0 79.0
household data on
per capita
expenditure
26 1998 0.172 22.0 78.0
26 1999 0.190 21.0 79.0
26 2002 0.233 15.0 85.0
Madagascar 6 1993 N/A 4.8 95.2 rural faritany
104 1993 N/A 15.4 84.6 rural
fivondrona
Elbers et al.
(2002)
1117 1993 N/A 18.1 81.9 rural firaisana
1993 estimated
household data on
per capita
expenditure
Madagascar 6 1993 N/A 7.7 92.3 urban faritany
103 1993 N/A 21.7 78.3 urban
fivondrona
Elbers et al.
(2002)
131 1993 N/A 23.2 76.8 urban firaisana
1993 estimated
household data on
per capita
expenditure
Mozambique 424 1996 N/A 22.0 78.0 administrative
post
146 1996 N/A 18.4 81.6
district
Elbers et al.
(2002)
11 1996 N/A 9.3 90.7
province
estimated
household data on
per capita
expenditure
25
Phillipines 2 1985 0.282 17.2 82.8 urban/rural
2 1988 0.264 16.6 83.4
2 1991 0.306 16.3 83.7
Balisacan and
Fuwa (2003)
2 1994 0.260 15.6 84.4
2 1997 0.303 17.5 82.5
family income and
expenditure survey
data on per capita
expenditure
Phillipines 13 1985 0.282 15.4 84.6 region
13 1988 0.264 13.0 87.0
13 1991 0.306 17.6 82.4
Balisacan and
Fuwa (2003)
13 1994 0.260 13.5 86.5
13 1997 0.303 15.1 84.9
family income and
expenditure survey
data on per capita
expenditure
Russia 77 1994 0.297 25.0 75.0 region
77 1995 0.282 27.0 73.0
Yemtsov
(2002)
77 1996 0.316 26.0 74.0
household budget
survey data on per
capita income
77 1997 0.337 23.0 77.0
77 1998 0.314 28.0 72.0
77 1999 0.329 31.0 69.0
Switzerland 3 1982 0.136 0.2 99.8
3 1992 0.159 0.6 99.4
Ernstetal.
(2000)
household data on
per capita income
Table 1B: Spatial decomposition of E
0
:earnings
Country No. of
groups
Year Total
inequality
Between
%
Within
%
Category Source Remarks
UK 12 1979 0.260 1.0 99.0 region Parker (1999)
12 1985 0.310 1.8 98.2
FES data on
employee income
12 1991 0.320 2.5 97.5
12 1994/5 0.330 2.4 97.6
UK 12 1979 1.850 2.5 97.5 region Parker (1999)
12 1985 0.650 3.0 97.0
FES data on self-
employment income
12 1991 0.780 9.8 90.2
12 1994/5 1.520 3.0 97.0
UK 11 1975 0.095 3.2 96.8 region Dickey (2001)
11 1980 0.094 4.3 95.7
11 1991 0.133 6.8 93.2
New Earnings
Survey (individual)
data
11 1995 0.152 7.2 92.8
UK 11 1991 0.213 12.2 87.8 region Dickey (2001)
11 1996 0.286 7.7 92.3
British Household
Panel Survey
(individual) data
26
Table 2. Correlation between shares of the between–group component
E
1
E
2
Gini
E
0
0.98 0.83 0.65
E
1
0.98 0.64
E
2
0.75
Table 3. Estimation results
Variable
Coefficient
estimate
T-ratio
Significance
level
Box-Cox
elasticity
number of groups
0.13 3.65 0.00 0.17
dummy for
E
1
-0.31 -1.12 0.26 -0.03
E
2
-0.39 -0.50 0.62 -0.00
Gini
3.51 8.20 0.00 0.13
dummy for
earnings
-1.50 -5.09 0.00 -0.13
`urban-rural
1.73 4.74 0.00 0.11
constant
2.63 12.01 0.00 1.24
R
2
= 0.39 Sample size = 185
27
Figure 1. Share of between component and number of groups: E
0
0
10
20
30
40
50
0 5 10 15 20 25
Number of groups
% share
Figure 2. Share of between component andnumberof groups: E
1
0
10
20
30
40
50
0 5 10 15 20 25
Number of groups
%share
28
Figure 3. Share of between component and numberof groups: E
2
0
10
20
30
40
50
0 5 10 15 20 25
Number of groups
% share
Figure 4. Share of between component and number of groups: Gini
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25
Number of groups
%share
