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Income inequality in Greece: EU-SILC evidence
K. Chrissis and A. Livada
Athens University of Economics and Business
Technical Report No 271
ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS
DEPARTMENT OF STATISTICS
DECEMBER 2013
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Abstract
The purpose of this paper is to examine income inequality in Greece from EU-SILC
(European Union Survey on Income and Living Conditions) micro data. The time
period of the analysis is from the year 2002 to the year 2010.
The empirical findings indicate that aggregate income inequality in Greece is in
higher level than the average of both European Union and Euro area.
The inequality indices are decomposed by population sub-groups and by income
sources. The empirical results indicate that the main contribution to total income
inequality is the ‘within’ inequality. The pattern is different only for the categorical
variable of education. The education level attained imposes effect on the distribution
of income. The decomposition by income components allows having a clear idea on
how each component contributes to the total inequality. The empirical findings
indicate that the main contribution to the total income inequality stems from salaries
and wages, income from the self employed, and to a lesser extent from pensions and
property income.
The analysis for the examination of the redistributive effects of social transfers andtaxes constitutes of the typical comparison of the main inequality measures (axiomatic
approach) and of second order stochastic dominance. The empirical findings suggest
that the social transfers smoothens inequalities. The same applies for taxes. It is not
clear, though, whether the very lower income class (the lowest two deciles) benefits
from the tax system. Finally, the incorporation of imputed rent (taken into account the
measurement difficulties) reduces aggregate income inequality.
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1. Introduction
The empirical study of economic inequality utilizes several data sources
either on aggregate or on micro level. The European Union has set up a
survey for collecting household data on income, poverty, social exclusion and
living conditions, known as European Union Survey on Income and Living
Conditions (EU SILC). This survey was launched in 2003 for Greece and can
be used for the estimation of income distribution.
More specifically, the European Union Survey on Income and Living
Conditions provides two types of annual data:
- Cross sectional data with variables on income, poverty, social
exclusion and other living conditions
- Longitudinal data for changes over time in individual level.
The data are produced on annual basis and the reference population is all
private households and their current members residing in the territory of the
Member State at the time of data collection. The year of the survey contains
data for the previous year; thus survey for 2011 illustrates information for the
year 20101.
EU SILC data contain information for various components of income.
Therefore, several variables that approach the concept of income have been
calculated and have been utilized to estimate the distribution of income in the
whole population. Seventeen (17) variables were compiled. The most
appropriate - according to the topic - have been used for income inequality
analysis [for details see Chrissis (2013)].
These variables describe the concept of income on household level. The size
of the household and the age of its members are important factors, therefore
the use of an equivalence scale is appropriate. In this study the "OECD-
modified scale" is utilized. This scale, first proposed by Haagenars et al.
(1994), assigns a value of 1 to the household head, of 0.5 to the second and
each subsequent person aged 14 and over and of 0.3 to each child aged under 14.
The time period of the analysis is from the year 2002 to the year 2010.
1 For more analytical technical details of the survey the interested user could visit the Eurostat’s
website.
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The structure of the paper is the following. Section 2 includes the empirical
findings for income inequality measurement. Section 3 and section 4 describe
the decomposition of income inequality by sub -groups and the decomposition
of income inequality by income sources respectively. Section 5 refers to the
redistributive effects of social transfers and taxes whereas section 6 describes
the effects of the inclusion of imputed rents in the income components.
Finally, section 7 concludes.
2. Measurement of income inequality
2.1. Statistical specification for EU-SILC data
Four inequality indices (with various parameters) are compiled for the EU
SILC micro data. The formulae for the indices are:
Gini index
(1)
Where and and
(3.29)
Generalized entropy index
(2)
Atkinson index
(3)
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Coefficient of Variation
(4)
The notations are
Symbol Indication
y Variable of interest
i Observation number
Value of the variable of interest for observation i
hw Sampling weight
Sampling weight for observation i
hs Size variable
Size of observation i
hg Group variable
Group of observation i
n Sample size
The estimations of aggregate inequality measures from EU SILC micro data
were conducted using software STATA/SE 11.0 (module DASP ver. 2.1).
2.2. Empirical results
The variable used for the estimation of income distribution is the ‘Total net
household income’. This variable includes net income on household level
taking into account, also, components of personal net income; it is noted that
we do not take into account the negative values in the variable net cash
benefits or losses from self -employment (including royalties). It has been
adjusted for the size of household and the age of the members of household
with the OECD-modified scale.
The indices2 that indicate the gap between the income shares of certain
portions of population are S80/S20 and S90/S10, which is simply the ratio
2 For inequality measurement from different data sources see Chrissis and Livada (2013).
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between the income share of upper and lower income classes. There has been
a small decrease in both indices; nevertheless the trend is not stable for the
whole period. The decrease is more obvious in the year 2009 especially for
S90/S10. Both ratios indicate increase for the year 2010. This implies that the
recession, which is more apparent from 2009, seems to affect the distribution
of income with ambiguous results.
The behavior of the aggregate inequality indices (GINI, Atkinson (0,5),
Atkinson (1,5), General Entropy (0), General Entropy (1), General Entropy
(2) and Coefficient of Variation) is rather stable with miniscule d ecline. In all
cases the absolute values are slightly changing in both directions (increase or
decrease); nevertheless, in all cases a small decrease is noted from 2008 to
2009 and a small increase from 2009 to 2010. This element, also, implies a
miniscule decline in inequality in the beginning of economic recession in
Greece and a small increase onwards.
Figure 1 contains the indices of S90/S10 and S80/S20 and Figure 2 illustrates
the trend of the seven aggregate inequality indices.
0,00
2,00
4,00
6,00
8,00
10,00
12,00
14,00
2002 2003 2004 2005 2006 2007 2008 2009 2010
FIG. 1. S90/S10 AND S80/S20 FOR GREECE
S90/S10 S80/S20
0,000
0,100
0,200
0,300
0,400
0,500
0,600
0,700
0,800
0,900
2002 2003 2004 2005 2006 2007 2008 2009 2010
FIG. 2. AGGREGATE INEQUALITY INDICES FOR GREECE
GINI Atkinson 0,5 Atkinson 1,5 GE(0)=Theil L
GE(1)=Theil T GE(2) CV
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International experience
The main variable used in this paper for the estimation of income distribution
is the ‘Total net household income’, which incorporates the net components
of household income without taking into account negative values for net cash
benefits or losses from self-employment (including royalties). This variable is
slightly different in interpretation and in compilation procedure from the
corresponding one (‘Total disposable household income (HY020)’) used by
ELSTAT.
Figures 3 and 4 illustrate the ratio S80/S20 and Gini coefficient for total
disposable household income for Greece and European Union 27 and Euro
Area 17. The reason for the sort period for comparison is due to the lack of
data for European averages.
The empirical findings indicate that aggregate income inequality in Greece is
in higher level than the average of both European Union and Euro area.
0
1
2
3
4
5
6
7
2004 2005 2006 2007 2008 2009 2010
FIG. 3. S80/S20 – INTERNATIONAL COMPARISON I
EU (27 countries) Euro area (17 countries) Greece
27
28
29
30
31
32
33
34
35
2004 2005 2006 2007 2008 2009 2010
FIG. 4. GINI COEFFICIENT – INTERNATIONAL COMPARISON II
EU (27 countries) Euro area (17 countries) Greece
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3. Decomposition of income inequality by sub-groups
3.1. Choice of categories
Certain indices can be decomposed by population subgroups in order to define
which components contribute to total inequality. The decomposition
procedure is applied to the ‘Total net household income’ .
Eight (8) categories were indented to be examined. These categories are
region, urbanization, citizenship of household head (hh), education of hh,
current economic status of hh, occupation status of hh, classification of
activity by NACE of hh and managerial position of hh. Nonetheless, the
limitations on data availability due to the response rate on these questionnaire
codes pose significant obstacles.
Table 1. Valid values of the categorical variables file Year
ofsurvey
2003(N=6665)
2004(N=6252)
2005(N=5568)
2006(N=5700)
2007(N=5643)
2008(N=6504)
2009(N=7036)
2010(N=7005)
2011(N=6029)
Region 6665 6252 5568 5700 5643 6504 7036 7005 6029
Urbanization 6665 6252 5568 5700 5643 6504 7036 7005 6029
Citizenship 5927 5522 4945 5059 5033 5803 6308 7005 6029
Education 5667 5275 4702 4814 4790 5540 6048 6779 6029Currenteconomic
status
5927 5522 4945 5059 5033 5803 6308 7005 6029
Occupation 4718 4475 4025 4142 4147 4783 5269 6388 5536
Activity by NACE
2480 2303 2042 2045 1994 2346 2488 3140 2334
ManagerialPosition
1540 2615 2294 2377 2396 2877 3169 3789 3154
Complete data availability exists for the categorical variables of region and
degree of urbanization. There are many missing values for all years for the
categories occupation, activity by NACE and managerial position. The valid
values are in satisfactory3 level for citizenship, education and current
economic status for 2010 and 2011 (reference year 2009 and 2010
respectively).
Therefore five (5) categorical variables are utilized; region and degree of
urbanization for all years and citizenship, education and current economic
3 According to Table 1 the coverage for both years is 100% with the exception of ‘education’ for 2010
(year of survey) which is 97%.
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status for 2010 and 2011 (reference year 2009 and 2010 respectively) 4. It is
noted, nonetheless, that estimations have been made for all variables for all
years and they are available upon request.
3.2. Technical details for income decomposition by sub-groups
Following Araar and Duclos (2009), Gini index and the Generalized Entropy
indices can be decomposed in order to determine the contribution of each
subgroup to total inequality. G is the number of population subgroups.
Gini index
(5)
Where
the population share of group g
the income share of group g
between-group inequality (when each individual is assigned the averageincome of his group)
R The residue implied by implied income overlap
It is noted that the fi rst component is ‘between’ contribution of inequality, the
second is the ‘within’ and the third is the ‘overlap’
Generalized entropy indices
The mathematical expression of decomposition is
(6)
Where
4 Details for the five categorical variables are presented in the Annex.
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Bf(k) is the proportion of the population found in subgroup k
Bm(k) is the mean income of group k
BI(k;θ) is inequality within group k
is population inequality if each individual in subgroup k is given themean income of subgroup k,m(k)
3.3. Empirical findings for income decomposition by sub-groups
The decomposition procedure can indicate the contribution of the population
subgroups’ inequality to the total inequality. According to this procedure it
can be defined whether the inequality derives from factors ‘between’ or
‘within’ the population subgroups. The four aggregate income inequality
indices [Gini coefficient, General Entropy 0 (GE0), General Entropy 1 (GE1)
and General Entropy 2 (GE2)] are decomposed by five categories ( region,
degree of urbanization , citizenship, education and current economic status).
The main results of the decomposition of aggregate inequality indices for the
categorical variable ‘region’ are presented in Table 2.
Table 2. Decomposition of aggregate inequality indices by ‘Region’
ge0 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 93,6% 94,4% 96,2% 96,7% 95,0% 95,5% 95,5% 95,7% 97,4%
between 6,4% 5,6% 3,8% 3,3% 4,9% 4,8% 4,8% 4,2% 2,5%
ge1 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 93,6% 94,4% 96,2% 96,7% 95,1% 95,7% 95,7% 95,8% 97,4%
between 6,5% 5,6% 3,8% 3,3% 4,7% 4,6% 4,6% 4,1% 2,4%
ge2 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 95,2% 95,9% 97,1% 97,6% 96,7% 97,0% 97,0% 97,1% 98,3%
between 4,8% 4,1% 2,9% 2,4% 3,3% 3,0% 3,0% 2,9% 1,7%
gini 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 18,5% 16,8% 14,5% 14,9% 15,2% 16,8% 16,7% 15,8% 13,4%
between 25,6% 23,9% 19,6% 18,5% 22,6% 20,6% 21,8% 20,7% 15,7%
overlap 56,0% 59,3% 65,9% 66,5% 62,2% 62,7% 61,6% 63,6% 70,9%
Source: Authors’ calculations
The findings indicate that decomposing aggregate inequality by region the
main contribution to total income inequality is the ‘within’ inequality. Therelative contribution for General Entropy (0) varies from 93,6% to 97,4%, for
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General Entropy (1) varies from 93,6% to 97,4% and for General Entropy (2)
varies from 95,2% to 98,3%. In all three GE’s cases a small increasing trend
exists. The picture is vaguer for the outcome for Gini coefficient, since a
component of ‘overlap’ effect exists. The ‘within’ inequality contribution is
smaller than the ‘between’, with no big differences nevertheless. According,
thus, to the empirical results, the aggregate income inequality is mainly due to
the differences in the income distribution ‘within’ each of the thirteen
regions.
Table 3 summarizes the results for the decomposition procedure for the
categorical variable ‘degree of urbanization’.
Table 3. Decomposition of aggregate inequality indices by ‘Degree of urbanization’
ge0 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 93,2% 92,7% 94,0% 94,8% 93,9% 96,1% 96,1% 95,9% 97,4%
between 6,8% 7,4% 5,9% 5,1% 6,0% 3,8% 3,8% 4,0% 2,6%
ge1 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 93,3% 92,7% 94,1% 95,0% 94,2% 96,3% 96,3% 96,1% 97,4%
between 6,6% 7,4% 5,8% 5,0% 5,7% 3,5% 3,5% 3,9% 2,5%
ge2 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 95,2% 94,7% 95,6% 96,3% 96,1% 97,7% 97,7% 97,3% 98,3%
between 4,8% 5,3% 4,4% 3,7% 3,9% 2,3% 2,3% 2,7% 1,7%
gini 2002 2003 2004 2005 2006 2007 2008 2009 2010
within 38,3% 38,4% 39,1% 38,7% 38,2% 38,5% 39,2% 39,4% 42,2%
between 24,6% 25,8% 22,9% 21,3% 23,2% 20,8% 18,0% 19,0% 15,0%
overlap 37,1% 35,8% 38,0% 39,9% 38,7% 40,7% 42,7% 41,6% 42,8%
Source: Authors’ calculations
The findings indicate, once again, that the main contribution to total income
inequality is the ‘within’ inequality. The relative contribution for General
Entropy (0) varies from 93,2% to 97,4%, for General Entropy (1) varies from
93,3% to 97,4% and for General Entropy (2) varies from 95,2% to 98,3%. In
all three cases a small increasing trend exists. These findings are verified also
from the outcome for Gini coefficient, despite of the existence of the
‘overlap’ effect. Similar to the case of regions, the aggregate income
inequality is mainly due to the differences in the income distribution ‘within’
the areas of different degree of urbanization.
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The main results of the decomposition of aggregate inequality indices for the
categorical variable ‘citizenship’ for the years 2009 and 2010 (year of survey
2010 and 2011 respectively) are presented in Table 4.
Table 4. Decomposition of income
inequality indices by ‘citizenship’
ge0 2009 2010
within 98,3% 99,2%
between 1,8% 0,9%
ge1 2009 2010
within 98,6% 99,3%
between 1,5% 0,8%
ge2 2009 2010
within 99,1% 99,5%
between 0,9% 0,5%
gini 2009 2010
within 91,4% 92,7%
between 4,7% 3,1%
overlap 3,8% 4,1%
Source: Authors’ calculations
The results indicate that the main contribution to the total inequality is the
‘within’ inequality. In all cases the relative contribution of the ‘within’
component for the general entropy indices exceeds 98%. Gini coefficient
indicates very high values of the ‘within’ component (over 91%) as well. This
certifies the a-priori expectations since the main percentage of the
respondents (over 95% for both years) have Greek citizenship.
Table 5 illustrates the decomposition according to the category of education.
Table 5. Decomposition of income
inequality indices by ‘education’
ge0 2009 2010
within 75,0% 74,9%
between23,4% 25,0%
ge1 2009 2010
within 75,4% 74,0%
between 23,7% 26,0%
ge2 2009 2010
within 81,5% 80,2%
between 18,1% 19,8%
gini 2009 2010
within 18,5% 18,0%
between 55,7% 50,0%
overlap 25,8% 32,0%
Source: Authors’ calculations
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The empirical findings indicate a different pattern. The main contribution to
the total inequality remains the ‘within’ inequality; nevertheless the effect of
between inequality is significant. The relative contribution of ‘between’
inequality is 23,4%-25%, 23,7%-26,1% and 18,1%-19,8% for General
Entropy (0) (1) and (2) respectively. The picture is vaguer for the outcome for
Gini coefficient, since a component of ‘overlap’ effect exists. The ‘within’
inequality contribution is smaller than the ‘between’. According, thus, to the
empirical results, the aggregate income inequality stems from the differences
in the income distribution ‘within’ the ‘education classes’, but there is strong
evidence that inequality is affected, also, from dif ference ‘between’
‘education classes’.
The final category for income decomposition by sub-groups is ‘current
economic status’.
Table 6. Decomposition of income
inequality indices by ‘current
economic status’
ge0 2009 2010
within 92,1% 88,8%
between 8,3% 11,9%
ge1 2009 2010
within 92,8% 89,5%
between 7,5% 11,1%
ge2 2009 2010
within 94,9% 92,6%
between 5,1% 7,4%
gini 2009 2010
within 24,1% 24,7%
between 27,6% 33,3%
overlap 48,2% 42,1%
Source: Authors’ calculations
The findings indicate, once again, that the main contribution to total income
inequality is the ‘within’ inequality. The relative contribution of ‘within’
inequality for all General Entropy indices exceeds 88% with a decrease being
detectable from 2009 to 2010. Similar to some of the previous cases, the
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‘overlap; effect poses certain difficulties. The ‘within’ inequality contribution
is smaller than the ‘between’, with no big differences nevertheless.
According, thus, to the empirical results, the aggregate income inequality is
mainly due to the differences in the income distribution ‘within’ each of
eleven ‘current economic status’ classes.
Comparing sub- groups’ inequality indices
Being the ‘within’ inequality the main contribution to aggregate inequality,
does not mean that there are no differences among the population sub-groups.
The income has a more ‘unequal’ distribution in the region of ‘Ipeiros’ and
‘Sterea Ellada’ and more ‘equal’ in the region of ‘Kriti’ and ‘ West
Makedonia’ (although in certain years some indices indicate other regions for
both cases; it is noted that there is evidence of increase of inequali ty in Attiki
for 2010). As far as the categorical variable of urbanization is concerned,
income inequality is more intense in the intermediate areas and less intense in
the densely populated areas. For the years 2009 and 2010 (year of survey
2010 and 2011 respectively) the inequality for citizenship, education and
current economic status is the following: Inequality is more intense in the
Greek citizens for 2009; nonetheless this is not the case for 2010. The income
has a more ‘unequal’ distribution among the people that have attained first
stage of tertiary education and more ‘equal’ in the education class of pre -
primary education. As far as the current economic status is concerned, income
inequality is more intense in self-employed (full time) and less intense in
people that are in compulsory military or community service.
4. Decomposition of income inequality by income sources
4.1. Technical details for income decomposition by income sources
In this section the decomposition of the Gini coefficient by income
components will be presented. This decomposition allows to have a clear idea
on how each component contributes to the total inequality. The Araar (2006a)
approach will be implemented. First, one supposes that the sum of K
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components equals the total income and the amount of component k, noted by
, equals or is greater than zero. The analytical decomposition of Gini
coefficient is:
Where is the income share of the component k, is the level of
component k for household I and is the single-parameter concentration
coefficient of component k. This is the mathematical expression of
decomposition according to Rao (1969). Araar (2006a) 5 proposes the
following decomposition of the Gini coefficient:
where definitions of symbols with (*) are similar to those already defined
except that Araar uses the translated income components instead of the usual
components, i.e. and . The
component is the variation effect (VE) and the component is the
constant effect (CE). It is noted that this decomposition is similar to Rao’s if
.
5 For more technical details the interested reader could see Araar (2006a) and for implications of the
constant effect impact could, also, see Shorrocks (1988) and Potter and Chatterjee (2002).
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4.2. Empirical findings for income decomposition by income sources
The decomposition by income sources will be applied for the Gini coefficient
for the years 2002-2010 (years of reference). The income components of the
underlying variable that is being used are presented in the following table.
Table 7. Income components
Code Income component
1 sum of net employee cash or near cash income
2 company car
3 sum of net cash benefits from self-employment (including royalties)_no negative values
4 sum of unemployment benefits
5 sum of old-age benefits
6 sum of survivor' benefits
7 sum of sickness benefits
8 sum of disability benefits
9 sum of education-related allowances
10 income from rental of a property or land
11 family/children related allowances
12 social exclusion not elsewhere classified
13 housing allowances
14 regular inter-household cash transfers received
15 interests, dividends, profit from capital investments in unincorporated business
16 income received by people aged under 16Source: Authors’ calculations and ELSTAT
The results of the decomposition procedure are presented in Table 8.
Table 8. Relative contribution of income components to total income inequality
Relative
contribution
2002
Relative
contribution
2003
Relative
contribution
2004
Relative
contribution
2005
Relative
contribution
2006
Relative
contribution
2007
Relative
contribution
2008
Relative
contribution
2009
Relative
contribution
2010
1. net employee
cash or near cash
income
58,19% 55,08% 55,08% 54,03% 52,92% 55,09% 54,26% 52,87% 51,99%
2. company car0,12% 0,16% 0,16% 0,18% 0,18% 0,18% 0,20% 0,21% 0,17%
3. sum of net cash
benefits from self-
employment
(including
royalties)
27,57% 32,93% 32,93% 31,38% 30,72% 28,93% 28,22% 27,82% 29,32%
4. unemployment
benefits 0,46% 0,21% 0,21% 0,14% 0,10% 0,16% 0,25% 0,33% 0,50%
5. old-age benefits 8,50% 6,88% 6,88% 8,30% 9,85% 10,55% 11,88% 14,49% 13,39%
6. survivor'
benefits-0,58% -1,08% -1,08% -0,98% -1,15% -1,36% -1,02% -1,31% -1,57%
7. sickness
benefits0,00% 0,05% 0,05% 0,02% 0,01% 0,07% 0,04% 0,05% 0,03%
8. disability
benefits-0,03% 0,05% 0,05% 0,09% 0,13% 0,01% 0,33% 0,52% 0,12%
9. education-
related allowances0,04% 0,09% 0,09% 0,04% 0,08% 0,09% 0,04% 0,05% 0,06%
10. income from
rental of a
property or land
5,28% 6,40% 6,40% 7,24% 7,63% 6,28% 5,87% 5,52% 6,13%
11. family/children
related allowances0,31% 0,30% 0,30% 0,27% 0,27% 0,24% 0,50% 0,41% 0,70%
12. social exclusion
not elsewhere
classified
-0,09% -0,33% -0,33% -0,33% -0,34% -0,35% -0,42% -0,42% -0,28%
13. housingallowances -0,01% -0,01% -0,01% -0,01% 0,00% 0,01% 0,01% -0,01% 0,02%
14. regular inter-
household cash-1,10% -0,98% -0,98% -1,08% -1,15% -0,76% -0,89% -1,27% -1,11%
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transfers received
15. interests,
dividends, profit
from capital
investments in
unincorporated
business
1,34% 0,27% 0,27% 0,69% 0,72% 0,85% 0,73% 0,73% 0,54%
16. income
received by people
aged under 16
0,00% 0,00% 0,00% 0,02% 0,01% 0,01% 0,00% 0,00% 0,00%
total 100,00% 100,00% 100,00% 100,00% 100,00% 100,00% 100,00% 100,00% 100,00%
Source: Authors’ calculations
The empirical findings indicate that the main contribution to the total income
inequality stems from salaries and wages, income from the self employed, and
to a lesser extent from pensions and property income. More specifically,
salaries and wages are the main source of income inequality; the level varies
from approximately from 52% to 58%. The second most important factor of
influence in the inequality structure is the income from self-employment with
a range of 28% to 33%. The volume of the effect that the other two
components pose to the aggregate inequality is significantly smaller. The
level of old age benefits (mostly pensions) and property income ranges from
approximately from 7% to 15% and from 5% to 8% respectively. The other
income components do not pose significant effect on the aggregate income
inequality.
An interesting fact is that, although the time period is short, the contribution
of income components changes. The effect of the salaries and wages
decreases (from 58,19% to 51,99%) whereas the trend of the pensions’ ef fect
is increasing (from 8,50% to 13,39%), although this trend seems to be
interrupted for 2010. The contribution of the self -employment income yielded
an increasing trend until 2005 followed by a decrease in the next years. The
pattern is similar for property income with the only difference being the
turning point (2007 instead of 2006).
5. Redistributive effects of social transfers and taxes
The components of total household income include various social t ransfers. In
the following section the redistributive effects of these transfers are reviewed.
Moreover, the effects of the taxation system on income distribution are
analyzed.
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5.1. Redistributive effects of social transfers
The study of the income distribution before and after social transfers is
important, since it reveals the impact of social policy. In order to quantify
these effects two variables are compiled.
Total net household income before social transfers except old-age and
survivor’s benefits_ no negative PY050N (HY022net_nn):
This variable includes income on household level taking into account, also,
components of personal income. It, therefore, includes net employee cash or
near cash income, company car, net cash benefits or losses from self-
employment including royalties), old-age benefits, survivor' benefits, income
from rental of a property or land, regular inter-household cash transfers
received, interests, dividends, profit from capital investments in
unincorporated business and income received by people aged under 16.
In this case we do not take into account the negative values in the variable
net cash benefits or losses from self-employment (including royalties).
Total net househol d in come before social t r ansfer s incl udi ng old -age and
survivor’s benefits_ no negative PY050N (HY023net_nn):
This variable includes income on household level taking into account, also,
components of personal income. It, therefore, includes net employee cash or
near cash income, company car, net cash benefits or losses from self-
employment (including royalties), income from rental of a property or land,
regular inter-household cash transfers received, interests, dividends, profit
from capital investments in unincorporated business and income received by
people aged under 16.
In this case we do not take into account the negative values in the variable net
cash benefits or losses from self-employment (including royalties).
The effect of the two types of pensions (old age and survivors) is isolated
since one of the most important policy tools is the pension system. The
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distribution of these two variables is compared with the corresponding one of
the ‘ Total net household income_ no negative PY050N (HY010net_nn)’ .
Therefore the analysis ‘moves’ from the variable with the less income
components (HY023_net_nn) to variable with all income components
(HY010net_nn)
It is noted that all variables under review have been adjusted for the size of
the household according to the "OECD-modified scale".
The analysis constitute of the typical comparison of the main inequality
measures (axiomatic approach) and of second order stochastic dominance.
In Table 9 the basic statistics, income shares and aggregate income inequality
measures under the three alternative household income definitions are
presented. Figure 5 presents the 10% top income shares of the three income
definitions.
Table 9. Redistributive effects of social transfers
2002 2003 2004 2005 2006 2007 2008 2009 2010
Average
HY023NET_NN 6.916 7.127 7.539 7.892 8.210 8.577 8.739 8.465 7.195HY022NET_NN 9.506 9.934 10.606 11.108 11.739 12.472 12.948 12.932 11.310
HY010NET_NN 9.756 10.211 10.945 11.480 12.133 12.905 13.441 13.503 11.813
Shares
Decile_01
HY023NET_NN 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
HY022NET_NN 0,02 0,02 0,02 0,02 0,02 0,02 0,02 0,02 0,02
HY010NET_NN 0,02 0,02 0,03 0,03 0,03 0,03 0,02 0,03 0,03
Decile_02
HY023NET_NN 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
HY022NET_NN 0,04 0,04 0,04 0,04 0,04 0,04 0,04 0,04 0,04
HY010NET_NN 0,04 0,04 0,04 0,04 0,04 0,04 0,04 0,05 0,04
Decile_03
HY023NET_NN 0,01 0,01 0,01 0,01 0,01 0,00 0,00 0,00 0,00HY022NET_NN 0,05 0,05 0,05 0,05 0,05 0,05 0,05 0,05 0,05
HY010NET_NN 0,05 0,06 0,05 0,05 0,05 0,06 0,06 0,06 0,06
Decile_04
HY023NET_NN 0,04 0,04 0,03 0,03 0,03 0,03 0,03 0,03 0,01
HY022NET_NN 0,06 0,06 0,06 0,06 0,06 0,06 0,06 0,06 0,06
HY010NET_NN 0,06 0,07 0,07 0,06 0,06 0,07 0,07 0,07 0,07
Decile_05
HY023NET_NN 0,07 0,07 0,06 0,06 0,06 0,06 0,06 0,06 0,05
HY022NET_NN 0,08 0,08 0,08 0,07 0,07 0,08 0,08 0,08 0,07
HY010NET_NN 0,08 0,08 0,08 0,08 0,08 0,08 0,08 0,08 0,08
Decile_06
HY023NET_NN 0,09 0,09 0,09 0,09 0,08 0,09 0,09 0,09 0,08
HY022NET_NN 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09HY010NET_NN 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09
Decile_07
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HY023NET_NN 0,11 0,12 0,12 0,11 0,11 0,11 0,11 0,11 0,11
HY022NET_NN 0,11 0,11 0,10 0,10 0,10 0,10 0,10 0,10 0,10
HY010NET_NN 0,10 0,11 0,10 0,10 0,10 0,10 0,10 0,10 0,10
Decile_08
HY023NET_NN 0,15 0,15 0,15 0,15 0,15 0,15 0,15 0,15 0,15
HY022NET_NN 0,13 0,13 0,13 0,13 0,12 0,13 0,12 0,12 0,13
HY010NET_NN 0,13 0,13 0,12 0,12 0,12 0,12 0,12 0,12 0,12Decile_09
HY023NET_NN 0,19 0,19 0,19 0,20 0,19 0,20 0,19 0,20 0,20
HY022NET_NN 0,16 0,16 0,16 0,16 0,16 0,16 0,15 0,16 0,16
HY010NET_NN 0,16 0,16 0,15 0,16 0,15 0,15 0,15 0,15 0,15
Decile_10
HY023NET_NN 0,34 0,33 0,35 0,35 0,36 0,36 0,37 0,37 0,39
HY022NET_NN 0,27 0,26 0,27 0,27 0,28 0,27 0,28 0,27 0,27
HY010NET_NN 0,26 0,25 0,26 0,27 0,27 0,26 0,27 0,26 0,26
GINI
HY023NET_NN 0,539 0,537 0,560 0,560 0,569 0,568 0,585 0,582 0,614
HY022NET_NN 0,366 0,354 0,365 0,369 0,373 0,363 0,370 0,358 0,368
HY010NET_NN 0,352 0,340 0,348 0,350 0,354 0,343 0,349 0,335 0,343
Atkinson 0,5HY023NET_NN 0,327 0,332 0,359 0,353 0,360 0,367 0,374 0,384 0,430
HY022NET_NN 0,116 0,112 0,117 0,116 0,120 0,116 0,117 0,112 0,125
HY010NET_NN 0,104 0,100 0,102 0,102 0,106 0,101 0,103 0,096 0,104
Atkinson 1,5
HY023NET_NN 0,461 0,491 0,533 0,532 0,558 0,558 0,570 0,587 0,608
HY022NET_NN 0,302 0,280 0,291 0,298 0,307 0,294 0,287 0,282 0,289
HY010NET_NN 0,280 0,259 0,269 0,268 0,273 0,267 0,265 0,254 0,263
GE(0)=Theil L
HY023NET_NN 0,357 0,365 0,398 0,408 0,433 0,419 0,437 0,435 0,455
HY022NET_NN 0,231 0,209 0,221 0,230 0,236 0,223 0,221 0,214 0,219
HY010NET_NN 0,214 0,193 0,204 0,207 0,211 0,200 0,200 0,190 0,198
GE(1)=Theil T
HY023NET_NN 0,304 0,292 0,313 0,325 0,351 0,335 0,355 0,353 0,353
HY022NET_NN 0,228 0,205 0,220 0,230 0,241 0,229 0,232 0,193 0,225
HY010NET_NN 0,216 0,193 0,207 0,213 0,222 0,210 0,213 0,199 0,207
GE(2)=CV
HY023NET_NN 0,641 0,626 0,673 0,690 0,776 0,781 0,848 0,813 0,946
HY022NET_NN 0,316 0,287 0,299 0,316 0,353 0,341 0,358 0,320 0,343
HY010NET_NN 0,295 0,268 0,276 0,288 0,322 0,310 0,324 0,284 0,305
CV
HY023NET_NN 1,132 1,119 1,160 1,175 1,245 1,250 1,303 1,275 1,375
HY022NET_NN 0,795 0,757 0,773 0,794 0,840 0,826 0,847 0,799 0,829
HY010NET_NN 0,769 0,732 0,742 0,759 0,803 0,788 0,805 0,754 0,782
Source: Authors’ calculations
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In all three cases the nominal average income increases for the whole time
period. As expected, the incorporation of social transfers increases average
income. The behavior of income shares reveals more significant results. The
lower classes yield an increased part of the generated income; nevertheless
the income shares remain in low levels. These levels are stable over time.
More specifically social transfers resulted in an increase from 0% to 2%-3%
for the lower 10% income share. The corresponding figures for the next lower
10% (decile 2) modified from 0% to 4%-5%. The amounts for decile 3 show
also an increase from 0%-1% to 5%-6%. The next two classes indicate an
increase of proportion of generated income; nevertheless not as intense as in
the previous cases. On the other side, the income shares for the upper
economic classes deteriorate. This is more intense for the upper 10% income
share. The proportion of income drops from a level of 34%-37% to 26%-27%.This decline is observed for the two previous 10% of the population but to a
significantly less degree. The impact for the middle class is very small since
the magnitude of changes in the income shares is small; almost zero for decile
6 and approximately at the level of 1% for decile 7.
The usage of two variables makes feasible the discrimination of the impact of
the types of social transfers. Old age pensions and survivor’ pensions pose the
most significant effect on the behavior of income shares. This is more obvious
in the three lower income shares and in the 10% top income share. The rest of
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
0,45
2002 2003 2004 2005 2006 2007 2008 2009 2010
FIG. 5. 10% Top Income Shares
HY023NET_NN HY022NET_NN HY010NET_NN
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the social transfers impose a small impact on income distribution being
negligible mainly in the middle income classes.
The empirical findings suggest that the social transfers smoothens the
inequalities, since the part of the income share of the upper classes is
decreasing. This effect is more intense for the 10% top income share. The
lower classes are benefited from the redistribution; still though the proportion
of the generated income for them is in low levels. The higher parts of middle
income classes are affected in low levels whereas the lower parts are
benefited; in less degree, though, than the lower income classes. From all the
types of social transfers the most significant effect derives from old age and
survivor’s benefits.
The decline of inequality is captured from the behavior of the aggregate
inequality measures [Gini, Atkinson (0,5), Atkinson (1,5), General Entropy
(0), General Entropy (1), General Entropy (2) and Coefficient of Variation].
Nevertheless, not all three cases yield a homogeneous pattern over time. Total
net household income before social transfers except old-age and survivor’s
benefits has a miniscule increase (except CV, which decreases), while total
net household income before social transfers including old-age and survivor’s
benefits has a minuscule decrease (except GE (2) and CV which are virtually
constant). The difference in GE (2) and CV may provide a justification for
the interception in the very tails of distributions (see below), since these
indices are sensitive to movements in the ‘ends’ of the distribution. Still
though, social transfers except old age and survivor’s benefi ts decrease
inequality and then social transfers including old age and survivor’s benefits
results in reduced aggregate income inequality.
The widely used approach to test the stochastic dominance in inequality is the
comparison between the Lorenz curves. Atkinson (1970) refers that all indices
that respect the Pigou-Dalton principle should indicate that inequality in A is
higher than inequality in B when is everywhere above . Thus,
distribution B inequality dominates distribution A at the second order if and
only if
(11)
where p refers to percentile [see also Araar (2006b)].
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The empirical results are verified to a significant extent from the calculation
of Lorenz curves for each distribution. The following figures illustrate the
‘movement’ of Lorenz curve when social transfers are incorporated in the
components of the aggregate income (the differences are available upon
request). It is apparent that the old age and survivor’s benefits have the most
influential impact on the distribution of income.
Figure 6. Stochastic dominance for social transfers: 2002-2010
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2002
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2003
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2004
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2005
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2006
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2007
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Source: Authors’ calculations
Applying the inequality dominance test we observe that there is no
intersection of the Lorenz curves for the years 2009 and 2008. The other years
bear ambiguous results (one to three intersections) but only in the tails of the
distributions. These cases occur below 0.009 percentile (from 0.003 to 0.009
– seven cases) and above 0.999 percentile (two cases) 6. The analytical results
are presented in the Annex. The main conclusions do not alter. Inequality
dominance test indicate that social transfers reduce aggregate income
inequality.
5.2. Redistributive effects of taxes
The study of the income distribution before and wealth and income taxes is
important, since it reveals the impact of taxation policy on income
distribution. In order to quantify these effects one variable is compiled. Due
to data restrictions the variable is calculated from the year 2006 (year of
6 The analytical results are available upon request.
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2008
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2009
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy023net_nn_eq
hy022net_nn_eq hy010net_nn_eq
Lorenz Curves_2010
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survey 2007) onwards. The analysis, thus, is confined to a shorter time
period.
Total net household income minus taxes_ no negative PY050N
(HY020net_nn):
This variable includes income on household level taking into account, also,
components of personal income. It, therefore, includes net employee cash or
near cash income, company car, net cash benefits or losses from self-
employment (including royalties), unemployment benefits, old-age benefits,
survivor' benefits, sickness benefits, disability benefits, education-related
allowances, income from rental of a property or land, family/children related
allowances, social exclusion not elsewhere classified, housing allowances,
regular inter-household cash transfers received, interests, dividends, profit
from capital investments in unincorporated business, income received by
people aged under 16 minus regular taxes on wealth and tax on income and
social insurance contributions (if applicable).
In this case we do not take into account the negative values in the variable net
cash benefits or losses from self-employment (including royalties).
It is noted that the variable tax on income hardly contains data for social
insurance contributions. This small distortion, nevertheless, should be taken
into account.
The effect of wealth taxes and taxes on income is isolated in order to estimate
the impact of taxation policy. The distribution of the new variable is
compared with the corresponding one of the ‘ Total net household income_ no
negative PY050N (HY 010net_nn)’ .
It is noted that both variables under consideration have been adjusted for the
size of the household according to the "OECD-modified scale".
As in this case of the redistributive effects of social transfers, the analysis
constitute of the typical comparison of the main inequality measures
(axiomatic approach) and of second order stochastic dominance.
In the Table 10 the basic statistics, income shares and aggregate income
inequality measures under the two alternative household income definitions
are presented.
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Table 10. Redistributive effects of taxes
2006 2007 2008 2009 2010
Average
HY010NET_NN 12.133 12.905 13.441 13.503 11.813
HY020NET_NN 8.577 9.340 9.866 10.064 8.809Shares
Decile_01
HY010NET_NN 0,03 0,03 0,02 0,03 0,03
HY020NET_NN 0,02 0,03 0,02 0,03 0,03
Decile_02
HY010NET_NN 0,04 0,04 0,04 0,05 0,04
HY020NET_NN 0,05 0,05 0,05 0,05 0,05
Decile_03
HY010NET_NN 0,05 0,06 0,06 0,06 0,06
HY020NET_NN 0,06 0,06 0,06 0,06 0,06
Decile_04
HY010NET_NN 0,06 0,07 0,07 0,07 0,07
HY020NET_NN 0,07 0,08 0,08 0,08 0,07Decile_05
HY010NET_NN 0,08 0,08 0,08 0,08 0,08
HY020NET_NN 0,09 0,09 0,09 0,09 0,08
Decile_06
HY010NET_NN 0,09 0,09 0,09 0,09 0,09
HY020NET_NN 0,10 0,10 0,10 0,10 0,10
Decile_07
HY010NET_NN 0,10 0,10 0,10 0,10 0,10
HY020NET_NN 0,11 0,11 0,11 0,11 0,11
Decile_08
HY010NET_NN 0,12 0,12 0,12 0,12 0,12
HY020NET_NN 0,13 0,12 0,12 0,12 0,13
Decile_09
HY010NET_NN 0,15 0,15 0,15 0,15 0,15
HY020NET_NN 0,15 0,14 0,14 0,14 0,15
Decile_10
HY010NET_NN 0,27 0,26 0,27 0,26 0,26
HY020NET_NN 0,22 0,22 0,23 0,22 0,23
S90/S10
HY010NET_NN 10,52 9,63 11,56 9,37 10,31
HY020NET_NN 9,54 8,38 10,76 7,45 9,05
S80/S20
HY010NET_NN 6,21 5,86 6,22 5,57 5,90
HY020NET_NN 5,06 4,73 5,16 4,39 5,10
GINI
HY010NET_NN 0,354 0,343 0,349 0,335 0,343
HY020NET_NN 0,299 0,289 0,299 0,278 0,305
Atkinson 0,5
HY010NET_NN 0,106 0,101 0,103 0,096 0,104
HY020NET_NN 0,073 0,071 0,075 0,066 0,078
Atkinson 1,5
HY010NET_NN 0,273 0,267 0,265 0,254 0,263
HY020NET_NN 0,220 0,212 0,229 0,198 0,228
GE(0)=Theil L
HY010NET_NN 0,211 0,200 0,200 0,190 0,198
HY020NET_NN 0,152 0,143 0,151 0,134 0,159
GE(1)=Theil T
HY010NET_NN 0,222 0,210 0,213 0,199 0,207
HY020NET_NN 0,143 0,138 0,145 0,126 0,153
GE(2)=CV
HY010NET_NN 0,322 0,310 0,324 0,284 0,305
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HY020NET_NN 0,184 0,186 0,206 0,156 0,202
CV
HY010NET_NN 0,803 0,788 0,805 0,754 0,782
HY020NET_NN 0,607 0,609 0,642 0,559 0,636
Source: Authors’ calculations
The average income for the two alternative household income definitions
increases for the whole time period. The average income after the
implementation of taxes decreases. The behavior of income shares reveals
more significant results. The income shares for the lower classes either
remain unchanged or slightly increases. The levels of the proportion of
generated income do not change for deciles 1 (around 3%) and 3 (around 6%)
and slightly increases for decile 2 (from 4% to 5%). Small increases are, also,
observed in the middle income classes; approximately 1% in each decile (4, 5,
6 and 7). In all cases the levels of income proportion are relatively stable over
time. Finally, the income shares for the upper economic classes decrease. This
is more intense for the upper 10% income share. The proportion of income
drops from a level of 26%-27% to 22%-23%. This decline is observed for the
previous 10% of the population but to a signi ficantly less degree (around 1%).
The empirical findings suggest that the taxation system smoothens
inequalities. The income share of the upper classes is decreasing. This effect
is more intense for the 10% top income share. The lower classes are either not
affected or slightly benefited from the taxes imposed. The middle income
classes are, also, slightly positively affected from the taxation system.
These findings are also supported by the trend of ratios S90/S10 and S80/S20.
The ratios are lower when taxes are imposed.
The decline of inequality is captured from the behavior of the aggregate
inequality measures [Gini, Atkinson (0,5), Atkinson (1,5), General Entropy(0), General Entropy (1), General Entropy (2) and Coefficient of Variation].
For both income definitions the indices suggest for the time trend a miniscule
decrease in aggregate inequality with a detectable increase in 2010.
As in the case of the analysis of redistributive effects of social transfers, the
approach of stochastic dominance is applied.
The empirical findings for the effects of taxation are verified to a significant
extent from the calculation of Lorenz curves for each distribution. The
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following figures illustrate the ‘movement’ of Lorenz curve when taxes are
imposed on the household net aggregate income.
Figure 7. Stochastic dominance for taxes: 2002-2010
Source: Authors’ calculations
Applying the inequality dominance test, nevertheless, some ambiguous results
are detected. In all years the Lorenz curves seem to intersect. The critical
percentile is 0.107 for 2010, 0.08 for 2009, 0.137 for 2008, 0.112 for 2007
and 0.131 for 2006. Below this intersection the curve for household net
aggregate income is above the corresponding curve after taxes. This imposes
an ambiguity for the benefits of taxes for the lower class. Still, though, the
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy020net_nn_eq
Lorenz Curves_TAXES_2006
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy020net_nn_eq
Lorenz Curves_TAXES_2007
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy020net_nn_eq
Lorenz Curves_TAXES_2008
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy020net_nn_eq
Lorenz Curves_TAXES_2009
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy020net_nn_eq
Lorenz Curves_TAXES 2010
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main conclusions do not alter. Inequality dominance test indicate that the
taxation system results in the reduction of aggregate income inequality.
6. Income inequality and imputed rent
The data set of EU-SILC contains information on imputed rent. The imputed
rent refers to the amounts that should be imputed for all households that do
not pay rent. The reason for nonpayment could be either because they are
owner-occupiers or because the pay rent at lower prices than the market price
or because the accommodation is provided for free.
In order to estimate the impact on income inequality the following variable is
compiled:
Total net household income_ imputed rent _ no negat i ve PY050N
(HY010net_nn_imp): It is the total net household income_ no negative
PY050N (HY010net_nn) with imputed rent.
There is difficulty in the measurement of imputed rent and this fact should be
taken into consideration in the interpretation of results.
It is noted that both variables under review have been adjusted for the size of
the household according to the "OECD-modified scale".
As in this case of the redistributive effects of social transfers and taxes, the
analysis constitute of the typical comparison of the main inequality measures
(axiomatic approach) and of second order stochastic dominance.
In the Table 11 the basic statistics, income shares and aggregate income
inequality measures of the comparison for the two distributions are presented.
Table 11. Impact of imputed rent
2002 2003 2004 2005 2006 2007 2008 2009 2010
Average
HY010NET_NN 9.756 10.211 10.945 11.480 12.133 12.905 13.441 13.503 11.813
HY010NET_NN_IMP 11.531 12.004 12.874 13.596 14.373 15.212 15.773 15.837 14.229
Shares
Decile_01
HY010NET_NN 0,02 0,02 0,03 0,03 0,03 0,03 0,02 0,03 0,03HY010NET_NN_IMP 0,03 0,03 0,03 0,03 0,03 0,03 0,03 0,03 0,03
Decile_02
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HY010NET_NN 0,04 0,04 0,04 0,04 0,04 0,04 0,04 0,05 0,04
HY010NET_NN_IMP 0,05 0,05 0,05 0,05 0,05 0,05 0,05 0,05 0,05
Decile_03
HY010NET_NN 0,05 0,06 0,05 0,05 0,05 0,06 0,06 0,06 0,06
HY010NET_NN_IMP 0,06 0,06 0,06 0,06 0,06 0,06 0,06 0,06 0,06
Decile_04
HY010NET_NN 0,06 0,07 0,07 0,06 0,06 0,07 0,07 0,07 0,07HY010NET_NN_IMP 0,07 0,07 0,07 0,07 0,07 0,07 0,07 0,07 0,07
Decile_05
HY010NET_NN 0,08 0,08 0,08 0,08 0,08 0,08 0,08 0,08 0,08
HY010NET_NN_IMP 0,08 0,08 0,08 0,08 0,08 0,08 0,08 0,08 0,08
Decile_06
HY010NET_NN 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09
HY010NET_NN_IMP 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09
Decile_07
HY010NET_NN 0,10 0,11 0,10 0,10 0,10 0,10 0,10 0,10 0,10
HY010NET_NN_IMP 0,10 0,11 0,10 0,10 0,10 0,10 0,10 0,10 0,10
Decile_08
HY010NET_NN 0,13 0,13 0,12 0,12 0,12 0,12 0,12 0,12 0,12
HY010NET_NN_IMP 0,12 0,12 0,12 0,12 0,12 0,12 0,12 0,12 0,12Decile_09
HY010NET_NN 0,16 0,16 0,15 0,16 0,15 0,15 0,15 0,15 0,15
HY010NET_NN_IMP 0,15 0,15 0,15 0,15 0,15 0,15 0,15 0,15 0,15
Decile_10
HY010NET_NN 0,26 0,25 0,26 0,27 0,27 0,26 0,27 0,26 0,26
HY010NET_NN_IMP 0,25 0,24 0,25 0,25 0,25 0,25 0,25 0,24 0,25
S90/S10
HY010NET_NN 11,11 10,45 10,41 10,05 10,52 9,63 11,56 9,37 10,31
HY010NET_NN_IMP 8,28 8,20 8,22 7,98 7,93 7,59 8,62 7,32 7,52
S80/S20
HY010NET_NN 6,38 5,98 6,14 6,12 6,21 5,86 6,22 5,57 5,90
HY010NET_NN_IMP 5,26 5,17 5,28 5,21 5,11 4,88 5,11 4,69 4,72
GINI
HY010NET_NN 0,352 0,340 0,348 0,350 0,354 0,343 0,349 0,335 0,343
HY010NET_NN_IMP 0,323 0,318 0,325 0,325 0,323 0,314 0,319 0,307 0,308
Atkinson 0,5
HY010NET_NN 0,104 0,100 0,102 0,102 0,106 0,101 0,103 0,096 0,104
HY010NET_NN_IMP 0,086 0,084 0,087 0,086 0,087 0,083 0,085 0,079 0,080
Atkinson 1,5
HY010NET_NN 0,280 0,259 0,269 0,268 0,273 0,267 0,265 0,254 0,263
HY010NET_NN_IMP 0,233 0,229 0,235 0,230 0,226 0,222 0,220 0,208 0,215
GE(0)=Theil L
HY010NET_NN 0,214 0,193 0,204 0,207 0,211 0,200 0,200 0,190 0,198
HY010NET_NN_IMP 0,176 0,170 0,178 0,176 0,174 0,166 0,166 0,157 0,161
GE(1)=Theil T
HY010NET_NN 0,216 0,193 0,207 0,213 0,222 0,210 0,213 0,199 0,207
HY010NET_NN_IMP 0,182 0,172 0,182 0,184 0,186 0,177 0,179 0,168 0,171
GE(2)=CV
HY010NET_NN 0,295 0,268 0,276 0,288 0,322 0,310 0,324 0,284 0,305
HY010NET_NN_IMP 0,241 0,227 0,234 0,243 0,260 0,249 0,260 0,230 0,238
CV
HY010NET_NN 0,769 0,732 0,742 0,759 0,803 0,788 0,805 0,754 0,782
HY010NET_NN_IMP 0,694 0,673 0,685 0,697 0,722 0,706 0,722 0,678 0,689
Source: Authors’ calculations
In both cases the average nominal income increases for the whole time period.
The incorporation of imputed rent has a positive impact on average income.
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The behavior of income shares reveals more significant results. The income
shares for the lower classes either remain unchanged or slightly increases.
The levels of the proportion of generated income do not change for deciles 1
(around 3%) and 3 (around 6%) and slightly increases for decile 2 (from 4%
to 5%). No significant changes are observed in the middle income classes.
The same patterns relatively apply for the lower parts of upper income
classes. Finally, the income shares for the 10% upper economic class
deteriorate. The proportion of income drops from a level of 26%-27% to 24%-
25%.
The empirical findings suggest that the incorporation of imputed rent
smoothens the inequalities. The lower classes are either not affected or
slightly benefited. The middle income classes and the lower parts of upper
classes are, relatively, not affected from the incorporation of imputed rent.
These findings are also supported from the ratios S90/S10 and S80/S20; both
are decreasing.
Finally, the decline of inequality is captured from the behavior of the
aggregate inequality measures [Gini, Atkinson (0,5), Atkinson (1,5), General
Entropy (0), General Entropy (1), General Entropy (2) and Coefficient of
Variation]. In both cases the indices suggest for the time trend a miniscule
decrease in aggregate inequality with an increase being detectable in 2010.
Figure 8. Stochastic dominance for imputed rent: 2002-2010
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed_2002
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed_2003
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Source: Authors’ calculations
Applying the inequality dominance test we observe that there is no
intersection of the Lorenz curves for the years 2009 and 2008. The other yearsindicate one to two intersections but only in the tails of the distributions.
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed_2004
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed 2005
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed_2006
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed_2007
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed_2008
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_Imputed_2009
0
. 2
. 4
. 6
. 8
1
0 .2 .4 .6 .8 1Percentiles (p)
45° line hy010net_nn_eq
hy010net_nn_imp_eq
Lorenz Curves_IMPUTED_2010
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These cases occur below 0.003 percentile (from 0.000 to 0.003 – seven cases)
and above 0.999 percentile (one case)7. The main conclusions do not alter.
Inequality dominance test indicate that the incorporation of imputed rent
(taken into account the measurement difficulties) reduce aggregate income
inequality.
7. Conclusions
This paper utilizes data from the European Union Survey on Income and
Living Conditions (EU SILC) for examining the structure of income
inequality. EU SILC includes micro data on income, on household and
personal level that can be used for the estimation of income distribution. The
size of the household and the age of its members are important factors,
therefore the use of an equivalence scale is appropriate. In this study the
"OECD-modified scale" is utilized. The time period of the analysis is from
the year 2002 to the year 2010.
The empirical findings indicate that aggregate income inequality in Greece is
in higher level than the average of both European Union and Euro area.
Certain indices can be decomposed by population subgroups in order to define
what components contribute to total inequality. According to this procedure it
can be defined whether the inequality derives from factors ‘between’ or
‘within’ the population subgroups. Five (5) categorical variables were used:
region, urbanization, citizenship of household head (hh), education of
household head, current economic status of household head. The empirical
findings indicate that the main contribution to total income inequality is the
‘within’ inequality. The pattern is different only for the categorical va riable
of education. The education level attained imposes effect on the distribution
of income. It is noted, nonetheless, that being the ‘within’ the main
contribution to aggregate inequality, does not mean that there are no
differences among the sub-groups.
7 The analytical results are available upon request.
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The decomposition by income components allows having a clear idea on how
each component (sixteen) contributes to the total inequality. The empirical
findings indicate that the main contribution to the total income inequality
stems from salaries and wages, income from the self employed, and to a lesser
extent from pensions and property income. More specifically, salaries and
wages are the main source of income inequality; the level varies from
approximately from 52% to 58%. The second most important factor of
influence in the inequality structure is income from self-employment with a
range of 28% to 33%. The volume of the effect that the other two components
poses to the aggregate inequality is significantly smaller. The level of old age
benefits (mostly pensions) and property income ranges approximately from
7% to 15% and from 5% to 8% respectively. The other income components do
not pose significant effect on the aggregate income inequality.
The analysis for the examination of the redistributive effects of social
transfers and taxes constitutes of the typical comparison of the main
inequality measures (axiomatic approach) and of second order stochastic
dominance. The empirical findings suggest that the social transfers smoothens
inequalities. The same applies for taxes. It is not clear, though, whether the
very lower income class (the lowest two deciles) benefits from the tax system.
Finally, the incorporation of imputed rent (taken into account the
measurement difficulties) reduces aggregate income inequality.
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ANNEX
The following tables indicate the values of assigned values of the five (5)
variables (region, degree of urbanization, citizenship, education and currenteconomic status).
Table A.6.1. Codes of categorical variable ‘Region’
NUTS code Code Name
GR11 1 Anatoliki Makedonia, Thraki
GR12 2 Kentriki Makedonia
GR13 3 Dytiki Makedonia
GR14 4 Thessalia
GR21 5 Ipeiros
GR22 6 Ionia Nisia
GR23 7 Dytiki Ellada
GR24 8 Sterea Ellada
GR25 9 Peloponnisos
GR30 10 Attiki
GR41 11 Voreio Aigaio
GR42 12 Notio Aigaio
GR43 13 Kriti
Source: Eurostat
The first column describes the regions according to NUTS level 2, the second
column describes the codes used in the decomposition analysis, whereas the
last column contains the names of the Greek regions.
Table A.6.2. Codes of categorical variable ‘Degree of urbanization’ Code Degree of urbanization
1 Densely populated area
2 Intermediate area
3 Thinly populated area
Source: Eurostat
According to classification of Eurostat, the densely populated area is a
contiguous set of local areas, each of which has a density superior to 500
inhabitants per square kilometer, where the total population is at least 50.000
inhabitants. The intermediate area is a contiguous set of local areas, not belonging to a densely populated area, each of which has a density superior to
100 inhabitants per square kilometer, and either with a total population for the
set of at least 50.000 or adjacent to densely-populated area. The thinly-
populated area is the area that does not belong to the two previous cases. The
column ‘code’ describes the labels used in the analysis.
Table A.6.3. Codes of categorical variable ‘Citizenship’
Code Citizenship
1 Greek
2 OtherSource: Eurostat
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According to Eurostat, citizenship is defined as the particular legal bond
between the individual and his/her State acquired by birth or naturalization,
whether by declaration, choice, option, marriage or other means according to
the national legislation. Eurostat notes that it generally corresponds to the
country issuing the passport.
Table A.6.4. Codes of categorical variable ‘Education’
Code Education (according to ISCED-97)
0 pre-primary education
1 primary education
2 lower secondary education
3 (upper) secondary education
4 post-secondary non tertiary education
5 first stage of tertiary education (not leading to an advanced research qualification)
6 second stage of tertiary education (leading to an advanced research qualification)
Source: Eurostat
According to Eurostat, educational attainment of a person is the highest level
of an educational programme the person has successfully completed and the
study field of the programme. The educational classification is the
International Standard Classification of Education (ISCED 1997).
Table A.6.5. Codes of categorical variable ‘Current Economic Status’
Code Self-defined current economic status
1 Employee working full-time2 Employee working part-time
3 Self-employed working full time (including family worker)
4 Self-employed working part-time (including family worker)
5 Unemployed
6 Pupil, student, further training, unpaid work experience
7 In retirement or in early retirement or has given up business
8 Permanently disabled or/and unfit to work
9 In compulsory military community or service
10 Fulfilling domestic tasks and care responsibilities
11 Other inactive person
Source: Eurostat
According to Eurostat, the concept of ‘current’ implies that any definite
changes in the activity situation are taken into account. Moreover, the
variable captures the person’s own perception of their main activity and it
may differ from the ILO (International Labor Organization) concept to the
extent that people’s perception could be different from the strict d efinitions of
ILO. The self-declared main activity status is, in principle, determined on the
basis of the most time spent. This classification is applied from 2009 and
onwards; for the previous years the codes were slightly different (9 in total).
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