PowerPoint bemutat

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István György Tóth and Márton Medgyesi Income
distribution in new (and old) EU member states   
Presentation based on paper submitted to the
joint OECD/University of Maryland International
Conference Measuring Poverty, Income Inequality,
and Social Exclusion Lessons from Europe 16-17
March 2009 Paris
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Research questions (raised by organisers of the
conference)

•         describe and assess the measures of
  inequality and poverty used in new Eastern
  European EU members.
•          examine the applicability of standard
  measures and the current or possible use of
  alternate measures.
•          povide some comparable estimates across
  countries at different stages of development
  using similar measures and explain the lessons to
  be learned from those comparisons.

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• In this paper we
• examine the distribution of incomes in EU member
  states
• (new and old), with standard methods and
  assumptions
• test if alternative measures and concepts affect
  the
• new member states systematically differently
• analyse determining factors of income
  inequality
• assess (with inadequate data) the differential
  role of
• incomes in welfare defined more broadly

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Data and definitions

• EU-SILC UDB 2006/1 released 01/03/2008
• refence year 2005
• income concept yearly net household monetary
  income
• country coverage EU27 (RO, BG and MT)
• two equivalence scales OECD II (1st adult1,
  other 14 members0.5, all members lt140.3) and
  per capita adjustment
• Bottom and top coding at 0.1 and at 99.95
  percentiles
• Research background SSO, OECD ineq paper,
• Tarki international comparisons

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OVERALL INEQUALITY RANKING
Gini indices and bootstrapped 95 confidence
intervals for EU countries, 2005

• Groups of countries (sometimes overlapping)
• Margin of error (statistical and
  non-statistical)
• Unequal LV, PT, LT
• Equal SE, DK, SI
• NMS in the whole spectrum

Source EU-SILC 2006. Note bootstrap confidence
intervals were obtained by 1000 replications. Hu
1 EU-SILC 2006. Hu2 U-Silc 2005. Hu 3 Tarki
Household Monitor 2005.
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INEQUALITY AND DEVELOPMENT LEVEL COMBINED
The income distributions of the countries of the
European Union (Euros, PPP)

• Methods
• Bars connect (euro, ppp) avg incomes of deciles
• Not shown variance at ends of distributions!!
• Conclusion
• Ranked by country avg incomes, NMS-s cluster at
  the bottom
• (presumably, roughly corresponding to GDP
  ranking)

Source EU-SILC (2006) Note The bottom of the
data bars represents the first decile, the top
represents the tenth decile and the marks in
between show the average incomes of the
individual deciles.
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Steps of pooling EU24 1 merge all datafiles 2
weight by all-EU population weight 3 identify
median brackets 4 present country distributions
by belonging to EU brackets
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Steps of pooling EU24 1 merge all datafiles 2
weight by all-EU population weight 3 identify
median brackets 4 present country distributions
by belonging to EU brackets
EU Frequency distribution
EU Pens parade
200
150
120
80
50
50
Eu median
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EU CITIZENS IN THE OVERALL EUROPEAN SOCIETY
The distribution of the population among
categories of the overall European income
distribution by country ()

• Findings
• The majority of the population in
• LT, LV, PL, EE SK, HU belong to
• the lt50med EU bracket
• This ratio in CZ and SI is lower
• The position of UK!
• (though slight diferences between
• countries in the range between UK and FI)

Source EU ILC (2006)
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INEQUALITY AND ECONOMIC DEVELOPMENT
GDP per capita (EU27100) and income inequality
in 2005

• negative correlation in both groupings
• cross section spells do not show this!

Source of data EUROSTAT NewCronos Database,
download 1st of June 2008. Variables GDP PPS
2005 (EU27100), Gini 2005 (except for Hungary
(2004). EU15 regression excludes LU.
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POVERTY AND ECONOMIC DEVELOPMENT
GDP per capita (EU27100) and income poverty in
2005

• Loosely identifiable clusters
• High poverty EU15 medit. and anglo-saxon
• Low poverty EU15 cont. and scandinavian
• High poverty NMS North and Ro
• Low poverty NMS CEU and BG
• This is cross section spells do not confirm!!

Source of data EUROSTAT NewCronos Database,
download 1st of June 2008. Variables GDP PPS
2005 (EU27100), At risk of poverty rate (after
social transfers) 2005 (except for Hungary
(2004). ). EU15 regression excludes LU.
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INEQUALITY SENSITIVITY ALTERNATIVE MEASURES
Values of various inequality measures in new and
old EU MS-s
- no systematic NMS effect
Source EU SILC 2006 Note the values of P90/P10
are divided by 10 to make it possible to use the
same scale for the various measures.
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INEQUALITY SENSITIVITY ALTERNATIVE EQ SCALES
Inequality levels in European countries (measured
by Gini based on per capita and equivalent
incomes), 2005

• Per cap ineq larger everywhere
• Small effect in large inequality countries
• - Larger effect in low inequality countries
• - NMS in both groups

Source EU-SILC (2006) Note Countries are
ranked by Ginis based on OECD 2 equivalent
incomes
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POVERTY SENSITIVITY ALTERNATIVE EQ SCALES
Poverty rates in European countries (based on per
capita and OECD 2 equivalent incomes), 2005

• - Shift to per capita may result higher and lower
  poverty rate
• Large increase in CEU NMSs
• Large decrease in LV and EE
• No change in LT and SI

Source EU-SILC (2006) Note Countries are
ranked by the difference of poverty rates.
Poverty threshold 60 of national median
income.  
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Decomposition by population subgroups Inequality
measure to be decomposed MLD index
MLD ?k vkMLDk ?k vk log (1/?k),
Within group Between group
inequality inequality Where vk nk/n and
?k?k/?
Subgroups (hh head characteristics) Age
18-35, 36-49, 50-64, 65 Education
less than upper secondary, upper secondary,
tertiary Employment employed, unempl. or
inact., retired Hhold structure head 18-64,
1,2,3 kids, head 65
Mookherjee-Shorrocks, 1982 Jenkins 1996, Jenkins
and Van Kerm, 2004)
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DECOMPOSITION BY DEMOGRAPHIC FACTORS
Fraction of inequality explained by demographic
factors age and household structure
Note Countries are ranked according to the MLD
index of total inequality.
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DECOMPOSITION BY EDUCATION AND EMPLOYMENT
Fraction of inequality explained by education and
employment status
Note Countries are ranked according to the MLD
index of total inequality.
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DECOMPOSITION SUMMARY
Percentage of inequalities explained by different
factors in the country groups, 2005
Age (gt5) North (and CY) Education
(gt15) Mediterranean countries (PT, CY, GR),
Former socialist countries (HU, LT, SI, PL),
LU, IE Employment (gt10) Baltics and
Anglo-Saxon countries plus FI, DK, BE, CZ
Note Percentages are simple country averages.
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Task measuring the relationship of income to a
composite variable that is supposed to measure
wealth or consumotion (stretched over the whole
distibution)     Predicted variables   1
wealth capacity index Housing conditions Rooms
per persons (variable HH030/HX040) , bath (HH080)
, flushing toilet (HH090), no leaking roof
(HH040), lack of problems with environment
(HS180), flat darkness (HS160), crime in
surroundings (HS190), noise in the neighbourhood
(HS170) Durables Telephone, colour tv, computer,
washing machine and car (in variables HS070 HS080
HS090 HS100 HS110, respectively). 2 consumption
ability index ability to make ends meet
(variable HS120, six category, from very easily
to with great difficulty), the ability to pay
for an unexpected expense (variable HS060 at the
level of 1/12 of the poverty threshold for a
household on average), or the ability to pay for
a week of holiday away from home (HS040) and an
ability to keep home adequately warm
(HH050). Construction sum of z-scores of
elements by country Predictors natural
logarithm of net person equivalent disposable
income Control age (-35, 36-49, 50-64 and 65),
education (less than secondary, secondary and
tertiary) and gender of household head Unit of
analysis hhold Regression simple OLS    
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THE EXPLANATORY POWER OF INCOMES
(Adjusted R2 and standardized ß for income in OLS
to explain wealth capacity index)
- The role of income is higher in NMS-s -
Explanation might come from the construction
of the index differential role of in kind
benefits differential extent of the informal
economy
Note countries are ranked by the value of the
standardized beta values. Controls age ,
education and gender of household head. All beta
estimates are significant at plt0.01
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One illustration of a potential explanation the
case of computer penetration in CZ and NL
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THE EXPLANATORY POWER OF INCOMES
(Adjusted R2 and standardized ß for income in OLS
to explain consumption capacity index)
- see previous
Note countries are ranked by the value of the
standardized beta values. Controls age ,
education and gender of household head. All
beta estimates are significant at plt0.01
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Summary

• - NMS group is just as heterogenous as EU15, in
  terms of levels of inequality
• - sensitivity analyses for alternative inequality
  measures and for alternative EQ scales do not
  show systematic biases
• - Nordic, Mediterranean, Anglo-saxon and
  continental countries are markedly different in
  terms of determining factors of inequality
• - within NMS, the Baltic countries and the others
  differ markedly in this respect
• - Income is more significant in NMS in
  determining wealth and consumption capacity
  status the reason might relate to design or to
  contextual factors
•   

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